experiments-covid.md

TREC-COVID Baselines

Important reproducibility notes: Anserini was upgraded to Lucene 9.3 at commit 272565 (8/2/2022). This upgrade created backward compatibility issues (see #1952), which means that the runs described on this page cannot be exactly reproduced with Lucene 9 code running on Lucene 8 indexes (since we need to disable consistent tie-breaking).

Following the Lucene upgrade, this page is no longer being maintained. For reproducibility purposes, however, runs with Lucene 8 (at v0.14.4) and Lucene 9 (at 5480dc) are captured and stored here. There are only minor differences in effectiveness between the two sets of runs.

In September 2023, the regression code was refactored such that the following commands run successfully (commits 88935f and 444eac):

python src/main/python/trec-covid/download_indexes.py --date 2020-07-16 &
python src/main/python/trec-covid/download_indexes.py --date 2020-06-19 &
python src/main/python/trec-covid/download_indexes.py --date 2020-05-19 &
python src/main/python/trec-covid/download_indexes.py --date 2020-05-01 &
python src/main/python/trec-covid/download_indexes.py --date 2020-04-10 &

nohup python src/main/python/trec-covid/generate_round5_baselines.py >& logs/log.trec-covid.round5 &
nohup python src/main/python/trec-covid/generate_round4_baselines.py >& logs/log.trec-covid.round4 &
nohup python src/main/python/trec-covid/generate_round3_baselines.py >& logs/log.trec-covid.round3 &
nohup python src/main/python/trec-covid/generate_round2_baselines.py >& logs/log.trec-covid.round2 &
nohup python src/main/python/trec-covid/generate_round1_baselines.py >& logs/log.trec-covid.round1 &

Specifically, the effectiveness of the runs generated by the scripts match the scores encoded in the scripts. However, the scores vary (in most cases, only slightly) from the scores reported below.

---

This document describes various baselines for the TREC-COVID Challenge, which uses the COVID-19 Open Research Dataset (CORD-19) from the Allen Institute for AI. Here, we focus on running retrieval experiments; for basic instructions on building Anserini indexes, see this page.

All the runs referenced on this page are stored in this repo. As an alternative to downloading each run separately, clone the repo and you'll have everything.

Quick Links

• Round 5
• Round 4
• Round 3
• Round 2 (Reproduction Commands)
• Round 1 (Reproduction Commands)

Round 5

These are runs that can be easily reproduced with Anserini, from pre-built indexes available here (version from 2020/07/16, the official corpus used in round 5). They were prepared for round 5 (for participants who wish to have a baseline run to rerank); to provide a sense of effectiveness, we present evaluation results with the cumulative qrels from rounds 1, 2, 3, and 4 (qrels_covid_d4_j0.5-4.txt provided by NIST, stored in our repo as qrels.covid-round4-cumulative.txt).

	index	field(s)	nDCG@10	J@10	R@1k	run file	checksum
1	abstract	query+question	0.4580	0.5880	0.4525	[download]	b1ccc364cc9dab03b383b71a51d3c6cb
2	abstract	UDel qgen	0.4912	0.6240	0.4714	[download]	ee4e3e6cf87dba2fd021fbb89bd07a89
3	full-text	query+question	0.3240	0.5660	0.3758	[download]	d7457dd746533326f2bf8e85834ecf5c
4	full-text	UDel qgen	0.4634	0.6460	0.4368	[download]	8387e4ad480ec4be7961c17d2ea326a1
5	paragraph	query+question	0.4077	0.6160	0.4877	[download]	62d713a1ed6a8bf25c1454c66182b573
6	paragraph	UDel qgen	0.4918	0.6440	0.5101	[download]	16b295fda9d1eccd4e1fa4c147657872
7	-	reciprocal rank fusion(1, 3, 5)	0.4696	0.6520	0.5027	[download]	16875b6d32a9b5ef96d7b59315b101a7
8	-	reciprocal rank fusion(2, 4, 6)	0.5077	0.6800	0.5378	[download]	8f7d663d551f831c65dceb8e4e9219c2
9	abstract	UDel qgen + RF	0.6177	0.6620	0.5505	[download]	909ccbbd55736eff60c7dbeff1404c94

IMPORTANT NOTES!!!

• These runs are performed at a3764c, 2020/07/23.
• J@10 refers to Judged@10 and R@1k refers to Recall@1000.
• The evaluation numbers are produced with the NIST-prepared cumulative qrels from rounds 1, 2, 3, and 4 (qrels_covid_d4_j0.5-4.txt provided by NIST, stored in our repo as qrels.covid-round4-cumulative.txt) on the round 5 collection (release of 7/16).
• For the abstract and full-text indexes, we request up to 10k hits for each topic; the number of actual hits retrieved is fairly close to this (a bit less because of deduping). For the paragraph index, we request up to 50k hits for each topic; because multiple paragraphs are retrieved from the same document, the number of unique documents in each list of hits is much smaller. A cautionary note: our experience is that choosing the top k documents to rerank has a large impact on end-to-end effectiveness. Reranking the top 100 seems to provide higher precision than top 1000, but the likely tradeoff is lower recall. It is very likely the case that you don't want to rerank all available hits.
• Row 9 represents the feedback baseline condition introduced in round 3: abstract index, UDel query generator, BM25+RM3 relevance feedback (100 feedback terms).
• (Updates 2020/07/27) Fixed a bug in the relevance feedback runs where we were using the round 3 cumulative qrels (instead of the round 4 ones).

The final runs submitted to NIST, after removing judgments from 1, 2, 3, and 4 (cumulatively), are as follows:

group	runtag	run file	checksum
anserini	r5.fusion1 = Row 7	[download]	12122c12089c2b07a8f6c7247aebe2f6
anserini	r5.fusion2 = Row 8	[download]	ff1a0bac315de6703b937c552b351e2a
anserini	r5.rf = Row 9	[download]	74e2a73b5ffd2908dc23b14c765171a1

We have written scripts that automate the reproduction of these baselines:

$ python src/main/python/trec-covid/download_indexes.py --date 2020-07-16
$ python src/main/python/trec-covid/generate_round5_baselines.py

Evaluation with Round 5 Qrels

Since the above runs were prepared for round 5, we do not know how well they actually performed until the round 5 judgments from NIST were released. Here, we provide these evaluation results.

Note that the runs posted on the TREC-COVID archive are not exactly the same the runs we submitted. According to NIST (from email to participants), they removed "documents that were previously judged but had id changes from the Round 5 submissions for scoring, even though the change in cord_uid was unknown at submission time." The actual evaluated runs are (mirrored from URL above):

group	runtag	run file	checksum
anserini	r5.fusion1 (NIST post-processed)	[download]	f1ebdd7f7b8403b53e89a5993fb55dd2
anserini	r5.fusion2 (NIST post-processed)	[download]	77ce612916becbb5ccfd6d891f797d1d
anserini	r5.rf (NIST post-processed)	[download]	dd765fa9491c585476735115eb966ea2

Effectiveness results (note that starting in Round 4, NIST changed from nDCG@10 to nDCG@20):

group	runtag	nDCG@20	J@20	AP	R@1k
anserini	r5.fusion1	0.5244	0.8490	0.2302	0.5615
anserini	r5.fusion1 (NIST post-processed)	0.5313	0.8570	0.2314	0.5615
anserini	r5.fusion2	0.5941	0.9080	0.2716	0.6012
anserini	r5.fusion2 (NIST post-processed)	0.6007	0.9150	0.2734	0.6012
anserini	r5.rf	0.7193	0.9270	0.3235	0.6378
anserini	r5.rf (NIST post-processed)	0.7346	0.9470	0.3280	0.6378

The scores of the post-processed runs match those reported by NIST. We see that NIST post-processing improves scores slightly.

Below, we report the effectiveness of the runs using the "complete" cumulative qrels file (covering rounds 1 through 5). This qrels file, provided by NIST as qrels-covid_d5_j0.5-5.txt, is stored in our repo as qrels.covid-complete.txt).

	index	field(s)	nDCG@10	J@10	nDCG@20	J@20	AP	R@1k	J@1k
1	abstract	query+question	0.6925	0.9740	0.6586	0.9700	0.3010	0.4636	0.4159
2	abstract	UDel qgen	0.7301	0.9980	0.6979	0.9900	0.3230	0.4839	0.4286
3	full-text	query+question	0.4709	0.8920	0.4382	0.8370	0.1777	0.3427	0.3397
4	full-text	UDel qgen	0.6286	0.9840	0.5973	0.9630	0.2391	0.4087	0.3875
5	paragraph	query+question	0.5832	0.9600	0.5659	0.9390	0.2808	0.4695	0.4412
6	paragraph	UDel qgen	0.6764	0.9840	0.6368	0.9740	0.3089	0.4949	0.4542
7	-	reciprocal rank fusion(1, 3, 5)	0.6469	0.9860	0.6184	0.9800	0.2952	0.4967	0.4675
8	-	reciprocal rank fusion(2, 4, 6)	0.6972	1.0000	0.6785	1.0000	0.3329	0.5313	0.4869
9	abstract	UDel qgen + RF	0.8395	1.0000	0.7955	0.9990	0.3911	0.5536	0.4607

Note that all of the results above can be reproduced with the following scripts:

$ python src/main/python/trec-covid/download_indexes.py --date 2020-07-16
$ python src/main/python/trec-covid/generate_round5_baselines.py

Round 4

These are runs that can be easily reproduced with Anserini, from pre-built indexes available here (version from 2020/06/19, the official corpus used in round 4). They were prepared for round 4 (for participants who wish to have a baseline run to rerank); to provide a sense of effectiveness, we present evaluation results with the cumulative qrels from rounds 1, 2, and 3 (qrels_covid_d3_j0.5-3.txt provided by NIST, stored in our repo as qrels.covid-round3-cumulative.txt).

	index	field(s)	nDCG@10	J@10	R@1k	run file	checksum
1	abstract	query+question	0.3143	0.4467	0.4257	[download]	56ac5a0410e235243ca6e9f0f00eefa1
2	abstract	UDel qgen	0.3260	0.4378	0.4432	[download]	115d6d2e308b47ffacbc642175095c74
3	full-text	query+question	0.2108	0.4044	0.3891	[download]	af0d10a5344f4007e6781e8d2959eb54
4	full-text	UDel qgen	0.3499	0.5067	0.4537	[download]	594d469b8f45cf808092a3d8e870eaf5
5	paragraph	query+question	0.3229	0.5267	0.4863	[download]	6f468b7b60aaa05fc215d237b5475aec
6	paragraph	UDel qgen	0.4016	0.5333	0.5050	[download]	b7b39629c12573ee0bfed8687dacc743
7	-	reciprocal rank fusion(1, 3, 5)	0.3424	0.5289	0.5033	[download]	8ae9d1fca05bd1d9bfe7b24d1bdbe270
8	-	reciprocal rank fusion(2, 4, 6)	0.4004	0.5400	0.5291	[download]	e1894209c815c96c6ddd4cacb578261a
9	abstract	UDel qgen + RF	0.4598	0.5044	0.5330	[download]	9d954f31e2f07e11ff559bcb14ef16af

IMPORTANT NOTES!!!

• These runs are performed at b8609a, at the release of Anserini 0.9.4.
• J@10 refers to Judged@10 and R@1k refers to Recall@1000.
• The evaluation numbers are produced with the NIST-prepared cumulative qrels from rounds 1, 2, and 3 (qrels_covid_d3_j0.5-3.txt provided by NIST, stored in our repo as qrels.covid-round3-cumulative.txt) on the round 4 collection (release of 6/19).
• For the abstract and full-text indexes, we request up to 10k hits for each topic; the number of actual hits retrieved is fairly close to this (a bit less because of deduping). For the paragraph index, we request up to 50k hits for each topic; because multiple paragraphs are retrieved from the same document, the number of unique documents in each list of hits is much smaller. A cautionary note: our experience is that choosing the top k documents to rerank has a large impact on end-to-end effectiveness. Reranking the top 100 seems to provide higher precision than top 1000, but the likely tradeoff is lower recall. It is very likely the case that you don't want to rerank all available hits.
• Row 9 represents the feedback baseline condition introduced in round 3: abstract index, UDel query generator, BM25+RM3 relevance feedback (100 feedback terms).

The final runs submitted to NIST, after removing judgments from 1, 2, and 3 (cumulatively), are as follows:

group	runtag	run file	checksum
anserini	r4.fusion1 = Row 7	[download]	a8ab52e12c151012adbfc8e37d666760
anserini	r4.fusion2 = Row 8	[download]	1500104c928f463f38e76b58b91d4c07
anserini	r4.rf = Row 9	[download]	41d746eb86a99d2f33068ebc195072cd

We have written scripts that automate the reproduction of these baselines:

$ python src/main/python/trec-covid/download_indexes.py --date 2020-06-19
$ python src/main/python/trec-covid/generate_round4_baselines.py

Evaluation with Round 4 Qrels

Since the above runs were prepared for round 4, we do not know how well they actually performed until the round 4 judgments from NIST were released. Here, we provide these evaluation results.

Note that the runs posted on the TREC-COVID archive are not exactly the same the runs we submitted. According to NIST (from email to participants), they removed "documents that were previously judged but had id changes from the Round 4 submissions for scoring, even though the change in cord_uid was unknown at submission time." The actual evaluated runs are (mirrored from URL above):

group	runtag	run file	checksum
anserini	r4.fusion1 (NIST post-processed)	[download]	b0ebafe36d8fc721ea6923da5837aa8c
anserini	r4.fusion2 (NIST post-processed)	[download]	e7e0b870c6822e7127df71608923e76b
anserini	r4.rf (NIST post-processed)	[download]	2fcd53854461e0cbe3c9170c0da234d9

Effectiveness results (note that NIST changed from nDCG@10 to nDCG@20 for this round):

group	runtag	nDCG@20	J@20	AP	R@1k
anserini	r4.fusion1	0.5204	0.7922	0.2656	0.6571
anserini	r4.fusion1 (NIST post-processed)	0.5244	0.7978	0.2666	0.6571
anserini	r4.fusion2	0.6047	0.8978	0.3078	0.6928
anserini	r4.fusion2 (NIST post-processed)	0.6089	0.9022	0.3088	0.6928
anserini	r4.rf	0.6940	0.9233	0.3506	0.6962
anserini	r4.rf (NIST post-processed)	0.6976	0.9278	0.3519	0.6962

The scores of the post-processed runs match those reported by NIST. We see that NIST post-processing improves scores slightly.

Below, we report the effectiveness of the runs using the cumulative qrels file from round 4. This qrels file, provided by NIST as qrels_covid_d4_j0.5-4.txt, is stored in our repo as qrels.covid-round4-cumulative.txt).

	index	field(s)	nDCG@10	J@10	nDCG@20	J@20	AP	R@1k	J@1k
1	abstract	query+question	0.6600	0.9356	0.6120	0.9111	0.2780	0.5019	0.2876
2	abstract	UDel qgen	0.7081	0.9844	0.6650	0.9622	0.2994	0.5233	0.2987
3	full-text	query+question	0.4192	0.8067	0.3984	0.7544	0.1712	0.4139	0.2740
4	full-text	UDel qgen	0.6110	0.9400	0.5668	0.8933	0.2344	0.4856	0.3079
5	paragraph	query+question	0.5610	0.9133	0.5324	0.8756	0.2713	0.5385	0.3386
6	paragraph	UDel qgen	0.6477	0.9644	0.6084	0.9322	0.2975	0.5625	0.3443
7	-	reciprocal rank fusion(1, 3, 5)	0.6271	0.9689	0.5968	0.9422	0.2904	0.5623	0.3519
8	-	reciprocal rank fusion(2, 4, 6)	0.6802	1.0000	0.6573	0.9956	0.3286	0.5946	0.3625
9	abstract	UDel qgen + RF	0.8056	1.0000	0.7649	0.9967	0.3663	0.5955	0.3229

Note that all of the results above can be reproduced with the following scripts:

$ python src/main/python/trec-covid/download_indexes.py --date 2020-06-19
$ python src/main/python/trec-covid/generate_round4_baselines.py

Round 3

These are runs that can be easily reproduced with Anserini, from pre-built indexes available here (version from 2020/05/19, the official corpus used in round 3). They were prepared for round 3 (for participants who wish to have a baseline run to rerank); to provide a sense of effectiveness, we present evaluation results with the union of round 1 and round 2 qrels.

	index	field(s)	nDCG@10	J@10	R@1k	run file	checksum
1	abstract	query+question	0.2118	0.3300	0.4398	[download]	d08d85c87e30d6c4abf54799806d282f
2	abstract	UDel qgen	0.2470	0.3375	0.4537	[download]	d552dff90995cd860a5727637f0be4d1
3	full-text	query+question	0.2337	0.4650	0.4817	[download]	6c9f4c09d842b887262ca84d61c61a1f
4	full-text	UDel qgen	0.3430	0.5025	0.5267	[download]	c5f9db7733c72eea78ece2ade44d3d35
5	paragraph	query+question	0.2848	0.5175	0.5527	[download]	872673b3e12c661748d8899f24d3ba48
6	paragraph	UDel qgen	0.3604	0.5050	0.5676	[download]	c1b966e4c3f387b6810211f339b35852
7	-	reciprocal rank fusion(1, 3, 5)	0.3093	0.4975	0.5566	[download]	61cbd73c6e60ba44f18ce967b5b0e5b3
8	-	reciprocal rank fusion(2, 4, 6)	0.3568	0.5250	0.5769	[download]	d7eabf3dab840104c88de925e918fdab
9	abstract	UDel qgen + RF	0.3633	0.3800	0.5722	[download]	e6a44f1f7183de10f892c6d922110934

IMPORTANT NOTES!!!

• These runs are performed at 2b4dcc2, at the release of Anserini 0.9.3.
• J@10 refers to Judged@10 and R@1k refers to Recall@1000.
• The evaluation numbers are produced with the union of both round 1 qrels and round 2 qrels on the round 3 collection (release of 5/19).
• For the abstract and full-text indexes, we request up to 10k hits for each topic; the number of actual hits retrieved is fairly close to this (a bit less because of deduping). For the paragraph index, we request up to 50k hits for each topic; because multiple paragraphs are retrieved from the same document, the number of unique documents in each list of hits is much smaller. A cautionary note: our experience is that choosing the top k documents to rerank has a large impact on end-to-end effectiveness. Reranking the top 100 seems to provide higher precision than top 1000, but the likely tradeoff is lower recall. It is very likely the case that you don't want to rerank all available hits.
• For reciprocal rank fusion, the underlying fusion library returns only up to 1000 hits per topic. This was a known issue for round 2, since the Anserini fusion script did not specify a larger value. However, this does appear to be a limitation in the underlying library, see this issue.
• Row 9 represents a new relevance feedback baseline condition introduced in round 3: abstract index, UDel query generator, BM25+RM3 relevance feedback (100 feedback terms). The code was in PR #1236 and had not been merged at the time of submission because we had not completed regression testing. The PR has since been merged.

The final runs submitted to NIST, after removing judgments from round 1 and round 2, are as follows:

group	runtag	run file	checksum
anserini	r3.fusion1 = Row 7	[download]	c1caf63a9c3b02f0b12e233112fc79a6
anserini	r3.fusion2 = Row 8	[download]	12679197846ed77306ecb2ca7895b011
anserini	r3.rf = Row 9	[download]	7192a08c5275b59d5ef18395917ff694

We resolved the issue from round 2 where the final submitted runs have less than 1000 hits per topic.

We have written scripts that automate the reproduction of these baselines:

$ python src/main/python/trec-covid/download_indexes.py --date 2020-05-19
$ python src/main/python/trec-covid/generate_round3_baselines.py

Note that these scripts were written after the release of the round 3 qrels (previously, the runs were generated by a series of shells commands). However, we have confirmed that they produce exactly the same output (i.e., identical checksums) as the runs generated previously. The history of this file in the repo contains those commands for historical/archival interest.

Evaluation with Round 3 Qrels

Since the above runs were prepared for round 3, we do not know how well they actually performed until the round 3 judgments from NIST were released. Here, we provide these evaluation results.

NIST provides the following caveat here:

Since there were previously judged documents whose doc-ids changed between the Round 1 and Round 2 judgment sets and the Round 3 data sets, these documents were removed from submissions by NIST. Almost all runs had some documents removed.

Thus, the runs submitted above were not the actual runs evaluated by NIST. They are, instead:

group	runtag	run file	checksum
anserini	r3.fusion1 (NIST post-processed)	[download]	f7c69c9bff381a847af86e5a8daf7526
anserini	r3.fusion2 (NIST post-processed)	[download]	84c5fd2c7de0a0282266033ac4f27c22
anserini	r3.rf (NIST post-processed)	[download]	3e79099639a9426cb53afe7066239011

Effectiveness results:

group	runtag	nDCG@10	J@10	AP	R@1k
anserini	r3.fusion1	0.5339	0.8400	0.2283	0.6160
anserini	r3.fusion1 (NIST post-processed)	0.5359	0.8475	0.2293	0.6160
anserini	r3.fusion2	0.6072	0.9025	0.2631	0.6441
anserini	r3.fusion2 (NIST post-processed)	0.6100	0.9100	0.2641	0.6441
anserini	r3.rf	0.6812	0.9600	0.2787	0.6399
anserini	r3.rf (NIST post-processed)	0.6883	0.9750	0.2817	0.6399

The scores of the post-processed runs match those reported by NIST. We see that NIST post-processing improves scores slightly.

Below, we report the effectiveness of the runs using the cumulative qrels file from round 3. This qrels file, provided by NIST as qrels_covid_d3_j0.5-3.txt, is stored in our repo as qrels.covid-round3-cumulative.txt.

	index	field(s)	nDCG@10	J@10	nDCG@20	J@20	AP	R@1k	J@1k
1	abstract	query+question	0.5781	0.8875	0.5359	0.8325	0.2348	0.5040	0.2351
2	abstract	UDel qgen	0.6291	0.9300	0.5972	0.8925	0.2525	0.5215	0.2370
3	full-text	query+question	0.3977	0.7500	0.3681	0.7213	0.1646	0.4708	0.2471
4	full-text	UDel qgen	0.5790	0.9050	0.5234	0.8525	0.2236	0.5313	0.2693
5	paragraph	query+question	0.5396	0.9425	0.5079	0.9050	0.2498	0.5766	0.2978
6	paragraph	UDel qgen	0.6327	0.9600	0.5793	0.9162	0.2753	0.5923	0.2956
7	-	reciprocal rank fusion(1, 3, 5)	0.5924	0.9625	0.5563	0.9362	0.2700	0.5956	0.3045
8	-	reciprocal rank fusion(2, 4, 6)	0.6515	0.9875	0.6200	0.9675	0.3027	0.6194	0.3076
9	abstract	UDel qgen + RF	0.7459	0.9875	0.7023	0.9637	0.3190	0.6125	0.2600

Note that all of the results above can be reproduced with the following scripts:

$ python src/main/python/trec-covid/download_indexes.py --date 2020-05-19
$ python src/main/python/trec-covid/generate_round3_baselines.py

Round 2

These are runs that can be easily reproduced with Anserini, from pre-built indexes available here (version from 2020/05/01, the official corpus used in round 2). They were prepared for round 2 (for participants who wish to have a baseline run to rerank), and so effectiveness is computed with round 1 qrels.

	index	field(s)	nDCG@10	J@10	R@1k	run file	checksum
1	abstract	query+question	0.3522	0.5371	0.6601	[download]	9cdea30a3881f9e60d3c61a890b094bd
2	abstract	UDel qgen	0.3781	0.5371	0.6485	[download]	1e1bcdf623f69799a2b1b2982f53c23d
3	full-text	query+question	0.2070	0.4286	0.5953	[download]	6d704c60cc2cf134430c36ec2a0a3faa
4	full-text	UDel qgen	0.3123	0.4229	0.6517	[download]	352a8b35a0626da21cab284bddb2e4e5
5	paragraph	query+question	0.2772	0.4400	0.7248	[download]	b48c9ffb3cf9b35269ca9321ac39e758
6	paragraph	UDel qgen	0.3353	0.4343	0.7196	[download]	580fd34fbbda855dd09e1cb94467cb19
7	-	reciprocal rank fusion(1, 3, 5)	0.3297	0.4657	0.7561	[download]	2a131517308d088c3f55afa0b8d5bb04
8	-	reciprocal rank fusion(2, 4, 6)	0.3679	0.4829	0.7511	[download]	9760124d8cfa03a0e3aae3a4c6e32550

IMPORTANT NOTES!!!

• These runs are performed at 39c9a92, at the release of Anserini 0.9.1.
• "UDel qgen" refers to query generator contributed by the University of Delaware (see below).
• The evaluation numbers are produced with round 1 qrels on the round 2 collection (release of 5/1).
• The above runs do not conform to NIST's residual collection guidelines. That is, those runs include documents from the round 1 qrels. If you use these runs as the basis for reranking, you must make sure you conform to the official round 2 guidelines from NIST. The reason for keeping documents from round 1 is so that it is possible to know the score distribution of relevant and non-relevant documents with respect to the new corpus.
• The above runs provide up to 10k hits for each topic (sometimes less because of deduping). A cautionary note: our experience is that choosing the top k documents to rerank has a large impact on end-to-end effectiveness. Reranking the top 100 seems to provide higher precision than top 1000, but the likely tradeoff is lower recall (although with such shallow pools currently, it's hard to tell). It is very likely the case that you don't want to rerank all 10k hits.

The final runs submitted to NIST, after removing round 1 judgments, are as follows:

group	runtag	run file	checksum
anserini	r2.fusion1	[download]	89544da0409435c74dd4f3dd5fc9dc62
anserini	r2.fusion2	[download]	774359c157c65bb7142d4f43b614e38f

We discovered at the last minute that the package we used to perform reciprocal rank fusion trimmed runs to 1000 hits per topic. Thus the final submitted runs have less than 1000 hits per topic after removal of round 1 judgments.

Exact commands for reproducing these runs are found further down on this page.

(Updates 2020/05/26) The effectiveness of the Anserini baselines according to official round 2 judgments from NIST:

group	runtag	nDCG@10	Judged@10	Recall@1000
anserini	r2.fusion1	0.4827	0.9543	0.6273
anserini	r2.fusion2	0.5553	0.9743	0.6630

Round 1

These are runs that can be easily reproduced with Anserini, from pre-built indexes available here (version from 2020/04/10, the official corpus used in round 1). They were prepared after round 1, and so we can report effectiveness results.

	index	field(s)	nDCG@10	Judged@10	Recall@1000
1	abstract	query	0.4100	0.8267	0.5279
2	abstract	question	0.5179	0.9833	0.6313
3	abstract	query+question	0.5514	0.9833	0.6989
4	abstract	query+question+narrative	0.5294	0.9333	0.6929
5	abstract	UDel query generator	0.5824	0.9567	0.6927
6	abstract	Covid19QueryGenerator	0.4520	0.6500	0.5061
7	full-text	query	0.3900	0.7433	0.6277
8	full-text	question	0.3439	0.9267	0.6389
9	full-text	query+question	0.4064	0.9367	0.6714
10	full-text	query+question+narrative	0.3280	0.7567	0.6591
11	full-text	UDel query generator	0.5407	0.9067	0.7214
12	full-text	Covid19QueryGenerator	0.2434	0.5233	0.5692
13	paragraph	query	0.4302	0.8400	0.4327
14	paragraph	question	0.4410	0.9167	0.5111
15	paragraph	query+question	0.5450	0.9733	0.5743
16	paragraph	query+question+narrative	0.4899	0.8967	0.5918
17	paragraph	UDel query generator	0.5544	0.9200	0.5640
18	paragraph	Covid19QueryGenerator	0.3180	0.5333	0.3552
19	-	reciprocal rank fusion(3, 9, 15)	0.5716	0.9867	0.8117
20	-	reciprocal rank fusion(5, 11, 17)	0.6019	0.9733	0.8121

IMPORTANT NOTE: These results cannot be reproduced using the indexer at HEAD because the indexing code has changed since the time the above indexes were generated. The results are only reproducible with the state of the indexer at the time of submission of TREC-COVID round 1 (which were conducted with the above indexes). Since it is not feasible to rerun and reevaluate with every indexer change, we have decided to perform all round 1 experiments only against the above indexes. For more discussion, see issue #1154; another major indexer change was #1101, which substantively changes the full-text and paragraph indexes.

The "UDel query generator" condition represents the query generator from run udel_fang_run3, contributed to the repo as part of commit 0d4bcd5 via #1142. Ablation analyses by lukuang revealed that the query generator provides the greatest contribution, and results above exceed udel_fang_run3 (thus making exact reproduction unnecessary).

For reference, the best automatic run is run sab20.1.meta.docs with nDCG@10 0.6080.

Why report nDCG@10 and Recall@1000? The first is one of the metrics used by the organizers. Given the pool depth of seven, nDCG@10 should be okay-ish, from the perspective of missing judgments, and nDCG is better than P@k since it captures relevance grades. Average precision is not included intentionally because of the shallow judgment pool, and hence likely to be very noisy. Recall@1000 captures the upper bound potential of downstream rerankers. Note that recall under the paragraph index isn't very good because of duplicates. Multiple paragraphs from the same article are retrieved, and duplicates are discarded; we start with top 1k hits, but end up with far fewer results per topic.

Caveats:

• These runs represent, essentially, testing on training data. Beware of generalization or lack thereof.
• Beware of unjudged documents.

Exact commands for reproducing these runs are found further down on this page.

Round 2: Reproduction Commands

Here are the reproduction commands for the individual runs.

First, download the pre-built indexes using our script:

python src/main/python/trec-covid/download_indexes.py --date 2020-05-01

Abstract runs:

target/appassembler/bin/SearchCollection -index indexes/lucene-index-cord19-abstract-2020-05-01 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round2.xml -topicfield query+question \
 -output runs/anserini.covid-r2.abstract.qq.bm25.txt -runtag anserini.covid-r2.abstract.qq.bm25.txt \
 -removedups -bm25 -hits 10000

target/appassembler/bin/SearchCollection -index indexes/lucene-index-cord19-abstract-2020-05-01 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round2-udel.xml -topicfield query \
 -output runs/anserini.covid-r2.abstract.qdel.bm25.txt -runtag anserini.covid-r2.abstract.qdel.bm25.txt \
 -removedups -bm25 -hits 10000

tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/anserini.covid-r2.abstract.qq.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/anserini.covid-r2.abstract.qdel.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'

python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/anserini.covid-r2.abstract.qq.bm25.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/anserini.covid-r2.abstract.qdel.bm25.txt

Full-text runs:

target/appassembler/bin/SearchCollection -index indexes/lucene-index-cord19-full-text-2020-05-01 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round2.xml -topicfield query+question \
 -output runs/anserini.covid-r2.full-text.qq.bm25.txt -runtag anserini.covid-r2.full-text.qq.bm25.txt \
 -removedups -bm25 -hits 10000

target/appassembler/bin/SearchCollection -index indexes/lucene-index-cord19-full-text-2020-05-01 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round2-udel.xml -topicfield query \
 -output runs/anserini.covid-r2.full-text.qdel.bm25.txt -runtag anserini.covid-r2.full-text.qdel.bm25.txt \
 -removedups -bm25 -hits 10000

tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/anserini.covid-r2.full-text.qq.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/anserini.covid-r2.full-text.qdel.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'

python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/anserini.covid-r2.full-text.qq.bm25.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/anserini.covid-r2.full-text.qdel.bm25.txt

Paragraph runs:

target/appassembler/bin/SearchCollection -index indexes/lucene-index-cord19-paragraph-2020-05-01 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round2.xml -topicfield query+question \
 -output runs/anserini.covid-r2.paragraph.qq.bm25.txt -runtag anserini.covid-r2.paragraph.qq.bm25.txt \
 -selectMaxPassage -bm25 -hits 10000

target/appassembler/bin/SearchCollection -index indexes/lucene-index-cord19-paragraph-2020-05-01 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round2-udel.xml -topicfield query \
 -output runs/anserini.covid-r2.paragraph.qdel.bm25.txt -runtag anserini.covid-r2.paragraph.qdel.bm25.txt \
 -selectMaxPassage -bm25 -hits 10000

tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/anserini.covid-r2.paragraph.qq.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/anserini.covid-r2.paragraph.qdel.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'

python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/anserini.covid-r2.paragraph.qq.bm25.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/anserini.covid-r2.paragraph.qdel.bm25.txt

We've written a convenience script to generate fusion runs that wraps trectools (v0.0.43):

python src/main/python/fusion.py --method RRF --out runs/anserini.covid-r2.fusion1.txt \
 --runs runs/anserini.covid-r2.abstract.qq.bm25.txt runs/anserini.covid-r2.full-text.qq.bm25.txt runs/anserini.covid-r2.paragraph.qq.bm25.txt

python src/main/python/fusion.py --method RRF --out runs/anserini.covid-r2.fusion2.txt \
 --runs runs/anserini.covid-r2.abstract.qdel.bm25.txt runs/anserini.covid-r2.full-text.qdel.bm25.txt runs/anserini.covid-r2.paragraph.qdel.bm25.txt

And to evaluate the fusion runs:

tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/anserini.covid-r2.fusion1.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/anserini.covid-r2.fusion2.txt | egrep '(ndcg_cut_10 |recall_1000 )'

python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/anserini.covid-r2.fusion1.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/anserini.covid-r2.fusion2.txt

To prepare the final runs for submission (removing round 1 judgments):

python tools/scripts/filter_run_with_qrels.py --discard --qrels tools/topics-and-qrels/qrels.covid-round1.txt \
 --input runs/anserini.covid-r2.fusion1.txt --output runs/anserini.r2.fusion1.txt --runtag r2.fusion1

python tools/scripts/filter_run_with_qrels.py --discard --qrels tools/topics-and-qrels/qrels.covid-round1.txt \
 --input runs/anserini.covid-r2.fusion2.txt --output runs/anserini.r2.fusion2.txt --runtag r2.fusion2

Evaluating runs with round 2 judgments:

tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round2.txt runs/anserini.r2.fusion1.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round2.txt runs/anserini.r2.fusion2.txt | egrep '(ndcg_cut_10 |recall_1000 )'

python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round2.txt --cutoffs 10 --run runs/anserini.r2.fusion1.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round2.txt --cutoffs 10 --run runs/anserini.r2.fusion2.txt

Round 1: Reproduction Commands

First, download the pre-built indexes using our script:

python src/main/python/trec-covid/download_indexes.py --date 2020-04-10

Here are the commands to generate the runs on the abstract index:

target/appassembler/bin/SearchCollection -index indexes/lucene-index-covid-2020-04-10 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round1.xml -topicfield query -removedups \
 -bm25 -output runs/run.covid-r1.abstract.query.bm25.txt

target/appassembler/bin/SearchCollection -index indexes/lucene-index-covid-2020-04-10 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round1.xml -topicfield question -removedups \
 -bm25 -output runs/run.covid-r1.abstract.question.bm25.txt

target/appassembler/bin/SearchCollection -index indexes/lucene-index-covid-2020-04-10 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round1.xml -topicfield query+question -removedups \
 -bm25 -output runs/run.covid-r1.abstract.query+question.bm25.txt

target/appassembler/bin/SearchCollection -index indexes/lucene-index-covid-2020-04-10 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round1.xml -topicfield query+question+narrative -removedups \
 -bm25 -output runs/run.covid-r1.abstract.query+question+narrative.bm25.txt

target/appassembler/bin/SearchCollection -index indexes/lucene-index-covid-2020-04-10 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round1-udel.xml -topicfield query -removedups \
 -bm25 -output runs/run.covid-r1.abstract.query-udel.bm25.txt

target/appassembler/bin/SearchCollection -index indexes/lucene-index-covid-2020-04-10 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round1.xml -topicfield query -querygenerator Covid19QueryGenerator -removedups \
 -bm25 -output runs/run.covid-r1.abstract.query-covid19.bm25.txt

Here are the commands to evaluate results on the abstract index:

tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.abstract.query.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.abstract.question.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.abstract.query+question.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.abstract.query+question+narrative.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.abstract.query-udel.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.abstract.query-covid19.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'

python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.abstract.query.bm25.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.abstract.question.bm25.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.abstract.query+question.bm25.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.abstract.query+question+narrative.bm25.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.abstract.query-udel.bm25.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.abstract.query-covid19.bm25.txt

Here are the commands to generate the runs on the full-text index:

target/appassembler/bin/SearchCollection -index indexes/lucene-index-covid-full-text-2020-04-10 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round1.xml -topicfield query -removedups \
 -bm25 -output runs/run.covid-r1.full-text.query.bm25.txt

target/appassembler/bin/SearchCollection -index indexes/lucene-index-covid-full-text-2020-04-10 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round1.xml -topicfield question -removedups \
 -bm25 -output runs/run.covid-r1.full-text.question.bm25.txt

target/appassembler/bin/SearchCollection -index indexes/lucene-index-covid-full-text-2020-04-10 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round1.xml -topicfield query+question -removedups \
 -bm25 -output runs/run.covid-r1.full-text.query+question.bm25.txt

target/appassembler/bin/SearchCollection -index indexes/lucene-index-covid-full-text-2020-04-10 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round1.xml -topicfield query+question+narrative -removedups \
 -bm25 -output runs/run.covid-r1.full-text.query+question+narrative.bm25.txt

target/appassembler/bin/SearchCollection -index indexes/lucene-index-covid-full-text-2020-04-10 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round1-udel.xml -topicfield query -removedups \
 -bm25 -output runs/run.covid-r1.full-text.query-udel.bm25.txt

target/appassembler/bin/SearchCollection -index indexes/lucene-index-covid-full-text-2020-04-10 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round1.xml -topicfield query -querygenerator Covid19QueryGenerator -removedups \
 -bm25 -output runs/run.covid-r1.full-text.query-covid19.bm25.txt

Here are the commands to evaluate results on the full-text index:

tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.full-text.query.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.full-text.question.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.full-text.query+question.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.full-text.query+question+narrative.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.full-text.query-udel.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.full-text.query-covid19.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'

python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.full-text.query.bm25.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.full-text.question.bm25.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.full-text.query+question.bm25.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.full-text.query+question+narrative.bm25.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.full-text.query-udel.bm25.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.full-text.query-covid19.bm25.txt

Here are the commands to generate the runs on the paragraph index:

target/appassembler/bin/SearchCollection -index indexes/lucene-index-covid-paragraph-2020-04-10 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round1.xml -topicfield query \
 -selectMaxPassage -bm25 -output runs/run.covid-r1.paragraph.query.bm25.txt

target/appassembler/bin/SearchCollection -index indexes/lucene-index-covid-paragraph-2020-04-10 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round1.xml -topicfield question \
 -selectMaxPassage -bm25 -output runs/run.covid-r1.paragraph.question.bm25.txt

target/appassembler/bin/SearchCollection -index indexes/lucene-index-covid-paragraph-2020-04-10 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round1.xml -topicfield query+question \
 -selectMaxPassage -bm25 -output runs/run.covid-r1.paragraph.query+question.bm25.txt

target/appassembler/bin/SearchCollection -index indexes/lucene-index-covid-paragraph-2020-04-10 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round1.xml -topicfield query+question+narrative \
 -selectMaxPassage -bm25 -output runs/run.covid-r1.paragraph.query+question+narrative.bm25.txt

target/appassembler/bin/SearchCollection -index indexes/lucene-index-covid-paragraph-2020-04-10 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round1-udel.xml -topicfield query \
 -selectMaxPassage -bm25 -output runs/run.covid-r1.paragraph.query-udel.bm25.txt

target/appassembler/bin/SearchCollection -index indexes/lucene-index-covid-paragraph-2020-04-10 \
 -topicReader Covid -topics tools/topics-and-qrels/topics.covid-round1.xml -topicfield query -querygenerator Covid19QueryGenerator \
 -selectMaxPassage -bm25 -output runs/run.covid-r1.paragraph.query-covid19.bm25.txt

Here are the commands to evaluate results on the paragraph index:

tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.paragraph.query.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.paragraph.question.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.paragraph.query+question.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.paragraph.query+question+narrative.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.paragraph.query-udel.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.paragraph.query-covid19.bm25.txt | egrep '(ndcg_cut_10 |recall_1000 )'

python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.paragraph.query.bm25.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.paragraph.question.bm25.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.paragraph.query+question.bm25.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.paragraph.query+question+narrative.bm25.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.paragraph.query-udel.bm25.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.paragraph.query-covid19.bm25.txt

We've written a convenience script to generate fusion runs that wraps trectools (v0.0.43):

python src/main/python/fusion.py --method RRF --out runs/run.covid-r1.fusion1.txt \
 --runs runs/run.covid-r1.abstract.query+question.bm25.txt runs/run.covid-r1.full-text.query+question.bm25.txt runs/run.covid-r1.paragraph.query+question.bm25.txt

python src/main/python/fusion.py --method RRF --out runs/run.covid-r1.fusion2.txt \
 --runs runs/run.covid-r1.abstract.query-udel.bm25.txt runs/run.covid-r1.full-text.query-udel.bm25.txt runs/run.covid-r1.paragraph.query-udel.bm25.txt

And to evalute the fusion runs:

tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.fusion1.txt | egrep '(ndcg_cut_10 |recall_1000 )'
tools/eval/trec_eval.9.0.4/trec_eval -c -M1000 -m all_trec tools/topics-and-qrels/qrels.covid-round1.txt runs/run.covid-r1.fusion2.txt | egrep '(ndcg_cut_10 |recall_1000 )'

python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.fusion1.txt
python tools/eval/measure_judged.py --qrels tools/topics-and-qrels/qrels.covid-round1.txt --cutoffs 10 --run runs/run.covid-r1.fusion2.txt