diff --git a/config.py b/config.py
index 5c4dcae..6111f5c 100644
--- a/config.py
+++ b/config.py
@@ -3,10 +3,10 @@
 API_TOKEN = ""
 
 
-CLASSIFICATION_PRETRAINED_MODEL = ""
+CLASSIFICATION_PRETRAINED_MODEL = "adsabs/ASTROBERT"
 CLASSIFICATION_PRETRAINED_MODEL_REVISION = "SciX-Categorizer"
 CLASSIFICATION_PRETRAINED_MODEL_TOKENIZER = "adsabs/ASTROBERT"
 
 CLASSIFICATION_THRESHOLDS = [0.06, 0.03, 0.04, 0.02, 0.99, 0.02, 0.02, 0.99]
-ADDITIONAL_EARTH_SCIENCE_PROCESSING = True
+ADDITIONAL_EARTH_SCIENCE_PROCESSING = False
 ADDITIONAL_EARTH_SCIENCE_PROCESSING_THRESHOLD = 0.015
diff --git a/harvest_solr.py b/harvest_solr.py
index b448a4c..5fbada4 100644
--- a/harvest_solr.py
+++ b/harvest_solr.py
@@ -105,20 +105,11 @@ def transform_r_json(r_json):
     Extract the needed information from the json response from the solr query.
     """
 
-    # Bibcoded and titles are always present
-    bibcodes = [doc['bibcode'] for doc in r_json['response']['docs']]
-    titles = [doc['title'][0] for doc in r_json['response']['docs']] # without [0] it returns a list
-    # Abstracts are not always present
-    abstracts = []
+    record_list = []
     for doc in r_json['response']['docs']:
-        if 'abstract' in doc:
-            abstracts.append(doc['abstract'])
-        else:
-            abstracts.append('')
-
-    record_list = [{'bibcode': bibcodes[i],
-                    'title' : titles[i],
-                    'abstract' : abstracts[i],
-                    'text': f'{titles[i]} {abstracts[i]}'} for i in range(len(bibcodes))]
+        if ('title' in doc) or ('abstract' in doc):
+            doc['text'] = f"{doc['title']} {doc['abstract']}"
+            record_list.append(doc)
+        
 
     return record_list
diff --git a/quick_classifier.py b/quick_classifier.py
index bc82f20..559e09a 100644
--- a/quick_classifier.py
+++ b/quick_classifier.py
@@ -13,6 +13,8 @@
 import os
 import csv
 import argparse
+import sys
+# sys.path.append("/Users/thomasallen/Code/ADS/classifier_script/venv/lib/python3.11/site-packages")
 
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
@@ -85,6 +87,8 @@ def write_batch_to_tsv(batch, header, filename, mode='w', include_header=True):
         # Harvest Title and Abstract from Solr
         bibcode_batch = bibcodes[output_idx:output_idx+output_batch]
         records = harvest_solr(bibcode_batch, start_index=0, fields='bibcode, title, abstract')
+        if len(records) == 0:
+            sys.exit('No records returned from harvesting Solr - exiting')
 
         for index, record in enumerate(records):
             record = score_record(record)