{"id":"W3174593637","doi":"10.21428/594757db.4ea59c2e","title":"Enhancing Pretrained Models with Domain Knowledge","year":2021,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Ford Motor Company","keywords":"Computer science; Variety (cybernetics); Domain (mathematical analysis); Domain knowledge; Artificial intelligence; Natural language processing; Language model; Software; Machine learning; Programming language","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00154837,0.002452078,0.001143309,0.00167268,0.0004587249,0.001453652,0.00200324,0.002068561,0.002858151],"category_scores_gemma":[0.007024915,0.0009127233,0.001325298,0.001432741,0.0007515337,0.00330688,0.00143822,0.004554211,0.002997408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008995677,"about_ca_system_score_gemma":0.001374969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008116751,"about_ca_topic_score_gemma":0.01866995,"domain_scores_codex":[0.9991778,0.0002484706,0.00004417582,0.0003359074,0.0001073002,0.00008639561],"domain_scores_gemma":[0.9956152,0.003009462,0.0001505762,0.0006184394,0.0005046497,0.0001015525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002891055,0.0006520399,0.006337454,0.0004213913,0.0004076838,0.0003688394,0.000327579,0.4970916,0.01386568,0.003401054,0.02762165,0.4492159],"study_design_scores_gemma":[0.00001710211,0.00005838024,0.0008505417,0.00005561015,0.00006588451,0.00006213335,0.00005131805,0.9859483,0.004306566,0.004076288,0.004484024,0.00002388572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1419114,0.005886023,0.8162534,0.002791767,0.0008742332,0.000330809,0.002863502,0.01743718,0.01165176],"genre_scores_gemma":[0.7472985,0.003196408,0.2148625,0.00259955,0.0005868941,0.0006366591,0.01671064,0.001290881,0.01281796],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008116751,"threshold_uncertainty_score":0.01613903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02137786640740907,"score_gpt":0.2384732776212545,"score_spread":0.2170954112138455,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}