{"id":"W4327645767","doi":"10.1007/978-3-031-28244-7_1","title":"Self-supervised Contrastive BERT Fine-tuning for Fusion-Based Reviewed-Item Retrieval","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Ranking (information retrieval); Artificial intelligence; Matching (statistics); Selection (genetic algorithm); Natural language processing; Information retrieval; Task (project management); Embedding; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001759521,0.0008296769,0.001814299,0.001729048,0.0005553995,0.0009251341,0.002309885,0.00128191,0.004671408],"category_scores_gemma":[0.003159323,0.0003736941,0.000841341,0.001723092,0.0005263562,0.001578602,0.001634974,0.001189746,0.00275086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005861128,"about_ca_system_score_gemma":0.0009269022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003833361,"about_ca_topic_score_gemma":0.007551578,"domain_scores_codex":[0.9989477,0.0002058605,0.00007564349,0.000311492,0.0003023011,0.000157022],"domain_scores_gemma":[0.9986299,0.0005416503,0.00008137157,0.0002182577,0.000458741,0.00007013304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008099151,0.0004609415,0.000992367,0.0002114445,0.0001554152,0.0000713173,0.0000822076,0.03149619,0.07000098,0.00163226,0.009489745,0.8845973],"study_design_scores_gemma":[0.00004485263,0.000189614,0.001528,0.00001759995,0.0001035758,0.0001401981,0.00003733134,0.9728082,0.01981399,0.002188648,0.003098437,0.00002949285],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04501799,0.00246863,0.9422174,0.0001585838,0.0002391701,0.0001804571,0.0004302488,0.005920604,0.003367017],"genre_scores_gemma":[0.5737909,0.0007173121,0.4110079,0.0004327456,0.0003822903,0.0002942898,0.002272365,0.0006278949,0.0104742],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004671408,"threshold_uncertainty_score":0.01562738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03059425526710696,"score_gpt":0.2597612823452757,"score_spread":0.2291670270781688,"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."}}