{"id":"W4407939460","doi":"10.1007/s10994-025-06752-x","title":"A contrastive neural disentanglement approach for query performance prediction","year":2025,"lang":"en","type":"article","venue":"Machine Learning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of Waterloo; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Artificial neural network; Natural language processing","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":[0.002414919,0.0007365352,0.0009495061,0.00163671,0.0004020722,0.00142924,0.001895235,0.0008662877,0.00269717],"category_scores_gemma":[0.008778914,0.0003221452,0.0004306669,0.001489886,0.0005522009,0.002755268,0.001416585,0.002053837,0.0005458133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009321145,"about_ca_system_score_gemma":0.0007518295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004867186,"about_ca_topic_score_gemma":0.006961329,"domain_scores_codex":[0.9987669,0.0003581817,0.00006364139,0.0003018451,0.0003849838,0.0001244155],"domain_scores_gemma":[0.9961756,0.002353674,0.0003037151,0.0004626443,0.0005820076,0.0001222521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00246515,0.001107013,0.01836908,0.0002207201,0.0002481618,0.0002856175,0.0002677334,0.1119223,0.0522722,0.052451,0.005726799,0.7546642],"study_design_scores_gemma":[0.00001791105,0.0001196758,0.002866092,0.000009366476,0.00003273458,0.00004969507,0.00002078107,0.9807998,0.003934805,0.01114053,0.0009903222,0.00001819748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1277003,0.001272948,0.861486,0.0007299286,0.0001748691,0.000130322,0.0004997913,0.001022153,0.006983682],"genre_scores_gemma":[0.901279,0.0002778984,0.09360649,0.0001719262,0.0001877787,0.00007961164,0.0004403817,0.0001004938,0.003856365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004867186,"threshold_uncertainty_score":0.01277143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009212424396369148,"score_gpt":0.238969669819153,"score_spread":0.2297572454227838,"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."}}