{"id":"W1629495025","doi":"10.48550/arxiv.1206.4647","title":"Active Learning for Matching Problems","year":2012,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Matching (statistics); Computer science; Active learning (machine learning); Probabilistic logic; Selection (genetic algorithm); Machine learning; Preference; Preference learning; Artificial intelligence; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002606235,0.0001118942,0.0001096766,0.00008784982,0.0002879984,0.00004879945,0.0004487092,0.00004872655,0.0000141771],"category_scores_gemma":[0.00003392499,0.0001176523,0.00007994564,0.0003165206,0.00002312498,0.0008012092,0.000168733,0.0002256008,0.00007045537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004781999,"about_ca_system_score_gemma":0.00002042272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004954279,"about_ca_topic_score_gemma":0.000002413132,"domain_scores_codex":[0.9991373,0.00007632101,0.00006971513,0.0002991182,0.00004381437,0.0003737075],"domain_scores_gemma":[0.9993889,0.0001329333,0.00008309783,0.0002227629,0.00004474753,0.000127515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000276612,0.0001685326,0.01751597,0.00005711599,0.00007700291,0.00001611554,0.004415629,0.3345197,0.0004579499,0.6082154,0.0001971117,0.03433183],"study_design_scores_gemma":[0.0007705946,0.0001272232,0.004138751,0.00002883244,0.0000227858,0.00001044765,0.0003335416,0.9612845,0.0003507653,0.015779,0.01676283,0.0003906831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1593524,0.00001989226,0.8372663,0.00007450837,0.0002151456,0.0001124874,5.767033e-7,0.0002181212,0.002740597],"genre_scores_gemma":[0.9905661,0.000006699609,0.005575252,0.00005187254,0.0001180022,8.940618e-7,0.000002536204,0.000009918468,0.003668777],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.831691,"threshold_uncertainty_score":0.4797721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04868786213904355,"score_gpt":0.190466202983027,"score_spread":0.1417783408439834,"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."}}