{"id":"W2152225808","doi":"10.1109/tkde.2010.33","title":"Asking Generalized Queries to Domain Experts to Improve Learning","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"China University of Geosciences; Shanghai Jiao Tong University; University of Pennsylvania","keywords":"Computer science; Oracle; Construct (python library); Classifier (UML); Ask price; Domain (mathematical analysis); Set (abstract data type); Machine learning; Class (philosophy); Labeled data; Active learning (machine learning); Artificial intelligence; Theoretical computer science; 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":[0.0002732096,0.0001839547,0.0001641339,0.0002256699,0.0002391404,0.0002076542,0.0006227248,0.00006061877,0.00001543046],"category_scores_gemma":[0.00002760398,0.0001796962,0.00003054804,0.000324134,0.000009896495,0.0003938798,0.00003720854,0.0004146756,0.00004767792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001413057,"about_ca_system_score_gemma":0.00002428297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004357141,"about_ca_topic_score_gemma":0.00005947459,"domain_scores_codex":[0.9988756,0.00002592394,0.0001618217,0.0005335399,0.000114085,0.0002889908],"domain_scores_gemma":[0.9988844,0.00009229813,0.00001803489,0.0007313287,0.00003187698,0.0002420316],"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":[0.00001824053,0.0001151089,0.00002011267,0.00005978722,0.00005204965,0.00001743925,0.00635638,0.07961161,0.2781409,0.002155364,0.0005999985,0.632853],"study_design_scores_gemma":[0.0004547292,0.0001891897,0.00008337916,0.00006748383,0.00001040173,0.00003408696,0.00006715989,0.7895482,0.02870722,0.00002746908,0.1802563,0.0005544176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04446828,0.00004838717,0.952889,0.0003232337,0.001622263,0.0001216346,0.00001280131,0.0003441256,0.0001703345],"genre_scores_gemma":[0.7668335,0.00001127286,0.2320894,0.0001019093,0.0002584073,0.00004708506,0.00000572267,0.00002917436,0.0006235254],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7223653,"threshold_uncertainty_score":0.7327798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01083209693741239,"score_gpt":0.265796040034446,"score_spread":0.2549639430970336,"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."}}