{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00988446,0.002244467,0.003020145,0.001613018,0.001090073,0.002825211,0.004128054,0.003694931,0.003855173],"category_scores_gemma":[0.04832206,0.0008146566,0.001162613,0.001757933,0.001898551,0.0093127,0.00430238,0.004149685,0.001297158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001454705,"about_ca_system_score_gemma":0.001849134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001917717,"about_ca_topic_score_gemma":0.003061764,"domain_scores_codex":[0.9883876,0.00538301,0.0008590032,0.002462897,0.002352913,0.0005544581],"domain_scores_gemma":[0.9429019,0.04266129,0.002257681,0.007372573,0.003924748,0.0008816862],"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":[0.001373935,0.001426422,0.01162209,0.0005643048,0.0002502292,0.0003642141,0.001998744,0.2347496,0.01768917,0.03109579,0.01708812,0.6817774],"study_design_scores_gemma":[0.0001074122,0.0001735658,0.0005102205,0.00001762523,0.00003922844,0.0001191885,0.0002555104,0.9607071,0.005059374,0.03015342,0.002831169,0.00002626151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04329183,0.0003066156,0.9513758,0.0007928361,0.00004704852,0.0002276499,0.0001200994,0.002371843,0.001466188],"genre_scores_gemma":[0.5127568,0.0002067417,0.4824381,0.0008016307,0.000201756,0.0004497688,0.0008665992,0.0003196863,0.001958931],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00988446,"threshold_uncertainty_score":0.05227464,"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."}}