{"id":"W2023453328","doi":"10.1145/1401890.1401950","title":"Active learning with direct query construction","year":2008,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Active learning (machine learning); Computer science; Machine learning; Semi-supervised learning; Decision tree; Artificial intelligence; Process (computing); Construct (python library); Tree (set theory); Labeled data; Instance-based learning; ID3 algorithm; Incremental decision tree; Decision tree learning; 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":[0.00006291425,0.00007302289,0.00008430386,0.0000513104,0.0002329818,0.00003179718,0.0001502701,0.00002191664,0.00003478808],"category_scores_gemma":[0.00002165338,0.00005226755,0.00002111136,0.0002085964,0.00005508842,0.0002592528,0.00004446374,0.0001934571,0.00004787221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001348628,"about_ca_system_score_gemma":0.00004055483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001228177,"about_ca_topic_score_gemma":0.000003631992,"domain_scores_codex":[0.9993922,0.00005935349,0.00006029818,0.0002094077,0.0001438719,0.0001349439],"domain_scores_gemma":[0.9996852,0.00005124923,0.00004116785,0.0001396905,0.00003698866,0.00004568168],"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":[0.00003785271,0.00008620039,0.07167973,0.00001104053,0.00007965942,0.0002069986,0.003860872,0.01005221,0.000404705,0.03263704,0.000918704,0.880025],"study_design_scores_gemma":[0.002535112,0.001556671,0.121435,0.00008659026,0.00001964778,0.004612158,0.0008061401,0.7575247,0.02059686,0.0009369614,0.08835526,0.001534903],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1150141,0.00001687135,0.7202151,0.0004301175,0.0001670902,0.00005234146,1.35936e-7,0.0006747511,0.1634295],"genre_scores_gemma":[0.8746864,0.00001007589,0.119514,0.00007896035,0.00006431545,0.000003500528,8.658403e-7,0.000005349763,0.005636591],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8784901,"threshold_uncertainty_score":0.2131409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006472633424440829,"score_gpt":0.1992903642217834,"score_spread":0.1928177307973425,"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."}}