{"id":"W3175423610","doi":"10.31234/osf.io/76mkg","title":"Sampling heuristics for active function learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Heuristics; Sampling (signal processing); Heuristic; Computer science; Simple (philosophy); Function (biology); Machine learning; Adaptation (eye); Artificial intelligence; Mathematical optimization; Mathematics; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001201411,0.00005910573,0.00007158864,0.00002565212,0.0001885554,0.0001142208,0.0001190422,0.00002718464,0.00003208565],"category_scores_gemma":[0.000255134,0.0000552825,0.00004452102,0.0001496843,0.000005597147,0.0001153855,0.00008167885,0.0001474511,0.00002243984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001270571,"about_ca_system_score_gemma":0.0000409126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006798799,"about_ca_topic_score_gemma":0.000001625496,"domain_scores_codex":[0.9994252,0.00003509546,0.00007979078,0.0002253607,0.00009130246,0.0001432639],"domain_scores_gemma":[0.9994764,0.0001968037,0.00003405687,0.0001393167,0.0001153122,0.00003805917],"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":[0.000007717221,0.00003439807,0.0003332497,0.00001767177,0.00002388682,0.000004720311,0.00028438,0.009236353,0.001062133,0.09487262,0.0005209142,0.893602],"study_design_scores_gemma":[0.0003058017,0.0001222646,0.001524641,0.00001066073,0.000008338646,0.00001421513,0.0001365164,0.8115172,0.002478353,0.007534028,0.1761841,0.0001639006],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001214437,0.00003063706,0.9911042,0.0005424715,0.0004111412,0.00003585233,4.711616e-7,0.0002074706,0.006453278],"genre_scores_gemma":[0.3298805,0.000007894876,0.6516169,0.0004787987,0.0003764506,0.0000137418,0.00001920837,0.00001286155,0.01759371],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.893438,"threshold_uncertainty_score":0.2254355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02290919935728985,"score_gpt":0.2844340931232484,"score_spread":0.2615248937659585,"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."}}