{"id":"W4323035109","doi":"10.1109/ner52421.2023.10123846","title":"Learning signatures of decision making from many individuals playing the same game","year":2023,"lang":"en","type":"article","venue":"","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Institute of Biomedical Imaging and Bioengineering; McKnight Foundation; National Institute of Mental Health; National Institute on Drug Abuse; National Institutes of Health; National Science Foundation","keywords":"Computer science; ENCODE; Space (punctuation); Sequence (biology); Artificial intelligence; Task (project management); Human behavior; Term (time); Machine learning; Process (computing); Scale (ratio)","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":[0.0007375075,0.0001192382,0.0001600488,0.0001895952,0.0001385284,0.0002028134,0.001860931,0.00007763264,0.00004548213],"category_scores_gemma":[0.0004454895,0.00008071984,0.00005444273,0.000674807,0.00004521769,0.000348202,0.001516209,0.0002657489,0.00006530465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001665105,"about_ca_system_score_gemma":0.00002612473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001037426,"about_ca_topic_score_gemma":0.000008005241,"domain_scores_codex":[0.9986161,0.00009673678,0.0002556271,0.0003399477,0.0004526676,0.0002389343],"domain_scores_gemma":[0.9973603,0.001735231,0.000149027,0.0006924858,0.00003636263,0.00002661561],"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":[0.00001034422,0.00003251912,0.02386151,0.0000121245,0.00007213597,0.00005795513,0.007885158,0.001224077,0.008912205,0.03975491,0.03143614,0.8867409],"study_design_scores_gemma":[0.0005669686,0.0003623693,0.3374097,0.001183374,0.00003886906,0.0000188154,0.001405569,0.3619514,0.03401043,0.2505017,0.01163305,0.0009177151],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4287458,0.00007758538,0.5672253,0.0003460121,0.0001556632,0.0001365856,0.00001550287,0.001328557,0.001968987],"genre_scores_gemma":[0.7726321,0.00001436257,0.2270649,0.0001648129,0.00002733823,0.000007526518,0.000008806809,0.000009218966,0.00007102099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8858232,"threshold_uncertainty_score":0.3458104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02617859748665355,"score_gpt":0.300911395368067,"score_spread":0.2747327978814135,"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."}}