{"id":"W3176447062","doi":"10.1609/aaai.v35i11.17161","title":"Interpretable Sequence Classification via Discrete Optimization","year":2021,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Schwartz/Reisman Emergency Medicine Institute; Vector Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research; Agencia Nacional de Investigación y Desarrollo; Microsoft Research","keywords":"Computer science; Artificial intelligence; Sequence (biology); Automaton; Machine learning; TRACE (psycholinguistics); Class (philosophy); Task (project management); Pattern recognition (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":[0.0001699577,0.00007570809,0.00008188,0.00004002494,0.0001000999,0.0001557843,0.0004002507,0.00004617491,0.000265364],"category_scores_gemma":[0.000131685,0.00007127062,0.00002819906,0.0004098749,0.00001613593,0.0005079556,0.0001782883,0.0001426377,0.00005807184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006218982,"about_ca_system_score_gemma":0.0001120428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007778119,"about_ca_topic_score_gemma":0.00001688355,"domain_scores_codex":[0.9989859,0.0001309575,0.000177521,0.0003551673,0.0001804087,0.0001699974],"domain_scores_gemma":[0.9990453,0.00006093018,0.00006400335,0.0005844109,0.0001750029,0.00007041468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007290278,0.00007748707,0.01808634,0.0001271324,0.00002599312,0.00006365948,0.00204601,0.374874,0.01086651,0.4147421,0.001640652,0.1774429],"study_design_scores_gemma":[0.00004620353,0.00001513117,0.001176432,0.00001421082,0.00000125776,0.00002744595,0.00001887411,0.9958136,0.0007573846,0.0009256276,0.001116136,0.00008770849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003681016,0.00004836278,0.9763573,0.006846089,0.0002596781,0.00006534797,5.650381e-7,0.0002635258,0.01579098],"genre_scores_gemma":[0.6054273,0.00001261623,0.3919354,0.0006801874,0.00002579653,0.000009696585,0.00001575988,0.000005394036,0.001887875],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6209396,"threshold_uncertainty_score":0.2906331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03444280696846177,"score_gpt":0.318180685123089,"score_spread":0.2837378781546273,"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."}}