{"id":"W3015333924","doi":"10.1145/3377930.3390226","title":"GeneCAI","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Army Research Office; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Computer science; Hyperparameter; Scalability; Pareto principle; Artificial intelligence; Multi-objective optimization; Machine learning; Mathematical optimization","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000991514,0.00009800446,0.0001040109,0.00003593293,0.00003235175,0.0002459946,0.001307555,0.00008529923,0.00004561037],"category_scores_gemma":[0.00005441547,0.0000862903,0.00004587836,0.0000822869,0.000007461206,0.00007207036,0.001915558,0.0003764047,0.0006063781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001333148,"about_ca_system_score_gemma":0.00008097908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005557526,"about_ca_topic_score_gemma":0.000003245158,"domain_scores_codex":[0.9991245,0.00004667023,0.00011944,0.0004718034,0.0001483033,0.00008933834],"domain_scores_gemma":[0.9989134,0.0000209849,0.00007300793,0.0008966666,0.00002594666,0.00007000733],"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":[0.00000116582,0.00002566214,0.0005147475,0.00007846603,0.0000213785,0.000009822164,0.0003159064,0.0004319836,0.0003757293,0.7056376,0.06029754,0.23229],"study_design_scores_gemma":[0.00007467955,0.00001902194,0.01120276,0.00001539671,0.000005562845,0.000003228254,0.000003844649,0.6751696,0.0002361161,0.03790993,0.2750559,0.0003038869],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001075247,0.00006584167,0.9215241,0.02342322,0.000386586,0.00006389477,0.000002886218,0.0005799741,0.05384598],"genre_scores_gemma":[0.4872974,0.00006047988,0.5064832,0.002936276,0.0003388607,0.00002644983,0.0001848309,0.00001339145,0.002659122],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6747377,"threshold_uncertainty_score":0.7793967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05057353322850214,"score_gpt":0.2905393398512218,"score_spread":0.2399658066227197,"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."}}