{"id":"W7066065766","doi":"","title":"A Framework for Regulatory Excellence","year":2015,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Excellence; Regulator; Regulatory reform; Regulatory state; Master regulator","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000386769,0.0001559884,0.0001384579,0.00001975321,0.00009561305,0.00004737984,0.0003070936,0.0002358069,0.00003977353],"category_scores_gemma":[0.0007738817,0.0001510526,0.00008868788,0.0000592443,0.00009386751,0.000007640137,0.0001280477,0.0001891088,0.0001318191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002257481,"about_ca_system_score_gemma":0.000107094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003080757,"about_ca_topic_score_gemma":0.00002621221,"domain_scores_codex":[0.9990216,0.00004056346,0.0002282276,0.0002443829,0.0001804825,0.0002847638],"domain_scores_gemma":[0.9988991,0.00003067277,0.0001069024,0.0005907781,0.0001239721,0.0002485999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001051788,0.0003720376,0.02251333,0.0005831003,0.0003477967,0.00001318235,0.001031827,0.003637658,0.05820448,0.553723,0.3499608,0.008561006],"study_design_scores_gemma":[0.0008536007,0.000457415,0.0005524307,0.00005194266,0.00002043203,0.00001907964,0.0001404334,0.0007789825,0.01808749,0.01278741,0.9658474,0.0004034323],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7542324,0.002963376,0.1874259,0.001620678,0.001927635,0.001230334,0.00007302056,0.0002257279,0.05030092],"genre_scores_gemma":[0.8506531,0.00002701739,0.1413164,0.002416435,0.0009185554,0.00007431059,0.0001129305,0.00004712036,0.004434131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6158865,"threshold_uncertainty_score":0.6159745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01777021943980322,"score_gpt":0.2807085241965123,"score_spread":0.2629383047567091,"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."}}