{"id":"W2408046200","doi":"10.1145/2896982.2896985","title":"Model management for regulatory compliance","year":2016,"lang":"en","type":"article","venue":"","topic":"Safety Systems Engineering in Autonomy","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; McMaster University","funders":"","keywords":"Exploit; Context (archaeology); Compliance (psychology); Risk analysis (engineering); Software; Computer security; Computer science; Process management; Service (business); Knowledge management; Business; Marketing","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":[0.00006501126,0.00009124341,0.00008739615,0.00003825108,0.00001369884,0.00000549358,0.0001128966,0.00002941343,0.00001872666],"category_scores_gemma":[0.000001876462,0.00006876964,0.00003729032,0.0000323921,0.000008565664,0.00006126932,0.00001376616,0.00001475979,0.00008901486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009453383,"about_ca_system_score_gemma":0.000002412026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.899885e-7,"about_ca_topic_score_gemma":3.396189e-7,"domain_scores_codex":[0.9995295,0.000001328213,0.000129646,0.0001066169,0.00005847291,0.0001743832],"domain_scores_gemma":[0.9996629,0.00002494291,0.000007264221,0.0002558721,0.00001237915,0.00003660578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005032807,0.000008895932,0.00005378662,0.0005868496,0.0001286858,0.000001271034,0.00003454375,0.7520155,0.01475321,0.1549929,0.03560222,0.04181718],"study_design_scores_gemma":[0.0004158624,0.00000440128,0.0006672111,0.0001018555,0.000005776812,0.000001502378,0.000004159567,0.9394081,0.001987524,0.00103936,0.05617189,0.000192317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001339166,0.00005909922,0.9568542,0.00006691814,0.0003049046,0.0002528111,0.000006538916,0.0008553263,0.04026104],"genre_scores_gemma":[0.913822,0.00000911048,0.07423656,0.00002311296,0.00004965637,0.0001615357,7.923384e-7,0.00004253572,0.01165468],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9124829,"threshold_uncertainty_score":0.2804345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03182417741040287,"score_gpt":0.2214483617015116,"score_spread":0.1896241842911088,"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."}}