{"id":"W4396708953","doi":"10.1007/s10479-024-05986-7","title":"A robust, resilience machine learning with risk approach: a case study of gas consumption","year":2024,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Center for Interuniversity Research and Analysis on Organizations","funders":"","keywords":"Gas consumption; Resilience (materials science); Consumption (sociology); Computer science; Artificial intelligence; Risk analysis (engineering); Economics; Business; Environmental economics; Sociology; Materials science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002771913,0.0006408927,0.0006508578,0.0009591756,0.0005743174,0.001214194,0.001354456,0.00193199,0.001553534],"category_scores_gemma":[0.008427927,0.0002711867,0.0006163369,0.0009585868,0.0009968565,0.001365654,0.0007976773,0.001014496,0.0001529715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001457332,"about_ca_system_score_gemma":0.0007753921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01409657,"about_ca_topic_score_gemma":0.009130834,"domain_scores_codex":[0.9991875,0.0004506051,0.00002892683,0.0001155786,0.0001428205,0.00007447684],"domain_scores_gemma":[0.9952812,0.003864475,0.0002054369,0.0002680259,0.0002795,0.000101231],"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":[0.0003724656,0.0002601648,0.009668543,0.0001012363,0.0000951797,0.001178889,0.0002603862,0.9383063,0.001450847,0.0132966,0.001571064,0.03343841],"study_design_scores_gemma":[0.00001811638,0.00008664678,0.001625512,0.000006159968,0.00001875035,0.0001235242,0.0001255625,0.9917047,0.0008406191,0.004937797,0.0004984668,0.00001399816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8358802,0.0007301248,0.1530179,0.002245171,0.00005335895,0.0001473267,0.000313982,0.0005139671,0.007098133],"genre_scores_gemma":[0.9820247,0.000099753,0.01621965,0.00003270689,0.00001418626,0.00002136983,0.00005856291,0.00003637145,0.001492693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01409657,"threshold_uncertainty_score":0.02802902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1717904189902343,"score_gpt":0.3865611991295567,"score_spread":0.2147707801393224,"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."}}