{"id":"W4319791556","doi":"10.1002/prs.12443","title":"Reducing the risk of intentional domino effects in process plants: A risk‐based minimax strategy","year":2023,"lang":"en","type":"article","venue":"Process Safety Progress","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimax; Risk analysis (engineering); Risk management; Risk assessment; Bayesian network; Computer science; Interdependence; Domino effect; Probabilistic risk assessment; Process (computing); Game theory; Probabilistic logic; Operations research; Computer security; Engineering; Mathematical optimization; Business; Economics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003340668,0.001166822,0.001209715,0.0009231472,0.0006662529,0.001497514,0.001676711,0.001625165,0.002634555],"category_scores_gemma":[0.006105329,0.000532632,0.0007459014,0.0004296207,0.002239708,0.001913985,0.002215001,0.001488441,0.0002042368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001451779,"about_ca_system_score_gemma":0.001434337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001340929,"about_ca_topic_score_gemma":0.0008725854,"domain_scores_codex":[0.9986019,0.00078182,0.00003501105,0.0001938689,0.0002374937,0.0001499787],"domain_scores_gemma":[0.996039,0.003020443,0.0004035824,0.0001342981,0.0001999036,0.0002027107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001301513,0.0001037921,0.0006094361,0.00008290579,0.0000988715,0.0001664143,0.0001140122,0.8746383,0.00403745,0.1042808,0.0007521581,0.01498571],"study_design_scores_gemma":[0.00002126625,0.000107993,0.0001324062,0.00001760636,0.00002012185,0.00003860855,0.00001898359,0.9571443,0.0007016219,0.04134689,0.0004360711,0.00001406495],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05641966,0.0002160026,0.9330762,0.0007584769,0.0000231827,0.0001018719,0.00003467279,0.00008357979,0.009286343],"genre_scores_gemma":[0.944814,0.0001862715,0.05086483,0.0001635482,0.0000275658,0.0001677374,0.00001862938,0.00002683014,0.003730523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003340668,"threshold_uncertainty_score":0.01766729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04066068976061939,"score_gpt":0.3717608377605483,"score_spread":0.3311001479999289,"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."}}