{"id":"W2036803902","doi":"10.1139/l10-035","title":"Technical hazard identification in water treatment using fault tree analysis","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of British Columbia","funders":"","keywords":"Fault tree analysis; Hazard analysis; Identification (biology); Hazard; Fault (geology); Reliability engineering; Computer science; Tree (set theory); Risk analysis (engineering); Engineering; Mathematics; Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0001868239,0.0001184635,0.0002287641,0.0009954306,0.00002518187,0.00007099787,0.000110188,0.00009579377,0.00005520339],"category_scores_gemma":[0.00001612919,0.00009867915,0.0001077215,0.0003503762,0.000008719542,0.0002465588,0.00000262817,0.0001816979,0.000002879002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002976658,"about_ca_system_score_gemma":0.00005768548,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008786474,"about_ca_topic_score_gemma":0.6845111,"domain_scores_codex":[0.9991839,0.000006826067,0.0004070595,0.00007559702,0.00008453894,0.0002421044],"domain_scores_gemma":[0.9995361,0.000008329611,0.0000350603,0.0001400167,0.00005378584,0.0002267326],"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":[5.953544e-7,0.000002814546,0.001907863,0.000009832347,0.0000659594,0.00003763747,0.0001966823,0.9580882,0.03950418,0.00001946956,0.00003835896,0.0001283842],"study_design_scores_gemma":[0.0003103236,0.00002019521,0.0104069,0.00003953683,0.0001708538,0.00010661,0.00002171905,0.97437,0.01123672,0.00001047696,0.003094774,0.0002118673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.830231,0.0001770822,0.1676889,0.00005572985,0.001200549,0.0001027593,0.000005534779,0.00004018629,0.0004982906],"genre_scores_gemma":[0.9989501,0.000005696028,0.0008306008,0.000002225466,0.0001415602,0.000002695437,0.000005845343,0.00002364433,0.00003763666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6836324,"threshold_uncertainty_score":0.4024019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007828175687240937,"score_gpt":0.1892912587867137,"score_spread":0.1814630830994728,"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."}}