{"id":"W2034283895","doi":"10.1002/sdr.338","title":"Learning from incidents: from normal accidents to high reliability","year":2006,"lang":"en","type":"article","venue":"System Dynamics Review","topic":"Complex Systems and Decision Making","field":"Decision Sciences","cited_by":226,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Reliability (semiconductor); Warning system; Process (computing); Computer science; Incident response; Warning signs; Computer security; Accident (philosophy); Risk analysis (engineering); Engineering; Business; Transport engineering; Telecommunications","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005777798,0.0003999144,0.001548479,0.0002630143,0.000348773,0.0007244922,0.001927424,0.0001510321,0.0008675504],"category_scores_gemma":[0.004026808,0.0003007415,0.0004373074,0.001554862,0.00003292093,0.0004759885,0.0009041872,0.0003301364,0.006089446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000682656,"about_ca_system_score_gemma":0.0000862274,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02333849,"about_ca_topic_score_gemma":0.003654194,"domain_scores_codex":[0.990185,0.001350186,0.003223188,0.001378166,0.003371046,0.0004924007],"domain_scores_gemma":[0.9932925,0.002287964,0.001161578,0.002110198,0.0008690434,0.000278736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000143168,0.0002684043,0.6769863,0.001541034,0.0001483046,0.0003257148,0.0005387905,0.02461544,0.0001949914,0.03128781,0.08329116,0.1806589],"study_design_scores_gemma":[0.00159881,0.0002035994,0.5388908,0.0308811,0.0002680861,0.00007554457,0.001640572,0.1402551,0.0000108612,0.05419178,0.2297659,0.002217884],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8299487,0.03661146,0.1158207,0.0005826272,0.003942509,0.002199322,0.0003478767,0.0003746653,0.01017221],"genre_scores_gemma":[0.9941112,0.0001240791,0.002472001,0.0003217229,0.0004337901,0.00006796416,0.00008876574,0.00003932456,0.002341112],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.178441,"threshold_uncertainty_score":0.9999444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04909349486495921,"score_gpt":0.356382589324226,"score_spread":0.3072890944592668,"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."}}