{"id":"W4293115928","doi":"10.11159/mmme22.123","title":"Analysis Of An Accident In The Mining Sector Using The Feyer and Williamson Method","year":2022,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Accident (philosophy); Computer science; Forensic engineering; Engineering; Philosophy; Epistemology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00164345,0.0008935358,0.0004327897,0.008071271,0.0008773646,0.001326792,0.000766539,0.0006969689,0.008354593],"category_scores_gemma":[0.00693364,0.0002465646,0.0009485079,0.003020642,0.0009047601,0.000955797,0.0006526909,0.0003914172,0.001044812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009766802,"about_ca_system_score_gemma":0.001552929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0100127,"about_ca_topic_score_gemma":0.01045203,"domain_scores_codex":[0.9976877,0.000686005,0.0002087068,0.0003613042,0.0008913748,0.0001648785],"domain_scores_gemma":[0.9967707,0.00219754,0.0003723997,0.0001438969,0.000480585,0.00003492273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000669627,0.0003035545,0.06242832,0.001089133,0.0002430266,0.003664328,0.00680801,0.03396795,0.03006531,0.07094803,0.004189578,0.7856231],"study_design_scores_gemma":[0.0001755227,0.001996886,0.222551,0.001388115,0.0008128717,0.007944309,0.01972787,0.4675676,0.06371611,0.09364964,0.1199105,0.0005595722],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1941126,0.0003825539,0.7786348,0.0002199892,0.00005998826,0.00122861,0.0008804397,0.0005197334,0.02396136],"genre_scores_gemma":[0.5086247,0.0005472933,0.4819668,0.00004613469,0.00003641492,0.0004945748,0.0007467116,0.00008173472,0.007455731],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0100127,"threshold_uncertainty_score":0.02794892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03784276806752307,"score_gpt":0.3236853576213747,"score_spread":0.2858425895538516,"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."}}