{"id":"W2302892696","doi":"10.1016/j.ssci.2016.02.024","title":"Using a case study fatality to depict the limits of proximity detection systems for articulating, underground machinery","year":2016,"lang":"en","type":"article","venue":"Safety Science","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Poison control; Engineering; Forensic engineering; Human factors and ergonomics; Computer science; Transport engineering; Risk analysis (engineering); Medical emergency; Business; Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.002137036,0.0001052529,0.0001476081,0.0001205329,0.0006649543,0.0000543831,0.0002102237,0.00003473081,0.00006191876],"category_scores_gemma":[0.000489095,0.00006039408,0.00005779342,0.0004006538,0.0001988214,0.0003556283,0.00005459398,0.00006837348,0.00002447562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001887975,"about_ca_system_score_gemma":0.00007305294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001396904,"about_ca_topic_score_gemma":0.00120062,"domain_scores_codex":[0.9983644,0.0002521211,0.0004608343,0.0003673992,0.0003001672,0.000255095],"domain_scores_gemma":[0.9985148,0.0004120568,0.0002378491,0.0004819671,0.0002630047,0.00009033233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002875675,0.004570441,0.2104082,0.000304656,0.0006163968,0.0002902264,0.1261773,0.01376238,0.3334223,0.08773664,0.000403919,0.2194318],"study_design_scores_gemma":[0.004513471,0.001656978,0.8514702,0.0002286213,0.0001634685,0.003617246,0.05951741,0.07014705,0.00429064,0.001073394,0.002487204,0.0008342651],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7938172,0.000007226915,0.2028314,0.0002198876,0.00119521,0.00109089,0.00001502092,0.00004531847,0.0007778322],"genre_scores_gemma":[0.9993322,2.430266e-7,0.0001864576,0.0000643617,0.00007289428,0.0000794236,2.282683e-7,0.00000773022,0.000256405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.641062,"threshold_uncertainty_score":0.5114358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1313199652201948,"score_gpt":0.4368325903944586,"score_spread":0.3055126251742638,"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."}}