{"id":"W4311078569","doi":"10.18280/ijsse.120509","title":"Relationship Between Occupational Risk and Personal Protective Equipment on the Example of Ferroalloy Production","year":2022,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ferroalloy; Personal protective equipment; Production (economics); Occupational exposure; Forensic engineering; Environmental health; Risk analysis (engineering); Engineering; Business; Medicine; Metallurgy; Materials science; Economics; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000723965,0.000094262,0.0001278775,0.0001335989,0.00009320938,0.00001506932,0.0001154723,0.0000299816,0.00001423523],"category_scores_gemma":[0.0004279676,0.00007932713,0.00004974489,0.00008509974,0.00002901071,0.00008362562,0.00005363494,0.0005010444,1.934778e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001217345,"about_ca_system_score_gemma":0.00001846165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002169434,"about_ca_topic_score_gemma":0.000001516448,"domain_scores_codex":[0.9991137,0.00003444114,0.0003067792,0.00008845628,0.0003742006,0.00008238488],"domain_scores_gemma":[0.9990976,0.0005991772,0.0001003031,0.00005707824,0.0001045033,0.00004127689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000149465,0.00006041568,0.06348594,0.00008217512,0.000259554,0.000005729455,0.003371396,0.9209055,0.0003698224,0.009965578,0.00007201736,0.001272456],"study_design_scores_gemma":[0.0006462716,0.0002803639,0.8905193,0.0001300402,0.00006177071,0.0001012336,0.0003046345,0.09879402,0.0008250372,0.002819875,0.005274282,0.0002431326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901074,0.0003228023,0.008338896,0.0003832791,0.0005876927,0.0001294161,0.00008178296,0.00001888377,0.00002988851],"genre_scores_gemma":[0.9994121,0.0001114394,0.0002462432,0.000005913818,0.0001950346,0.000008655458,0.000006743368,0.00001095066,0.000002911371],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8270334,"threshold_uncertainty_score":0.3234867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01791344649508277,"score_gpt":0.2278955042257952,"score_spread":0.2099820577307124,"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."}}