{"id":"W2408940203","doi":"10.1016/j.ssci.2016.05.015","title":"The accident early warning system for iron and steel enterprises based on combination weighting and Grey Prediction Model GM (1, 1)","year":2016,"lang":"en","type":"article","venue":"Safety Science","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":64,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; Program for New Century Excellent Talents in University; National Natural Science Foundation of China","keywords":"Weighting; Warning system; Analytic hierarchy process; Hazard; Composite index; Early warning system; Engineering; Computer science; Statistics; Operations research; Mathematics; Composite indicator; Econometrics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.003988682,0.00008893744,0.0001038263,0.0001149988,0.004312378,0.00003638374,0.0001760177,0.00006324832,0.000002824893],"category_scores_gemma":[0.0009933094,0.00004955029,0.00001784143,0.0001833708,0.0002312998,0.0003893964,0.00009221372,0.0001702679,0.000009723934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003703218,"about_ca_system_score_gemma":0.0005447187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006410365,"about_ca_topic_score_gemma":0.00003500739,"domain_scores_codex":[0.9980462,0.0002054456,0.0003567885,0.0003304313,0.0005706411,0.000490459],"domain_scores_gemma":[0.996382,0.002714593,0.0001618908,0.0001900519,0.0003566098,0.0001948311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002257384,0.00005288515,0.8259501,0.000657193,0.000004542148,5.761191e-7,0.002081298,0.0006214807,0.002615912,0.04076665,0.0001223034,0.1248697],"study_design_scores_gemma":[0.001120312,0.0002472106,0.6436639,0.0005721855,0.000003366178,4.718644e-7,0.0003666904,0.3531533,0.00004720218,0.0005342768,0.0002319034,0.00005918867],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.93859,0.00008566926,0.04798545,0.007800329,0.0007573545,0.002390536,0.0000773393,0.00008966784,0.00222368],"genre_scores_gemma":[0.9988294,0.00007458331,0.0003491841,0.000110841,0.00007235795,0.0001972201,0.000003510874,0.00000815043,0.0003547577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3525318,"threshold_uncertainty_score":0.9969839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04117457047205211,"score_gpt":0.3908463748538496,"score_spread":0.3496718043817975,"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."}}