{"id":"W4224271966","doi":"10.1016/j.ergon.2022.103293","title":"An adaptive model for human factors assessment in maritime operations","year":2022,"lang":"en","type":"article","venue":"International Journal of Industrial Ergonomics","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Human error; Risk analysis (engineering); Bayesian network; Operations research; Engineering; Accident analysis; Accident (philosophy); Dynamic Bayesian network; Computer science; Transport engineering; Artificial intelligence; Reliability engineering; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007278218,0.0006881465,0.0008621661,0.0006330612,0.0003920498,0.001047493,0.001318438,0.001297613,0.002745765],"category_scores_gemma":[0.002706914,0.0003638489,0.0006874242,0.0005103268,0.0003912438,0.0008464186,0.0007748618,0.001027415,0.0004985542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008043288,"about_ca_system_score_gemma":0.001103872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02691663,"about_ca_topic_score_gemma":0.0143525,"domain_scores_codex":[0.999523,0.000158721,0.00002418499,0.0001385574,0.00009353585,0.00006200931],"domain_scores_gemma":[0.9992402,0.0004274505,0.00006190025,0.00003865043,0.0001919575,0.00003987413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007245667,0.00008839897,0.001132648,0.00002428173,0.00003877246,0.00004061947,0.00005434719,0.978762,0.0006683465,0.001392347,0.000258586,0.01746721],"study_design_scores_gemma":[0.000004205091,0.00001727892,0.0002267622,0.000001921777,0.000006023544,0.000003682277,0.000004959966,0.999161,0.00004987233,0.0004607827,0.00006063436,0.000002884538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08233372,0.0001930497,0.9123529,0.0002698442,0.00008190258,0.0001187112,0.0002084576,0.0006050466,0.003836252],"genre_scores_gemma":[0.9591077,0.0001253286,0.03771813,0.00006022548,0.00002759991,0.0002251661,0.0001609091,0.00003188379,0.002543173],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02691663,"threshold_uncertainty_score":0.0535199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05954393900575312,"score_gpt":0.3154329545844627,"score_spread":0.2558890155787096,"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."}}