{"id":"W2258233657","doi":"10.1016/j.promfg.2015.07.815","title":"Human Factors and Ergonomics in Transportation Control Systems","year":2015,"lang":"en","type":"article","venue":"Procedia Manufacturing","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Automation; Cognitive ergonomics; Workload; Control (management); Human error; Context (archaeology); Human factors and ergonomics; Engineering; Risk analysis (engineering); Work (physics); Transport engineering; Systems engineering; Computer science; Poison control; Business; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001714284,0.0001228012,0.0001708304,0.0001495892,0.00004865135,0.00004661336,0.00006384141,0.00008368573,0.0001400779],"category_scores_gemma":[0.00001359042,0.0001146396,0.00002492116,0.00002855513,0.00001978164,0.0001944102,0.000003398441,0.0001495996,0.00005673151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007375165,"about_ca_system_score_gemma":0.0000135763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003028078,"about_ca_topic_score_gemma":0.0001834804,"domain_scores_codex":[0.9991754,0.00003852837,0.0003150529,0.0002124351,0.0000880166,0.000170518],"domain_scores_gemma":[0.9996238,0.00004114733,0.0001043127,0.0001000598,0.00002547943,0.0001051634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001139965,0.0006149457,0.7518929,0.0006056195,0.000391295,0.0001012123,0.1806556,0.009298379,0.001383072,0.03979875,0.006079828,0.00803838],"study_design_scores_gemma":[0.002003328,0.00004857107,0.986337,0.00002528469,0.00001378822,0.000008450633,0.004963012,0.0005069418,0.0009052773,0.0001591989,0.004827964,0.0002011693],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944049,0.00007292358,0.0003016346,0.00004488009,0.0007640577,0.0002502539,0.00001073013,0.0001043595,0.004046295],"genre_scores_gemma":[0.9991511,0.000001744913,0.00001430962,0.00005544591,0.00009220909,0.00005131857,0.00002136945,0.00001583346,0.000596636],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2344441,"threshold_uncertainty_score":0.4674868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03896139900104988,"score_gpt":0.3168171867269711,"score_spread":0.2778557877259212,"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."}}