{"id":"W4385794592","doi":"10.1007/978-3-031-37667-2","title":"Understanding Human Errors in Construction Industry","year":2023,"lang":"en","type":"book","venue":"Digital innovations in architecture, engineering and construction","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Nicolet Chartrand Knoll (Canada)","funders":"","keywords":"Human error; Risk analysis (engineering); Computer science; Engineering; Construction engineering; Forensic 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":[0.0006188019,0.0004636017,0.0001968463,0.001363659,0.0006768652,0.002795167,0.0006477627,0.001308509,0.00625145],"category_scores_gemma":[0.002476585,0.0002198966,0.0002349471,0.0009841232,0.002301534,0.003948193,0.001132654,0.001117807,0.0006410402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00180241,"about_ca_system_score_gemma":0.002181262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03173389,"about_ca_topic_score_gemma":0.04704765,"domain_scores_codex":[0.9996542,0.0001091378,0.00001377047,0.00004122361,0.0001365776,0.00004501477],"domain_scores_gemma":[0.9982324,0.001376597,0.0001167876,0.00004697828,0.0001871179,0.00004027127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005408197,0.0001448154,0.02015752,0.0004578913,0.0000375436,0.0006418208,0.05508574,0.01534022,0.001212256,0.2799805,0.0903957,0.5364919],"study_design_scores_gemma":[0.000008321167,0.00005752035,0.03974579,0.00170533,0.00003323512,0.0007277598,0.06623098,0.01599332,0.001303045,0.5304571,0.3436901,0.00004748614],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1660398,0.04718086,0.09545754,0.04756804,0.0006439961,0.0000805265,0.0005330454,0.0003946465,0.6421015],"genre_scores_gemma":[0.7418153,0.03218028,0.02537583,0.00277459,0.0003815842,0.00006832083,0.000628398,0.000142798,0.1966329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03173389,"threshold_uncertainty_score":0.06309837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.118267962970379,"score_gpt":0.3830429619066167,"score_spread":0.2647749989362377,"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."}}