{"id":"W2238402573","doi":"10.1139/cjce-2015-0143","title":"A fuzzy logic approach to posture-based ergonomic analysis for field observation and assessment of construction manual operations","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Industry Canada; University of Alberta","keywords":"Fuzzy logic; Human factors and ergonomics; Engineering; Modular design; Field (mathematics); Joint (building); Risk analysis (engineering); Computer science; Poison control; Artificial intelligence; Civil engineering; Medicine; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.00115317,0.0005971304,0.0004441656,0.002059038,0.000601792,0.001546358,0.0007625442,0.0006409177,0.001386442],"category_scores_gemma":[0.002518282,0.0002743955,0.0007559058,0.001043734,0.000496964,0.0007158018,0.0005628534,0.0004947463,0.0003165863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001024635,"about_ca_system_score_gemma":0.001148936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008801195,"about_ca_topic_score_gemma":0.008447532,"domain_scores_codex":[0.999206,0.0002823475,0.00008533572,0.0001245082,0.0002501523,0.00005170205],"domain_scores_gemma":[0.9992298,0.0003766984,0.0001046746,0.00003367169,0.0002209956,0.00003414664],"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":[0.0005130832,0.0006522803,0.01595728,0.0006187944,0.0002243235,0.001060045,0.001661912,0.3567962,0.05756535,0.03027221,0.001996006,0.5326825],"study_design_scores_gemma":[0.00002755024,0.0002207741,0.005178633,0.00009171518,0.00007555114,0.00020483,0.0003986202,0.9764057,0.003658338,0.01201667,0.001658855,0.0000628679],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02356354,0.0002044734,0.9730405,0.0001057125,0.00002453769,0.0001364867,0.0001028099,0.0001781321,0.002643911],"genre_scores_gemma":[0.553755,0.0004032764,0.4436841,0.00007928422,0.00005221664,0.0003721335,0.0001686257,0.00001557395,0.001469775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008801195,"threshold_uncertainty_score":0.01749992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01515772044499062,"score_gpt":0.2588649151336165,"score_spread":0.2437071946886259,"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."}}