{"id":"W2325887549","doi":"10.1061/40671(2003)9","title":"Measuring and Estimating Steel Drafting Productivity","year":2003,"lang":"en","type":"article","venue":"","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial neural network; Scope (computer science); Productivity; Process (computing); Work (physics); Industrial engineering; Computer science; Engineering; Manufacturing engineering; Machine learning; Mechanical engineering","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":[0.001122077,0.0006813009,0.0004068383,0.001419006,0.0002325568,0.0006867903,0.0005171677,0.0005600365,0.0007962233],"category_scores_gemma":[0.004190139,0.0003040817,0.0002675917,0.001011183,0.0002218225,0.001070079,0.000494365,0.0003541378,0.0003812507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005677014,"about_ca_system_score_gemma":0.0007286032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003263661,"about_ca_topic_score_gemma":0.003454827,"domain_scores_codex":[0.9986959,0.0002715497,0.00007825124,0.0001937618,0.0006955686,0.00006509032],"domain_scores_gemma":[0.998696,0.0004116387,0.000221341,0.0001438051,0.0004936772,0.00003355391],"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":[0.0002426065,0.0002418442,0.03547451,0.0003210173,0.00006682433,0.0001467637,0.000266648,0.3059772,0.1378852,0.001945681,0.0008137931,0.5166178],"study_design_scores_gemma":[0.00001986491,0.0002266522,0.02242081,0.0000227406,0.00003652936,0.00008070334,0.00008043856,0.8996776,0.07472178,0.001086989,0.001587132,0.00003872351],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1799795,0.0001094304,0.8167031,0.00005655313,0.00001703876,0.00009961056,0.0001628252,0.0008793247,0.001992694],"genre_scores_gemma":[0.7723719,0.0002061987,0.2253899,0.00001377402,0.0000134791,0.00008847477,0.0003096324,0.00005873788,0.001547991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003263661,"threshold_uncertainty_score":0.006489336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01789987758938369,"score_gpt":0.1936470372851729,"score_spread":0.1757471596957892,"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."}}