{"id":"W2098163046","doi":"10.24908/pceea.v0i0.3718","title":"MAPPING ENGINEERING DEVELOPMENT PROCESSES FOR PROCESS IMPROVEMENT","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Process (computing); Production (economics); Process management; Computer science; Risk analysis (engineering); Quality (philosophy); Productivity; Action (physics); Data collection; Systems engineering; Knowledge management; Engineering; Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.03540072,0.002276728,0.001123151,0.009720862,0.00349831,0.0101954,0.003150205,0.001991246,0.01173159],"category_scores_gemma":[0.05478469,0.001089914,0.002015074,0.0102693,0.003472944,0.01107019,0.006975062,0.002779957,0.003675326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006888222,"about_ca_system_score_gemma":0.02231892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008041365,"about_ca_topic_score_gemma":0.00521594,"domain_scores_codex":[0.9659352,0.0211137,0.002398013,0.002530133,0.007177713,0.0008453933],"domain_scores_gemma":[0.9571751,0.01918155,0.002598303,0.009984951,0.01039798,0.0006621996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.00008399449,0.0002878938,0.002907194,0.002557639,0.000108967,0.0002159838,0.007656966,0.0267662,0.002715206,0.2753415,0.009388927,0.6719695],"study_design_scores_gemma":[0.0001470223,0.0004879371,0.004824311,0.003587217,0.0001439839,0.0004228539,0.01238082,0.06471445,0.008174514,0.4792552,0.4256485,0.0002130786],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004796735,0.001500573,0.9597172,0.002253117,0.0001378946,0.001670242,0.0003465665,0.001523892,0.02805376],"genre_scores_gemma":[0.03158388,0.001261876,0.962177,0.000124231,0.00001921727,0.001235659,0.0004873868,0.0002143653,0.002896473],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03540072,"threshold_uncertainty_score":0.1872191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04663149348274823,"score_gpt":0.3434962359995957,"score_spread":0.2968647425168474,"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."}}