{"id":"W2170146169","doi":"10.24908/pceea.v0i0.3915","title":"SUCCESSFUL METHODS FOR DEVELOPING INTRODUCTORY DESIGN LABS AS PREPARATION FOR UPPER YEAR DISCIPLINE SELECTION AND MULTI-DISCIPLINARY DESIGN","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Discipline; Terminology; Selection (genetic algorithm); Computer science; Process (computing); Engineering design process; Mathematics education; Engineering management; Engineering ethics; Engineering; Psychology; Artificial intelligence; Sociology; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01678207,0.001574881,0.0005390489,0.001807026,0.001417905,0.003268714,0.002491672,0.001149235,0.009349392],"category_scores_gemma":[0.06096273,0.0009693911,0.0006722545,0.001316511,0.001120131,0.002278242,0.002502135,0.001717256,0.00436502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001344312,"about_ca_system_score_gemma":0.003780978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005786579,"about_ca_topic_score_gemma":0.002181173,"domain_scores_codex":[0.9803822,0.01129746,0.001215435,0.002143077,0.004407577,0.000554295],"domain_scores_gemma":[0.9254512,0.03770978,0.005408809,0.01500643,0.0139998,0.002424058],"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":[0.0003634944,0.002358001,0.005072038,0.0009592784,0.00004494926,0.0002902338,0.01195258,0.003917399,0.04454928,0.01088115,0.007292246,0.9123194],"study_design_scores_gemma":[0.001790648,0.009303977,0.03496157,0.002235474,0.0004331557,0.005233557,0.01577781,0.05843498,0.2705032,0.04280929,0.5574718,0.001044536],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02619656,0.0001108406,0.9595872,0.0002082462,0.0000749973,0.003826665,0.0001099462,0.001480564,0.00840495],"genre_scores_gemma":[0.02877214,0.00008201635,0.9637052,0.00005339843,0.00001346368,0.003320266,0.0001365441,0.0002795802,0.003637236],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01678207,"threshold_uncertainty_score":0.0887531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0279744193978923,"score_gpt":0.2866332794720338,"score_spread":0.2586588600741415,"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."}}