{"id":"W4308713143","doi":"10.24908/pceea.vi.15908","title":"Multi-Disciplinary Design Activity for Undergraduate and Graduate Engineering Students","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Experimental Learning in Engineering","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Context (archaeology); Computer science; Variety (cybernetics); Data collection; Discipline; Finite element method; Instrumentation (computer programming); Engineering education; Software engineering; Engineering; Engineering management; Artificial intelligence; Structural engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005909921,0.0002360994,0.0002126539,0.0003345073,0.0003359371,0.0001062906,0.0003875129,0.00008484788,0.000005685818],"category_scores_gemma":[0.0003331528,0.0002796228,0.00008609863,0.0004421193,0.000011048,0.0002227993,0.0001070755,0.0004239113,0.000001366595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003165842,"about_ca_system_score_gemma":0.0001469414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004779062,"about_ca_topic_score_gemma":0.000121029,"domain_scores_codex":[0.9986797,0.000008803242,0.0002484283,0.0002312563,0.0004108345,0.0004209585],"domain_scores_gemma":[0.9993207,0.000102564,0.0001380707,0.000108002,0.0001383036,0.0001923725],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007496415,0.00009317099,0.01223619,0.0005136622,0.0002462138,2.151608e-7,0.001871033,0.9147733,0.06288733,0.0007924659,0.006143586,0.0004353302],"study_design_scores_gemma":[0.0008079084,0.00008155058,0.1230345,0.000153823,0.0001011471,0.00001580897,0.0005631748,0.8484944,0.01784495,0.00009013583,0.007992508,0.0008201686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899176,0.0003336573,0.003525826,0.001077964,0.003100896,0.001324325,0.00006759329,0.0004890664,0.0001631044],"genre_scores_gemma":[0.987502,0.00001253816,0.01117735,0.0000282932,0.00008778362,0.0005021437,0.000007646311,0.000103861,0.0005783328],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1107983,"threshold_uncertainty_score":0.9999656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02057914166432132,"score_gpt":0.2519954257612942,"score_spread":0.2314162840969729,"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."}}