{"id":"W2126657415","doi":"10.24908/pceea.v0i0.4846","title":"SMALL-GROUP TUTORIALS AND OPEN BRAINSTORMING FOR PROBLEM-SOLVING IN ENVIRONMENTAL ENGINEERING SYSTEMS","year":2013,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Brainstorming; Class (philosophy); Coaching; Engineering education; Computer science; Group work; Resource (disambiguation); Work (physics); Mathematics education; Problem-based learning; Engineering management; Engineering; Psychology; Artificial intelligence; Mechanical engineering","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":[0.003560793,0.002150447,0.0007203268,0.001715945,0.001366223,0.001572396,0.00312419,0.001328387,0.05928599],"category_scores_gemma":[0.01162175,0.0005815327,0.0008565468,0.001005698,0.0008071343,0.001973284,0.003375292,0.001871265,0.01213568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008790557,"about_ca_system_score_gemma":0.00127068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000390459,"about_ca_topic_score_gemma":0.002103957,"domain_scores_codex":[0.998363,0.0008223393,0.00008771403,0.0002768481,0.0002916613,0.0001584585],"domain_scores_gemma":[0.9908842,0.005908052,0.0003192737,0.0009299059,0.0007160509,0.001242446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001159621,0.00629603,0.00125124,0.002044224,0.00006086744,0.0007130292,0.01115086,0.007939119,0.0353179,0.01078707,0.1266521,0.7966279],"study_design_scores_gemma":[0.001658025,0.00644601,0.01429169,0.001224568,0.0001524464,0.002184102,0.006443776,0.06011492,0.04212763,0.08928121,0.7757176,0.0003580671],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.09587248,0.001100399,0.772645,0.001825566,0.0011433,0.008965907,0.001052326,0.01454082,0.1028542],"genre_scores_gemma":[0.158259,0.0008221114,0.773811,0.0006161785,0.0005262669,0.009395699,0.001627118,0.001198089,0.05374446],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.05928599,"threshold_uncertainty_score":0.1983314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008436396259165023,"score_gpt":0.1841209908500528,"score_spread":0.1756845945908878,"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."}}