{"id":"W3003101128","doi":"10.24908/pceea.vi0.13783","title":"CONTINUOUS IMPROVEMENTS IN THIRD YEAR CHEMICAL ENGINEERING DESIGN","year":2019,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Problem and Project Based Learning","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Environmental remediation; Petrochemical; Curriculum; Leaching (pedology); Waste management; Engineering; Process engineering; Computer science; Engineering management; Environmental science; Political science; Law","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":[],"consensus_categories":[],"category_scores_codex":[0.001197447,0.00009981637,0.0001417476,0.0002383828,0.00008885468,0.00009201383,0.0002915212,0.0001638107,0.0000494599],"category_scores_gemma":[0.001600363,0.0001078302,0.00005218183,0.000553649,0.00001027666,0.0002321491,0.00001889477,0.0002648355,0.00002372272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002256624,"about_ca_system_score_gemma":0.001000499,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02411394,"about_ca_topic_score_gemma":0.00216219,"domain_scores_codex":[0.9989271,0.00001283554,0.0002129095,0.0001582488,0.0003190396,0.000369829],"domain_scores_gemma":[0.9992906,0.00008401903,0.0001899527,0.00006108695,0.0002392352,0.0001350387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001352069,0.0001310789,0.8235162,0.0004152194,0.0001229399,2.080191e-7,0.02434892,0.004925616,0.06350247,0.06213743,0.01763657,0.003249808],"study_design_scores_gemma":[0.003708787,0.0001821132,0.4484025,0.002422855,0.0001854482,0.000003446325,0.01353996,0.02258792,0.05637833,0.002352537,0.4471371,0.003099059],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9730292,0.00007675112,0.00003563283,0.003723693,0.001798719,0.001101334,0.000006366884,0.0001196768,0.02010862],"genre_scores_gemma":[0.995598,0.000005259875,0.001464804,0.0001314055,0.0001326658,0.00003985531,0.000002116192,0.00001946092,0.002606452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4295005,"threshold_uncertainty_score":0.9823846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005611847800021367,"score_gpt":0.2126728673980735,"score_spread":0.2070610195980521,"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."}}