{"id":"W2737623604","doi":"10.1016/j.ece.2017.07.003","title":"Psychrometric charts in color: An example of active learning for chemical engineering students and faculty members","year":2017,"lang":"en","type":"article","venue":"Education for Chemical Engineers","topic":"Experimental Learning in Engineering","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Psychrometrics; Active learning (machine learning); Class (philosophy); Computer science; Color-coding; Coding (social sciences); Mathematics education; Engineering drawing; Multimedia; Medical education; Psychology; Engineering; Mechanical engineering; Artificial intelligence; Mathematics; Medicine; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.0001655518,0.000233957,0.0002986269,0.0002392536,0.00005407057,0.00005667777,0.0003145784,0.0001536173,0.000007482884],"category_scores_gemma":[0.0003999647,0.0002849837,0.00007153832,0.000146232,0.00003910815,0.0002938864,0.00004243436,0.0002458375,8.114712e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002285304,"about_ca_system_score_gemma":0.0000232647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004425004,"about_ca_topic_score_gemma":6.400342e-7,"domain_scores_codex":[0.9989144,0.000004130355,0.000286549,0.0002995253,0.0001682286,0.0003271638],"domain_scores_gemma":[0.9993091,0.0001460236,0.00007509527,0.00025323,0.00005586894,0.0001606554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006028025,0.000225684,0.001402596,0.0006516344,0.000121559,3.008757e-7,0.004244111,0.1737605,0.8133367,0.0004997225,0.0002317151,0.00546515],"study_design_scores_gemma":[0.002259223,0.0001289293,0.004721476,0.0002017439,0.00004570208,0.000006233761,0.001179452,0.3854724,0.6004795,0.00003095124,0.004740558,0.0007337101],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955667,0.0001324971,0.002991539,0.00001488737,0.000476568,0.0005150318,0.000014857,0.0001621888,0.0001257019],"genre_scores_gemma":[0.9844821,0.000009567763,0.01474423,0.000004097228,0.0001309625,0.000371211,0.0001369684,0.00008137471,0.00003949631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2128572,"threshold_uncertainty_score":0.9999602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03041415822971031,"score_gpt":0.3265110202428042,"score_spread":0.2960968620130939,"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."}}