{"id":"W4384827146","doi":"10.32920/23709624.v1","title":"Data Science in the Chemical Engineering Curriculum","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Statistics Education and Methodologies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Curriculum; Data science; Point (geometry); Visualization; Data visualization; Certificate; Data management; Data mining; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008754516,0.0008991697,0.0008039242,0.003700725,0.002204925,0.009366688,0.00175984,0.003182975,0.05788504],"category_scores_gemma":[0.02408216,0.0009739747,0.0008846346,0.006782749,0.003206793,0.00987226,0.006263569,0.006629588,0.03569726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006347866,"about_ca_system_score_gemma":0.01048489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002461129,"about_ca_topic_score_gemma":0.002946689,"domain_scores_codex":[0.9946712,0.001276798,0.0004374933,0.0008584979,0.002324027,0.0004319109],"domain_scores_gemma":[0.9833252,0.006778285,0.0009232733,0.002250314,0.003573104,0.003149764],"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":[0.00003772937,0.0001830061,0.001900148,0.0009335059,0.000021037,0.0001490141,0.0006552975,0.001733857,0.00131793,0.2977222,0.3293313,0.366015],"study_design_scores_gemma":[0.00000591469,0.00002254574,0.001035857,0.000459768,0.000004218062,0.0001842183,0.0002954561,0.001209061,0.0005448047,0.1238666,0.872358,0.00001358169],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01280495,0.05619006,0.2438573,0.2686498,0.02095546,0.0004349821,0.003486406,0.002942374,0.3906788],"genre_scores_gemma":[0.1280415,0.08724853,0.2631516,0.0444523,0.01827369,0.0007244358,0.006480697,0.003206576,0.4484207],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.05788504,"threshold_uncertainty_score":0.1936448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.569697592277889,"score_gpt":0.5186618176488325,"score_spread":0.05103577462905651,"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."}}