{"id":"W4403764113","doi":"10.24908/pceea.2023.17137","title":"A Case Study in VLSI Education during the COVID-19 Pandemic","year":2024,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; CMC Microsystems","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Virology; Medicine; Infectious disease (medical specialty); Disease; Internal medicine","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":[0.0005805922,0.0001724216,0.0001324712,0.0005257628,0.0001514633,0.0003058884,0.0002837236,0.0001473825,0.00002774084],"category_scores_gemma":[0.0005200029,0.0001531601,0.00006686943,0.001138942,0.00001501529,0.0005405872,0.00001575098,0.000470941,0.00001308258],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006170098,"about_ca_system_score_gemma":0.00134069,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.008646685,"about_ca_topic_score_gemma":0.01971694,"domain_scores_codex":[0.9988517,0.00000898192,0.0003981463,0.0001706442,0.0002808855,0.0002896457],"domain_scores_gemma":[0.9993337,0.00009361946,0.00007858939,0.0001222276,0.0001572313,0.0002146038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004052105,0.0002797897,0.8205008,0.003624475,0.0003732559,0.0000142336,0.0431658,0.06944716,0.001052572,0.007234178,0.04834169,0.005962004],"study_design_scores_gemma":[0.002074816,0.0001012192,0.4420321,0.001953833,0.0005002926,0.004993694,0.1128654,0.05690289,0.001490055,0.001808914,0.3720809,0.003195867],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890851,0.0002110498,0.00001032526,0.001458345,0.002211279,0.0007367348,0.00002367865,0.0002915633,0.005971945],"genre_scores_gemma":[0.9979762,0.00001153985,0.00003516019,0.0001320594,0.00018511,0.000276122,0.000004552504,0.00004527326,0.001333938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3784687,"threshold_uncertainty_score":0.9981707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01638036329633498,"score_gpt":0.2468709485688797,"score_spread":0.2304905852725447,"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."}}