{"id":"W3036024779","doi":"10.24908/pceea.vi0.14200","title":"EXPANDED CROSS-DISCIPLINE IMPLEMENTATION STRATEGY OF DISCOVERY: A BIOMEDICAL ENGINEERING-THEMED EDUCATION PROGRAM BRIDGING SECONDARY AND POST-SECONDARY LEARNING","year":2020,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Biomedical and Engineering Education","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Curriculum; Bridging (networking); Context (archaeology); Active learning (machine learning); Experiential learning; Student engagement; Engineering education; Engineering ethics; Engineering; Mathematics education; Pedagogy; Computer science; Psychology; Engineering management","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.0003089627,0.0002653059,0.0002932079,0.0003451541,0.000112928,0.0001794269,0.0002523336,0.000220682,0.00005506658],"category_scores_gemma":[0.000485436,0.0002726456,0.0000977915,0.00075216,0.00004517556,0.0005710317,0.00004320231,0.0005303976,0.000002536957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007317826,"about_ca_system_score_gemma":0.001164877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001448982,"about_ca_topic_score_gemma":0.000283561,"domain_scores_codex":[0.9983683,0.000007333824,0.0005751659,0.0002731516,0.0003750538,0.0004010308],"domain_scores_gemma":[0.9987909,0.00004690906,0.0002562494,0.00009361327,0.0003867937,0.0004255025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005104459,0.0008096121,0.09252056,0.02716831,0.00129886,7.505267e-7,0.01610078,0.02240182,0.3018675,0.008605025,0.03153299,0.4976428],"study_design_scores_gemma":[0.001322945,0.0002899719,0.851696,0.0007125986,0.0002125076,0.00002196311,0.002938895,0.07427589,0.02281264,0.0001475246,0.04436753,0.001201545],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936886,0.000569265,0.0001783499,0.002634613,0.001341786,0.0007558374,0.00008215699,0.0003185847,0.0004307347],"genre_scores_gemma":[0.9976402,0.00004801514,0.001203509,0.0001019987,0.0004078233,0.000180951,0.0001779005,0.00006688838,0.0001727481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7591754,"threshold_uncertainty_score":0.9999726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005138652699275595,"score_gpt":0.2373514229632758,"score_spread":0.2322127702640002,"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."}}