{"id":"W1886361645","doi":"10.1002/bmb.20823","title":"A guide to using case‐based learning in biochemistry education","year":2014,"lang":"en","type":"article","venue":"Biochemistry and Molecular Biology Education","topic":"Problem and Project Based Learning","field":"Social Sciences","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Memorization; Rote learning; Active learning (machine learning); Class (philosophy); Process (computing); Psychology; Value (mathematics); Teaching method; Mathematics education; Chemistry; Biochemistry; Computer science; Cooperative learning; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0006549721,0.0001099708,0.0001029626,0.00008978212,0.0002674154,0.00003841205,0.00009432441,0.0002145898,0.00002136127],"category_scores_gemma":[0.0009111929,0.0001283608,0.00002767451,0.0002614548,0.0000986994,0.00003219304,0.00002512277,0.0001547652,0.000002984578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001402766,"about_ca_system_score_gemma":0.001861579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003121355,"about_ca_topic_score_gemma":0.00005056883,"domain_scores_codex":[0.9989833,0.0002248596,0.0001678102,0.0003291776,0.00006140366,0.0002334558],"domain_scores_gemma":[0.9994948,0.00005379854,0.00008217469,0.0001389817,0.00009362201,0.0001366458],"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":[0.00001505014,0.0001278445,0.009981301,0.00007852773,0.000006300159,0.000003890783,0.0009279143,0.0001205317,0.908928,0.001302624,0.0003299977,0.07817797],"study_design_scores_gemma":[0.0004441836,0.00009439702,0.0002016755,0.0002928223,0.00004135145,0.0001173116,0.007073337,0.0007469404,0.6835953,0.0009930491,0.3057089,0.0006907865],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9818406,0.0004693826,0.005721647,0.001588863,0.0001637028,0.0001617464,9.928956e-7,0.00003879608,0.01001433],"genre_scores_gemma":[0.9880479,0.000008206525,0.009791072,0.001057214,0.0002112872,0.00004304984,0.00005377767,0.000009060994,0.0007784115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3053789,"threshold_uncertainty_score":0.5234399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01010405474044868,"score_gpt":0.3602833571514276,"score_spread":0.3501793024109789,"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."}}