{"id":"W2904771518","doi":"10.1152/advan.00128.2018","title":"Phys-MAPS: a programmatic physiology assessment for introductory and advanced undergraduates","year":2018,"lang":"en","type":"article","venue":"AJP Advances in Physiology Education","topic":"Biomedical and Engineering Education","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Science Foundation","keywords":"CLARITY; Curriculum; Statement (logic); Concept inventory; Mathematics education; Physiology; Class (philosophy); Process (computing); Psychology; Computer science; Medical education; Medicine; Artificial intelligence; Biology; Pedagogy","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":[0.004800091,0.0009129607,0.000646691,0.002466897,0.0005459636,0.001372337,0.001257703,0.0006089749,0.01215853],"category_scores_gemma":[0.01612149,0.0004441187,0.0008848555,0.0008554461,0.0003242364,0.001523606,0.002616942,0.001341285,0.00531097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000586193,"about_ca_system_score_gemma":0.002591434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008070909,"about_ca_topic_score_gemma":0.002029098,"domain_scores_codex":[0.9970686,0.001174742,0.0003285887,0.0003577441,0.0008899614,0.0001803893],"domain_scores_gemma":[0.9918578,0.003067403,0.0008634927,0.0004112876,0.002649509,0.001150465],"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":[0.001011688,0.002921855,0.06670105,0.000677909,0.00009347684,0.0002372083,0.001346217,0.00227595,0.0130886,0.001791676,0.05855285,0.8513016],"study_design_scores_gemma":[0.0009498627,0.008647835,0.6547313,0.000858686,0.0002314115,0.002220754,0.002067462,0.04120192,0.03064776,0.01178833,0.2461806,0.0004741342],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4654817,0.0006285662,0.4092278,0.004010255,0.00102846,0.01803576,0.01616843,0.03420667,0.05121234],"genre_scores_gemma":[0.3930571,0.0009975261,0.5445576,0.001203987,0.0004032842,0.02310169,0.01332885,0.001853445,0.02149639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01215853,"threshold_uncertainty_score":0.04067427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004446057110038387,"score_gpt":0.2818341653623943,"score_spread":0.2773881082523559,"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."}}