{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008543097,0.0001569915,0.000197849,0.0001096037,0.00004534719,0.000008536972,0.0001018847,0.00008418617,0.00001044386],"category_scores_gemma":[0.00005385382,0.000148584,0.00002347489,0.0001946603,0.0002037484,0.0002525159,0.00001695823,0.0001248353,0.000007287626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009628262,"about_ca_system_score_gemma":0.00006807171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003871252,"about_ca_topic_score_gemma":0.00001300506,"domain_scores_codex":[0.9991735,0.00002516979,0.000209606,0.0002770899,0.00004301035,0.0002716178],"domain_scores_gemma":[0.9995458,0.0001090114,0.00004350883,0.0001827347,0.00006304794,0.00005593789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005248507,0.0004773493,0.0005043682,0.002216057,0.00005631584,5.399528e-8,0.001239208,0.002022117,0.3720867,0.009869479,0.002526061,0.6089498],"study_design_scores_gemma":[0.002080986,0.001176655,0.2597477,0.0005161967,0.00006881219,0.0000127939,0.002079942,0.03466083,0.01333625,0.4108344,0.2740998,0.001385633],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9843108,0.00443613,0.004097539,0.0007608556,0.005099783,0.0006691628,0.000005956931,0.0002388411,0.0003808992],"genre_scores_gemma":[0.9810008,0.000883845,0.01629793,0.00008502614,0.0008600238,0.0006846914,0.0001279294,0.00002381163,0.00003586922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6075642,"threshold_uncertainty_score":0.6059079,"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."}}