{"id":"W2176836730","doi":"10.2196/mededu.4479","title":"Semantic Indexing of Medical Learning Objects: Medical Students' Usage of a Semantic Network","year":2015,"lang":"en","type":"article","venue":"JMIR Medical Education","topic":"Open Education and E-Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Usability; Computer science; World Wide Web; Search engine indexing; Information retrieval; Consistency (knowledge bases); Semantic network; Human–computer interaction; Multimedia; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002810908,0.0002913506,0.0001947598,0.001927186,0.0005500564,0.001895444,0.0003916335,0.0005903578,0.00253124],"category_scores_gemma":[0.01205598,0.000125882,0.000317355,0.001238398,0.0008224608,0.002863261,0.001979718,0.0003666511,0.0005422589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006163277,"about_ca_system_score_gemma":0.0006989361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004806551,"about_ca_topic_score_gemma":0.0005667466,"domain_scores_codex":[0.9985588,0.0006815338,0.0001435995,0.0001695697,0.0003572625,0.00008927313],"domain_scores_gemma":[0.9922245,0.005381873,0.0009372061,0.0003898361,0.0005519119,0.0005147432],"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":[0.0008019233,0.001006547,0.3935052,0.001465992,0.0001102124,0.001536178,0.09279365,0.001519389,0.02746365,0.004463935,0.003148007,0.4721853],"study_design_scores_gemma":[0.0001452052,0.004935229,0.5872942,0.001089094,0.0005854138,0.01341761,0.1577596,0.02364884,0.04595787,0.01841128,0.1463296,0.0004261434],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9826239,0.0003162602,0.009877499,0.0004524062,0.0000210207,0.00008504029,0.0001131244,0.0002232653,0.006287652],"genre_scores_gemma":[0.9890682,0.0003179323,0.009346459,0.00008003715,0.00001083049,0.00004435721,0.0001309902,0.00002523323,0.0009759322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002810908,"threshold_uncertainty_score":0.0148657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01736563803777485,"score_gpt":0.3543463600467904,"score_spread":0.3369807220090156,"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."}}