{"id":"W2588206244","doi":"10.1002/aet2.10029","title":"Multiple Wins: Embracing Technology to Increase Efficiency and Maximize Efforts","year":2017,"lang":"en","type":"article","venue":"AEM Education and Training","topic":"Health Sciences Research and Education","field":"Health Professions","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Scholarship; Computer science; Management science; Engineering ethics; Engineering management; Political science; Engineering","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":[0.05064492,0.001625154,0.001194912,0.006501035,0.005656622,0.02145611,0.004589516,0.003917841,0.009047865],"category_scores_gemma":[0.08424667,0.0009095832,0.001158008,0.004571618,0.01196443,0.04113989,0.02138076,0.004064003,0.004393021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003207395,"about_ca_system_score_gemma":0.009759362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008313448,"about_ca_topic_score_gemma":0.00157221,"domain_scores_codex":[0.9572715,0.02544576,0.001752069,0.002794863,0.01071767,0.002018342],"domain_scores_gemma":[0.9222568,0.0524319,0.005088061,0.01078175,0.005881781,0.003559691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002661405,0.0002971519,0.006553326,0.001101384,0.0001435139,0.0002342437,0.01878529,0.00160922,0.002962771,0.2373772,0.01427796,0.7163917],"study_design_scores_gemma":[0.0002992858,0.00150016,0.004374631,0.0020169,0.0003393288,0.001224528,0.02557511,0.009527729,0.01035105,0.6501922,0.2942709,0.0003280689],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06949634,0.006944548,0.6822177,0.06338625,0.001601483,0.001189012,0.000113227,0.00331342,0.1717382],"genre_scores_gemma":[0.4545409,0.004075216,0.503115,0.006630498,0.001090008,0.001233387,0.00008604396,0.0007998231,0.02842925],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05064492,"threshold_uncertainty_score":0.2678391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1222620950855482,"score_gpt":0.4923957909013796,"score_spread":0.3701336958158314,"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."}}