{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001114551,0.00008792494,0.0001397366,0.0002639983,0.003144569,0.00007349748,0.0001801296,0.0001338075,0.00006656687],"category_scores_gemma":[0.003324401,0.00007881283,0.00001264084,0.0001686776,0.0001441565,0.0002632628,0.0001174921,0.0003706811,0.0000589054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005399798,"about_ca_system_score_gemma":0.001344985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001361852,"about_ca_topic_score_gemma":0.0003593185,"domain_scores_codex":[0.9987434,0.0000831779,0.0002449526,0.0002997706,0.0001373495,0.0004913505],"domain_scores_gemma":[0.9986859,0.0002698709,0.0001353789,0.0003179753,0.0001244267,0.00046645],"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":[0.00003074482,0.0001145191,0.3682585,0.0002063904,0.000006358203,0.00000101629,0.04195057,0.000001807285,0.000357944,0.005204005,0.003438748,0.5804294],"study_design_scores_gemma":[0.0007053212,0.0001215094,0.8933759,0.0003986692,0.000008188257,0.00001461939,0.05118809,0.0008686653,0.00003159772,0.002699066,0.05038299,0.0002053861],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9691381,0.0001857295,0.0002182146,0.01623463,0.0009169255,0.0005742145,0.000002471144,0.0000479146,0.01268173],"genre_scores_gemma":[0.9940273,0.00005040388,0.002642789,0.001623245,0.0002471276,0.0002057741,0.000005221579,0.000008613147,0.00118959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.580224,"threshold_uncertainty_score":0.9981532,"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."}}