{"id":"W6931638403","doi":"10.5683/sp2/0e4ltf","title":"Clinical Clerkship case series","year":2021,"lang":"en","type":"dataset","venue":"Borealis","topic":"Mathematics, Computing, and Information Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Zoom; Series (stratigraphy); Clinical clerkship; Focus (optics); Field (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009383985,0.0002557696,0.0004596973,0.0001151354,0.0002315013,0.0007834012,0.001152861,0.0003000756,0.00005941607],"category_scores_gemma":[0.0005819529,0.0002347463,0.0001750429,0.0002308059,0.0000788984,0.0007584557,0.0006881859,0.000441035,0.00004756303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002720832,"about_ca_system_score_gemma":0.000336855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008159439,"about_ca_topic_score_gemma":0.0008775214,"domain_scores_codex":[0.9978536,0.0001307807,0.0009732658,0.0003968463,0.0003556524,0.0002898748],"domain_scores_gemma":[0.9973236,0.0002036301,0.0005870385,0.001472709,0.0002504654,0.0001625499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[7.401003e-7,0.00003733684,0.000001742485,0.0002902682,0.00002067577,0.0009920499,0.0001603518,0.000002372567,1.20572e-8,0.001607627,0.9879046,0.008982237],"study_design_scores_gemma":[0.0001114683,0.00003253727,0.000005606233,0.0001461648,0.00001969979,0.004026569,0.00005450227,0.002239384,0.000003408171,0.001167167,0.9919137,0.0002797875],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000003376912,0.0001601202,0.04135932,0.0002530415,0.0007463529,0.0001009911,0.9552237,0.0001491397,0.002003912],"genre_scores_gemma":[0.000004141269,0.0001896759,0.02511258,0.001219902,0.0005023991,0.00000859218,0.9728044,0.000009835653,0.0001485317],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01758059,"threshold_uncertainty_score":0.9572678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05237662697595363,"score_gpt":0.3303488105022518,"score_spread":0.2779721835262982,"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."}}