{"id":"W1841050277","doi":"","title":"Staying human during residency training. 4th edition: How to survive and thrive after medical school","year":2011,"lang":"en","type":"article","venue":"PubMed Central","topic":"Diversity and Career in Medicine","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Suite; Library science; Medical education; Computer science; Data science; Psychology; Medicine; History","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001415406,0.0001018739,0.000150922,0.00007385603,0.0004870609,0.0001015384,0.0003183937,0.0001163876,0.00225173],"category_scores_gemma":[0.001092049,0.0001005742,0.00004075688,0.0001606494,0.0003647543,0.0003652703,0.0001155713,0.0002306363,0.00001268781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001597207,"about_ca_system_score_gemma":0.000286218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009927192,"about_ca_topic_score_gemma":0.004682159,"domain_scores_codex":[0.9973866,0.0001408597,0.0001148762,0.0002597198,0.001033197,0.001064758],"domain_scores_gemma":[0.9979619,0.0000395822,0.00003818241,0.0001067855,0.00005553345,0.001798058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001931315,0.00009760537,0.5224045,0.00006042679,0.00009633726,0.0007036262,0.4381235,2.3807e-7,0.00001925965,0.0112809,0.01282684,0.01419359],"study_design_scores_gemma":[0.0004589391,0.00003379043,0.922456,0.00006151208,0.00002602257,0.00000323084,0.07219219,2.791301e-7,0.00004461346,0.0008508875,0.003715289,0.0001572632],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9649467,0.0001299199,0.00005121652,0.005304822,0.001143512,0.0003072844,0.00001209839,0.00007407655,0.02803037],"genre_scores_gemma":[0.9958972,0.00008403233,0.0001053378,0.0007712943,0.001958932,0.00004714961,0.000003642986,0.000007090487,0.00112533],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4000514,"threshold_uncertainty_score":0.9986603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05247917294531464,"score_gpt":0.256458951435687,"score_spread":0.2039797784903723,"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."}}