{"id":"W2096273440","doi":"10.1186/1471-2458-13-541","title":"Traffic medicine–related research: a scientometric analysis","year":2013,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Healthcare Systems and Public Health","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Public health; Biostatistics; Medicine; Productivity; Regional science; Bibliometrics; Web of science; Environmental health; Library science; Economic growth; Geography; Economics; Meta-analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"not_applicable","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics","insufficient_payload"],"consensus_categories":["bibliometrics","insufficient_payload"],"category_scores_codex":[0.0312199,0.0003273383,0.00148597,0.01522785,0.0008074744,0.0002000777,0.0004631768,0.0003657473,0.004741319],"category_scores_gemma":[0.006450779,0.000242087,0.0003045674,0.0549935,0.0004625238,0.0004557482,0.00009951075,0.001316639,0.001514051],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001805482,"about_ca_system_score_gemma":0.01649936,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03550722,"about_ca_topic_score_gemma":0.001313546,"domain_scores_codex":[0.9880914,0.002940032,0.002066782,0.001077046,0.002868718,0.002955989],"domain_scores_gemma":[0.9890196,0.00103053,0.0004087104,0.001456834,0.002238173,0.005846104],"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":[0.000028066,0.00126783,0.1923758,0.003530587,0.0009312868,0.00003842018,0.01158326,0.00002652232,0.0000192974,0.01842006,0.2998252,0.4719537],"study_design_scores_gemma":[0.002089375,0.001577913,0.6416886,0.0001815198,0.00005432129,0.0001605203,0.004034937,0.009329032,2.958887e-7,0.0001549952,0.3404475,0.0002809403],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6506792,0.003238872,0.001188581,0.3149554,0.0008105217,0.003862065,0.00001753958,0.0005609024,0.02468697],"genre_scores_gemma":[0.9867709,0.0002671888,0.0009946283,0.005342573,0.0005719404,0.0002509891,0.0001773241,0.00005595972,0.005568546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4716727,"threshold_uncertainty_score":0.9992634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3371280988953731,"score_gpt":0.4857274100707129,"score_spread":0.1485993111753398,"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."}}