{"id":"W2908202864","doi":"","title":"Bibliometric analysis of Traffic Medicine – related publications: 2008 – 2015","year":2019,"lang":"en","type":"article","venue":"IUG Journal of Natural Studies","topic":"Traffic and Road Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scopus; Road traffic; Web of science; Geography; Medicine; Transport engineering; MEDLINE; Political science; Engineering","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":"observational","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":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.0004520398,0.0001269986,0.0006610418,0.03884839,0.00003671155,0.000006459641,0.0002067109,0.00006315854,0.0001039954],"category_scores_gemma":[0.0002577937,0.00008044373,0.0002142438,0.09014015,0.00007916195,0.0002156347,0.00002179365,0.0002961737,0.00001072225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006832804,"about_ca_system_score_gemma":0.00001534278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001406662,"about_ca_topic_score_gemma":0.000005300325,"domain_scores_codex":[0.9986429,0.00002710567,0.000692901,0.00008199869,0.0004006235,0.0001544701],"domain_scores_gemma":[0.9986305,0.0002722529,0.0002812475,0.0001360564,0.0006215157,0.00005843329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00008515769,0.0001366518,0.0298569,0.0002308671,0.0344517,0.00001934772,0.002780176,0.6068072,0.00104051,0.0003170747,0.1962951,0.1279793],"study_design_scores_gemma":[0.001684743,0.0002617332,0.9555066,0.0001898008,0.002456466,0.00006028358,0.001670432,0.01908157,0.00005756634,0.00003390756,0.01872338,0.0002735161],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7693722,0.2274168,0.00002250743,0.001185936,0.001035274,0.00006339628,0.0000045549,0.00004902561,0.000850293],"genre_scores_gemma":[0.9816293,0.01752253,0.000162785,0.00001318175,0.00006690803,6.02778e-7,0.000003996361,0.000009675726,0.0005910192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9256497,"threshold_uncertainty_score":0.9720454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01993017348047422,"score_gpt":0.295849673311439,"score_spread":0.2759194998309648,"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."}}