{"id":"W2927667709","doi":"10.1093/rheumatology/kez060.011","title":"184. THE MICROBIOME OF TEMPORAL ARTERIES","year":2019,"lang":"en","type":"article","venue":"Lara D. Veeken","topic":"Paleopathology and ancient diseases","field":"Arts and Humanities","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Microbiome; Temporal artery; Cardiology; Pathology; Bioinformatics; Disease; Vasculitis; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002629802,0.0002868046,0.0002486747,0.0005358469,0.0006370237,0.0009386896,0.0001242724,0.0004419241,0.002268546],"category_scores_gemma":[0.0006549347,0.0001395738,0.0002152839,0.0006135266,0.0003522873,0.0003634116,0.0006152639,0.000245689,0.0005503251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002882929,"about_ca_system_score_gemma":0.0004851362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00194418,"about_ca_topic_score_gemma":0.001646172,"domain_scores_codex":[0.9997566,0.00004795603,0.00001926255,0.00006314851,0.00005270125,0.00006033349],"domain_scores_gemma":[0.9997756,0.00002818082,0.00007469428,0.00001532637,0.00005852989,0.00004767695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001690074,0.0001338439,0.4291143,0.0007400622,0.0001580848,0.002077772,0.002333656,0.000251685,0.5028591,0.0005533947,0.001430701,0.0586574],"study_design_scores_gemma":[0.00005004844,0.0009977237,0.9428327,0.0002001668,0.0002114042,0.00934893,0.00326385,0.0008845658,0.02505514,0.0009737011,0.01614993,0.00003181372],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929016,0.002974092,0.0005856432,0.000277019,0.00003965431,0.00003394693,0.001018362,0.00001643852,0.002153098],"genre_scores_gemma":[0.9939865,0.002232283,0.00154334,0.0002299159,0.00006061924,0.00004391346,0.0009362587,0.000008451252,0.0009587416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002268546,"threshold_uncertainty_score":0.007589042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01844401706337076,"score_gpt":0.220113053973524,"score_spread":0.2016690369101532,"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."}}