{"id":"W4394168686","doi":"10.6084/m9.figshare.8087687","title":"Additional file 1: of Enhancing the one health initiative by using whole genome sequencing to monitor antimicrobial resistance of animal pathogens: Vet-LIRN collaborative project with veterinary diagnostic laboratories in United States and Canada","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Microbial infections and disease research","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Antibiotic resistance; Whole genome sequencing; Animal health; Veterinary medicine; Genome; Biology; Biotechnology; Medicine; Microbiology; Genetics; Antibiotics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003090373,0.001599702,0.002041254,0.005271353,0.001491632,0.003559113,0.003623952,0.002217352,0.5327253],"category_scores_gemma":[0.02554134,0.001068475,0.001777658,0.01128315,0.0007169259,0.002344339,0.002757107,0.001737856,0.1162944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003624881,"about_ca_system_score_gemma":0.008658553,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1066039,"about_ca_topic_score_gemma":0.155708,"domain_scores_codex":[0.9982748,0.0002827869,0.000324801,0.0005155347,0.0003190463,0.0002830054],"domain_scores_gemma":[0.9813175,0.01071305,0.001269102,0.001800664,0.003808992,0.0010907],"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":[0.0001180054,0.00002044091,0.001258588,0.003314725,0.00005569621,0.00002122205,0.00003898507,0.0002289253,0.00005166851,0.0006027474,0.9925101,0.001778978],"study_design_scores_gemma":[0.001859977,0.00004788318,0.01383047,0.00340855,0.0001927566,0.00009233951,0.0002424856,0.0003551988,0.0003133894,0.003162767,0.9764016,0.00009270992],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001108533,0.000006476863,0.00001302834,0.00001753263,0.000003519345,0.000007806266,0.9997721,0.00002819354,0.0001401539],"genre_scores_gemma":[0.0004451177,0.00005782423,0.000449281,0.00009403325,0.000008652587,0.0002697487,0.9977175,0.0001233232,0.0008345305],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8933961,"threshold_uncertainty_score":0.6665106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0519346307723819,"score_gpt":0.2949351567830059,"score_spread":0.2430005260106239,"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."}}