{"id":"W4394423415","doi":"10.6084/m9.figshare.8087696","title":"Additional file 2: 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":"Animal health; Antibiotic resistance; Veterinary medicine; Whole genome sequencing; Biology; Biotechnology; Genome; Microbiology; Medicine; 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":[0.002253325,0.00148685,0.002023673,0.003835414,0.001165006,0.002843605,0.003061205,0.002074793,0.4785984],"category_scores_gemma":[0.01733221,0.0008282819,0.001486971,0.007650658,0.0005389115,0.00173116,0.002097421,0.001552471,0.09396206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002527149,"about_ca_system_score_gemma":0.00515872,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07052688,"about_ca_topic_score_gemma":0.1124845,"domain_scores_codex":[0.9988405,0.0001917343,0.0001738722,0.0003860772,0.0002099681,0.0001978845],"domain_scores_gemma":[0.9880179,0.007426993,0.000758246,0.00108202,0.002045047,0.0006697965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001300372,0.00002412765,0.001709938,0.003282736,0.00007234006,0.00002817845,0.00003543451,0.0003299348,0.00006399091,0.0005141342,0.991751,0.00205819],"study_design_scores_gemma":[0.002424008,0.00006498757,0.02061396,0.003340057,0.0002530264,0.0001156877,0.0002337301,0.0006521661,0.000346176,0.003587168,0.9682696,0.00009933681],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001612705,0.00000670942,0.00001437041,0.00001633996,0.000002830516,0.000005469514,0.9997986,0.00002793601,0.0001114994],"genre_scores_gemma":[0.0006385251,0.00004875471,0.0004419531,0.0000920676,0.000007391954,0.0002116972,0.9977542,0.0001149205,0.000690536],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9294731,"threshold_uncertainty_score":0.7437161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05068070018688688,"score_gpt":0.2950339604708767,"score_spread":0.2443532602839898,"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."}}