{"id":"W3028685851","doi":"10.3389/fvets.2020.00301","title":"Extraction and Detection of Avian Influenza Virus From Wetland Sediment Using Enrichment-Based Targeted Resequencing","year":2020,"lang":"en","type":"article","venue":"Frontiers in Veterinary Science","topic":"Influenza Virus Research Studies","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; Ministry of Health; Ministry of Agriculture; University of British Columbia; BC Centre for Disease Control","funders":"Genome British Columbia; Agriculture and Agri-Food Canada; Canadian Food Inspection Agency; British Columbia Ministry of Agriculture and Lands; Genome Canada","keywords":"Biology; False positive paradox; Real-time polymerase chain reaction; Influenza A virus; Virus; Population; Virology; Influenza A virus subtype H5N1; RNA extraction; RNA; Medicine; Gene; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006308979,0.0006506621,0.0005034006,0.001092858,0.0003584906,0.0005003347,0.0002925291,0.0003714903,0.0005805159],"category_scores_gemma":[0.001145172,0.0003088411,0.0007682695,0.0005611104,0.0002424403,0.0002233831,0.0003299762,0.0003963603,0.0005713666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002564452,"about_ca_system_score_gemma":0.0003107432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002579678,"about_ca_topic_score_gemma":0.006740707,"domain_scores_codex":[0.9995407,0.00007333788,0.00004543935,0.0001375644,0.0001418105,0.0000612129],"domain_scores_gemma":[0.9995213,0.0001480223,0.0001025918,0.00005184527,0.0001519612,0.0000243978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007732482,0.00003449195,0.005534044,0.00009235579,0.00002311424,0.0001048442,0.0001275715,0.0003825129,0.9886962,0.00004608487,0.0000771794,0.00480424],"study_design_scores_gemma":[0.0000291543,0.0008975336,0.1971776,0.00006771334,0.0002297681,0.001271972,0.0004771957,0.01754417,0.7752343,0.0002620481,0.006744376,0.00006413177],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9360716,0.0006830237,0.05748717,0.00007371452,0.00002766958,0.0003462355,0.003911023,0.0003224385,0.001077111],"genre_scores_gemma":[0.8671,0.0008479033,0.1160646,0.0002506955,0.00002519747,0.0002832767,0.0131871,0.0001858057,0.002055465],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002579678,"threshold_uncertainty_score":0.005129337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1007593840536351,"score_gpt":0.3701358992502883,"score_spread":0.2693765151966532,"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."}}