{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005084554,0.0001438555,0.0002831408,0.0004729092,0.000197622,0.00003021019,0.0001377225,0.00005881581,0.00001129152],"category_scores_gemma":[0.0004596126,0.000137091,0.00003355342,0.00104617,0.0005899194,0.0004407739,0.0001271266,0.0002439111,0.000001675063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004140734,"about_ca_system_score_gemma":0.0002792546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007344621,"about_ca_topic_score_gemma":0.000004983826,"domain_scores_codex":[0.9981835,0.00008289298,0.0003232483,0.0004421765,0.0006228448,0.0003453549],"domain_scores_gemma":[0.9993282,0.00004776934,0.0001224744,0.0001917682,0.0001170773,0.0001927124],"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":[0.0009925279,0.00003514858,0.03703856,0.00007360405,0.00001659305,0.00006924063,0.000839256,0.0004095008,0.9571409,6.273631e-7,0.0000153302,0.003368756],"study_design_scores_gemma":[0.003100603,0.003117287,0.3110204,0.0003161447,0.00004954811,0.00002314786,0.001783291,0.1512637,0.5267088,0.00004876825,0.002256008,0.0003122081],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892689,0.001296192,0.008560625,0.0001089857,0.0002875884,0.0003682124,0.00001589044,0.00002957841,0.00006403748],"genre_scores_gemma":[0.9742718,0.00005429407,0.02519412,0.0004011116,0.00005385379,0.00001004848,0.000001931234,0.000009934216,0.000002894412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.430432,"threshold_uncertainty_score":0.5590407,"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."}}