{"id":"W4389792460","doi":"10.1080/07060661.2023.2290041","title":"Metabarcoding for plant pathologists","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Plant Pathology","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agricultural Research Service; U.S. Department of Agriculture","keywords":"Amplicon; False positive paradox; Biology; Computational biology; Environmental DNA; DNA extraction; False positives and false negatives; Taxonomic rank; Polymerase chain reaction; Genetics; Computer science; Ecology; Biodiversity; Machine learning; Gene","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.01215497,0.002768692,0.002779403,0.008615711,0.002209635,0.005609517,0.004037481,0.002273419,0.03555224],"category_scores_gemma":[0.03817637,0.002452258,0.002677344,0.007435232,0.001148832,0.004277159,0.004270529,0.005633285,0.03021424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001481153,"about_ca_system_score_gemma":0.005688493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002597595,"about_ca_topic_score_gemma":0.00610147,"domain_scores_codex":[0.9935583,0.002083314,0.0009730117,0.001993263,0.0011495,0.0002426498],"domain_scores_gemma":[0.9831905,0.006565068,0.002202323,0.003270188,0.004045587,0.0007263144],"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":[0.001365378,0.0002447496,0.006857836,0.00731703,0.001508544,0.001004782,0.002452957,0.001587931,0.03927873,0.02525368,0.5489368,0.3641917],"study_design_scores_gemma":[0.0003225557,0.0001605514,0.007355191,0.001297104,0.0003823781,0.0009377528,0.0004476382,0.01304748,0.02226719,0.03382163,0.9197121,0.0002483753],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006214596,0.007208972,0.7070756,0.003828939,0.002798363,0.001319651,0.07300472,0.1804981,0.01805102],"genre_scores_gemma":[0.006736767,0.001190077,0.9441813,0.0008434155,0.000238383,0.001517659,0.03026616,0.009938054,0.005088213],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03555224,"threshold_uncertainty_score":0.1189341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0342200228446316,"score_gpt":0.2165106759535286,"score_spread":0.182290653108897,"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."}}