{"id":"W4412434429","doi":"10.1094/phytofr-02-25-0017-r","title":"Fungal Community Profiling and Pathogen Detection in Conifer Seed Lots: Benchmarking Oxford Nanopore DNA Metabarcoding Against Conventional Methods","year":2025,"lang":"en","type":"article","venue":"PhytoFrontiers™","topic":"Plant Pathogens and Fungal Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of British Columbia; Cegep de Sainte Foy; Natural Resources Canada; Alberta Ministry of Agriculture and Forestry; Canadian Forest Service","funders":"Canadian Forest Service","keywords":"Profiling (computer programming); Benchmarking; Fungal pathogen; DNA profiling; Biology; Nanopore sequencing; Nanopore; Computational biology; Pathogen; DNA; DNA sequencing; Genetics; Computer science; Nanotechnology; Materials science","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":[0.0009725004,0.0004689457,0.0003343924,0.0006955035,0.0003126829,0.0006653087,0.0005087696,0.0005015523,0.0005786995],"category_scores_gemma":[0.001204035,0.0001990136,0.0003117569,0.0005539504,0.0003823034,0.0003623423,0.0004669257,0.000268171,0.000320742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000385416,"about_ca_system_score_gemma":0.0003623749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005449458,"about_ca_topic_score_gemma":0.01056166,"domain_scores_codex":[0.9990979,0.0001358235,0.00007283586,0.0003288514,0.0002829302,0.00008149827],"domain_scores_gemma":[0.9992231,0.0002504674,0.0001914362,0.00005082374,0.0002247439,0.00005935112],"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":[0.0002764712,0.00004383279,0.01795865,0.000270482,0.00004688341,0.0001220159,0.0001620866,0.001604301,0.9533583,0.0001917607,0.0002747839,0.02569043],"study_design_scores_gemma":[0.00001879906,0.0005526873,0.06600317,0.00009151144,0.0001009622,0.0007776336,0.00030764,0.02657329,0.8997937,0.0002378303,0.005466814,0.0000760762],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9427429,0.00212313,0.04820285,0.0001959809,0.00004434867,0.0001190461,0.003705758,0.0007878484,0.002078083],"genre_scores_gemma":[0.8077777,0.001574455,0.1835098,0.0002428657,0.00002335801,0.0001418631,0.004953925,0.000108801,0.001667121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005449458,"threshold_uncertainty_score":0.01083547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0136208469786706,"score_gpt":0.2788863987584745,"score_spread":0.2652655517798039,"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."}}