{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005941497,0.0001821925,0.0002231744,0.0001186296,0.0002458454,0.0000402556,0.0001252815,0.0001314438,0.000003944152],"category_scores_gemma":[0.0001290001,0.0001898681,0.0001021886,0.0001490134,0.00007993131,0.00001353868,0.0001548498,0.000214246,3.813456e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003039341,"about_ca_system_score_gemma":0.00004630415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001239581,"about_ca_topic_score_gemma":0.0000628394,"domain_scores_codex":[0.9986991,0.0004074302,0.0002548223,0.0002952121,0.00008615809,0.0002572701],"domain_scores_gemma":[0.9995469,0.00003919726,0.00008068606,0.0002086118,0.00005650163,0.00006814577],"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":[0.00009753318,0.00007120334,0.07041878,0.00008210193,0.00008709465,0.000006135841,0.00003148445,0.00001626496,0.9135423,0.00006219423,0.0000642255,0.01552065],"study_design_scores_gemma":[0.002369899,0.0002362115,0.2809156,0.0002392609,0.0002004206,0.00002758102,0.001363591,0.007188453,0.6890926,0.0005405282,0.01706294,0.0007629186],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9721448,0.00468556,0.02013301,0.00002413772,0.000359574,0.0002626183,0.0002727458,0.00001962662,0.002097892],"genre_scores_gemma":[0.9922522,0.0004571946,0.006412938,0.0002129501,0.00008205749,0.00004357711,0.0003856425,0.00001400532,0.000139478],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2244498,"threshold_uncertainty_score":0.7742596,"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."}}