{"id":"W4387940385","doi":"10.3390/plants12213678","title":"ASVmaker: A New Tool to Improve Taxonomic Identifications for Amplicon Sequencing Data","year":2023,"lang":"en","type":"article","venue":"Plants","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère de l'Agriculture, des Pêcheries et de l'Alimentation; Université Laval; Institut de Recherche et de Développement en Agroenvironnement","funders":"Ministère de l'Agriculture, des Pêcheries et de l'Alimentation; U.S. Department of Veterans Affairs","keywords":"Amplicon; Amplicon sequencing; Computer science; Identification (biology); Taxonomy (biology); Taxonomic rank; Data mining; Information retrieval; Computational biology; Biology; Gene; Genetics; Polymerase chain reaction; Ecology","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":[0.008790111,0.003274584,0.001933334,0.007639064,0.001716215,0.004487521,0.003615414,0.002176902,0.01270234],"category_scores_gemma":[0.02086242,0.001702039,0.002583717,0.00489052,0.0008790642,0.005314453,0.00391368,0.003312605,0.008050659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000946396,"about_ca_system_score_gemma":0.001765019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009931538,"about_ca_topic_score_gemma":0.001813727,"domain_scores_codex":[0.9939308,0.001614139,0.0009423894,0.001722233,0.001483989,0.000306335],"domain_scores_gemma":[0.9929203,0.004156512,0.0009006653,0.0009632275,0.0007649889,0.0002943022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001612564,0.0004719493,0.007794054,0.003557349,0.0009419547,0.001041144,0.001952539,0.008522511,0.07875717,0.01257164,0.1441601,0.738617],"study_design_scores_gemma":[0.0005396788,0.0005640439,0.008344091,0.00126839,0.0004359926,0.002490098,0.0006174477,0.3401947,0.1365222,0.04631697,0.4620499,0.0006565718],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006079659,0.0007942327,0.7793357,0.000265168,0.0004285214,0.0002845654,0.006738679,0.2043148,0.001758518],"genre_scores_gemma":[0.02277496,0.000353524,0.9486594,0.0003018397,0.00009837175,0.0006332534,0.01112842,0.01453671,0.00151356],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01270234,"threshold_uncertainty_score":0.04648709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09027962046519729,"score_gpt":0.3066734037759288,"score_spread":0.2163937833107316,"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."}}