{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001303813,0.00008808573,0.00008991196,0.00003222732,0.00008958196,0.00003252107,0.0003047609,0.00004602569,0.000003231736],"category_scores_gemma":[0.0001035385,0.00009118073,0.0000294317,0.00004851062,0.000009896358,9.502428e-7,0.0002898067,0.00002138459,0.00009157421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001108871,"about_ca_system_score_gemma":0.00009316947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000628823,"about_ca_topic_score_gemma":0.0001432851,"domain_scores_codex":[0.9992296,0.000006149045,0.0001381844,0.0003797477,0.00003974827,0.0002065934],"domain_scores_gemma":[0.9992721,0.00001982731,0.00003864896,0.0005855463,0.00002217826,0.00006167327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002033937,0.00000417981,0.0002952023,0.000007209374,0.00004936994,3.795132e-7,0.00003817067,0.00005558278,0.9234134,0.00004155318,0.06947438,0.006600229],"study_design_scores_gemma":[0.000711876,0.0001520844,0.01293864,0.00001417674,0.00004202983,0.00000774147,0.0002333211,0.000795914,0.2503178,0.0005858588,0.7337821,0.000418551],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934848,0.0001998074,0.003119525,0.0002982074,0.0003765902,0.0004600799,0.001830305,0.00001110712,0.0002195816],"genre_scores_gemma":[0.9911,0.0002017066,0.003739068,0.0003252864,0.000486971,0.0001114,0.001388019,0.00002195884,0.002625654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6730956,"threshold_uncertainty_score":0.3718242,"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."}}