{"id":"W4390721461","doi":"10.1038/s41467-023-44521-3","title":"VESPA: an optimized protocol for accurate metabarcoding-based characterization of vertebrate eukaryotic endosymbiont and parasite assemblages","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; University of New Brunswick","funders":"National Institute of Allergy and Infectious Diseases; National Institute on Aging; National Institutes of Health; U.S. Department of Health and Human Services","keywords":"Vertebrate; Evolutionary biology; Parasite hosting; Biology; Computational biology; Protocol (science); Computer science; Genetics; Gene; Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003626781,0.003378473,0.00198693,0.003419492,0.001684766,0.001668804,0.001990459,0.001684865,0.005475529],"category_scores_gemma":[0.006950134,0.002002818,0.00156587,0.002103576,0.0009550415,0.00123289,0.002332172,0.003401719,0.008162295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004286892,"about_ca_system_score_gemma":0.002053541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001227553,"about_ca_topic_score_gemma":0.004356856,"domain_scores_codex":[0.9958306,0.0009896534,0.0006043897,0.001319808,0.0008822367,0.0003732639],"domain_scores_gemma":[0.9980665,0.0006374312,0.0002699023,0.0004112368,0.0005042556,0.0001107484],"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":[0.0007990541,0.0001854665,0.003077037,0.001560511,0.0003147219,0.0006580843,0.0008232219,0.001368284,0.9043813,0.002321297,0.01483549,0.06967546],"study_design_scores_gemma":[0.0002180759,0.0007475503,0.009762595,0.0003704625,0.000290065,0.002404598,0.0003098476,0.01809349,0.7218964,0.002884278,0.2426367,0.0003859469],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04000208,0.002666114,0.9077735,0.0004031294,0.0004637679,0.002764148,0.02336689,0.01784435,0.004716023],"genre_scores_gemma":[0.02792209,0.00123041,0.9253596,0.0004025776,0.00006693689,0.004627453,0.03305909,0.003248836,0.004082979],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005475529,"threshold_uncertainty_score":0.01918048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03614112117328823,"score_gpt":0.3253337798531385,"score_spread":0.2891926586798503,"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."}}