{"id":"W2992473023","doi":"10.1038/s41598-019-54532-0","title":"Variations in terrestrial arthropod DNA metabarcoding methods recovers robust beta diversity but variable richness and site indicators","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; University of Guelph; Ontario Forest Research Institute; Laurentian University; Natural Resources Canada","funders":"Canadian Forest Service; U.S. Forest Service","keywords":"Species richness; DNA barcoding; Arthropod; Biodiversity; Biology; Ecology; Taxon; Environmental DNA; Sampling (signal processing); Fauna; Beta diversity; Computer 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002395794,0.000197605,0.0002900428,0.0002185449,0.0009359417,0.0001417063,0.000220478,0.00009965096,0.001282188],"category_scores_gemma":[0.0001240121,0.0001987755,0.00006803632,0.0009047418,0.0006093855,0.0006898617,0.002062827,0.0001823613,0.0002069801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003457279,"about_ca_system_score_gemma":0.00001239376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008531024,"about_ca_topic_score_gemma":0.0000889938,"domain_scores_codex":[0.9974138,0.0001725373,0.0003676461,0.001051874,0.0005894224,0.0004047924],"domain_scores_gemma":[0.9988811,0.0001131867,0.0002900418,0.0005757516,0.000003496448,0.0001364294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00000769806,0.00007181465,0.9807732,0.000005564665,0.00002211777,0.00003975815,0.0008825661,0.000772666,0.0156842,0.0000130346,0.001027224,0.0007001451],"study_design_scores_gemma":[0.0006870194,0.00004899885,0.9717723,0.00002943442,0.0001170785,0.00004112968,0.001157803,0.000493271,0.006273528,0.001542726,0.01727723,0.0005594976],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916521,0.0000433774,0.0007799882,0.00007396827,0.003487021,0.0004653402,0.0000144916,0.00003874276,0.003444991],"genre_scores_gemma":[0.9449506,0.000023648,0.05246957,0.00003980605,0.00002383265,0.000008087084,0.00004062013,0.00001117399,0.002432604],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05168958,"threshold_uncertainty_score":0.9996307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01832460535991919,"score_gpt":0.2377320907605925,"score_spread":0.2194074854006733,"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."}}