{"id":"W2783143970","doi":"10.1111/fwb.13220","title":"Scaling up <scp>DNA</scp> metabarcoding for freshwater macrozoobenthos monitoring","year":2018,"lang":"en","type":"article","venue":"Freshwater Biology","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":128,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Canada First Research Excellence Fund","keywords":"Environmental DNA; Workflow; Biology; DNA sequencing; Illumina dye sequencing; DNA extraction; Sample (material); Invertebrate; Metagenomics; Computational biology; Ecology; Computer science; Biodiversity; DNA; Genetics; Polymerase chain reaction; Database; Gene","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003217203,0.0003142789,0.0003066948,0.00006828956,0.000635431,0.00004359936,0.0005206378,0.0001877612,0.001020094],"category_scores_gemma":[0.0001073921,0.000262304,0.0001470994,0.0001097394,0.0009515713,0.0002013047,0.0009991197,0.0001397574,0.002387573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001936976,"about_ca_system_score_gemma":0.000001849358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002603212,"about_ca_topic_score_gemma":0.0002872857,"domain_scores_codex":[0.9978532,0.00008115128,0.0002984816,0.0007190934,0.0001768581,0.0008711843],"domain_scores_gemma":[0.9992105,0.00018324,0.0001033117,0.0003510647,0.00001351564,0.0001383566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001325177,0.00003798314,0.7018176,0.00001019181,0.0000766517,0.000003511866,0.001462562,0.00001042592,0.2792561,0.00003003352,0.01538969,0.001892015],"study_design_scores_gemma":[0.0005638232,0.0001831984,0.04087743,0.00001362939,0.00005117461,0.00000593135,0.0004988647,0.0001513379,0.5344244,0.0006928829,0.422363,0.0001743777],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946233,0.000129121,0.001139586,0.0001395376,0.00183431,0.0004215215,0.0003411739,0.0001135001,0.001257974],"genre_scores_gemma":[0.9428892,0.00007954757,0.05210501,0.0003382787,0.0007660689,0.00009227749,0.00008525082,0.00004346089,0.003600964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6609401,"threshold_uncertainty_score":0.9999829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02887978838532438,"score_gpt":0.2591293047903333,"score_spread":0.2302495164050089,"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."}}