{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00787563,0.0008181881,0.0008023775,0.002011216,0.000919325,0.002480353,0.001709661,0.001321548,0.003011469],"category_scores_gemma":[0.009994619,0.0005992797,0.001051708,0.001619116,0.0007254138,0.002100611,0.002202662,0.001464271,0.002617076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006619059,"about_ca_system_score_gemma":0.002352851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004330322,"about_ca_topic_score_gemma":0.01405758,"domain_scores_codex":[0.9957289,0.001339182,0.0003855914,0.0009588674,0.001396278,0.0001911909],"domain_scores_gemma":[0.9919001,0.001959305,0.001144346,0.00133738,0.003054266,0.0006046389],"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":[0.0002245365,0.0001347735,0.02177609,0.001208177,0.0002488735,0.0004524686,0.0006819636,0.003756796,0.6651431,0.003139096,0.0102809,0.2929533],"study_design_scores_gemma":[0.0001468207,0.00104342,0.07814412,0.0009823665,0.0004742841,0.001996709,0.001052318,0.0481793,0.6235588,0.01147471,0.2325446,0.0004025596],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.110902,0.00197412,0.8596284,0.003690712,0.000773099,0.001451591,0.007179778,0.007070077,0.007330235],"genre_scores_gemma":[0.05075927,0.000883785,0.9414175,0.0006777112,0.00009374994,0.0004880179,0.003541299,0.0004259711,0.001712775],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00787563,"threshold_uncertainty_score":0.04165083,"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."}}