{"id":"W2921060969","doi":"10.1101/578591","title":"Studying ecosystems with DNA metabarcoding: lessons from aquatic biomonitoring","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; Natural Resources Canada; Environment and Climate Change Canada; University of Guelph; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada; Ontario Genomics; Ontario Genomics Institute; Genome Canada","keywords":"Data science; Identification (biology); Terminology; Sample (material); Computer science; Comparability; Ecology; Environmental resource management; Biology; Environmental 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02066612,0.0009375433,0.0009836818,0.002898525,0.00108977,0.00392974,0.003421588,0.003223305,0.0009883473],"category_scores_gemma":[0.04423535,0.0005346626,0.0009622588,0.003833588,0.004244547,0.006865706,0.003211904,0.004809278,0.0005341496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001411008,"about_ca_system_score_gemma":0.002696812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00677825,"about_ca_topic_score_gemma":0.01304601,"domain_scores_codex":[0.9955331,0.002527698,0.0002833808,0.0005233216,0.0009694541,0.0001629854],"domain_scores_gemma":[0.9634064,0.02286554,0.002022634,0.003083369,0.006991579,0.001630423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002663202,0.0002982568,0.09763736,0.004873134,0.0006676997,0.001703517,0.005219501,0.01361394,0.01368682,0.05131436,0.02736547,0.7833537],"study_design_scores_gemma":[0.00006760226,0.0006164685,0.06136744,0.00569756,0.000570839,0.002510377,0.007731912,0.02798132,0.01984757,0.6123062,0.2608612,0.000441441],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1166867,0.2210648,0.4041957,0.2302336,0.00365951,0.0003032129,0.00146051,0.001180778,0.0212152],"genre_scores_gemma":[0.3006387,0.1219455,0.540699,0.02951785,0.002689612,0.0002401055,0.0007851581,0.0004318288,0.003052264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02066612,"threshold_uncertainty_score":0.1092942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0266658059093431,"score_gpt":0.2208304755735807,"score_spread":0.1941646696642376,"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."}}