{"id":"W2961165167","doi":"10.3897/mbmg.3.34735","title":"Advancing the use of molecular methods for routine freshwater macroinvertebrate biomonitoring – the need for calibration experiments","year":2019,"lang":"en","type":"article","venue":"Metabarcoding and Metagenomics","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Fundação para a Ciência e a Tecnologia; Universität Duisburg-Essen; Universität Zürich; Svenska Forskningsrådet Formas; Nordisk Ministerråd; Velux Stiftung; Bundesministerium für Bildung und Forschung; Norges Forskningsråd; Academy of Finland; European Cooperation in Science and Technology; European Commission; Canada First Research Excellence Fund; Miljødirektoratet; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Vetenskapsrådet; National Science Foundation","keywords":"Environmental DNA; Freshwater ecosystem; Aquatic ecosystem; Environmental science; Ecology; Ecosystem; Biology; Biodiversity","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":[0.08647275,0.001469303,0.001515856,0.002417226,0.0008517709,0.004047247,0.003734148,0.004089444,0.001329769],"category_scores_gemma":[0.0902308,0.001322867,0.001316986,0.002123198,0.004778961,0.005234497,0.003893502,0.006755623,0.001345007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001620722,"about_ca_system_score_gemma":0.003088163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001305036,"about_ca_topic_score_gemma":0.00225414,"domain_scores_codex":[0.9383347,0.03201648,0.00361753,0.006529136,0.01871748,0.0007846926],"domain_scores_gemma":[0.9143094,0.04487249,0.006628757,0.0134748,0.01977682,0.000937685],"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":[0.0003322044,0.000413191,0.01359004,0.005182231,0.0004140095,0.0001979533,0.001920715,0.008898423,0.5840662,0.02755374,0.00258425,0.3548471],"study_design_scores_gemma":[0.0001093294,0.004093208,0.01964396,0.005172403,0.0005249967,0.001619131,0.001346401,0.03026226,0.6934202,0.05947415,0.1837233,0.0006107566],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02799723,0.01826639,0.9431114,0.004951959,0.0006076949,0.001035661,0.0005698194,0.0004576417,0.003002232],"genre_scores_gemma":[0.05932182,0.01491921,0.9194164,0.002211707,0.00028084,0.001481907,0.0007144885,0.0002265901,0.001427057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08647275,"threshold_uncertainty_score":0.4573171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0440541181304854,"score_gpt":0.2917671528969023,"score_spread":0.2477130347664169,"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."}}