{"id":"W4321485702","doi":"10.1016/j.biocon.2023.109963","title":"Prioritizing taxa for genetic reference database development to advance inland water conservation","year":2023,"lang":"en","type":"article","venue":"Biological Conservation","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Université du Québec à Montréal; Université de Montréal; McGill Genome Centre; Concordia University; Université du Québec à Trois-Rivières; Université du Québec à Chicoutimi; Carleton University; Environment and Climate Change Canada; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Groupe de recherche interuniversitaire en limnologie; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Genome Canada","keywords":"Barcode; Biodiversity; Taxon; Workflow; DNA barcoding; Environmental resource management; Ecology; Biology; Endangered species; Water resources; Data deficient; Geography; Database; Business; Habitat; Computer science; Environmental science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.01906692,0.0009158598,0.001163357,0.01336084,0.002823332,0.005597068,0.003696388,0.001104296,0.008958451],"category_scores_gemma":[0.04073051,0.0006128142,0.000833357,0.01427934,0.0007363647,0.004040754,0.004002363,0.001720417,0.004586996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003743476,"about_ca_system_score_gemma":0.0151976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08373593,"about_ca_topic_score_gemma":0.2472298,"domain_scores_codex":[0.9917355,0.002239748,0.001278351,0.001727323,0.002333485,0.0006855136],"domain_scores_gemma":[0.9752586,0.004088809,0.003260045,0.004035653,0.01222019,0.001136632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007317198,0.0003310289,0.169683,0.003387421,0.0004522576,0.001189021,0.008702985,0.004237069,0.07377634,0.0261739,0.09536717,0.6159682],"study_design_scores_gemma":[0.0001398942,0.0002229661,0.1705011,0.003282768,0.0004816472,0.001428443,0.007763249,0.01929817,0.04099251,0.01711671,0.7385336,0.0002389234],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2184066,0.01205582,0.5302854,0.008138233,0.001696584,0.002799027,0.146488,0.0117354,0.06839494],"genre_scores_gemma":[0.126419,0.002085026,0.7427852,0.001295583,0.0001425071,0.0008675044,0.119021,0.001534534,0.005849635],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08373593,"threshold_uncertainty_score":0.1664971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08702657531650097,"score_gpt":0.2675140338339848,"score_spread":0.1804874585174838,"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."}}