{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002764774,0.000133882,0.0001207446,0.00003075603,0.0003269764,0.00001936223,0.0001663233,0.0000709637,0.0001086087],"category_scores_gemma":[0.0001742807,0.0001001226,0.00002104754,0.0001687882,0.0001174664,0.0001281665,0.0004097017,0.00005307923,0.001996337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001548051,"about_ca_system_score_gemma":0.000003489371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007652241,"about_ca_topic_score_gemma":0.00003368467,"domain_scores_codex":[0.9988714,0.00004134616,0.0002173116,0.0004037735,0.0001661351,0.0003000748],"domain_scores_gemma":[0.9995912,0.0001258105,0.00004854515,0.0001502678,0.00001290227,0.00007125549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005098028,0.00002350627,0.8491866,0.00001115971,0.000006210021,0.000003722195,0.0001985213,0.00003659456,0.1398655,0.00002247539,0.007034024,0.003560745],"study_design_scores_gemma":[0.0001828437,0.0000810889,0.7868558,0.000009848233,0.000003613746,0.000001002787,0.00007857857,0.0001202732,0.006076984,0.00009145549,0.206343,0.0001554377],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956611,0.00001195187,0.001826454,0.001434117,0.00007809701,0.0006272917,0.0000628519,0.0001032876,0.0001948212],"genre_scores_gemma":[0.8553328,0.00009720651,0.1386382,0.004017932,0.00003261596,0.0002924463,0.0008970943,0.00001083344,0.0006808175],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.199309,"threshold_uncertainty_score":0.9987807,"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."}}