{"id":"W3138438889","doi":"10.1111/ddi.13262","title":"Screening marker sensitivity: Optimizing eDNA‐based rare species detection","year":2021,"lang":"en","type":"article","venue":"Diversity and Distributions","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China; Canada Research Chairs","keywords":"Environmental DNA; Biology; Endangered species; Ecology; Abundance (ecology); Invasive species; Genetic marker; Biodiversity; Computational biology; Evolutionary biology; Genetics; Habitat; 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":[0.04888286,0.000928968,0.001770947,0.007171257,0.0003706604,0.00422716,0.001923336,0.001455728,0.001974778],"category_scores_gemma":[0.08754022,0.0008398107,0.001580077,0.004143408,0.001269246,0.00378332,0.001630318,0.0009507739,0.0007831898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009116321,"about_ca_system_score_gemma":0.00159908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008097597,"about_ca_topic_score_gemma":0.001773408,"domain_scores_codex":[0.9722717,0.01402293,0.005057788,0.003456659,0.004834992,0.0003557629],"domain_scores_gemma":[0.8535144,0.1136213,0.0117725,0.00322243,0.01750959,0.0003596384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001042647,0.0001477204,0.05771373,0.08651938,0.003281558,0.0009689573,0.002940171,0.004635952,0.2026808,0.005005007,0.003325738,0.6317384],"study_design_scores_gemma":[0.0003380938,0.00218894,0.09153273,0.02799093,0.01248477,0.004675361,0.004036653,0.01527166,0.6092573,0.01608898,0.2155836,0.0005510222],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"methods","genre_scores_codex":[0.1984814,0.3904511,0.3808454,0.007469285,0.002012742,0.001906359,0.003826764,0.001158155,0.01384876],"genre_scores_gemma":[0.5049877,0.07419598,0.4109518,0.003080616,0.0005390175,0.001386069,0.001878881,0.0002999131,0.002679998],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04888286,"threshold_uncertainty_score":0.2585204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02247829375729149,"score_gpt":0.1925780508392172,"score_spread":0.1700997570819257,"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."}}