{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0001008006,0.0001060283,0.0001005016,0.00001583135,0.002893336,0.00003224317,0.00005127514,0.00005409377,0.0007465044],"category_scores_gemma":[0.00004939136,0.0001239196,0.00006535563,0.0001624707,0.0003370902,0.0002197237,0.001845679,0.0001021623,0.00007406342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001636063,"about_ca_system_score_gemma":0.000001799128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002083863,"about_ca_topic_score_gemma":0.0001453255,"domain_scores_codex":[0.9992015,0.00006406883,0.00006450996,0.0002776502,0.0001989515,0.0001933079],"domain_scores_gemma":[0.9996836,0.00006413404,0.00003227725,0.0001235868,0.000008359591,0.00008809305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001584929,0.00006782093,0.9918841,0.000005216416,0.00002769306,0.00008926056,0.0002354572,0.000436713,0.004976005,0.00002536616,0.0009643401,0.001272242],"study_design_scores_gemma":[0.0002904466,0.00001412003,0.9843849,0.000008438868,0.00006683207,0.00001224874,0.001455207,0.000981266,0.008790638,0.00002537328,0.003805885,0.0001646592],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9496676,0.00004409983,0.04694665,0.0004260989,0.00007388667,0.00007236103,0.0004544589,0.00005280663,0.002262013],"genre_scores_gemma":[0.9938362,0.00003879186,0.005497837,0.000152232,0.00001336961,0.000001003846,0.0001125496,0.000002393939,0.0003456348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04416856,"threshold_uncertainty_score":0.9984047,"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."}}