{"id":"W3112053409","doi":"10.1002/edn3.171","title":"Lateral and longitudinal fish environmental DNA distribution in dynamic riverine habitats","year":2020,"lang":"en","type":"article","venue":"Environmental DNA","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Österreichische Forschungsförderungsgesellschaft","keywords":"Environmental DNA; Habitat; River ecosystem; Environmental science; Fish migration; Biology; Ecology; Freshwater fish; Snow; Fish <Actinopterygii>; Hydrology (agriculture); Fishery; Biodiversity; Geography; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001173727,0.0004767959,0.0003667604,0.00003181452,0.000295843,0.00004014684,0.0003330907,0.000141451,0.003234938],"category_scores_gemma":[0.00001306591,0.0005217743,0.000109776,0.0001254797,0.001117553,0.0005539052,0.001413695,0.0003278936,0.001814906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001022454,"about_ca_system_score_gemma":0.000001456725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005820673,"about_ca_topic_score_gemma":0.00005281795,"domain_scores_codex":[0.9972999,0.00008869358,0.0003945319,0.00101499,0.0005677946,0.0006340771],"domain_scores_gemma":[0.9992134,0.00004107067,0.0001229341,0.0002730585,2.882263e-7,0.0003492476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007894538,0.0002517905,0.9482322,0.00001067409,0.00002713539,0.0001144366,0.0007193328,0.0001177029,0.04728947,0.000002564444,0.001053027,0.002102665],"study_design_scores_gemma":[0.001234444,0.0002758733,0.9887156,0.00001111142,0.00003510238,0.0000256415,0.0003480775,0.0007045101,0.002499043,0.00003548336,0.005567045,0.0005480716],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968287,0.000139344,0.00003693369,0.00120127,0.00008609676,0.000466301,0.0008607167,0.0000590739,0.0003215504],"genre_scores_gemma":[0.9973733,0.0005407302,0.0004417489,0.0007936927,0.00003317318,0.00002514753,0.0005317499,0.00003475659,0.0002256294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04479043,"threshold_uncertainty_score":0.9997234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008022807901971707,"score_gpt":0.1843294899568425,"score_spread":0.1763066820548707,"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."}}