{"id":"W4409190872","doi":"10.1002/edn3.70089","title":"Combining <scp>eDNA</scp> Metabarcoding, Hydrology‐Based Modeling and Camera Trap Datasets to Assess the Potential of River <scp>eDNA</scp> in Monitoring Terrestrial Mammals","year":2025,"lang":"en","type":"article","venue":"Environmental DNA","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Horizon 2020 Framework Programme; Ministry of Environment; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; European Commission","keywords":"Trap (plumbing); Environmental DNA; Environmental science; Hydrology (agriculture); Ecology; Geology; Biology; Biodiversity; Environmental engineering; Geotechnical engineering","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006552927,0.0005321408,0.0005797282,0.00020091,0.0005373334,0.00006466142,0.0008293145,0.0002054732,0.00005839062],"category_scores_gemma":[0.0001397185,0.00049922,0.0001655974,0.0003033564,0.001105093,0.000424659,0.001808169,0.0004939747,0.0001029018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000451707,"about_ca_system_score_gemma":0.000008467759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004105775,"about_ca_topic_score_gemma":0.00004391412,"domain_scores_codex":[0.9964747,0.0003024421,0.000657518,0.0009991986,0.0007567999,0.0008092722],"domain_scores_gemma":[0.9984561,0.0004925201,0.0002227863,0.0006206236,0.000001424967,0.0002065073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000299987,0.0004236998,0.8014136,0.00002489951,0.0001926,0.00003670927,0.001649932,0.05226288,0.1413606,0.00001202893,0.001030029,0.001563064],"study_design_scores_gemma":[0.003119324,0.0004843056,0.9071865,0.0001120551,0.0002930726,0.00001007937,0.005661523,0.01048122,0.06834996,0.0001759584,0.003866303,0.0002596942],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963675,0.0004651711,0.0006174448,0.0001290838,0.0004461093,0.000832089,0.0005431653,0.00003841594,0.000560981],"genre_scores_gemma":[0.9962334,0.0002380543,0.002765984,0.0003058081,0.00005663319,0.00006283496,0.0001099723,0.00003365449,0.0001936286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1057729,"threshold_uncertainty_score":0.999746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02255680286474175,"score_gpt":0.2438184994372458,"score_spread":0.2212616965725041,"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."}}