{"id":"W4383197108","doi":"10.1002/jwmg.22463","title":"Environmental DNA surveys can underestimate amphibian occupancy and overestimate detection probability: implications for practice","year":2023,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Zoo","funders":"U.S. Forest Service; National Institute of Food and Agriculture; Cenovus Energy; Rocky Mountain Research Station; U.S. Department of Agriculture","keywords":"Occupancy; Environmental DNA; Sampling (signal processing); Ecology; Boreal; Leopard; Lithobates; Distance sampling; Sampling design; Environmental science; Biology; Abundance (ecology); Amphibian; Biodiversity; Computer science; Population","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1318018,0.001277186,0.001528834,0.00266396,0.001164608,0.004467336,0.004533944,0.002263925,0.002112188],"category_scores_gemma":[0.3979931,0.0008583913,0.0007093637,0.003080876,0.004770926,0.003267814,0.002625315,0.002673924,0.0006204111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006439372,"about_ca_system_score_gemma":0.009643925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07989409,"about_ca_topic_score_gemma":0.1207767,"domain_scores_codex":[0.8907472,0.0757274,0.00720614,0.007680228,0.01747093,0.001168072],"domain_scores_gemma":[0.4719055,0.4285987,0.04335408,0.01333371,0.03892374,0.003884094],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003760084,0.0002314367,0.704152,0.003124075,0.001099616,0.0004095794,0.006013942,0.00964897,0.0004502351,0.004459393,0.02511197,0.2449227],"study_design_scores_gemma":[0.0003758427,0.001526651,0.7081069,0.02168573,0.001453931,0.001779504,0.01801309,0.1067976,0.002786171,0.08275364,0.05415152,0.0005693456],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4541779,0.0738046,0.2032785,0.2263823,0.002433192,0.001333959,0.002571318,0.001695276,0.03432279],"genre_scores_gemma":[0.9133096,0.006112647,0.0667233,0.0119391,0.000480326,0.000401258,0.0002244731,0.0001462884,0.000662916],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8681983,"threshold_uncertainty_score":0.6970426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02940747488820515,"score_gpt":0.2628499000229014,"score_spread":0.2334424251346963,"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."}}