{"id":"W2935276756","doi":"10.1002/ecs2.2639","title":"Species‐specific differences in detection and occupancy probabilities help drive ability to detect trends in occupancy","year":2019,"lang":"en","type":"article","venue":"Ecosphere","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Parks Canada","funders":"Alberta Parks; Parks Canada; Yellowstone to Yukon Conservation Initiative; Alberta Biodiversity Monitoring Institute; University of Montana; Panthera","keywords":"Occupancy; Statistical power; Replicate; Abundance (ecology); Statistics; Environmental science; Sampling (signal processing); Ecology; Biology; Computer science; Mathematics; Detector","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":[],"consensus_categories":[],"category_scores_codex":[0.005993068,0.0003058352,0.0004344774,0.0008540575,0.0004146545,0.001120947,0.0006906257,0.0006355395,0.003141864],"category_scores_gemma":[0.03492732,0.0004306923,0.0004292647,0.0005372587,0.001065828,0.001400618,0.0007339165,0.0006382701,0.0003358918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003644883,"about_ca_system_score_gemma":0.0002698005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00483303,"about_ca_topic_score_gemma":0.007633373,"domain_scores_codex":[0.996766,0.001329277,0.0002430713,0.0009832557,0.0004362176,0.0002421569],"domain_scores_gemma":[0.9612716,0.02839601,0.005765019,0.002026176,0.001690714,0.0008504823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002149814,0.0001083976,0.9723123,0.00008220926,0.0003098388,0.0000381071,0.0003663352,0.001957439,0.007690648,0.0003596572,0.0003085484,0.01625147],"study_design_scores_gemma":[0.000006533424,0.0001752058,0.9866642,0.00001555713,0.00005902837,0.0001146401,0.0002123695,0.009744003,0.001844663,0.0008263986,0.0003197864,0.000017484],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883455,0.0001532231,0.008929982,0.0001011281,0.00001268189,0.0000369608,0.0002024324,0.0000836612,0.002134353],"genre_scores_gemma":[0.9985732,0.00001299771,0.001234799,0.00002413065,0.000003358242,0.000008745644,0.00005340059,0.000007761575,0.00008153209],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005993068,"threshold_uncertainty_score":0.03169477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01206776803110206,"score_gpt":0.2069386986749862,"score_spread":0.1948709306438841,"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."}}