{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002696611,0.0001543304,0.0002169235,0.00005012724,0.00006499978,0.00002623681,0.000139119,0.0001205056,0.008056172],"category_scores_gemma":[0.00004080396,0.0001469243,0.00003160862,0.0004742166,0.0001010043,0.0002758312,0.0001168592,0.0001783763,0.0007020532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002772208,"about_ca_system_score_gemma":0.000008986087,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005033387,"about_ca_topic_score_gemma":0.03480519,"domain_scores_codex":[0.9986806,0.0001083434,0.0002701306,0.0004916234,0.0001306018,0.0003187121],"domain_scores_gemma":[0.9995341,0.0001002654,0.00005415704,0.0002443535,0.000005855421,0.0000613237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006811917,0.00004468306,0.9710326,0.000009216197,0.000001536737,0.000001751153,0.0006380414,0.00005028461,0.0007459196,0.00003219,0.000457626,0.02691799],"study_design_scores_gemma":[0.0003476066,0.0001734864,0.9961658,0.00001664885,0.000001615305,0.000001726602,0.0004387583,0.0001748196,0.0002338946,0.001425951,0.0008464858,0.00017328],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9849632,0.00005304987,0.00003327537,0.0002451034,0.0001992294,0.0003682571,0.000004232188,0.0000303065,0.0141033],"genre_scores_gemma":[0.9974385,0.00001697265,0.0003352051,0.0001062621,0.00002338214,0.00007187722,0.000001922934,0.000007337367,0.00199847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03430185,"threshold_uncertainty_score":0.9928506,"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."}}