{"id":"W2888978368","doi":"10.1650/condor-18-32.1","title":"Evaluating time-removal models for estimating availability of boreal birds during point count surveys: Sample size requirements and model complexity","year":2018,"lang":"en","type":"article","venue":"Ornithological Applications","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Yukon University; Université Laval; Alberta Environment and Protected Areas; University of Alberta","funders":"","keywords":"Covariate; Statistics; Variance (accounting); Sampling (signal processing); Boreal; Sample size determination; Count data; Econometrics; Sample (material); Variable (mathematics); Sampling bias; Population; Breeding bird survey; Mathematics; Ecology; Computer science; Habitat; Demography; Biology; Accounting; Poisson distribution","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":[],"consensus_categories":[],"category_scores_codex":[0.001885669,0.0001243268,0.0001952048,0.00001413093,0.0004899091,0.00001476908,0.0001745338,0.00009929657,0.0003617014],"category_scores_gemma":[0.0007880568,0.0001108294,0.00003935006,0.0001485464,0.0008520485,0.0001758703,0.0002104877,0.0000835367,0.00004102264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000946763,"about_ca_system_score_gemma":0.00001972238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004663092,"about_ca_topic_score_gemma":0.00006062046,"domain_scores_codex":[0.9986156,0.0001559891,0.000386752,0.0004251626,0.0001850703,0.0002314461],"domain_scores_gemma":[0.9986274,0.0007482394,0.0001952161,0.000281574,0.00007866024,0.00006894173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002376525,0.0009386016,0.9031071,0.0001089561,0.00004630847,6.662892e-7,0.0006163082,0.04129266,0.0325012,0.002533593,0.0004369113,0.01818005],"study_design_scores_gemma":[0.0002621126,0.00009642378,0.4289326,0.000004066041,0.00001625848,0.000004259219,0.00001173023,0.4767008,0.0001805645,0.09369411,0.000005892344,0.00009122086],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6603082,0.000001883707,0.3380437,0.0001920448,0.00000872023,0.00058215,0.00008986235,0.00004373962,0.0007297266],"genre_scores_gemma":[0.5865548,6.528366e-7,0.4130042,0.00009111056,0.00002143562,0.0002555072,0.00002186748,0.000005814164,0.00004463864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4741745,"threshold_uncertainty_score":0.4519492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1190023004170918,"score_gpt":0.3431588996774338,"score_spread":0.2241565992603421,"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."}}