{"id":"W2132360826","doi":"10.1111/2041-210x.12106","title":"Calibrating indices of avian density from non‐standardized survey data: making the most of a messy situation","year":2013,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Avian ecology and behavior","field":"Environmental Science","cited_by":135,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Alberta Biodiversity Monitoring Institute; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Fish and Wildlife Service","keywords":"Breeding bird survey; Sampling (signal processing); Covariate; Songbird; Range (aeronautics); Statistics; Survey data collection; RADIUS; Count data; Ecology; Geography; Environmental science; Mathematics; Habitat; Computer science; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.06328522,0.001017555,0.001836099,0.00240782,0.0008244303,0.002845763,0.002498693,0.001102519,0.0007830577],"category_scores_gemma":[0.1821187,0.001350994,0.001140222,0.002815521,0.002474916,0.003499435,0.003444866,0.002107782,0.0005279998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008168261,"about_ca_system_score_gemma":0.001109327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004657651,"about_ca_topic_score_gemma":0.005685932,"domain_scores_codex":[0.9543818,0.03567311,0.002588934,0.003576855,0.00346661,0.0003126785],"domain_scores_gemma":[0.8502563,0.07574706,0.01500736,0.0463916,0.0117591,0.000838655],"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":[0.0004188585,0.0002338191,0.635778,0.001207499,0.003755231,0.0001889046,0.002149188,0.07009368,0.005530744,0.009550165,0.006429607,0.2646644],"study_design_scores_gemma":[0.0001950637,0.0008438769,0.5037014,0.001357813,0.001115751,0.0005492248,0.003258116,0.3664338,0.01162333,0.08771432,0.02266206,0.0005453729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3927349,0.001253945,0.6004906,0.00158071,0.0002515478,0.0002823861,0.0009810163,0.0007930829,0.001631775],"genre_scores_gemma":[0.7533804,0.0005621258,0.242889,0.0006519996,0.0002163946,0.0006170598,0.001091924,0.0003274139,0.0002637741],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06328522,"threshold_uncertainty_score":0.3346882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04222651582612921,"score_gpt":0.3494875924357468,"score_spread":0.3072610766096177,"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."}}