{"id":"W3117279564","doi":"10.1093/ornithapp/duaa065","title":"North American Breeding Bird Survey status and trend estimates to inform a wide range of conservation needs, using a flexible Bayesian hierarchical generalized additive model","year":2020,"lang":"en","type":"article","venue":"Ornithological applications","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Environment and Climate Change Canada","funders":"Environment and Climate Change Canada","keywords":"Generalized additive model; Breeding bird survey; Range (aeronautics); Bayesian hierarchical modeling; Bayesian probability; Population; Statistics; Component (thermodynamics); Statistical model; Covariate; Population model; Econometrics; Model selection; Computer science; Geography; Bayesian inference; Mathematics; Demography","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001064617,0.0001405185,0.0002367496,0.00004567998,0.0001854291,0.00003319077,0.0001326828,0.00004147971,0.001127731],"category_scores_gemma":[0.000180823,0.00012203,0.00003650181,0.00134789,0.0004619459,0.0001013109,0.0001871131,0.0001042879,0.00005710533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001117329,"about_ca_system_score_gemma":0.00001280532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002763655,"about_ca_topic_score_gemma":0.002358864,"domain_scores_codex":[0.9989728,0.00003954272,0.0002622075,0.0002690369,0.0001888981,0.000267545],"domain_scores_gemma":[0.9992466,0.0001696198,0.0001241345,0.0001241168,0.00002731674,0.0003082438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008727531,0.00006225127,0.9934214,0.000005658723,0.000007070894,4.834568e-7,0.0004243448,0.0008920057,0.001750205,0.0004159689,0.0005850896,0.002348199],"study_design_scores_gemma":[0.0002695675,0.0001219586,0.985032,0.000002303688,0.00001721925,0.000002275612,0.0003815728,0.01208121,0.0001603051,0.0001137253,0.001659565,0.0001583189],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9330318,0.000007166443,0.06329913,0.00137373,0.000003668468,0.0003993346,0.001041835,0.0001029484,0.0007403639],"genre_scores_gemma":[0.9854399,0.00004650938,0.01216531,0.001835931,0.00001007061,0.0001631818,0.0003122002,0.00001263303,0.00001428518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05240806,"threshold_uncertainty_score":0.9997854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08735655606816758,"score_gpt":0.2995312739708001,"score_spread":0.2121747179026326,"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."}}