{"id":"W4213413702","doi":"10.1111/ibi.13045","title":"Predicting population trends of birds worldwide with big data and machine learning","year":2022,"lang":"en","type":"article","venue":"Ibis","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto and Region Conservation Authority","funders":"","keywords":"Population; Ecology; Geography; IUCN Red List; Threatened species; Endangered species; Population size; Biology; Demography; Habitat","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001433951,0.00004273696,0.00005534237,0.0000259857,0.0001528692,0.000008570351,0.0001079199,0.000007634548,0.01445945],"category_scores_gemma":[0.00001365601,0.00003763726,0.000006884184,0.0002611732,0.0000351131,0.00006788475,0.0003981118,0.00007266422,0.000005344896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008188884,"about_ca_system_score_gemma":0.000001226755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001557323,"about_ca_topic_score_gemma":0.001840061,"domain_scores_codex":[0.999479,0.0000277038,0.00007808283,0.0001482252,0.0001847152,0.00008229948],"domain_scores_gemma":[0.9997528,0.00001205586,0.00005991006,0.0001477991,0.000001452525,0.0000259513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001691375,0.00001747248,0.9745557,0.000002345348,0.00000297897,0.000001213401,0.0001458849,0.0002037455,0.0005372731,0.00001365283,0.0003923448,0.02411052],"study_design_scores_gemma":[0.0002382677,0.00009265793,0.9563423,0.000002745126,0.00001074202,0.000009189722,0.000802563,0.005071437,0.00007856661,0.00000483196,0.03728342,0.00006328683],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927544,0.00004564963,0.00002787264,0.0002520479,0.00004072313,0.00002512142,0.0002107451,0.0000272577,0.006616196],"genre_scores_gemma":[0.9986516,0.000009315935,0.00002906423,0.00003888296,0.00001001586,0.000003339206,0.0005140307,0.000005387042,0.0007383937],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03689107,"threshold_uncertainty_score":0.9864415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05157589854719987,"score_gpt":0.2523701162502307,"score_spread":0.2007942177030308,"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."}}