{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001728215,0.00111219,0.0005840646,0.003247879,0.0003134667,0.001325697,0.0007257077,0.0006572094,0.0007105386],"category_scores_gemma":[0.006656589,0.0003521881,0.0007826843,0.00334773,0.0003658044,0.002344029,0.001166715,0.001147388,0.0005695489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004992906,"about_ca_system_score_gemma":0.0004884057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01025145,"about_ca_topic_score_gemma":0.022813,"domain_scores_codex":[0.9992269,0.0002431406,0.00007448638,0.0001994903,0.0001921957,0.00006372233],"domain_scores_gemma":[0.9951741,0.002315104,0.0007627364,0.0007176228,0.0007637402,0.0002667703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001363882,0.0003155782,0.7081851,0.0005833643,0.0005277276,0.0003677499,0.000356771,0.08234095,0.001889892,0.002533892,0.02276747,0.1799951],"study_design_scores_gemma":[0.00002965975,0.0002253699,0.4268122,0.0006117493,0.0002372619,0.0003752822,0.001433737,0.5045049,0.002986935,0.02034928,0.0423144,0.0001192064],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7853422,0.01065519,0.1034423,0.009836419,0.001049237,0.0003343711,0.07537375,0.002652165,0.01131436],"genre_scores_gemma":[0.8567901,0.003121429,0.07479231,0.001157133,0.0005876118,0.0002304041,0.06177475,0.0001445011,0.001401772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01025145,"threshold_uncertainty_score":0.02038354,"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."}}