{"id":"W3103332312","doi":"10.1111/geb.13210","title":"RivFishTIME: A global database of fish time‐series to study global change ecology in riverine systems","year":2020,"lang":"en","type":"article","venue":"Global Ecology and Biogeography","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"WWF International; National Forest Foundation; Deutsche Forschungsgemeinschaft; New Mexico Department of Game and Fish; Conselho Nacional de Desenvolvimento Científico e Tecnológico; National Fish and Wildlife Foundation; Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; Tennessee Valley Authority; World Wildlife Fund","keywords":"Database; Ecology; Geography; Macroecology; Biodiversity; Actinopterygii; Freshwater fish; Abundance (ecology); Ichthyology; Global change; Fish <Actinopterygii>; Climate change; Fishery; Biology; Computer science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002661293,0.0002679805,0.0005263222,0.00004326761,0.0001699932,0.00001423905,0.0003676398,0.0001622952,0.000462798],"category_scores_gemma":[0.00007146018,0.0002599746,0.00006759448,0.001369693,0.0005862959,0.0002254899,0.001406193,0.00008698845,0.000177386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001038428,"about_ca_system_score_gemma":0.000008801385,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001607044,"about_ca_topic_score_gemma":0.03224048,"domain_scores_codex":[0.9980625,0.0002057513,0.0003811358,0.0006716273,0.000153265,0.0005257352],"domain_scores_gemma":[0.9994201,0.00003061464,0.0001270703,0.0002140397,0.00001686246,0.0001913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000297419,0.0005129376,0.9762363,0.00002947508,0.0001613035,0.0001339965,0.0001230955,0.00001844849,0.000004130472,0.0003476082,0.0218151,0.0003201166],"study_design_scores_gemma":[0.001130245,0.001902637,0.9921086,0.000006184939,0.00008768441,0.00001529269,0.000480744,0.0000306675,0.000001652514,0.0002775111,0.003734614,0.0002241252],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868781,0.00007699701,0.000006465517,0.005939684,0.0002960715,0.001344303,0.001113024,0.00006258237,0.004282779],"genre_scores_gemma":[0.9933344,0.0001194784,0.0002200955,0.006062544,0.00003754458,0.0001749909,0.00003853785,0.000003939168,0.00000843766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03063344,"threshold_uncertainty_score":0.9999852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01068491830349787,"score_gpt":0.2241132993561918,"score_spread":0.2134283810526939,"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."}}