{"id":"W4220740180","doi":"10.1002/ecy.3683","title":"Accounting for temporal change in multiple biodiversity patterns improves the inference of metacommunity processes","year":2022,"lang":"en","type":"article","venue":"Ecology","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University; Österreichische Forschungsgemeinschaft; Nemzeti Kutatási Fejlesztési és Innovációs Hivatal; Killam Trusts; KU Leuven; Magyar Tudományos Akadémia; Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; Fonds Wetenschappelijk Onderzoek; Deutsche Forschungsgemeinschaft; University of British Columbia; Alexander von Humboldt-Stiftung","keywords":"Metacommunity; Ecology; Biological dispersal; Temporal scales; Sampling (signal processing); Species evenness; Statistics; Species richness; Computer science; Biology; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0003859682,0.00004854767,0.0001019122,0.00002587651,0.0004526206,0.000002375319,0.0002398553,0.00002417271,0.0002770185],"category_scores_gemma":[0.0002285764,0.00004002944,0.00001961916,0.0001290045,0.0001430533,0.00008704012,0.0006035179,0.0001170726,0.0000053483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005496134,"about_ca_system_score_gemma":0.00001138519,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001682525,"about_ca_topic_score_gemma":0.07200808,"domain_scores_codex":[0.9995177,0.00009795305,0.0001080031,0.0001001397,0.0000456017,0.000130548],"domain_scores_gemma":[0.9991885,0.0006006978,0.0001080146,0.00008474037,0.00001050241,0.000007479641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001301382,0.00008096141,0.9966928,0.00001726499,0.000007827969,4.036703e-7,0.0025572,0.0002221847,0.00006700406,0.00002977439,0.00003399736,0.0002775973],"study_design_scores_gemma":[0.0002260795,0.0001054969,0.9953948,6.090776e-7,0.000006955221,4.729569e-7,0.001275371,0.002266809,0.00004291395,0.0004223592,0.0002144316,0.00004374935],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985824,0.00001933587,0.00004967583,0.0008040378,0.00009968603,0.0003145393,0.00004631143,0.000007023231,0.00007699858],"genre_scores_gemma":[0.9991581,0.000009775861,0.0001213139,0.0004083202,0.000004327178,0.0002668107,0.00001197767,0.000001580973,0.00001779037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07032555,"threshold_uncertainty_score":0.9449254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04037877107010127,"score_gpt":0.2651484889045546,"score_spread":0.2247697178344533,"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."}}