{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003897265,0.0004914156,0.0006578655,0.001289505,0.0005645036,0.0011235,0.0008904957,0.0006556947,0.0009619261],"category_scores_gemma":[0.0159011,0.0004004904,0.001536062,0.0009419624,0.0004899848,0.002453205,0.001066972,0.001032983,0.0001715136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003813406,"about_ca_system_score_gemma":0.000515953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005729231,"about_ca_topic_score_gemma":0.007639827,"domain_scores_codex":[0.9991177,0.00034644,0.00007411539,0.0002994931,0.0001076444,0.00005462367],"domain_scores_gemma":[0.9903335,0.006043864,0.001323528,0.001636557,0.0003001725,0.0003623575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002597388,0.0001734541,0.356003,0.0001632111,0.0009375747,0.000182429,0.0002501667,0.5506908,0.008804021,0.006227769,0.0005818587,0.07572591],"study_design_scores_gemma":[0.00001148976,0.00008961144,0.0602239,0.00001648047,0.00008050011,0.00009522102,0.00006634009,0.925733,0.001623918,0.0113901,0.0006340659,0.00003541845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6880118,0.0002899386,0.3093126,0.0001955161,0.00004122812,0.00003212405,0.0005159974,0.0006649187,0.0009359309],"genre_scores_gemma":[0.9573128,0.00007539033,0.04191492,0.00003770036,0.00001704869,0.00002655698,0.0004504919,0.00006076512,0.0001044309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005729231,"threshold_uncertainty_score":0.02061093,"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."}}