{"id":"W3000434011","doi":"10.1038/s41597-019-0344-7","title":"A global database for metacommunity ecology, integrating species, traits, environment and space","year":2020,"lang":"en","type":"article","venue":"Scientific Data","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Université de Montréal","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Deutsche Forschungsgemeinschaft; Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Metacommunity; Ecology; Trait; Biodiversity; Ecosystem; Taxon; Community; Database; Biology; Macroecology; Spatial ecology; Geography; Computer science; Biological dispersal","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":[0.0007492331,0.0001359102,0.0001451964,0.00001024508,0.0004862471,0.000236968,0.001079534,0.00003843669,0.02221852],"category_scores_gemma":[0.0003138042,0.0001218853,0.00002913004,0.0002616272,0.0007319011,0.0004276412,0.002795381,0.00009102262,0.0004690366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002057312,"about_ca_system_score_gemma":0.00001505417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001173173,"about_ca_topic_score_gemma":0.001623876,"domain_scores_codex":[0.9984931,0.00006057711,0.0001770494,0.0007065173,0.0002554433,0.0003072797],"domain_scores_gemma":[0.9988148,0.00005523815,0.00007275612,0.0008173843,0.000005634023,0.0002342528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002533299,0.0001520795,0.01373773,0.00003039526,0.00002011367,0.000003346599,0.0003542662,0.000007260284,0.009940681,0.004506188,0.965452,0.005770617],"study_design_scores_gemma":[0.0003753236,0.0000467627,0.06167727,0.000003665247,0.00003357759,0.000004386313,0.002800479,0.002626401,0.00021202,0.0001028299,0.9319468,0.0001704648],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5949129,0.0006749031,0.02128893,0.03109471,0.001423325,0.002432507,0.30721,0.0002963092,0.04066643],"genre_scores_gemma":[0.9243233,0.0001732778,0.02072821,0.0026561,0.0001462078,0.00007336623,0.04930291,0.0000310604,0.002565522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3294105,"threshold_uncertainty_score":0.9786753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1139196362106543,"score_gpt":0.2891992433229184,"score_spread":0.175279607112264,"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."}}