{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001930526,0.0013782,0.001760492,0.009558388,0.0007258377,0.00264005,0.002642829,0.001273906,0.01242369],"category_scores_gemma":[0.01102714,0.0007228237,0.001308561,0.0145117,0.000369526,0.002953732,0.003411252,0.001557908,0.009216169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009721151,"about_ca_system_score_gemma":0.003078232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009242784,"about_ca_topic_score_gemma":0.01468267,"domain_scores_codex":[0.9981868,0.0002837923,0.0006008737,0.0004151039,0.0003822375,0.0001311879],"domain_scores_gemma":[0.9939177,0.001630964,0.001277736,0.001357711,0.001131942,0.0006838989],"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":[0.0006741955,0.0002352604,0.05875196,0.00963573,0.001124952,0.0005397948,0.0007950322,0.006438518,0.008220137,0.01762763,0.7912259,0.1047309],"study_design_scores_gemma":[0.0002163204,0.00006519518,0.05228285,0.000818115,0.0002793178,0.000499874,0.0003624631,0.005280606,0.002246653,0.01143824,0.9264091,0.0001012817],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002975977,0.0004194102,0.00583984,0.0001059055,0.00003002867,0.00007065845,0.9872745,0.001843295,0.001440256],"genre_scores_gemma":[0.00502234,0.0002473125,0.00954261,0.00006459504,0.00001190482,0.0002474297,0.9843597,0.000178515,0.0003255943],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01242369,"threshold_uncertainty_score":0.04156142,"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."}}