{"id":"W3162096660","doi":"10.1038/s41597-021-00912-z","title":"Global data on earthworm abundance, biomass, diversity and corresponding environmental properties","year":2021,"lang":"en","type":"article","venue":"Scientific Data","topic":"Invertebrate Taxonomy and Ecology","field":"Agricultural and Biological Sciences","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; Natural Resources Canada; University of Toronto; Université de Sherbrooke; McGill University; Saint Mary's University","funders":"Biotechnology and Biological Sciences Research Council; Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; Bundesministerium für Bildung und Forschung; Academy of Finland; Russian Foundation for Basic Research; Agence Nationale de la Recherche; Deutsche Forschungsgemeinschaft; Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Earthworm; Abundance (ecology); Biomass (ecology); Diversity (politics); Ecology; Environmental science; Macroecology; Biodiversity; Biology","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":[0.001579383,0.001129459,0.00117789,0.01898027,0.0003274266,0.001220914,0.0008019219,0.0006042312,0.01821641],"category_scores_gemma":[0.008003294,0.0004297516,0.001024603,0.03171022,0.0004884241,0.001177588,0.001282891,0.0007426434,0.01008236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006763741,"about_ca_system_score_gemma":0.001835909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01175221,"about_ca_topic_score_gemma":0.01695617,"domain_scores_codex":[0.9985454,0.0001467827,0.0004156104,0.0003069518,0.0004510995,0.0001341766],"domain_scores_gemma":[0.9906888,0.002677871,0.002674564,0.0009184878,0.002546804,0.0004934418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0006383972,0.0001870566,0.2360074,0.03747693,0.001667502,0.0007691219,0.001476229,0.003978115,0.006547707,0.006028637,0.5405175,0.1647055],"study_design_scores_gemma":[0.00007333888,0.00005541286,0.3096474,0.002010876,0.0003748249,0.0003887219,0.0006866445,0.0002757605,0.00124518,0.001596888,0.6835696,0.00007541699],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.006737038,0.001581122,0.000390527,0.00008547604,0.00003360468,0.00003261662,0.987642,0.0001017047,0.003395845],"genre_scores_gemma":[0.01708557,0.002612943,0.001902293,0.00007375089,0.00004597605,0.0002267765,0.9765899,0.00005493495,0.001407922],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01898027,"threshold_uncertainty_score":0.06093991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0991375172588764,"score_gpt":0.221497634683466,"score_spread":0.1223601174245896,"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."}}