{"id":"W6925038146","doi":"10.16904/envidat.87","title":"Soil net nitrogen mineralisation across global grasslands","year":2019,"lang":"en","type":"dataset","venue":"EnviDat","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; Universidad de Buenos Aires; Nature Conservancy; Deutsche Forschungsgemeinschaft; University of Minnesota; National Science Foundation","keywords":"Edaphic; Nitrogen; Nitrogen cycle; Abundance (ecology); Global change; Grassland; Range (aeronautics); Climate change","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":[0.000573718,0.000957309,0.0007999715,0.002319624,0.0002548164,0.0009620878,0.0008590348,0.000650985,0.01085584],"category_scores_gemma":[0.001383897,0.0002696074,0.0007078956,0.004693456,0.0001677482,0.0006816133,0.0009211216,0.0004799773,0.01076845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007681608,"about_ca_system_score_gemma":0.0006739422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01606847,"about_ca_topic_score_gemma":0.02229602,"domain_scores_codex":[0.9995021,0.00006035657,0.00006871657,0.0001880569,0.0001285125,0.00005223107],"domain_scores_gemma":[0.9993011,0.00009239731,0.0001800669,0.0001006455,0.0002399119,0.00008582514],"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":[0.0005841699,0.0001186325,0.0849444,0.004353951,0.0009011762,0.0001758042,0.0001895121,0.003538174,0.003625193,0.001505591,0.8719599,0.02810358],"study_design_scores_gemma":[0.0004568707,0.000068893,0.2124792,0.0004704998,0.0001905927,0.0002450895,0.0002253325,0.00176805,0.001831014,0.001352453,0.7808315,0.00008044014],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003467488,0.0002222869,0.00007989647,0.00003931294,0.00001557357,0.000004707578,0.9951884,0.0001523254,0.0008301049],"genre_scores_gemma":[0.007462327,0.0001685243,0.0003802342,0.00003816989,0.000008591383,0.00002715607,0.9914294,0.00003536904,0.0004502113],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01606847,"threshold_uncertainty_score":0.03631645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01960163850943245,"score_gpt":0.2891905111247366,"score_spread":0.2695888726153042,"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."}}