{"id":"W2130560194","doi":"10.1371/journal.pone.0105992","title":"SoilGrids1km — Global Soil Information Based on Automated Mapping","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":1297,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Joint Research Centre; Agriculture and Agri-Food Canada; Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, Gobierno de México; Chinese Academy of Sciences; European Commission; Bill and Melinda Gates Foundation; Institute of Soil Science, Chinese Academy of Sciences; Alliance for a Green Revolution in Africa; U.S. Department of Agriculture","keywords":"Soil map; Digital soil mapping; Soil carbon; USDA soil taxonomy; Environmental science; Pedotransfer function; Soil science; Soil survey; Soil water; Soil organic matter; Cation-exchange capacity; Silt; Soil classification; Soil test; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001161812,0.001096984,0.0004833056,0.002372115,0.0003014047,0.0009465461,0.001390026,0.0005210171,0.005710996],"category_scores_gemma":[0.003126661,0.0003641348,0.0008757597,0.002596169,0.000266651,0.001679208,0.001457345,0.0004991586,0.0024814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00081967,"about_ca_system_score_gemma":0.001329845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0275132,"about_ca_topic_score_gemma":0.02149181,"domain_scores_codex":[0.9994826,0.00007756774,0.00003960212,0.0001890627,0.0001591401,0.00005192437],"domain_scores_gemma":[0.998956,0.0001982372,0.0001665022,0.0003442442,0.0002231059,0.0001118861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008696778,0.0003714038,0.07717246,0.0006712851,0.0004349615,0.0002978876,0.0003696218,0.353906,0.008629369,0.006350296,0.1862406,0.3646865],"study_design_scores_gemma":[0.0001811301,0.0000907701,0.03554018,0.00007145102,0.00006409491,0.00007429227,0.0001640853,0.9003026,0.006410845,0.006806126,0.05020019,0.00009420954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1912944,0.0005957547,0.2089363,0.0006831684,0.0002328108,0.0006808192,0.3733755,0.2130926,0.01110858],"genre_scores_gemma":[0.3924525,0.0003106268,0.229658,0.0001355918,0.00005546599,0.0006378166,0.3724242,0.002368373,0.001957285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0275132,"threshold_uncertainty_score":0.0547061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01615228860210913,"score_gpt":0.1963834306529935,"score_spread":0.1802311420508844,"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."}}