{"id":"W2059542853","doi":"10.1016/j.geoderma.2014.06.032","title":"Digital mapping of soil properties in Canadian managed forests at 250m of resolution using the k-nearest neighbor method","year":2014,"lang":"en","type":"article","venue":"Geoderma","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":145,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"U.S. Geological Survey; National Aeronautics and Space Administration","keywords":"Soil map; Environmental science; Mean squared error; Soil texture; Digital soil mapping; Scale (ratio); Soil science; Hydrology (agriculture); Physical geography; Soil water; Statistics; Mathematics; Geography; Cartography; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002581767,0.0002583904,0.0003158127,0.002731538,0.001042519,0.0008681576,0.0008250806,0.0001991461,0.001574254],"category_scores_gemma":[0.001146241,0.000208411,0.0003060782,0.005919113,0.0003048794,0.0002978269,0.0005106817,0.0002385977,0.0002645519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008113672,"about_ca_system_score_gemma":0.01050731,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9866465,"about_ca_topic_score_gemma":0.9938861,"domain_scores_codex":[0.9995477,0.00001697636,0.00001930278,0.0000721543,0.0002425411,0.0001012552],"domain_scores_gemma":[0.9994695,0.00003541026,0.00004273098,0.00003453332,0.00037682,0.00004095606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004985729,0.0002202112,0.5715451,0.0004236332,0.000288419,0.0003321487,0.001326663,0.05520622,0.01247721,0.003449248,0.01792663,0.3363059],"study_design_scores_gemma":[0.00003675551,0.00001325224,0.937313,0.00006287434,0.00005880716,0.00009255177,0.0006959375,0.04852228,0.001535384,0.0003506994,0.01126349,0.00005509258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.959976,0.001234321,0.006528853,0.0001650906,0.00002458439,0.0001201555,0.02104601,0.0003744104,0.0105306],"genre_scores_gemma":[0.9797528,0.0003690775,0.01106492,0.00001837289,0.000005148524,0.00004033717,0.006907546,0.00001808331,0.001823659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01335347,"threshold_uncertainty_score":0.05886906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02371114405044822,"score_gpt":0.2289416690899809,"score_spread":0.2052305250395327,"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."}}