{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003516992,0.00007744025,0.0001173624,0.00005750355,0.0001101412,0.00001838285,0.0001495783,0.00003599739,0.00004261137],"category_scores_gemma":[0.0001284145,0.00005917175,0.00002740304,0.0001648717,0.0001315875,0.0001061909,0.0001467404,0.00006000272,0.000009484544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001545955,"about_ca_system_score_gemma":0.00002309027,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.287438,"about_ca_topic_score_gemma":0.4819411,"domain_scores_codex":[0.9991525,0.00006040115,0.0002046433,0.0001419022,0.0001636155,0.0002769833],"domain_scores_gemma":[0.9995944,0.00005585141,0.00008882207,0.0001877934,0.000009736044,0.00006335638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002096234,0.00003132009,0.8050507,0.00009293405,0.00001649119,0.000007495986,0.002403124,0.1435836,0.007361798,0.00055138,0.0003281755,0.04055213],"study_design_scores_gemma":[0.0001493012,0.00001247975,0.5725812,0.00005027563,0.000005510823,0.000005367797,0.0002020768,0.4237395,0.0007612949,0.0004625972,0.001944791,0.00008558244],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9826533,0.00003011719,0.008431378,0.0001532836,0.00004920201,0.0001481979,0.00001616081,0.000005648153,0.008512751],"genre_scores_gemma":[0.9980456,0.00000238778,0.001654965,0.00005974725,0.00001237446,0.000004354681,0.000004725778,0.000007979294,0.0002079253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.280156,"threshold_uncertainty_score":0.717307,"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."}}