{"id":"W4311054739","doi":"10.3390/land11122180","title":"Comparison of Random Forest and Kriging Models for Soil Organic Carbon Mapping in the Himalayan Region of Kashmir","year":2022,"lang":"en","type":"article","venue":"Land","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Variogram; Kriging; Mean squared error; Random forest; Soil carbon; Environmental science; Spatial variability; Soil science; Spatial distribution; Digital soil mapping; Geostatistics; Sampling (signal processing); Statistics; Coefficient of determination; Spatial analysis; Mathematics; Soil map; Computer science; Soil water; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002484938,0.00004306446,0.0001235224,0.00002160686,0.00006016755,0.000004840223,0.00008497595,0.00001079097,0.000009849947],"category_scores_gemma":[0.00001543839,0.00003380909,0.00001407931,0.00009817564,0.0000369839,0.00002233906,0.00007970323,0.00005117325,6.873261e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001635173,"about_ca_system_score_gemma":0.000004254679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001786737,"about_ca_topic_score_gemma":0.001423621,"domain_scores_codex":[0.9995254,0.00003348861,0.0001437761,0.00009287469,0.0001122131,0.000092306],"domain_scores_gemma":[0.9996918,0.0001308812,0.00008129422,0.00008230868,0.000002718525,0.00001096479],"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":[0.00003814881,0.00003232033,0.9396102,0.0000313957,0.000004024804,0.000001411363,0.003650087,0.05413278,0.001225082,0.0002062917,0.00008380716,0.0009844077],"study_design_scores_gemma":[0.002385866,0.0001097786,0.1506655,0.00002756316,0.00001841664,0.000009296176,0.002862078,0.8346366,0.0001575334,0.008449413,0.0005638428,0.0001141197],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957094,0.00008472618,0.002998298,0.00008434074,0.00002534158,0.0001945488,0.000004947789,0.000002842419,0.0008955816],"genre_scores_gemma":[0.9996881,0.000009937892,0.0002205277,0.00002500585,0.000006051018,0.00002204035,0.000004943156,0.000004362711,0.00001898739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7889448,"threshold_uncertainty_score":0.2701024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02744966249828611,"score_gpt":0.2474577521351156,"score_spread":0.2200080896368295,"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."}}