{"id":"W4220757256","doi":"10.1016/j.geodrs.2022.e00495","title":"Incorporating spatial uncertainty maps into soil sampling improves digital soil mapping classification accuracy in Ontario, Canada","year":2022,"lang":"en","type":"article","venue":"Geoderma Regional","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University; Natural Resources Canada; Canadian Forest Service","funders":"Canadian Forest Service","keywords":"Digital soil mapping; Soil map; Environmental science; Sampling (signal processing); Topographic Wetness Index; Soil texture; Soil science; Spatial variability; Water content; Soil water; Hydrology (agriculture); Remote sensing; Statistics; Digital elevation model; Computer science; Mathematics; Geography; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001136717,0.0002299812,0.0003123632,0.001086769,0.001595252,0.001735888,0.0009151624,0.000311594,0.001724401],"category_scores_gemma":[0.005518038,0.0002741416,0.0002780686,0.002566715,0.0005870042,0.0005058708,0.0006254203,0.0003065215,0.0002727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03010524,"about_ca_system_score_gemma":0.0302118,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9974437,"about_ca_topic_score_gemma":0.999226,"domain_scores_codex":[0.9991903,0.00009475157,0.00005565002,0.0001495617,0.0003657734,0.0001440088],"domain_scores_gemma":[0.9965539,0.0006071731,0.0002381204,0.0001462699,0.002311474,0.0001431104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000467864,0.0001185818,0.7168273,0.0003000888,0.0001825392,0.0002293484,0.002365543,0.04026059,0.003763821,0.001790308,0.01349658,0.2201975],"study_design_scores_gemma":[0.00006105908,0.00003089213,0.8746336,0.0001280998,0.0001302352,0.00005392202,0.002363282,0.1034085,0.00231149,0.0007924751,0.01602028,0.00006630929],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9789987,0.0005950627,0.004311639,0.000760298,0.0000281008,0.00008357128,0.004030697,0.0002051463,0.01098683],"genre_scores_gemma":[0.9887441,0.0003117103,0.005296278,0.0000650483,0.000006087929,0.00001986907,0.001275683,0.00003173189,0.004249523],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03010524,"threshold_uncertainty_score":0.2184299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02723772449586692,"score_gpt":0.2205783969271931,"score_spread":0.1933406724313262,"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."}}