{"id":"W3121560041","doi":"10.1139/cjss-2020-0103","title":"Impacts of coarse-resolution soil maps and high-resolution digital-elevation-model-generated attributes on modelling forest soil zinc and copper","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Soil Science","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China","keywords":"Digital elevation model; Soil science; Environmental science; Scale (ratio); Terrain; Zinc; Elevation (ballistics); Soil map; Soil texture; Resolution (logic); Remote sensing; Hydrology (agriculture); Geology; Soil water; Mathematics; Chemistry; Geography; Cartography; Geotechnical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0007109732,0.0007062185,0.0003639976,0.0004084327,0.0001567052,0.0005870929,0.0005206267,0.0004316591,0.000385129],"category_scores_gemma":[0.001396314,0.0003283447,0.0006028851,0.0003568357,0.000287149,0.0005395452,0.0003534649,0.0002869085,0.00007142017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008928066,"about_ca_system_score_gemma":0.0006041681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04183037,"about_ca_topic_score_gemma":0.02995448,"domain_scores_codex":[0.9997985,0.0000640267,0.0000183425,0.00005982105,0.00003032971,0.00002901518],"domain_scores_gemma":[0.9994821,0.0003095898,0.00005667153,0.00004902684,0.00007512801,0.00002736901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004723065,0.00003258546,0.01474553,0.00001653113,0.00002924532,0.00003196365,0.00001666567,0.9791461,0.001062469,0.00005141028,0.0000311148,0.00478916],"study_design_scores_gemma":[0.000005996466,0.00001054493,0.004921899,0.000002065764,0.000009020822,0.000004664102,0.000009419615,0.9944777,0.0004641649,0.00004811667,0.00004205585,0.000004309538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893765,0.00008265997,0.009610403,0.00002673369,0.000007795864,0.00001655364,0.0001789152,0.0001654866,0.0005348492],"genre_scores_gemma":[0.9956884,0.00003662387,0.003960308,0.000006464263,0.000001630797,0.00001149858,0.0001530876,0.000006813046,0.0001352165],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04183037,"threshold_uncertainty_score":0.08317375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02044956421500361,"score_gpt":0.212403539901731,"score_spread":0.1919539756867274,"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."}}