{"id":"W3207531566","doi":"10.3390/f12111430","title":"Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data","year":2021,"lang":"en","type":"article","venue":"Forests","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China","keywords":"Environmental science; Terrain; Vegetation (pathology); Nutrient; Remote sensing; Hydrology (agriculture); Soil nutrients; Soil science; Soil water; Ecology; Geography; Geology; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.0002470931,0.0004715475,0.00023784,0.0007577506,0.0001503922,0.0002814938,0.0002799987,0.0003042514,0.0003479389],"category_scores_gemma":[0.0005079059,0.0001236163,0.0004494726,0.0006852272,0.0001277168,0.0003906644,0.0002260077,0.0001871866,0.0001069946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004769631,"about_ca_system_score_gemma":0.0005923821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05749677,"about_ca_topic_score_gemma":0.04750717,"domain_scores_codex":[0.9999088,0.00001160091,0.000006632534,0.00003075446,0.00002130557,0.00002087408],"domain_scores_gemma":[0.9998757,0.00002651246,0.00002057096,0.00001158708,0.00005066358,0.00001497487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001956596,0.0001395854,0.5745206,0.000112122,0.000133164,0.0004774965,0.0000985913,0.3536516,0.01012519,0.0003908901,0.001658363,0.05849671],"study_design_scores_gemma":[0.00002645287,0.00003373032,0.1957098,0.00001166601,0.0000336412,0.00003349095,0.00009193946,0.8013829,0.001871091,0.0002043119,0.0005840844,0.00001690286],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963651,0.00007490549,0.002026728,0.00003754782,0.000007420207,0.00001077134,0.0009086938,0.00008134521,0.0004875976],"genre_scores_gemma":[0.9958208,0.00006197388,0.002469934,0.000007258584,0.000003097696,0.00001193371,0.001428422,0.000004106834,0.0001925791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05749677,"threshold_uncertainty_score":0.1143242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04558354294670711,"score_gpt":0.2506156977712954,"score_spread":0.2050321548245883,"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."}}