{"id":"W3047224322","doi":"10.5194/isprs-annals-v-3-2020-541-2020","title":"ESTIMATION OF SOIL BULK DENSITY AND CARBON USING MULTI-SOURCE REMOTELY SENSED DATA","year":2020,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Ministry of Agriculture, Food and Rural Affairs; Natural Sciences and Engineering Research Council of Canada; Natural Resources Canada; Ministry of Natural Resources; Ontario Ministry of Agriculture, Food and Rural Affairs; Ontario Ministry of Natural Resources and Forestry","keywords":"Multispectral image; Environmental science; Covariate; Soil carbon; Lidar; Soil science; Remote sensing; Bulk density; Standard deviation; Digital elevation model; Vegetation (pathology); Atmospheric sciences; Geography; Statistics; Mathematics; Soil water; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006715418,0.000114771,0.0001881009,0.0000556848,0.0002490067,0.00008632975,0.0002166017,0.00004868989,0.000002308035],"category_scores_gemma":[0.0008105358,0.00008884456,0.00002905495,0.0004496411,0.0006049033,0.0002431846,0.0004583392,0.00008539994,7.513174e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007485947,"about_ca_system_score_gemma":0.00002532826,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2931755,"about_ca_topic_score_gemma":0.005364319,"domain_scores_codex":[0.9987552,0.00006613628,0.0003860735,0.0001979328,0.0004155283,0.000179143],"domain_scores_gemma":[0.9991067,0.00008861728,0.0004249636,0.0002377577,0.00004844314,0.00009351189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001777595,0.000005044412,0.0008869648,0.00003819492,0.000006825828,2.576159e-7,0.001591934,0.0244805,0.005237052,6.37351e-7,0.00003129529,0.9677035],"study_design_scores_gemma":[0.0001468515,0.00004602961,0.006391596,0.00004952003,0.00001535349,0.00001097518,0.0004218283,0.975633,0.01694831,0.00008734673,0.0001481041,0.0001010849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6208822,0.00001398417,0.378307,0.0004642942,0.00006124889,0.0001116604,0.00001158237,0.00001025507,0.0001377987],"genre_scores_gemma":[0.9860753,0.00003025903,0.01332467,0.0005454444,0.00001195589,1.334749e-8,0.000005890095,0.000003585893,0.000002891661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9676024,"threshold_uncertainty_score":0.7115313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1011628233847286,"score_gpt":0.3116853750537759,"score_spread":0.2105225516690472,"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."}}