{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007397837,0.0006433523,0.0003325342,0.001524045,0.0003453846,0.0005675455,0.0007119146,0.0002763773,0.001197487],"category_scores_gemma":[0.002224345,0.0002507329,0.0005006142,0.001765131,0.0002887237,0.0004004631,0.0003277549,0.0002135906,0.0003691423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004324975,"about_ca_system_score_gemma":0.003288635,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.7922445,"about_ca_topic_score_gemma":0.8668202,"domain_scores_codex":[0.9997194,0.0000402191,0.00001557247,0.00007735271,0.00009985805,0.00004749121],"domain_scores_gemma":[0.9990587,0.0002516639,0.0001453164,0.00009835404,0.0003976002,0.0000485307],"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":[0.0001357318,0.00008869906,0.8653635,0.0001255291,0.0001683538,0.0001528788,0.0002128163,0.09947754,0.002056434,0.0003819812,0.001491876,0.03034464],"study_design_scores_gemma":[0.00001876555,0.00002858386,0.8217778,0.00002322874,0.00002927195,0.00004475339,0.0002496653,0.1746098,0.0009953572,0.0002390892,0.001958383,0.00002525239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9771689,0.000119416,0.005921385,0.00003335574,0.000004476251,0.00009501613,0.01535639,0.0001832034,0.001117932],"genre_scores_gemma":[0.9793569,0.0000649435,0.007556207,0.000007169401,0.000002651037,0.00006810902,0.01214294,0.00001887312,0.0007821471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7922445,"threshold_uncertainty_score":0.417958,"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."}}