{"id":"W4401466613","doi":"10.1016/j.geomat.2024.100002","title":"Enhancing soil organic carbon estimation accuracy: Integrating spatial vegetation dynamics and temporal analysis with Sentinel 2 imagery","year":2024,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Soil carbon; Remote sensing; Vegetation (pathology); Soil science; Geology; Soil water; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006631463,0.0004714652,0.0002411682,0.0009976526,0.0001558074,0.0004359912,0.0003494524,0.0002053009,0.0003973156],"category_scores_gemma":[0.001044811,0.0001748722,0.0004832992,0.0009973025,0.0001337252,0.000763726,0.0004107101,0.0002482291,0.0001566964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002350755,"about_ca_system_score_gemma":0.0004214297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009088159,"about_ca_topic_score_gemma":0.02571631,"domain_scores_codex":[0.9997219,0.00007226454,0.00001767885,0.00007199352,0.00007599689,0.00004016294],"domain_scores_gemma":[0.9995578,0.0001093061,0.0001029907,0.00005472613,0.0001594798,0.00001581031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003686852,0.0001991055,0.2983676,0.0005655272,0.0005084181,0.0004029904,0.0006502729,0.1226136,0.1270247,0.001953013,0.003046257,0.4442998],"study_design_scores_gemma":[0.00002717726,0.0001443572,0.2332319,0.00007358989,0.0002758315,0.0003514054,0.0006943968,0.727651,0.02758838,0.001910246,0.007909532,0.0001421268],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7878303,0.0009268555,0.2030645,0.0003117171,0.0001583848,0.00005484102,0.001542403,0.001109193,0.00500183],"genre_scores_gemma":[0.863696,0.0003832169,0.1339178,0.0000662743,0.00005168664,0.00004003186,0.001162498,0.00006254137,0.0006199389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009088159,"threshold_uncertainty_score":0.01807052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004116595954139163,"score_gpt":0.2155043766518397,"score_spread":0.2113877806977006,"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."}}