{"id":"W4409324757","doi":"10.1007/s10661-025-13972-0","title":"Variability analysis of soil organic carbon content across land use types and its digital mapping using machine learning and deep learning algorithms","year":2025,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières; Innovation and Economic Development Trois Rivières","funders":"","keywords":"Soil carbon; Digital soil mapping; Algorithm; Total organic carbon; Land use; Environmental science; Ecotoxicology; Carbon fibers; Computer science; Artificial intelligence; Machine learning; Soil science; Soil classification; Environmental chemistry; Chemistry; Soil water; Ecology; Biology","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":[0.0004721642,0.000214754,0.000196637,0.001190085,0.000195906,0.0004816894,0.0003456204,0.0002733826,0.0005052314],"category_scores_gemma":[0.001267614,0.0001134259,0.0004478505,0.001178447,0.0002151032,0.0004935316,0.0003525954,0.0002973027,0.0001145378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004732585,"about_ca_system_score_gemma":0.000354736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01094966,"about_ca_topic_score_gemma":0.009789088,"domain_scores_codex":[0.9998327,0.00001680261,0.000009007575,0.00007345088,0.0000435685,0.00002427939],"domain_scores_gemma":[0.9993788,0.0002413212,0.0001183412,0.00007929733,0.0001516086,0.00003056181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006895236,0.0003166406,0.4958982,0.0001295866,0.0005124524,0.0002614358,0.0001783775,0.295294,0.03921414,0.002390622,0.002005009,0.1631101],"study_design_scores_gemma":[0.000009710296,0.00002676431,0.207331,0.000006529793,0.00003678423,0.00006845106,0.00007259152,0.7857676,0.004777114,0.001370486,0.000512373,0.00002066362],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9710872,0.00009631266,0.0265795,0.00006282677,0.00001127949,0.00000832813,0.001146606,0.0002363594,0.0007716823],"genre_scores_gemma":[0.9957552,0.000016695,0.003346623,0.000006069315,0.000003598578,0.00000463615,0.0006796307,0.00001752076,0.0001701405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01094966,"threshold_uncertainty_score":0.02177185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01816350387728679,"score_gpt":0.267671127503592,"score_spread":0.2495076236263052,"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."}}