{"id":"W2509663643","doi":"10.1080/00224561.2006.12435907","title":"Landscape segmentation modeling in agricultural fields: Correlating soil pH to herbicide persistence","year":2006,"lang":"en","type":"article","venue":"Journal of Soil and Water Conservation","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Landform; Persistence (discontinuity); Environmental science; Soil water; Spatial variability; Agriculture; Digital elevation model; Soil science; Physical geography; Agronomy; Ecology; Geography; Geology; Geomorphology; Remote sensing; Biology; Mathematics; Geotechnical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001966885,0.0002818647,0.0002764935,0.0006587696,0.000156925,0.0004510773,0.0002369342,0.0002374652,0.0002789698],"category_scores_gemma":[0.0006894884,0.0001387619,0.0001961967,0.0006463314,0.000153505,0.0002674091,0.0001536608,0.0001052524,0.00004823788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007229511,"about_ca_system_score_gemma":0.0005016541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07036683,"about_ca_topic_score_gemma":0.07570545,"domain_scores_codex":[0.9999256,0.00001798998,0.000003866814,0.0000243443,0.00001299383,0.00001529909],"domain_scores_gemma":[0.9998179,0.00007797073,0.00004229459,0.0000121558,0.00003336639,0.00001626629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0004702615,0.0001713588,0.2408832,0.00004749305,0.00009794807,0.0001091146,0.0001962605,0.6665912,0.01506062,0.0004136946,0.000275557,0.07568318],"study_design_scores_gemma":[0.000007608171,0.00005018578,0.05928148,0.000002694111,0.00001297585,0.00002416168,0.00005423066,0.9389219,0.001228576,0.0002489439,0.0001612209,0.000005995426],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9795695,0.00004915167,0.0197057,0.00002028071,0.000001289913,0.00001487018,0.000150296,0.0001586885,0.0003302642],"genre_scores_gemma":[0.9910412,0.00002451037,0.008460376,0.000003771602,0.000001037577,0.00000913707,0.000288028,0.000007420986,0.0001644412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07036683,"threshold_uncertainty_score":0.1399145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01425380282758824,"score_gpt":0.1928094974200766,"score_spread":0.1785556945924884,"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."}}