{"id":"W2123079653","doi":"10.5539/jas.v6n5p120","title":"Using DSSAT-CENTURY Model to Simulate Soil Organic Carbon Dynamics Under a Low-Input Maize Cropping System","year":2014,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Soil Carbon and Nitrogen Dynamics","field":"Agricultural and Biological Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"DSSAT; Soil carbon; Environmental science; Tillage; Soil fertility; Cropping system; Conservation agriculture; Soil water; Agronomy; Soil management; Soil organic matter; Agroforestry; Agriculture; Crop yield; Soil science; Crop; Ecology; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001019934,0.0002756797,0.0004002228,0.00006703103,0.0004445971,0.0003017463,0.0008798729,0.0001235494,0.000007347888],"category_scores_gemma":[0.0001497481,0.0000973452,0.0002072905,0.001650576,0.000146773,0.0004279048,0.0002230802,0.0003048353,0.000006337572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008446623,"about_ca_system_score_gemma":0.00008550921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002525814,"about_ca_topic_score_gemma":0.0005951183,"domain_scores_codex":[0.997249,0.00009034605,0.0006041829,0.0003765386,0.001031174,0.0006487265],"domain_scores_gemma":[0.998084,0.0001078418,0.0004784646,0.0000981319,0.0007067372,0.0005248451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002373979,0.00004223171,0.0005978926,0.00001207908,0.0000126821,0.000005574593,0.0001089374,0.1273238,0.8702263,0.0005648438,0.000007665745,0.001074275],"study_design_scores_gemma":[0.0002663534,0.0002508432,0.06952945,0.0002064512,0.00005223141,0.0003431228,0.001733702,0.9227861,0.004289518,0.0001178427,0.000009601948,0.0004148494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977796,0.00005163638,0.000366382,0.000512936,0.0005886864,0.0001534969,0.000005548677,0.00005187895,0.0004898132],"genre_scores_gemma":[0.9985136,0.00001105902,0.0007355857,0.0002693166,0.0004188857,9.383837e-7,0.000002225039,0.000002575418,0.00004582956],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8659368,"threshold_uncertainty_score":0.3969622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01929643295966613,"score_gpt":0.2283709607866085,"score_spread":0.2090745278269424,"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."}}