{"id":"W4386206493","doi":"10.1016/j.still.2023.105858","title":"Legume cover crops enhance soil organic carbon via microbial necromass in orchard alleyways","year":2023,"lang":"en","type":"article","venue":"Soil and Tillage Research","topic":"Soil Carbon and Nitrogen Dynamics","field":"Agricultural and Biological Sciences","cited_by":57,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Fundamental Research Funds for the Central Universities; Guangzhou Institute of Geochemistry, Chinese Academy of Sciences; Earmarked Fund for China Agriculture Research System; National University's Basic Research Foundation of China; Agriculture Research System of China; Sichuan Agricultural University; Chinese Academy of Sciences","keywords":"Orchard; Cover crop; Soil carbon; Legume; Cover (algebra); Agroforestry; Environmental science; Carbon fibers; Soil cover; Agronomy; Biology; Soil science; Mathematics; Engineering; Soil water","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":[0.0001629485,0.0002484361,0.0002719538,0.0003167886,0.0005393472,0.0006814977,0.0004070833,0.0002584035,0.001737868],"category_scores_gemma":[0.0003525833,0.0002846265,0.0002184405,0.0001908732,0.0003312183,0.0007069639,0.0006480511,0.0004838571,0.0001819971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001123941,"about_ca_system_score_gemma":0.0006496759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01927324,"about_ca_topic_score_gemma":0.1020423,"domain_scores_codex":[0.9998367,0.00002155512,0.00000832485,0.00004685505,0.00002455224,0.00006203521],"domain_scores_gemma":[0.9992648,0.0001357827,0.0001267539,0.00005298705,0.00006742225,0.0003522316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001348885,0.0002379477,0.05741883,0.00004842474,0.00007938738,0.0001115498,0.0003477648,0.0002380022,0.93646,0.0001943466,0.00009069608,0.003424247],"study_design_scores_gemma":[0.0000279622,0.00035735,0.9412859,0.00000685432,0.00004126315,0.00006418105,0.000326215,0.0009646425,0.05598085,0.000104376,0.0008296063,0.00001092732],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996904,0.00003737903,0.00004157036,0.000008343772,8.43023e-7,0.000002426284,0.00002797041,0.000004263565,0.0001867603],"genre_scores_gemma":[0.9979489,0.00005670642,0.0001588671,0.0000269406,0.000001698412,0.000007353568,0.0001455771,0.00001009949,0.001643816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01927324,"threshold_uncertainty_score":0.03832215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0287082640728797,"score_gpt":0.2787148987527696,"score_spread":0.2500066346798899,"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."}}