{"id":"W4409871225","doi":"10.1016/j.fcr.2025.109946","title":"Long-term organic material application enhances black soil productivity by improving aggregate stability and dissolved organic matter dynamics","year":2025,"lang":"en","type":"article","venue":"Field Crops Research","topic":"Soil Carbon and Nitrogen Dynamics","field":"Agricultural and Biological Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"National Key Research and Development Program of China; Chinese Academy of Agricultural Sciences; Ministry of Science and Technology of the People's Republic of China; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Term (time); Productivity; Organic matter; Environmental science; Aggregate (composite); Soil organic matter; Dissolved organic carbon; Stability (learning theory); Soil science; Chemistry; Environmental chemistry; Soil water; Materials science; Computer science; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006169917,0.0001375759,0.0001678488,0.00002115444,0.0003100919,0.0002254197,0.0002962673,0.0001642992,0.0007947648],"category_scores_gemma":[0.0001714881,0.00006737118,0.00003685445,0.0004632606,0.0001969885,0.0001163071,0.0003339963,0.0003653677,0.00002920076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009464344,"about_ca_system_score_gemma":0.00002892789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001510512,"about_ca_topic_score_gemma":0.01360885,"domain_scores_codex":[0.9984096,0.0002007294,0.0001941642,0.0005255697,0.0002683146,0.0004016634],"domain_scores_gemma":[0.9992825,0.0002539254,0.00005112187,0.0001546189,0.0001771355,0.00008064904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007654408,0.00007043993,0.2147038,0.00006865912,0.000009360824,8.881127e-7,0.00003806844,5.180626e-8,0.7694701,0.00002418555,0.0001183983,0.01541947],"study_design_scores_gemma":[0.0001295007,0.0001765534,0.3110467,0.00002118065,0.00001003413,0.000002014752,0.0001889014,0.001806714,0.6859087,0.0004641329,0.00005797471,0.0001876093],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961396,0.00008064425,0.00007038363,0.0027472,0.00009572624,0.0004121823,0.00003253265,0.00005387906,0.0003678096],"genre_scores_gemma":[0.9985136,0.00007733068,0.000006236758,0.00006765233,0.0001121907,0.00004142361,0.000116297,0.000002166152,0.001063107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09634284,"threshold_uncertainty_score":0.8702118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01341340364715751,"score_gpt":0.2700280029557842,"score_spread":0.2566145993086267,"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."}}