{"id":"W4409787690","doi":"10.61091/jcmcc127a-291","title":"Agricultural carbon financial innovation and carbon emission reduction effect: an empirical study based on system GMM","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Impact of AI and Big Data on Business and Society","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Carbon fibers; Agriculture; Reduction (mathematics); Empirical research; Business; Natural resource economics; Economics; Materials science; Mathematics; Geography; Composite material","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":[0.006246225,0.0003041833,0.0008878602,0.0004771148,0.0004493322,0.0006171349,0.0003912096,0.0002396995,8.750628e-7],"category_scores_gemma":[0.002228593,0.0001943558,0.0001081728,0.001461074,0.00006894976,0.0002539019,0.0001605555,0.0005016618,1.780669e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001434344,"about_ca_system_score_gemma":0.0002264217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000255824,"about_ca_topic_score_gemma":2.492596e-7,"domain_scores_codex":[0.9960082,0.0004448864,0.0014875,0.0003588684,0.001445649,0.0002549059],"domain_scores_gemma":[0.9962173,0.0009383952,0.001143034,0.0003148798,0.001243758,0.0001426646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.005433181,0.01612856,0.149417,0.001976926,0.0005350703,0.0002009896,0.01725787,0.0007379617,0.01483193,0.7265332,0.004298419,0.06264898],"study_design_scores_gemma":[0.05913625,0.03307977,0.1926729,0.005742264,0.001221738,0.0003667167,0.03919205,0.2717581,0.007049194,0.3863819,0.0007385318,0.002660661],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9856336,0.00004223409,0.0002921298,0.0002260235,0.01262016,0.0003960995,0.000001526,0.00002858716,0.0007597041],"genre_scores_gemma":[0.998671,0.000001731913,0.0001567427,0.00002408231,0.001124192,0.000002049194,0.000002321779,0.00001104657,0.000006828684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3401513,"threshold_uncertainty_score":0.7925599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03512385674429449,"score_gpt":0.3444651417401556,"score_spread":0.3093412849958612,"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."}}