{"id":"W4409795000","doi":"10.61091/jcmcc127b-379","title":"Research on Decentralized Energy Trading Mechanism and Carbon Footprint Traceability Based on Heterogeneous Blockchain and Federated Reinforcement Learning for Interactive Energy Markets","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Blockchain; Traceability; Carbon footprint; Reinforcement learning; Mechanism (biology); Energy (signal processing); Footprint; Computer science; Business; Computer security; Artificial intelligence; Geology; Greenhouse gas","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002819317,0.0002780349,0.0006069866,0.0004997079,0.0006961706,0.0003206893,0.0004111357,0.0002445407,5.393161e-7],"category_scores_gemma":[0.0004567757,0.000258275,0.00009792343,0.0004139632,0.0001143077,0.00003547692,0.0003004019,0.0006437933,2.280137e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001824418,"about_ca_system_score_gemma":0.0001362436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002573425,"about_ca_topic_score_gemma":0.000001550536,"domain_scores_codex":[0.9975379,0.0003607772,0.0007978057,0.0004410569,0.0004629693,0.0003995664],"domain_scores_gemma":[0.9958855,0.002642064,0.0005080594,0.0002842937,0.0005259668,0.0001541317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000333242,0.0004182914,0.00001266429,0.00008935254,0.00008621682,0.000007783122,0.0003172695,0.000608517,0.0003433489,0.9865629,0.000008477024,0.01121195],"study_design_scores_gemma":[0.002433802,0.001026261,0.000008829397,0.000224146,0.00001893288,0.00001388669,0.0001081776,0.5416635,0.006769769,0.4474512,0.0001540846,0.0001274349],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7490011,0.0002383464,0.246559,0.0008461883,0.002411834,0.0004761087,5.643724e-7,0.00009500657,0.0003718091],"genre_scores_gemma":[0.9966115,0.0000505994,0.00314558,0.00004706886,0.0001007503,0.00002300795,5.709522e-7,0.00001627593,0.00000459799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.541055,"threshold_uncertainty_score":0.9999869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01618176306371685,"score_gpt":0.2862493212279451,"score_spread":0.2700675581642282,"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."}}