{"id":"W4409603112","doi":"10.61091/jcmcc127b-125","title":"Study on Constructing a Risk Monitoring and Early Warning Model for Transactions in Southern Regional Electricity Market under New Power System Based on Time Series Bayesian Networks","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Smart Grid and Power Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Warning system; Electricity; Electricity market; Series (stratigraphy); Bayesian network; Bayesian probability; Time series; Econometrics; Computer science; Power market; Power (physics); Electric power system; Data mining; Operations research; Business; Economics; Artificial intelligence; Machine learning; Engineering; Telecommunications","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":[0.001359474,0.0003084593,0.0007201489,0.0003459326,0.0002669199,0.0002022396,0.0001634743,0.000168228,9.487765e-7],"category_scores_gemma":[0.00009021497,0.0002949743,0.0001277711,0.0003036769,0.00002558807,0.0001141237,0.00001837111,0.0006437164,1.874414e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001741131,"about_ca_system_score_gemma":0.0001081041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001440142,"about_ca_topic_score_gemma":7.891152e-7,"domain_scores_codex":[0.9980977,0.0001336966,0.0009092918,0.0002065603,0.0003368763,0.0003158341],"domain_scores_gemma":[0.9980417,0.001130506,0.0003985314,0.0001579828,0.0001424567,0.0001288599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001936991,0.00116035,0.01602606,0.0009076003,0.001041152,0.00004262062,0.01958168,0.8866181,0.0001632179,0.07001159,0.0005025057,0.002008079],"study_design_scores_gemma":[0.006372415,0.000883713,0.0003880354,0.001730253,0.0001403451,0.00002383541,0.004619971,0.9775371,0.00004878527,0.00791388,0.00001898628,0.0003226752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7000802,0.0001313195,0.2871471,0.00002366775,0.01169792,0.0005291096,0.000002715313,0.00007668279,0.0003113392],"genre_scores_gemma":[0.9974835,0.000004606434,0.00145375,0.000003404633,0.0009890532,0.000004623564,2.177412e-7,0.00004281528,0.00001806662],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2974033,"threshold_uncertainty_score":0.9999502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009256614420352641,"score_gpt":0.2204428828850094,"score_spread":0.2111862684646567,"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."}}