{"id":"W4409795007","doi":"10.61091/jcmcc127b-479","title":"Machine Learning-Based State Monitoring and Regulation Characterization of Distribution Grid with High Percentage Distributed Resource Access","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":"Grid; Resource (disambiguation); Computer science; State (computer science); Resource distribution; Distribution (mathematics); Distributed computing; Characterization (materials science); Resource allocation; Computer network; Mathematics; Materials science; Nanotechnology","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":[0.0005969604,0.0002091199,0.0005186117,0.0001409863,0.0001553436,0.0001752908,0.0001585264,0.00009339675,0.000001246178],"category_scores_gemma":[0.0000785673,0.000184212,0.00005231393,0.0003025465,0.00004707475,0.0002057134,0.00006652182,0.0003085767,9.612983e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008021879,"about_ca_system_score_gemma":0.00004665487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001063234,"about_ca_topic_score_gemma":1.914789e-7,"domain_scores_codex":[0.9985315,0.00008091784,0.0007536029,0.0001231876,0.0003317394,0.0001790044],"domain_scores_gemma":[0.9987383,0.0002379708,0.0005507962,0.0001216292,0.000270087,0.00008121806],"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":[0.004804439,0.003400753,0.3097543,0.02150891,0.003255094,0.0001330454,0.007616062,0.1823817,0.1245701,0.3307584,0.001931902,0.009885255],"study_design_scores_gemma":[0.0385019,0.003859819,0.16978,0.01428142,0.001170599,0.0001620453,0.0007407196,0.5786107,0.1356215,0.03181707,0.02307427,0.002379854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9484717,0.0001376215,0.04046855,0.00003820318,0.01057431,0.0001965362,0.0000300011,0.00005205276,0.00003104545],"genre_scores_gemma":[0.9988678,0.00003823845,0.0001340343,0.000001491743,0.0008664621,0.000001462958,0.00006506179,0.00002233162,0.000003147381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.396229,"threshold_uncertainty_score":0.7511946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006086365097127786,"score_gpt":0.218068707623308,"score_spread":0.2119823425261802,"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."}}