{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004918245,0.0004123311,0.0005011824,0.0004847587,0.0002389551,0.0007039631,0.0004592389,0.0003197875,0.0005887224],"category_scores_gemma":[0.001514368,0.0001504045,0.0002971916,0.0005075853,0.000327032,0.0007383859,0.0003066275,0.0004051447,0.0001178684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004828293,"about_ca_system_score_gemma":0.0002543171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003326805,"about_ca_topic_score_gemma":0.00222367,"domain_scores_codex":[0.9997442,0.00006044816,0.00001734024,0.00008004552,0.00006805865,0.0000298834],"domain_scores_gemma":[0.999455,0.0002350939,0.00009971994,0.00005611472,0.0001364658,0.00001755958],"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":[0.000176657,0.00009493183,0.009196934,0.00006998586,0.00004486294,0.000106231,0.00009655102,0.8971868,0.007978023,0.003616279,0.0007975543,0.0806352],"study_design_scores_gemma":[8.306793e-7,0.000007433726,0.0007067336,8.973531e-7,0.000001772868,0.000005748569,0.000004514148,0.9985499,0.000382556,0.0002938421,0.00004371611,0.00000207289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2820026,0.0001981443,0.7118559,0.0001803502,0.00002637655,0.00006075043,0.0001269267,0.0007502576,0.004798712],"genre_scores_gemma":[0.9916235,0.00004492309,0.007744121,0.00001231106,0.000005568497,0.00001898215,0.00007803258,0.000009244854,0.0004633315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003326805,"threshold_uncertainty_score":0.006614864,"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."}}