{"id":"W4403124918","doi":"10.1109/pesgm51994.2024.10689072","title":"Synergizing Smart Electric Railway Networks with Integrated Wind Generation: An Optimal Energy Management Approach Considering Stochastic and Probabilistic Analysis","year":2024,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Probabilistic logic; Wind power; Computer science; Energy management; Stochastic process; Energy (signal processing); Reliability engineering; Engineering; Electrical engineering; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000765903,0.001011113,0.001057534,0.0005893116,0.0004377791,0.001386936,0.0008536556,0.001421314,0.002247497],"category_scores_gemma":[0.001357319,0.0007527167,0.0009911314,0.0006139609,0.0006285195,0.001122569,0.0008422863,0.0007961156,0.0002068739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001088051,"about_ca_system_score_gemma":0.001296096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01350515,"about_ca_topic_score_gemma":0.01444023,"domain_scores_codex":[0.9996821,0.0001203817,0.00001370853,0.00006163579,0.00006656555,0.00005557327],"domain_scores_gemma":[0.9995265,0.0002625842,0.0000901491,0.00001527025,0.00007431066,0.00003107126],"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":[0.000008588963,0.000007325947,0.0001606397,0.000009040858,0.00001088922,0.00002918428,0.000004481431,0.9964234,0.0001103042,0.001927198,0.00008033423,0.001228582],"study_design_scores_gemma":[0.000002391057,0.000007325858,0.00005723873,0.00000191171,0.000004839004,0.000003713673,0.000003894001,0.9989957,0.00002256159,0.0008104101,0.00008809843,0.000001923509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07533046,0.0006552448,0.9060684,0.0006999479,0.00008963615,0.0001061127,0.0002393222,0.0002487401,0.01656212],"genre_scores_gemma":[0.9750185,0.0004228248,0.01985969,0.00005955042,0.0000493295,0.00009392384,0.00009255247,0.00004537639,0.004358184],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01350515,"threshold_uncertainty_score":0.02685308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008782190297902618,"score_gpt":0.1845907260453676,"score_spread":0.175808535747465,"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."}}