{"id":"W2000152137","doi":"10.1243/095440804774134244","title":"Mathematical methods of combining deterministic/probabilistic criteria in short-term generating reserve scheduling","year":2004,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering","topic":"Power System Reliability and Maintenance","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Probabilistic logic; Scheduling (production processes); Computer science; Term (time); Electric power system; Reliability engineering; Operator (biology); Mathematical optimization; Process (computing); Operations research; Risk analysis (engineering); Power (physics); Engineering; Mathematics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002587899,0.0003494357,0.001154071,0.0002962484,0.00004024479,0.00002446551,0.0008337718,0.0002822821,0.000007669418],"category_scores_gemma":[0.004258777,0.0002865308,0.0003351416,0.0007095991,0.0001041374,0.0004345621,0.000136481,0.0007750364,3.693247e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002332216,"about_ca_system_score_gemma":0.0001358506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000248645,"about_ca_topic_score_gemma":9.133983e-7,"domain_scores_codex":[0.9962865,0.00002379349,0.00238272,0.0002423008,0.0006363721,0.0004283333],"domain_scores_gemma":[0.9983147,0.0002404042,0.0004138088,0.0002090368,0.0006355965,0.0001864783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005011074,0.0001335058,0.000008445684,0.003820599,0.00007582248,0.000003703382,0.0003314486,0.5715946,0.3734937,0.05029014,0.000002216434,0.0001957592],"study_design_scores_gemma":[0.0008625175,0.00023366,0.00001433483,0.00559736,0.000104924,0.000129286,0.0002389126,0.4886232,0.4981261,0.005789829,0.00001538827,0.0002645608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6282271,0.0002566955,0.3700611,0.00005377617,0.0009525288,0.0003161229,0.000007001418,0.00005694154,0.00006869543],"genre_scores_gemma":[0.9130977,0.00004075685,0.08670352,0.000005410916,0.000090534,0.00001917316,5.004629e-7,0.00004150569,9.17687e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2848706,"threshold_uncertainty_score":0.9999587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02441819257303188,"score_gpt":0.2935015320625268,"score_spread":0.2690833394894949,"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."}}