{"id":"W4387986941","doi":"10.1109/tempr.2023.3327903","title":"Tight and Compact Data-Driven Linear Relaxations for Constraint Screening in Unit Commitment","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Energy Markets Policy and Regulation","topic":"Electric Power System Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Constraint (computer-aided design); Set (abstract data type); Mathematical optimization; Mathematics; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.0001949434,0.0001329891,0.0001462588,0.0005512068,0.0001596983,0.00002855054,0.00006753601,0.000102375,0.000007028758],"category_scores_gemma":[0.00001187505,0.0001387133,0.00002213946,0.0005645525,0.00004305973,0.0002070199,0.000001643556,0.00008454615,0.000001016288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000450508,"about_ca_system_score_gemma":0.00002909903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001429021,"about_ca_topic_score_gemma":0.0002840461,"domain_scores_codex":[0.9992037,0.0000634367,0.00024622,0.0001953737,0.00009751665,0.0001937414],"domain_scores_gemma":[0.9993836,0.0002653016,0.0000409509,0.0002180196,0.0000264396,0.00006565547],"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":[0.00003295697,0.0000192168,0.00004809552,0.00003968645,0.00005491364,7.126535e-7,0.000122922,0.974111,0.0002746106,0.001670676,0.000360088,0.02326511],"study_design_scores_gemma":[0.0006669101,0.0000305928,0.004654475,0.00007207918,0.00002252578,0.000008708508,0.00002631905,0.9897755,0.0006821986,0.0001416628,0.00377887,0.0001401406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01331462,0.00004591827,0.9844355,0.0005353956,0.0001581046,0.0002771068,0.0001502608,0.0002668568,0.000816261],"genre_scores_gemma":[0.9970348,0.0003338076,0.001691669,0.00002627035,0.00005931724,0.00003539292,0.000213924,0.0000285543,0.000576275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9837202,"threshold_uncertainty_score":0.5656562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03351206978138462,"score_gpt":0.2713972873424749,"score_spread":0.2378852175610903,"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."}}