{"id":"W3124215582","doi":"10.1016/j.eneco.2016.03.013","title":"Measuring demand responses to wholesale electricity prices using market power indices","year":2016,"lang":"en","type":"article","venue":"Energy Economics","topic":"Electric Power System Optimization","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Economics; Price elasticity of demand; Lerner index; Market power; Cournot competition; Econometrics; Natural gas prices; Index (typography); Price index; Electricity market; Demand curve; Electricity; Agricultural economics; Microeconomics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003138453,0.0001779772,0.0002086997,0.0003106642,0.00006882292,0.00006597097,0.0002010578,0.00009954704,0.00005163185],"category_scores_gemma":[0.00006451859,0.0001632214,0.00004294914,0.0002100295,0.00001154662,0.0003426843,0.00003714435,0.00004632525,0.00001324922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004090196,"about_ca_system_score_gemma":0.00004208195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002670244,"about_ca_topic_score_gemma":0.00006659875,"domain_scores_codex":[0.9989948,0.00004446539,0.000302038,0.0002442252,0.00006482704,0.0003496994],"domain_scores_gemma":[0.9994001,0.0001329067,0.0000760371,0.0002448828,0.00003175219,0.000114282],"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":[0.0008103177,0.0001727904,0.1332451,0.000207395,0.001211674,0.00004259613,0.001563845,0.6902355,0.1127925,0.005045113,0.01056329,0.04410991],"study_design_scores_gemma":[0.002104641,0.0002711156,0.01466995,0.0004275735,0.00009206232,0.0001276662,0.00007993547,0.5297054,0.3212969,0.0007246998,0.1278809,0.002619167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8335935,0.0004524856,0.1588589,0.00004188679,0.0003671116,0.00007623566,0.000008079282,0.0002085084,0.006393253],"genre_scores_gemma":[0.9964458,0.0002131063,0.00248101,0.00005005415,0.00009606964,0.0000141565,0.000001105773,0.00005797156,0.0006407761],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2085044,"threshold_uncertainty_score":0.6655975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01287710347201598,"score_gpt":0.1849853007411125,"score_spread":0.1721081972690965,"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."}}