{"id":"W3192545956","doi":"","title":"A Bilevel Model for Large-scale Time-and-Level-of-Use Pricing","year":2019,"lang":"en","type":"preprint","venue":"LillOA (Université de Lille (University Of Lille))","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Bilevel optimization; Scale (ratio); Computer science; Econometrics; Economics; Algorithm; Geography","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":[0.0005234807,0.0004801778,0.0009818344,0.001017044,0.000543826,0.00007628811,0.0008694251,0.0005569798,0.0004249122],"category_scores_gemma":[0.00005684824,0.0006423552,0.000565762,0.0004590754,0.0001549219,0.001007091,0.003779833,0.0004386381,0.00003927799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002033909,"about_ca_system_score_gemma":0.0001760014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003149724,"about_ca_topic_score_gemma":0.00139187,"domain_scores_codex":[0.997844,0.00003256459,0.0003078164,0.0008268652,0.0003823661,0.0006063527],"domain_scores_gemma":[0.9976114,0.00023393,0.0008900937,0.0007461092,0.0004480466,0.00007040633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01292455,0.003258721,0.5850918,0.03158236,0.00541938,0.0004334311,0.07521813,0.05896011,0.03382105,0.02462445,0.07808045,0.09058557],"study_design_scores_gemma":[0.004526408,0.00005636247,0.05461188,0.0008645859,0.002138957,0.000008525182,0.005052046,0.9036683,0.00007193269,0.0008465446,0.02666225,0.001492156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9366021,0.0002310603,0.05104293,0.0006674369,0.000345889,0.001329051,0.0008913386,0.0001754983,0.008714703],"genre_scores_gemma":[0.9491885,0.0002625979,0.01751337,0.0002189583,0.0001209864,0.000001662024,0.0003745943,0.0001008916,0.03221849],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8447083,"threshold_uncertainty_score":0.9996028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04681532245028092,"score_gpt":0.2117193462144465,"score_spread":0.1649040237641655,"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."}}