{"id":"W2065752649","doi":"10.1109/icdcs.2012.52","title":"Towards Optimal Capacity Segmentation with Hybrid Cloud Pricing","year":2012,"lang":"en","type":"article","venue":"","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Common value auction; Reservation; Revenue management; Revenue equivalence; Combinatorial auction; Revenue; Mathematical optimization; Market segmentation; Cloud computing; Reservation price; Spot market; Dynamic pricing; Reverse auction; Forward auction; Operations research; Microeconomics; Auction theory; Economics; Finance","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":[0.001919347,0.0009366428,0.001604545,0.0006410889,0.000488635,0.001856259,0.001533339,0.001287135,0.002094861],"category_scores_gemma":[0.006013005,0.000969953,0.0006570723,0.001082892,0.00150245,0.002226421,0.001690023,0.001392613,0.0003094914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001755947,"about_ca_system_score_gemma":0.002165389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003396866,"about_ca_topic_score_gemma":0.002231048,"domain_scores_codex":[0.9988145,0.0005125512,0.00003930715,0.000205764,0.0002022449,0.0002256266],"domain_scores_gemma":[0.9976904,0.001539477,0.0002350724,0.0002152134,0.0001774768,0.0001423983],"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":[0.00009195162,0.00007044409,0.000351036,0.00004111923,0.00003214026,0.00005035516,0.0000612607,0.9346365,0.001403471,0.04558856,0.0009939843,0.01667921],"study_design_scores_gemma":[0.000007074452,0.00001079902,0.00003871535,0.000002425855,0.000002206686,0.000008443344,0.000006533782,0.9837914,0.0001778776,0.01578759,0.0001635884,0.000003273667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0494614,0.0002796109,0.9442709,0.0002992815,0.00003517938,0.00006508987,0.00005900536,0.0002485598,0.005281059],"genre_scores_gemma":[0.8736454,0.0001772718,0.1238668,0.0001422558,0.00005261086,0.00009627894,0.00006928181,0.00009085842,0.001859237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003396866,"threshold_uncertainty_score":0.01274037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1005582240481655,"score_gpt":0.3624922905559849,"score_spread":0.2619340665078194,"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."}}