{"id":"W1794309874","doi":"10.1007/978-3-642-02011-7_10","title":"A Heuristic for Fair Correlation-Aware Resource Placement","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Overhead (engineering); Heuristic; Exploit; Scheme (mathematics); Mathematical optimization; Bounded function; Graph; Distributed computing; Algorithm; Theoretical computer science; Artificial intelligence; Mathematics","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":[0.0007520895,0.0005786572,0.0005556165,0.0005880662,0.0004095029,0.0005466475,0.002792143,0.0003684601,0.00001419946],"category_scores_gemma":[0.0001367631,0.0005387106,0.0001919955,0.0005556879,0.0003054495,0.0003344368,0.000711252,0.000656704,0.0000338108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003314421,"about_ca_system_score_gemma":0.0005272673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005712356,"about_ca_topic_score_gemma":0.00001644138,"domain_scores_codex":[0.9960006,0.00002768804,0.0005971252,0.001680156,0.0008952345,0.0007991664],"domain_scores_gemma":[0.9965327,0.001277116,0.0003434184,0.001391699,0.0002471169,0.0002078865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001679057,0.00003071153,0.00002632178,0.0000380469,0.00001036038,0.00003584168,0.0004194895,0.2642681,0.000001584312,0.01583708,0.001039245,0.7182764],"study_design_scores_gemma":[0.0004676548,0.0004106205,0.00008170885,0.0004955623,0.00001324408,0.00004350083,1.893265e-7,0.8426165,0.00002862856,0.1266361,0.02850579,0.0007004726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000005200982,0.0008209714,0.9936298,0.0009921778,0.001438075,0.0007882967,0.00001077094,0.0003089998,0.002005666],"genre_scores_gemma":[0.06516638,0.00008773054,0.9196346,0.008082788,0.002275875,0.00008595894,0.00006592423,0.000121135,0.004479606],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.717576,"threshold_uncertainty_score":0.9997064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01491045815004099,"score_gpt":0.2363237681073886,"score_spread":0.2214133099573476,"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."}}