{"id":"W7026874876","doi":"","title":"Algorithmes de gestion des ressources radio pour la
\\ncommunication périphérique à périphérique (D2D) dans
\\nles réseaux cellulaires sans fil.","year":2017,"lang":"en","type":"dissertation","venue":"EspaceINRS (National Institute for Scientific Research (Canada))","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Resource allocation; Cellular network; Scheduling (production processes); Power control; Radio resource management; Cellular communication; Wireless; Resource management (computing); Spectral efficiency","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001537884,0.001337367,0.001363964,0.001373074,0.00104704,0.002174241,0.003107092,0.00204129,0.008256416],"category_scores_gemma":[0.004127052,0.0005329647,0.001142053,0.001023664,0.0009658741,0.002247322,0.001854532,0.001764786,0.002739571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00161514,"about_ca_system_score_gemma":0.003153212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006155888,"about_ca_topic_score_gemma":0.009426147,"domain_scores_codex":[0.9989549,0.0002118796,0.0000880892,0.0003013321,0.0002680957,0.0001757053],"domain_scores_gemma":[0.9985127,0.0007889116,0.0001408688,0.000161634,0.0003210693,0.00007492791],"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":[0.0003839401,0.0002541614,0.00139747,0.000252062,0.00009420995,0.0001426659,0.000413044,0.385019,0.00864049,0.06062043,0.008135002,0.5346475],"study_design_scores_gemma":[0.00008198851,0.00007434865,0.0001897005,0.00002434357,0.00001951127,0.00004755024,0.00006277189,0.9821618,0.00293237,0.009551055,0.004829726,0.00002491677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005607847,0.000273484,0.9898515,0.0001665886,0.00007750974,0.0001572812,0.00004843608,0.0006493055,0.003168101],"genre_scores_gemma":[0.1161927,0.0004639501,0.8723387,0.0001962451,0.0001039615,0.0007109679,0.0003761384,0.0001959702,0.009421355],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008256416,"threshold_uncertainty_score":0.02762043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1268561409256864,"score_gpt":0.3919291136142573,"score_spread":0.2650729726885709,"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."}}