{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.04005482,0.0007413797,0.000829734,0.002277505,0.009487265,0.008684978,0.008256764,0.0005457154,0.0002170964],"category_scores_gemma":[0.0371257,0.0006835889,0.00038262,0.002306652,0.002365011,0.001725084,0.0005684423,0.001430157,0.00005833039],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003499739,"about_ca_system_score_gemma":0.01884294,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6422646,"about_ca_topic_score_gemma":0.9980825,"domain_scores_codex":[0.9807251,0.002035693,0.001673326,0.002912938,0.01103541,0.001617548],"domain_scores_gemma":[0.9789209,0.005607698,0.001305086,0.003808342,0.009623749,0.0007342445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002288049,0.0002957304,0.0006271101,0.0003057258,0.0001900249,0.00007946071,0.004639547,0.02201932,0.001733506,0.01107363,0.8802447,0.07856242],"study_design_scores_gemma":[0.0006354254,0.00005383474,0.0157274,0.0006260903,0.00003699305,0.0000214624,0.00677832,0.05510473,0.004174152,0.02869912,0.8873647,0.0007777037],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4315597,0.007110553,0.06292249,0.1141197,0.03994599,0.010227,0.01242717,0.0007416584,0.3209458],"genre_scores_gemma":[0.7496551,0.0002409398,0.005530118,0.00008340659,0.0007385644,0.0005478306,0.009757685,0.00009992273,0.2333464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3558179,"threshold_uncertainty_score":0.9995615,"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."}}