{"id":"W4404952379","doi":"10.1109/tccn.2024.3508783","title":"A Survey of Graph-Based Resource Management in Wireless Networks—Part I: Optimization Approaches","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Victoria","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Wireless network; Wireless; Resource management (computing); Graph; Computer network; Theoretical computer science; Telecommunications","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":[0.001112768,0.001772288,0.001660125,0.002003872,0.0005495258,0.002208524,0.001952544,0.001384553,0.00389515],"category_scores_gemma":[0.002372921,0.0007012105,0.001208956,0.005055082,0.0008770286,0.003759841,0.001110148,0.001845291,0.001298772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001431283,"about_ca_system_score_gemma":0.001103089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002569135,"about_ca_topic_score_gemma":0.001969524,"domain_scores_codex":[0.9992349,0.0002433476,0.00007045058,0.0001704608,0.000219492,0.00006139356],"domain_scores_gemma":[0.998881,0.0007962094,0.000065453,0.00007372187,0.0001458201,0.00003771123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006079976,0.000203238,0.001170294,0.004742831,0.00018124,0.0001625185,0.000146804,0.1616457,0.001368656,0.2269322,0.04034456,0.5630411],"study_design_scores_gemma":[0.00002800572,0.000208903,0.001262206,0.001716982,0.0001340021,0.0005508829,0.0002286992,0.3889111,0.001225291,0.3327232,0.2729087,0.0001019698],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.004306395,0.4127535,0.5461298,0.003751478,0.001050376,0.000128277,0.0004709901,0.0004011585,0.03100806],"genre_scores_gemma":[0.09189894,0.7130516,0.1775749,0.001752555,0.003940973,0.0002993205,0.001158541,0.0003446893,0.009978535],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.00389515,"threshold_uncertainty_score":0.01303065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09058678297912597,"score_gpt":0.276203392835273,"score_spread":0.185616609856147,"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."}}