{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":115,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":115,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"66dd8c5e685c","filters":{"venue":"IEEE Transactions on Cognitive Communications and Networking"}},"results":[{"id":"W3035788210","doi":"10.1109/tccn.2020.3003036","title":"Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":241,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Reinforcement learning; Markov decision process; Edge computing; Mobile edge computing; Distributed computing; Server; Computation offloading; Scheduling (production processes); Schedule; Workload; Cloudlet; Computer network; Cloud computing; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Markov process; Operating system","authors":[{"name":"Mushu Li","is_ca":true},{"name":"Jie Gao","is_ca":true},{"name":"Lian Zhao","is_ca":true},{"name":"Xuemin Shen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04792263489770058,"gpt":0.2795189155115557,"spread":0.2315962806138551,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000886654,0.000612221,0.0008546078,0.0003215739,0.0004370793,0.0006879259,0.001135359,0.0007883955,0.001044462],"category_scores_gemma":[0.002436973,0.0003367569,0.000369624,0.0003544428,0.0007646825,0.0008233155,0.001055441,0.00123856,0.0001453126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001232135,"about_ca_system_score_gemma":0.001333955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01183754,"about_ca_topic_score_gemma":0.01013331,"domain_scores_codex":[0.999599,0.0001033041,0.00001838429,0.00009151698,0.00007922642,0.0001085684],"domain_scores_gemma":[0.9992208,0.0004342135,0.00007721762,0.00004548698,0.0001591294,0.00006305625],"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":[0.00003283618,0.00002875333,0.0004617802,0.00001492544,0.00001467138,0.00002450432,0.00002243987,0.9793459,0.0004936233,0.004256247,0.000381382,0.01492303],"study_design_scores_gemma":[0.000001448458,0.000003961053,0.00001906296,4.848527e-7,8.650138e-7,0.000001059027,0.000001446137,0.9990453,0.0000495948,0.0008412679,0.00003478711,6.712045e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05557088,0.000385753,0.9409024,0.0003664571,0.00005718105,0.00003058352,0.00003202059,0.0003043289,0.002350416],"genre_scores_gemma":[0.9725032,0.0001096213,0.02571016,0.00009431937,0.00001921818,0.00004310858,0.00004386061,0.00002064702,0.001455722],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01183754,"threshold_uncertainty_score":0.02353728,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3137932838","doi":"10.1109/tccn.2021.3066619","title":"Delay-Aware and Energy-Efficient Computation Offloading in Mobile-Edge Computing Using Deep Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":226,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"Computation offloading; Reinforcement learning; Server; Edge computing; Cloud computing; Mobile edge computing; Mobile device; Enhanced Data Rates for GSM Evolution; Edge device","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.03976490221543554,"gpt":0.2897704064381183,"spread":0.2500055042226828,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006026602,0.000674062,0.0008238111,0.0001979338,0.0003280881,0.0006631571,0.000991761,0.0006570052,0.0009935049],"category_scores_gemma":[0.001406362,0.0003008928,0.0002651977,0.0002472508,0.0006533916,0.0007038108,0.0007698628,0.00103061,0.0001199904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000803961,"about_ca_system_score_gemma":0.00116861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006699137,"about_ca_topic_score_gemma":0.00758503,"domain_scores_codex":[0.9997215,0.00006446234,0.00001134601,0.0000642134,0.00004872508,0.00008976121],"domain_scores_gemma":[0.9994583,0.0003048656,0.00006182236,0.0000274852,0.00009107419,0.00005650078],"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":[0.00008494175,0.00008301919,0.0006393547,0.00003415784,0.00001791181,0.00005689948,0.00002898667,0.9733218,0.001541417,0.00218432,0.0006291991,0.02137798],"study_design_scores_gemma":[0.000002857503,0.000009475967,0.00002616803,0.000001002881,0.000001441457,0.000002461833,0.000001974772,0.9992906,0.0001334048,0.0004935377,0.00003610413,9.883256e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1056681,0.0007872776,0.8885361,0.0005465653,0.00008533468,0.00005847313,0.00004716432,0.0004771434,0.003793913],"genre_scores_gemma":[0.980175,0.000116397,0.01835951,0.0001131757,0.00001670863,0.00003400748,0.00002507139,0.00001938368,0.001140711],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006699137,"threshold_uncertainty_score":0.01332033,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3170790803","doi":"10.1109/tccn.2021.3084406","title":"Fast-Convergent Federated Learning With Adaptive Weighting","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":208,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Computer science; Node (physics); Weighting; Convergence (economics); Orchestration; Artificial intelligence; Theoretical computer science","authors":[{"name":"Hongda Wu","is_ca":true},{"name":"Ping Wang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04948721404327679,"gpt":0.2725010338649894,"spread":0.2230138198217126,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004369757,0.001539601,0.002351564,0.0009062549,0.0009618285,0.001827004,0.003594248,0.002135896,0.002637051],"category_scores_gemma":[0.01026396,0.0007593822,0.000967425,0.001278608,0.001459645,0.003438784,0.00342118,0.002306287,0.001225914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001320392,"about_ca_system_score_gemma":0.002688715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005820953,"about_ca_topic_score_gemma":0.006444129,"domain_scores_codex":[0.997335,0.000741924,0.0001467407,0.0008140577,0.0005865319,0.0003758045],"domain_scores_gemma":[0.9956899,0.001623752,0.0002761862,0.001177155,0.0009980402,0.0002349662],"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":[0.0004725178,0.0002608397,0.001149592,0.00007590794,0.0001009674,0.0001327437,0.0001039921,0.8554786,0.002488554,0.01141272,0.003358711,0.1249647],"study_design_scores_gemma":[0.00001449588,0.0000207907,0.00003697976,0.000002445051,0.000004136237,0.00000974358,0.000008365539,0.9942577,0.0005413069,0.00490985,0.0001904885,0.000003738832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01412917,0.000131913,0.9830065,0.0002051682,0.00005596276,0.00004542166,0.00005609308,0.001149048,0.001220652],"genre_scores_gemma":[0.7003859,0.0001337613,0.2920154,0.0004807316,0.00009298338,0.0002344474,0.0004421996,0.0002227839,0.005991785],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005820953,"threshold_uncertainty_score":0.02310973,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3090944066","doi":"10.1109/tccn.2020.3027696","title":"UAV-Assisted Wireless Energy and Data Transfer With Deep Reinforcement Learning","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":113,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Reinforcement learning; Computer science; Markov decision process; Wireless; Partially observable Markov decision process; Real-time computing; Data transmission; Markov process; Distributed computing; Markov chain; Computer network; Markov model; Artificial intelligence; Machine learning; Telecommunications","authors":[{"name":"Zehui Xiong","is_ca":false},{"name":"Yang Zhang","is_ca":false},{"name":"Wei Yang Bryan Lim","is_ca":false},{"name":"Jiawen Kang","is_ca":false},{"name":"Dusit Niyato","is_ca":false},{"name":"Cyril Leung","is_ca":true},{"name":"Chunyan Miao","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0449217732959111,"gpt":0.2440623563199031,"spread":0.199140583023992,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007123254,0.0006622684,0.0008404847,0.0002164099,0.0003161544,0.0005930719,0.0009043568,0.0009070828,0.001568109],"category_scores_gemma":[0.001565152,0.0002963663,0.0003900184,0.0002621004,0.0006841124,0.000780085,0.0009184622,0.001158723,0.0001575339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008094932,"about_ca_system_score_gemma":0.001126738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007338695,"about_ca_topic_score_gemma":0.005709926,"domain_scores_codex":[0.9997358,0.00007286971,0.00001271712,0.00006181753,0.00005762863,0.00005906625],"domain_scores_gemma":[0.9993137,0.0003840556,0.0001024547,0.00003803094,0.0001163479,0.00004532781],"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":[0.00002969049,0.00003169733,0.0003436837,0.00002683901,0.00001161527,0.00003888634,0.0000158924,0.9878312,0.000512989,0.002106723,0.0002936792,0.0087571],"study_design_scores_gemma":[0.000002790503,0.00001046812,0.00002203686,0.00000134806,0.000001383878,0.000003028259,0.000001672745,0.9993611,0.00008577597,0.0004573887,0.00005186653,0.000001095329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05657794,0.0005578503,0.9370514,0.0004436777,0.00007656498,0.0000592127,0.00005671028,0.0003617051,0.00481487],"genre_scores_gemma":[0.9785639,0.0001100142,0.01955876,0.00009849087,0.00001272049,0.0000568297,0.00003659612,0.00001462051,0.001548049],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007338695,"threshold_uncertainty_score":0.01459199,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3048635505","doi":"10.1109/tccn.2020.3016096","title":"Robust Secure Beamforming for Wireless Powered Cognitive Satellite-Terrestrial Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Satellite Communication Systems","field":"Engineering","cited_by":88,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; Concordia University","funders":"Shanghai Aerospace Science and Technology Innovation Foundation","keywords":"Computer science; Beamforming; Base station; Wireless; Optimization problem; Benchmark (surveying); Iterative method; Mathematical optimization; Computer network; Telecommunications; Algorithm; Mathematics","authors":[{"name":"Zhi Lin","is_ca":false},{"name":"Min Lin","is_ca":false},{"name":"Wei‐Ping Zhu","is_ca":true},{"name":"Jun-Bo Wang","is_ca":false},{"name":"Julian Cheng","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09893019344119698,"gpt":0.2714573008821349,"spread":0.1725271074409379,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006951778,0.0008412556,0.0005673952,0.0003126003,0.0003166937,0.0006940918,0.0005109836,0.0007265979,0.001471109],"category_scores_gemma":[0.001881747,0.0002634706,0.0003645694,0.0006724794,0.000658555,0.0007770334,0.0009220795,0.0007589853,0.0003946567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005297862,"about_ca_system_score_gemma":0.0007487811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001520498,"about_ca_topic_score_gemma":0.001882786,"domain_scores_codex":[0.9995534,0.0001496154,0.00001603982,0.00006423918,0.0001562264,0.00006042578],"domain_scores_gemma":[0.9994667,0.0003372185,0.00007073969,0.00003431066,0.00007157055,0.00001933032],"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":[0.0001027556,0.00002557119,0.0002664384,0.0000969922,0.0000356497,0.00009646206,0.00005852673,0.8930205,0.01024407,0.0323065,0.001110519,0.06263597],"study_design_scores_gemma":[0.000009792394,0.00003239283,0.00005464322,0.00000670718,0.000005680302,0.00002670812,0.00001428641,0.9926425,0.0009374234,0.005783712,0.0004806369,0.000005579368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006329606,0.0002238449,0.9914631,0.00008525648,0.00002185366,0.0000165534,0.00001902975,0.00005871972,0.001781974],"genre_scores_gemma":[0.7802258,0.001216492,0.2137255,0.0001998603,0.0000779586,0.0001879283,0.0001143723,0.00003763746,0.004214553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001520498,"threshold_uncertainty_score":0.004921317,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3017748691","doi":"10.1109/tccn.2020.2988908","title":"Delay-Aware VNF Scheduling: A Reinforcement Learning Approach With Variable Action Set","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Reinforcement learning; Markov decision process; Distributed computing; Scheduling (production processes); Quality of service; Q-learning; Integer programming; Mathematical optimization; Markov process; Computer network; Algorithm; Artificial intelligence","authors":[{"name":"Junling Li","is_ca":true},{"name":"Weisen Shi","is_ca":true},{"name":"Ning Zhang","is_ca":false},{"name":"Xuemin Shen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08454553673967595,"gpt":0.278670215954831,"spread":0.1941246792151551,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001506513,0.00105479,0.001420425,0.0005675496,0.0004302133,0.0007678592,0.00192561,0.001122072,0.001614509],"category_scores_gemma":[0.002966959,0.0004577294,0.0005509852,0.0005278775,0.0009098839,0.0007800407,0.0008997619,0.001491406,0.0001581197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001415992,"about_ca_system_score_gemma":0.00191269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009540848,"about_ca_topic_score_gemma":0.006669924,"domain_scores_codex":[0.9992815,0.0002530233,0.00002846813,0.0001546779,0.0001472068,0.0001351182],"domain_scores_gemma":[0.9986032,0.0008831777,0.0001919675,0.00004581234,0.000177219,0.00009858212],"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":[0.00002547441,0.00003204866,0.0001957522,0.00002272827,0.00001741228,0.00003280855,0.00001920875,0.9869171,0.0002885601,0.003109224,0.0001934901,0.009146155],"study_design_scores_gemma":[0.000005211687,0.00001258215,0.00001963264,0.000002021178,0.000002606687,0.000003793413,0.000002077196,0.9988239,0.00006037743,0.0009727775,0.00009313569,0.000001853083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01638012,0.0003148005,0.9803167,0.0002460352,0.00005772604,0.0000636413,0.00002923168,0.0001946258,0.002397222],"genre_scores_gemma":[0.8862398,0.0002839626,0.1107987,0.0001685602,0.00006603433,0.0002308111,0.00006795094,0.00004807651,0.002096022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009540848,"threshold_uncertainty_score":0.01897067,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4293143556","doi":"10.1109/tccn.2022.3187098","title":"UAV-Aided Aerial Reconfigurable Intelligent Surface Communications With Massive MIMO System","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"Engineering and Physical Sciences Research Council; Queen's University; Queen's University Belfast; Royal Academy of Engineering","keywords":"Computer science; Base station; MIMO; Beamforming; Coordinate descent; Benchmark (surveying); Throughput; Precoding; Real-time computing; Wireless; Computer network; Algorithm; Telecommunications","authors":[{"name":"Minh-Hien T. Nguyen","is_ca":false},{"name":"Emiliano Garcia‐Palacios","is_ca":false},{"name":"Tan Do‐Duy","is_ca":false},{"name":"Octavia A. Dobre","is_ca":true},{"name":"Trung Q. Duong","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03749830188036622,"gpt":0.2506376786355902,"spread":0.2131393767552239,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000126051,0.0003933354,0.0003734523,0.0001498581,0.0002047032,0.0003165467,0.0004073835,0.000328079,0.001118593],"category_scores_gemma":[0.0002228547,0.0001141235,0.000263565,0.0002309656,0.0002126666,0.0003173015,0.0004499073,0.0003457545,0.0003431641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002040628,"about_ca_system_score_gemma":0.0003001478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001556303,"about_ca_topic_score_gemma":0.002210043,"domain_scores_codex":[0.9998513,0.00003507643,0.000004675393,0.00002995277,0.0000489436,0.00003000964],"domain_scores_gemma":[0.9999098,0.00002810593,0.00001870062,0.00001425506,0.00001978859,0.000009329417],"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":[0.0002404409,0.00005091939,0.001459895,0.0001264822,0.0000534811,0.0006355001,0.0001358556,0.8035191,0.06963261,0.006002964,0.001925048,0.1162176],"study_design_scores_gemma":[0.00001598552,0.0001434213,0.0003138096,0.000005352651,0.00001275138,0.00008971414,0.00002384231,0.9925453,0.005157542,0.0006990256,0.000984499,0.000008694584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1272128,0.0004034172,0.8602234,0.0001807331,0.0000986689,0.00004443702,0.00008328843,0.0007735713,0.01097979],"genre_scores_gemma":[0.9387539,0.0001077718,0.05814863,0.00005725738,0.00002498624,0.00003679509,0.00005697697,0.000009263981,0.002804478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001556303,"threshold_uncertainty_score":0.003742099,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4401326099","doi":"10.1109/tccn.2024.3438379","title":"Generative AI for Secure Physical Layer Communications: A Survey","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Physical layer; Computer network; Layer (electronics); Telecommunications; Wireless","authors":[{"name":"Changyuan Zhao","is_ca":false},{"name":"Hongyang Du","is_ca":false},{"name":"Dusit Niyato","is_ca":false},{"name":"Jiawen Kang","is_ca":false},{"name":"Zehui Xiong","is_ca":false},{"name":"Dong In Kim","is_ca":false},{"name":"Xuemin Shen","is_ca":true},{"name":"Khaled B. Letaief","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08242531335143641,"gpt":0.3473854756081475,"spread":0.264960162256711,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001192753,0.001203199,0.001064023,0.001054636,0.0003700675,0.001914631,0.001428219,0.001572463,0.003241776],"category_scores_gemma":[0.002762689,0.0005649819,0.001009008,0.001437402,0.001322437,0.002024057,0.001394665,0.00227637,0.001069125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000924786,"about_ca_system_score_gemma":0.0008685258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002005409,"about_ca_topic_score_gemma":0.001270489,"domain_scores_codex":[0.9994013,0.0002157122,0.00004448175,0.0001048019,0.0001941133,0.0000396807],"domain_scores_gemma":[0.9980478,0.001588556,0.00005771565,0.0001381011,0.0001292336,0.00003863878],"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":[0.00005932905,0.0001254367,0.00206645,0.002508977,0.0002365044,0.0002813849,0.000387166,0.3210804,0.002518731,0.2901046,0.009819916,0.3708111],"study_design_scores_gemma":[0.00001528608,0.0001447156,0.0007501243,0.0006569561,0.00007037226,0.0004800077,0.0001667201,0.7231886,0.00168251,0.1938864,0.07888643,0.00007181808],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.006852006,0.1396648,0.8125952,0.003005372,0.0004462178,0.00009307185,0.0001728344,0.00055509,0.03661541],"genre_scores_gemma":[0.4039325,0.3617291,0.2115394,0.001903843,0.002258427,0.00038675,0.0006224041,0.0004487615,0.01717894],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.003241776,"threshold_uncertainty_score":0.01084483,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4404627796","doi":"10.1109/tccn.2024.3504489","title":"RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Diffusion; Sampling (signal processing); Telecommunications","authors":[{"name":"Xiucheng Wang","is_ca":false},{"name":"Keda Tao","is_ca":false},{"name":"Nan Cheng","is_ca":false},{"name":"Zhisheng Yin","is_ca":false},{"name":"Zan Li","is_ca":false},{"name":"Yuan Zhang","is_ca":false},{"name":"Xuemin Shen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04172909688352386,"gpt":0.3117781576834992,"spread":0.2700490607999754,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007928341,0.0008267192,0.0007842798,0.000711514,0.0003454811,0.0008514184,0.002151249,0.0009581905,0.00221164],"category_scores_gemma":[0.002267059,0.0005395809,0.00104177,0.000830333,0.000703192,0.001447733,0.001373466,0.001541609,0.0007783652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008763645,"about_ca_system_score_gemma":0.0007598534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00592827,"about_ca_topic_score_gemma":0.005758942,"domain_scores_codex":[0.9995822,0.00009842257,0.00001951427,0.0001224204,0.0001329746,0.00004445299],"domain_scores_gemma":[0.9993203,0.0003221485,0.0000738331,0.0001027215,0.0001386343,0.00004229233],"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":[0.0001196388,0.0000506975,0.001008215,0.00009903215,0.00005686748,0.0001363476,0.0001151848,0.7664394,0.006983037,0.02180664,0.00322132,0.1999636],"study_design_scores_gemma":[0.000003086548,0.000007909306,0.00005941939,0.000002200167,0.000003996374,0.00002425627,0.000003353185,0.9968582,0.0006218927,0.001857587,0.0005531882,0.000004887941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003464233,0.0001142497,0.9951745,0.00006303671,0.00002349941,0.00001533641,0.00004523896,0.0003952198,0.0007047786],"genre_scores_gemma":[0.4423234,0.0006701927,0.5455521,0.0003070541,0.0001259982,0.0001973078,0.0009515516,0.000396432,0.009475947],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00592827,"threshold_uncertainty_score":0.01178753,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3201976554","doi":"10.1109/tccn.2021.3116251","title":"On Joint Offloading and Resource Allocation: A Double Deep Q-Network Approach","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Reinforcement learning; Resource allocation; Cellular network; Mobile edge computing; Distributed computing; Base station; Telecommunications link; Computer network; Deep learning; Edge computing; Wireless network; Resource management (computing); Q-learning; Computation offloading; Transmitter power output; Enhanced Data Rates for GSM Evolution; Wireless; Artificial intelligence; Server; Channel (broadcasting); Telecommunications; Transmitter","authors":[{"name":"Fahime Khoramnejad","is_ca":true},{"name":"Melike Erol‐Kantarci","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05920463564747824,"gpt":0.2650668162793273,"spread":0.205862180631849,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001201189,0.0007937817,0.001104763,0.0004032608,0.0004758672,0.0007007873,0.001472985,0.001108998,0.002519003],"category_scores_gemma":[0.002173764,0.0003835478,0.0003719358,0.0004330486,0.001011242,0.001499088,0.001265131,0.001104943,0.0002583266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001022165,"about_ca_system_score_gemma":0.001402188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007283611,"about_ca_topic_score_gemma":0.007029614,"domain_scores_codex":[0.999519,0.0001532394,0.00002001243,0.00009941286,0.00008578919,0.0001226121],"domain_scores_gemma":[0.998983,0.0006144696,0.00008750981,0.00006478609,0.0001657075,0.00008455534],"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":[0.0001324531,0.000102087,0.0008096796,0.00004988139,0.00002934599,0.0000793408,0.00005109045,0.9261526,0.001311643,0.01073374,0.001286708,0.05926143],"study_design_scores_gemma":[0.000006027115,0.00001387871,0.00003414306,0.000001921957,0.000002294887,0.000007031663,0.000003874574,0.9972669,0.000129707,0.00240696,0.0001257329,0.000001670395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02972988,0.0003330804,0.9661531,0.0004727677,0.00005603772,0.0000529295,0.0000344453,0.0002566214,0.002911209],"genre_scores_gemma":[0.8724313,0.0002079268,0.122317,0.000456412,0.00005671595,0.0001162103,0.00008719387,0.00006378521,0.004263667],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007283611,"threshold_uncertainty_score":0.01448238,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3080073809","doi":"10.1109/tccn.2020.3018157","title":"Dynamic Resource Scaling for VNF Over Nonstationary Traffic: A Learning Approach","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Provisioning; Quality of service; Resource allocation; Scalability; Computer network; Software-defined networking; Markov decision process; Markov process; Traffic generation model; Distributed computing","authors":[{"name":"Kaige Qu","is_ca":true},{"name":"Weihua Zhuang","is_ca":true},{"name":"Xuemin Shen","is_ca":true},{"name":"Xu Li","is_ca":true},{"name":"Jaya Rao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04431378056773156,"gpt":0.2776108599146176,"spread":0.2332970793468861,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000942449,0.0008640576,0.0009646611,0.0005667426,0.0003852206,0.0006972124,0.00135203,0.001114232,0.001166602],"category_scores_gemma":[0.003190889,0.0004469531,0.0005264184,0.000624088,0.0007705127,0.001090011,0.0008892387,0.001588129,0.0001996408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001001127,"about_ca_system_score_gemma":0.001319902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007535289,"about_ca_topic_score_gemma":0.00583008,"domain_scores_codex":[0.9996781,0.00007163793,0.0000177898,0.0001002563,0.00006510813,0.00006711847],"domain_scores_gemma":[0.9988579,0.000698788,0.0001268203,0.00005580936,0.0001990289,0.00006173187],"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":[0.00003278773,0.00006014213,0.0007276459,0.00003046506,0.00002064391,0.00004068865,0.00003396854,0.9501237,0.0008623153,0.003740891,0.0005527266,0.04377416],"study_design_scores_gemma":[9.089162e-7,0.000005049364,0.00002512754,0.000001172702,0.000001291796,0.000002319443,0.000001486051,0.9992238,0.00006193047,0.0006329892,0.00004287537,0.000001028959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03640464,0.000440333,0.9609073,0.0004199812,0.00004110182,0.00003362137,0.00003997482,0.0003145676,0.001398544],"genre_scores_gemma":[0.9010738,0.0004528406,0.09478382,0.0002705332,0.0001203456,0.0001354172,0.0001450012,0.00006797555,0.002950275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007535289,"threshold_uncertainty_score":0.01498282,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2765551136","doi":"10.1109/tccn.2017.2769121","title":"Stackelberg Equilibria of an Anti-Jamming Game in Cooperative Cognitive Radio Networks","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Stackelberg competition; Cognitive radio; Jamming; Computer science; Game theory; Deception; Computer security; Strategy; Backward induction; Computer network; Wireless; Channel (broadcasting); Set (abstract data type); Telecommunications; Mathematical economics; Mathematics","authors":[{"name":"Ismail K. Ahmed","is_ca":true},{"name":"Abraham O. Fapojuwo","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04071875252153063,"gpt":0.3034718984963976,"spread":0.2627531459748669,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002419544,0.001409579,0.001394884,0.001240429,0.001036416,0.00267411,0.00157581,0.002002646,0.003537806],"category_scores_gemma":[0.007229306,0.000551926,0.0009712054,0.0006828402,0.00258615,0.002786757,0.001726355,0.001561122,0.0004209695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002418098,"about_ca_system_score_gemma":0.001909153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003640649,"about_ca_topic_score_gemma":0.002253262,"domain_scores_codex":[0.9986229,0.0006932827,0.00005196631,0.0001619444,0.0002212691,0.0002485786],"domain_scores_gemma":[0.9963061,0.002625243,0.0004796775,0.00007801144,0.0002568964,0.0002540645],"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":[0.000264763,0.0001197955,0.00115397,0.0001119303,0.00009498284,0.0004702635,0.0005196051,0.545832,0.002714042,0.4392959,0.001100762,0.008322014],"study_design_scores_gemma":[0.00004567011,0.0001084173,0.0001925325,0.00002010889,0.00002209174,0.00005689594,0.0001228039,0.8594909,0.0004799037,0.1389979,0.0004381338,0.00002465429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2775454,0.0003414499,0.6928487,0.0007492348,0.00004476555,0.000184577,0.0001493311,0.0001391052,0.02799747],"genre_scores_gemma":[0.9735832,0.0003254513,0.02204163,0.00007833284,0.00002043822,0.0002175761,0.00005593825,0.000016046,0.003661509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003640649,"threshold_uncertainty_score":0.01754463,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3005301866","doi":"10.1109/tccn.2020.2971703","title":"A Secure Spectrum Handoff Mechanism in Cognitive Radio Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea","keywords":"Computer science; Cognitive radio; Emulation; Computer network; Handover; Throughput; Transmission (telecommunications); Cognitive network; Secure transmission; Authentication (law); Mechanism (biology); Cognition; Computer security; Wireless; Telecommunications; Encryption","authors":[{"name":"Geetanjali Rathee","is_ca":false},{"name":"Naveen Jaglan","is_ca":false},{"name":"Sahil Garg","is_ca":true},{"name":"Bong Jun Choi","is_ca":false},{"name":"Kim‐Kwang Raymond Choo","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04003560654503288,"gpt":0.2640151462681938,"spread":0.223979539723161,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001482358,0.0005675099,0.0004766958,0.0006501882,0.0007285248,0.001059662,0.001044481,0.001140981,0.00061084],"category_scores_gemma":[0.004513007,0.0002386757,0.0004023487,0.000330956,0.00094893,0.00145518,0.001368114,0.0008695679,0.0002216969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004420276,"about_ca_system_score_gemma":0.0008120195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009198021,"about_ca_topic_score_gemma":0.0005532422,"domain_scores_codex":[0.9985315,0.0002754226,0.000100962,0.0002222781,0.000695275,0.0001746509],"domain_scores_gemma":[0.9978471,0.000527034,0.0006597243,0.0005073781,0.0003403977,0.0001183518],"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":[0.001528747,0.0004195342,0.006271409,0.000457053,0.0003038819,0.00182714,0.001054385,0.236156,0.2529635,0.185206,0.003476666,0.3103358],"study_design_scores_gemma":[0.00008669579,0.0007108875,0.001525961,0.00004634494,0.0001132097,0.001069089,0.00009283288,0.9354905,0.03414351,0.02200877,0.004624323,0.0000879501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1174813,0.001223771,0.8761009,0.0003634907,0.0002154452,0.0001445162,0.00002533471,0.0007260163,0.003719229],"genre_scores_gemma":[0.9741228,0.000194228,0.02483075,0.00006430322,0.00004807114,0.00003746806,0.000009697378,0.000007519411,0.0006851149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001482358,"threshold_uncertainty_score":0.007839501,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3191505721","doi":"10.1109/tccn.2021.3103531","title":"Resource Allocation in Cognitive Radio-Enabled UAV Communication","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina; University of Ottawa; Carleton University","funders":"","keywords":"Cognitive radio; Computer science; Throughput; Unavailability; Underlay; Spectrum management; Resource allocation; Wireless; Interference (communication); Wireless network; Heuristic; Computer network; Transmitter power output; Transmission (telecommunications); Channel (broadcasting); Telecommunications; Transmitter; Signal-to-noise ratio (imaging); Engineering","authors":[{"name":"Sina Khoshabi Nobar","is_ca":true},{"name":"Mohamed H. Ahmed","is_ca":true},{"name":"Yasser Morgan","is_ca":true},{"name":"S. Mahmoud","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02797899759800147,"gpt":0.2550288006567106,"spread":0.2270498030587091,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005938275,0.0003735534,0.0004546319,0.0003260041,0.0003908078,0.0007021992,0.0006038446,0.0005406036,0.0007154553],"category_scores_gemma":[0.00183462,0.0002400935,0.0001816435,0.0005566141,0.0007737539,0.0006556613,0.0005711112,0.0003357612,0.00009082613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007985126,"about_ca_system_score_gemma":0.0007829134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007048841,"about_ca_topic_score_gemma":0.005863245,"domain_scores_codex":[0.9996374,0.0001607953,0.000007739311,0.00003535829,0.00006331433,0.00009539241],"domain_scores_gemma":[0.9993948,0.0004310802,0.00006645071,0.00002278552,0.00005486131,0.00003012101],"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":[0.00002744058,0.00001258655,0.0001413866,0.00001587542,0.000006907531,0.00004691181,0.00001570019,0.9899497,0.000704464,0.00474027,0.0001622566,0.00417646],"study_design_scores_gemma":[0.000004568737,0.00002017974,0.00006870406,0.000002204507,0.000002444667,0.00001041192,0.00001523591,0.9973872,0.0001829925,0.002181554,0.0001219047,0.000002675907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2184678,0.001954141,0.7617179,0.0003663266,0.00008644549,0.00006766734,0.00005646323,0.0001716772,0.01711157],"genre_scores_gemma":[0.986563,0.0002603481,0.01230678,0.00003196401,0.0000111666,0.00002131212,0.000008526212,0.000006702513,0.0007902052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007048841,"threshold_uncertainty_score":0.01401561,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3201634655","doi":"10.1109/tccn.2021.3114147","title":"Improving Energy Efficiency and QoS of LPWANs for IoT Using Q-Learning Based Data Routing","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Computer network; Scalability; Testbed; Quality of service; Wireless sensor network; Efficient energy use; Data transmission; Energy consumption; Distributed computing","authors":[{"name":"Om Jee Pandey","is_ca":false},{"name":"Tankala Yuvaraj","is_ca":false},{"name":"Joseph K. Paul","is_ca":false},{"name":"Ha H. Nguyen","is_ca":true},{"name":"Karthikay Gundepudi","is_ca":false},{"name":"Mahendra K. Shukla","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0731012213402395,"gpt":0.300688706020099,"spread":0.2275874846798595,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001491968,0.0006028966,0.0006855093,0.0004048557,0.0006124676,0.0006855995,0.001152862,0.0006788915,0.0008659794],"category_scores_gemma":[0.002977011,0.000229107,0.0003387866,0.0004099403,0.0006350436,0.001184888,0.0008824482,0.0008293908,0.0001480585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007720194,"about_ca_system_score_gemma":0.001006537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002447745,"about_ca_topic_score_gemma":0.00280715,"domain_scores_codex":[0.9995133,0.0001566539,0.00002724346,0.0001025875,0.0001285988,0.00007163616],"domain_scores_gemma":[0.9983824,0.0009463732,0.0001813205,0.0001037269,0.0003017422,0.00008453563],"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":[0.0001254765,0.0002644254,0.001752006,0.0000948287,0.00004158442,0.0001081914,0.00009350251,0.8674712,0.01098449,0.008254162,0.001055622,0.1097545],"study_design_scores_gemma":[0.000004052119,0.00003040844,0.00007285914,0.000001740443,0.000002832069,0.00001208574,0.000007164229,0.9980172,0.0005715151,0.001152143,0.0001252866,0.00000279091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0322779,0.0002285339,0.965281,0.0001958001,0.00004599507,0.00006522242,0.00001677135,0.0002396377,0.001649187],"genre_scores_gemma":[0.9243562,0.0001758607,0.07426269,0.00009280446,0.00003194385,0.00008015756,0.00003572281,0.00002086827,0.0009437539],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002447745,"threshold_uncertainty_score":0.007890344,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3040349264","doi":"10.1109/tccn.2020.3005921","title":"Intelligent Optimization of Availability and Communication Cost in Satellite-UAV Mobile Edge Caching System With Fault-Tolerant Codes","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Base station; Erasure code; Fault tolerance; Real-time computing; Edge computing; Distributed computing; Exploit; Communications satellite; Enhanced Data Rates for GSM Evolution; Communications system; Satellite; Computer network; Telecommunications","authors":[{"name":"Shushi Gu","is_ca":false},{"name":"Ye Wang","is_ca":false},{"name":"Niannian Wang","is_ca":false},{"name":"Wen Wu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04122268077337909,"gpt":0.2661970606720668,"spread":0.2249743798986877,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004633002,0.0005537613,0.0005614929,0.0003106887,0.0005014914,0.0006753998,0.000566214,0.0004377085,0.0005511795],"category_scores_gemma":[0.001512966,0.0001740253,0.000211524,0.0004032341,0.0004491367,0.0007040828,0.0004623836,0.0002959983,0.00005483544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001255602,"about_ca_system_score_gemma":0.001148061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00874808,"about_ca_topic_score_gemma":0.006609085,"domain_scores_codex":[0.999649,0.00008387692,0.0000159676,0.00006261869,0.00007542293,0.000113064],"domain_scores_gemma":[0.9991267,0.0004297079,0.0001652133,0.00003622104,0.0001909637,0.00005118353],"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":[0.00008958967,0.00002171729,0.0007140211,0.00003759524,0.00001386838,0.00007892821,0.00003772374,0.9844082,0.003800019,0.003066447,0.0002642394,0.00746764],"study_design_scores_gemma":[0.00000636699,0.00003404403,0.0001790406,0.000002397064,0.000007593158,0.00001960402,0.00001610756,0.9982339,0.0008132403,0.0006211909,0.0000624425,0.000003992051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.574504,0.001081729,0.4182922,0.0004458368,0.00004766067,0.00006857785,0.00008517462,0.0002062232,0.005268622],"genre_scores_gemma":[0.9954447,0.00007832573,0.004097462,0.00001257848,0.000003431034,0.00001182241,0.000009912624,0.000004013306,0.0003378221],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00874808,"threshold_uncertainty_score":0.0173943,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4312461449","doi":"10.1109/tccn.2022.3216406","title":"Hybrid Time-Switching and Power-Splitting EH Relaying for RIS-NOMA Downlink","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Telecommunications link; Beamforming; Relay; Spectral efficiency; Transmitter power output; Diversity gain; Noma; Transmission (telecommunications); Wireless; Maximum power transfer theorem; Computer network; Transmit diversity; Electronic engineering; Signal-to-noise ratio (imaging); MIMO; Power (physics); Transmitter; Telecommunications; Channel (broadcasting); Fading; Engineering; Physics","authors":[{"name":"Guoan Zhang","is_ca":false},{"name":"Xiaohui Gu","is_ca":false},{"name":"Wei Duan","is_ca":false},{"name":"Miaowen Wen","is_ca":false},{"name":"Jaeho Choi","is_ca":false},{"name":"Feifei Gao","is_ca":false},{"name":"Pin‐Han Ho","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02117357330874641,"gpt":0.2486706280172455,"spread":0.2274970547084991,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002584774,0.0004128256,0.0002538213,0.0002000628,0.000299334,0.000368356,0.0005162981,0.0002656798,0.0006101755],"category_scores_gemma":[0.0004195097,0.00009688953,0.0002593278,0.0002449691,0.0003457,0.0004405353,0.0004989262,0.0002400995,0.0001841784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002535274,"about_ca_system_score_gemma":0.000224511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005182069,"about_ca_topic_score_gemma":0.001388961,"domain_scores_codex":[0.9997873,0.00005780345,0.00001170549,0.00003719025,0.00006366626,0.00004239182],"domain_scores_gemma":[0.9997887,0.00007864857,0.00003478508,0.00004596144,0.00003732169,0.00001463464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006955594,0.0002397017,0.004680026,0.0002596683,0.0001555806,0.001478702,0.000534849,0.2758859,0.3867772,0.05226232,0.001779295,0.2752512],"study_design_scores_gemma":[0.00003014741,0.0004502241,0.0008872967,0.00001083719,0.00005660941,0.0007097748,0.0001122782,0.9301718,0.05742822,0.006943515,0.003170102,0.00002919711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2627132,0.0005400866,0.7274891,0.0001725554,0.00006133231,0.00004443125,0.00005954025,0.0003508396,0.008568911],"genre_scores_gemma":[0.9778978,0.00008860878,0.02094512,0.00003571051,0.00001379541,0.00001296588,0.00002020521,0.000003811451,0.0009820805],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0006101755,"threshold_uncertainty_score":0.002041221,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3002723509","doi":"10.1109/tccn.2020.2969623","title":"Joint D2D Assignment, Bandwidth and Power Allocation in Cognitive UAV-Enabled Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Institute for Computational Science and Technology; Queen's University; Queen's University Belfast; Royal Academy of Engineering; Department for Business, Energy and Industrial Strategy, UK Government; National Science Foundation","keywords":"Computer science; Optimization problem; Mathematical optimization; Leverage (statistics); Cognitive radio; Telecommunications link; Bandwidth allocation; Computational complexity theory; Binary number; Convex optimization; Bandwidth (computing); Base station; Convergence (economics); Quality of service; Distributed computing; Computer network; Algorithm; Wireless; Regular polygon; Telecommunications; Artificial intelligence; Mathematics","authors":[{"name":"Huy T. Nguyen","is_ca":false},{"name":"Hoang Duong Tuan","is_ca":false},{"name":"Trung Q. Duong","is_ca":false},{"name":"H. Vincent Poor","is_ca":false},{"name":"Won–Joo Hwang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03890537196643324,"gpt":0.2450795308718488,"spread":0.2061741589054155,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008474143,0.0008061097,0.0006358051,0.0004233961,0.0004662103,0.001113003,0.001027678,0.0008614197,0.0006676869],"category_scores_gemma":[0.002241178,0.0003891141,0.0003119209,0.0007552864,0.0009859129,0.0009644833,0.001205819,0.0006092251,0.0001154381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001024557,"about_ca_system_score_gemma":0.001058305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006816494,"about_ca_topic_score_gemma":0.005326594,"domain_scores_codex":[0.9993396,0.0002492909,0.00001963478,0.0001078511,0.0001336693,0.0001499372],"domain_scores_gemma":[0.9994521,0.0003342514,0.00007867699,0.0000351614,0.00005794045,0.00004195593],"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":[0.00008422778,0.00004455771,0.0003744716,0.00006049166,0.00002014125,0.00009452326,0.00004691358,0.9503579,0.001781657,0.02162619,0.0005134041,0.02499563],"study_design_scores_gemma":[0.000007707925,0.00002033231,0.00006938318,0.000002986968,0.000004847485,0.00001645561,0.00001414293,0.9943727,0.0003671492,0.004864841,0.0002554026,0.000004027827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03758737,0.0005026439,0.9585091,0.0001668886,0.00003907051,0.00002892091,0.00002860606,0.0000588654,0.003078599],"genre_scores_gemma":[0.939244,0.0002905767,0.0588527,0.00006680503,0.00003181737,0.00005818576,0.00002430285,0.00001420346,0.001417515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006816494,"threshold_uncertainty_score":0.01355368,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3021299077","doi":"10.1109/tccn.2020.2991436","title":"Opportunistic Utilization of Dynamic Multi-UAV in Device-to-Device Communication Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"National University of Defense Technology; National Natural Science Foundation of China","keywords":"Computer science; Transmission (telecommunications); Computer network; Matching (statistics); Channel (broadcasting); Convergence (economics); Network topology; Selection (genetic algorithm); Upload; Distributed computing; Real-time computing; Telecommunications; Artificial intelligence","authors":[{"name":"Dianxiong Liu","is_ca":false},{"name":"Yuhua Xu","is_ca":false},{"name":"Jinlong Wang","is_ca":false},{"name":"Jin Chen","is_ca":false},{"name":"Qihui Wu","is_ca":false},{"name":"Alagan Anpalagan","is_ca":true},{"name":"Kun Xu","is_ca":false},{"name":"Yuli Zhang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09580382084456163,"gpt":0.3054548546692817,"spread":0.2096510338247201,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001309289,0.001033671,0.001089407,0.0007227296,0.001129537,0.001404851,0.001947518,0.001086801,0.001228703],"category_scores_gemma":[0.003443704,0.0005489882,0.0006366579,0.00114889,0.001112036,0.002339063,0.001679744,0.0006490109,0.0001337609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001446895,"about_ca_system_score_gemma":0.0008728535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005047215,"about_ca_topic_score_gemma":0.004577654,"domain_scores_codex":[0.9988233,0.0004094991,0.00003880431,0.0002348409,0.0001806229,0.0003129091],"domain_scores_gemma":[0.9979766,0.00124897,0.0003145711,0.0001212433,0.0001662001,0.0001724033],"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":[0.0001367004,0.00007075298,0.001636262,0.0001489654,0.00007100638,0.0006739179,0.0001721124,0.9219581,0.003050874,0.05552067,0.00124584,0.01531475],"study_design_scores_gemma":[0.000007300177,0.00004798349,0.0002227858,0.000006280933,0.00001611397,0.0001080382,0.00006574134,0.9906051,0.0003173971,0.007899734,0.0006940707,0.000009559202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1403652,0.001650819,0.8505551,0.0005407706,0.0001271391,0.0001300833,0.0001148185,0.0001330636,0.006383017],"genre_scores_gemma":[0.9860373,0.0004256937,0.01191697,0.00005813754,0.00002714451,0.00005157178,0.00002777073,0.00001250574,0.001442822],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005047215,"threshold_uncertainty_score":0.01049793,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3152904640","doi":"10.1109/tccn.2021.3074178","title":"Physical-Layer Security on Maximal Ratio Combining for SIMO Cognitive Radio Networks Over Cascaded κ-μ Fading Channels","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fading; Transmitter; Cognitive radio; Transmission (telecommunications); Computer science; Notation; Topology (electrical circuits); Algorithm; Mathematics; Computer network; Telecommunications; Channel (broadcasting); Combinatorics; Arithmetic; Wireless","authors":[{"name":"Deemah H. Tashman","is_ca":true},{"name":"Walaa Hamouda","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04295668249818185,"gpt":0.297516699221967,"spread":0.2545600167237851,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001371704,0.001154528,0.0006487905,0.0004220989,0.0006111638,0.001102756,0.0006687632,0.0006775897,0.001019657],"category_scores_gemma":[0.004239978,0.0003331196,0.0006057959,0.0004901843,0.001517186,0.00154824,0.001377237,0.0008591985,0.0002512017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001046201,"about_ca_system_score_gemma":0.0008794806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001352987,"about_ca_topic_score_gemma":0.00107312,"domain_scores_codex":[0.9987441,0.0003917975,0.00004309676,0.0002035987,0.0003771466,0.0002401758],"domain_scores_gemma":[0.9975039,0.001629015,0.0003334342,0.0002059566,0.00026098,0.00006662206],"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":[0.001306659,0.0001251515,0.002896519,0.0003852882,0.0002262519,0.001030409,0.0004468043,0.6188356,0.04906794,0.2455455,0.002370341,0.07776339],"study_design_scores_gemma":[0.00003510118,0.0002881479,0.0008109021,0.00004642538,0.00006868592,0.0004095586,0.0000824272,0.9481896,0.00722964,0.041451,0.001337587,0.00005099546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1429731,0.001693068,0.8409886,0.0006064687,0.0001188827,0.00005308526,0.00008548245,0.0001840552,0.0132973],"genre_scores_gemma":[0.9817359,0.0008699144,0.01610993,0.0001171208,0.00008852325,0.00003615067,0.00002271846,0.00000876723,0.00101109],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001371704,"threshold_uncertainty_score":0.007590711,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3120002035","doi":"10.1109/tccn.2020.3048105","title":"Prediction and Modeling of Spectrum Occupancy for Dynamic Spectrum Access Systems","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Autoregressive–moving-average model; Autoregressive model; Aperiodic graph; Moving average; Hidden Markov model; Time series; Markov chain; Algorithm; Spectral density; Filter (signal processing); Artificial intelligence; Statistics; Mathematics; Machine learning; Telecommunications","authors":[{"name":"Hamed Mosavat-Jahromi","is_ca":true},{"name":"Yue Li","is_ca":true},{"name":"Lin Cai","is_ca":true},{"name":"Jianping Pan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0691298802324729,"gpt":0.305817504080423,"spread":0.2366876238479501,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005504831,0.0005618037,0.0005007748,0.000437126,0.0002353267,0.0004753805,0.0005734089,0.000380797,0.0004061532],"category_scores_gemma":[0.001958783,0.0003196473,0.0003089654,0.0004367585,0.0003411537,0.0006909377,0.0003811758,0.0007082496,0.0001392412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004776856,"about_ca_system_score_gemma":0.0004854748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006587206,"about_ca_topic_score_gemma":0.006793933,"domain_scores_codex":[0.999754,0.00005838769,0.00001668113,0.00007423307,0.00005570053,0.00004103698],"domain_scores_gemma":[0.999341,0.0003955592,0.00009777516,0.00007062752,0.00007186965,0.00002322127],"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":[0.00006652243,0.00005482589,0.003974574,0.00003056393,0.00001853837,0.00003631599,0.00004067567,0.9720937,0.002844245,0.001560788,0.0002596391,0.01901959],"study_design_scores_gemma":[6.377523e-7,0.000005659159,0.0005301323,8.847704e-7,0.000001180009,0.000003916879,0.000003318087,0.9987469,0.0002722744,0.0003921563,0.00004119001,0.000001689739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3520419,0.0002385042,0.6453314,0.0001754992,0.00003864851,0.00003371825,0.0003664014,0.0006656306,0.001108317],"genre_scores_gemma":[0.9813162,0.00006894215,0.01789005,0.00001706524,0.00001346993,0.00003427918,0.0002653635,0.00002122633,0.0003733764],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006587206,"threshold_uncertainty_score":0.0130977,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4401326389","doi":"10.1109/tccn.2024.3438359","title":"On the Performance of Rate Splitting Multiple Access for ISAC in Device-to-Multi-Device IoT Communications","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Full-Duplex Wireless Communications","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"Chengdu Science and Technology Program","keywords":"Computer science; Computer network; Internet of Things; Telecommunications; Embedded system","authors":[{"name":"Sutanu Ghosh","is_ca":false},{"name":"Keshav Singh","is_ca":false},{"name":"Haejoon Jung","is_ca":false},{"name":"Chih–Peng Li","is_ca":false},{"name":"Trung Q. Duong","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1029023515747931,"gpt":0.3317520800449196,"spread":0.2288497284701265,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002933759,0.001497748,0.000955543,0.0009450057,0.0008629983,0.001288354,0.0009519033,0.001052179,0.001418253],"category_scores_gemma":[0.01282408,0.0003605062,0.0006800719,0.001118795,0.001495317,0.002041252,0.001284322,0.001194795,0.000260279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00142854,"about_ca_system_score_gemma":0.0009512705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003763346,"about_ca_topic_score_gemma":0.002800697,"domain_scores_codex":[0.9976537,0.0008361619,0.00007715644,0.0002504778,0.0008199331,0.0003626544],"domain_scores_gemma":[0.9886724,0.008189255,0.0008909199,0.0005247853,0.001583864,0.00013875],"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":[0.000187437,0.00006763414,0.002318406,0.0002207619,0.00008481125,0.0003392903,0.0002354348,0.9385192,0.008254476,0.03004535,0.0005692357,0.01915803],"study_design_scores_gemma":[0.000003413219,0.0001122343,0.0004892277,0.0000231877,0.00002629578,0.0001649963,0.0000579722,0.9945405,0.001431291,0.00291619,0.000218575,0.00001613685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3100933,0.008574799,0.6533964,0.0009707053,0.0002222347,0.0001332877,0.0001685368,0.000541536,0.02589926],"genre_scores_gemma":[0.9853967,0.001560671,0.01199116,0.0000882173,0.00006094775,0.00003172781,0.00004322131,0.00003818103,0.0007891758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003763346,"threshold_uncertainty_score":0.01551539,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2218218593","doi":"10.1109/tccn.2015.2488649","title":"Adaptive Caching in the YouTube Content Distribution Network: A Revealed Preference Game-Theoretic Learning Approach","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Huawei Technologies (Canada); University of British Columbia","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Server; Computer science; Regret; Cache; Computer network; Latency (audio); Utility maximization problem; Stackelberg competition; Nash equilibrium; Distributed computing; Mathematical optimization; Machine learning; Utility maximization","authors":[{"name":"William Hoiles","is_ca":true},{"name":"Omid Namvar Gharehshiran","is_ca":true},{"name":"Vikram Krishnamurthy","is_ca":true},{"name":"Ngọc-Dũng Đào","is_ca":true},{"name":"Hang Zhang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1837459926735084,"gpt":0.2739955013048865,"spread":0.0902495086313781,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001624727,0.0007321819,0.001066314,0.0006289429,0.0006380079,0.001281284,0.001641437,0.001415792,0.00135776],"category_scores_gemma":[0.005820793,0.0004053567,0.0006536673,0.0007921801,0.001425887,0.001886546,0.0009807013,0.001247698,0.0001238587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003266156,"about_ca_system_score_gemma":0.001369648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02130966,"about_ca_topic_score_gemma":0.01422267,"domain_scores_codex":[0.9992183,0.0004147138,0.00002399846,0.0001130983,0.000107023,0.0001229345],"domain_scores_gemma":[0.9965677,0.002536955,0.0003523807,0.00009747139,0.0002771125,0.0001683429],"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":[0.00004652017,0.00003358769,0.0007914182,0.00003111332,0.00002758667,0.0001311462,0.00007346266,0.953176,0.0004609074,0.04032876,0.0004900734,0.004409469],"study_design_scores_gemma":[0.000003642053,0.000006427499,0.00007093283,0.00000151532,0.000002864927,0.000006954942,0.00001136428,0.9932647,0.00004157922,0.006515945,0.00007079091,0.000003288808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1316355,0.0005742181,0.8591685,0.001404471,0.00004077371,0.0001087459,0.0001302017,0.0001234649,0.006814254],"genre_scores_gemma":[0.9697167,0.0003491948,0.02653063,0.0001158044,0.00003399976,0.0001007561,0.00004540438,0.00001877477,0.003088648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02130966,"threshold_uncertainty_score":0.04237121,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4402217571","doi":"10.1109/tccn.2024.3454280","title":"Secure Task Offloading in Blockchain-Enabled MEC Networks With Improved PBFT Consensus","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University; University of Windsor","funders":"Key Research and Development Projects of Shaanxi Province; National Natural Science Foundation of China","keywords":"Blockchain; Computer science; Task (project management); Consensus algorithm; Computer network; Distributed computing; Computer security","authors":[{"name":"Jianbo Du","is_ca":false},{"name":"Zuting Yu","is_ca":false},{"name":"Aijing Sun","is_ca":false},{"name":"Jing Jiang","is_ca":false},{"name":"Haitao Zhao","is_ca":false},{"name":"Ning Zhang","is_ca":true},{"name":"Celimuge Wu","is_ca":false},{"name":"F. Richard Yu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01547300570766963,"gpt":0.2476941962121704,"spread":0.2322211905045007,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009751102,0.0007874187,0.001104472,0.0003888065,0.000874801,0.0008393119,0.001138733,0.001022572,0.002557117],"category_scores_gemma":[0.002758346,0.000325073,0.000414536,0.0006087001,0.0008181437,0.001709091,0.00153146,0.0009894492,0.0003199989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000932964,"about_ca_system_score_gemma":0.001472361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006359366,"about_ca_topic_score_gemma":0.005314539,"domain_scores_codex":[0.999166,0.0001988077,0.00003281522,0.0002128732,0.0001839546,0.0002055774],"domain_scores_gemma":[0.9983137,0.0008351287,0.0002066105,0.000183925,0.0003135567,0.0001471139],"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":[0.0001308783,0.00002988476,0.0004838965,0.00004686343,0.00001978427,0.0001471637,0.00007106201,0.9715657,0.002179479,0.008866386,0.000646839,0.01581207],"study_design_scores_gemma":[0.000008676944,0.0000230626,0.00005060832,0.000002396454,0.000002942344,0.00001601711,0.00001285422,0.9953448,0.0003123077,0.004032351,0.0001904562,0.000003501752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09297061,0.0004032382,0.9006339,0.0004023223,0.00007634007,0.00007517007,0.00009553673,0.0002546473,0.005088241],"genre_scores_gemma":[0.9734741,0.0001624363,0.02323462,0.00007051664,0.00002065361,0.00007430631,0.00007750264,0.00002476155,0.002861067],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006359366,"threshold_uncertainty_score":0.01264471,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2997749137","doi":"10.1109/tccn.2019.2963149","title":"SDATP: An SDN-Based Traffic-Adaptive and Service-Oriented Transmission Protocol","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Computer science; Computer network; Retransmission; Network packet; Distributed computing; Transmission delay; Packet loss","authors":[{"name":"Jiayin Chen","is_ca":true},{"name":"Qiang Ye","is_ca":false},{"name":"Wei Quan","is_ca":false},{"name":"Si Yan","is_ca":true},{"name":"Phu Thinh","is_ca":false},{"name":"Peng Yang","is_ca":true},{"name":"Weihua Zhuang","is_ca":true},{"name":"Xuemin Shen","is_ca":true},{"name":"Xu Li","is_ca":true},{"name":"Jaya Rao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04065389638978855,"gpt":0.2828174567223354,"spread":0.2421635603325468,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009945509,0.0005457464,0.0006501938,0.0006399305,0.0005790764,0.0008884328,0.002088091,0.00059597,0.0008062437],"category_scores_gemma":[0.001901336,0.0002005049,0.000345,0.0008869307,0.0005351122,0.001162195,0.001059788,0.0009671421,0.0001997019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007629898,"about_ca_system_score_gemma":0.001350221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00244212,"about_ca_topic_score_gemma":0.002981008,"domain_scores_codex":[0.999369,0.0001741494,0.00006007721,0.0001015322,0.000233077,0.00006231921],"domain_scores_gemma":[0.9992359,0.000223754,0.00009702914,0.00008964029,0.0003010584,0.00005268656],"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":[0.000472789,0.0002581621,0.001782617,0.0006143421,0.0001809581,0.0006086789,0.0002421956,0.3861186,0.04406605,0.1064144,0.02018455,0.4390567],"study_design_scores_gemma":[0.00003603805,0.0001536312,0.0001739204,0.00002238424,0.00003512449,0.0002917859,0.00002333743,0.9782193,0.003488141,0.007208644,0.0103204,0.00002734387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01032684,0.0007341996,0.9846144,0.0002255101,0.0002818363,0.000178799,0.00009921492,0.001064754,0.002474509],"genre_scores_gemma":[0.7031574,0.001421714,0.2903481,0.000472265,0.0001950649,0.0006435084,0.0006622023,0.0001184801,0.002981393],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00244212,"threshold_uncertainty_score":0.005535841,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3215024008","doi":"10.1109/tccn.2021.3130979","title":"Artificial Noise Aided Secure Communications for Cooperative NOMA Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Noma; Relay; Computer network; Eavesdropping; Beamforming; Single antenna interference cancellation; Artificial noise; Decoding methods; Telecommunications link; Wireless; Wireless network; Spectral efficiency; Physical layer; Telecommunications; Power (physics)","authors":[{"name":"Zhanghua Cao","is_ca":false},{"name":"Xiaodong Ji","is_ca":false},{"name":"Jue Wang","is_ca":false},{"name":"Wei Wang","is_ca":false},{"name":"Kanapathippillai Cumanan","is_ca":false},{"name":"Zhiguo Ding","is_ca":false},{"name":"Octavia A. Dobre","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05075840772439402,"gpt":0.289244126911414,"spread":0.23848571918702,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006732701,0.0005124019,0.0003336314,0.0003530311,0.0006017258,0.0006001096,0.0005593621,0.0006292585,0.001018699],"category_scores_gemma":[0.001793695,0.0001534302,0.0002808004,0.0003668906,0.0009300457,0.0007143282,0.001079507,0.0006046135,0.0003134667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005702804,"about_ca_system_score_gemma":0.0005447852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005072668,"about_ca_topic_score_gemma":0.0009312317,"domain_scores_codex":[0.9994722,0.0002281195,0.00002264668,0.00006166712,0.0001608422,0.00005457458],"domain_scores_gemma":[0.9991135,0.0004818324,0.0001345981,0.00009264588,0.0001496722,0.00002769822],"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":[0.0008322708,0.0001207106,0.001551904,0.0003991362,0.00008525854,0.0008028437,0.0006050059,0.4389526,0.1118079,0.2796142,0.002871583,0.1623566],"study_design_scores_gemma":[0.00002405517,0.0001852104,0.0001904245,0.0000263378,0.000018513,0.0002131242,0.00004217001,0.9703023,0.009622143,0.0145703,0.004780403,0.0000250243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04536387,0.000826854,0.946956,0.0003282563,0.0001166617,0.00004590539,0.00003235428,0.000124555,0.006205484],"genre_scores_gemma":[0.9276309,0.0006896502,0.06752727,0.0001448131,0.00006386782,0.00009981795,0.00004531452,0.00001091976,0.0037874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001018699,"threshold_uncertainty_score":0.004137695,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4390187147","doi":"10.1109/tccn.2023.3346824","title":"Dynamic Neural Network-Based Resource Management for Mobile Edge Computing in 6G Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor; Queen's University; University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Inference; Mobile edge computing; Resource management (computing); Resource allocation; Artificial neural network; Task (project management); Enhanced Data Rates for GSM Evolution; Computational resource; Edge computing; Edge device; Distributed computing; Computational complexity theory; Artificial intelligence; Computer network; Algorithm; Cloud computing","authors":[{"name":"Longfei Ma","is_ca":false},{"name":"Nan Cheng","is_ca":false},{"name":"Conghao Zhou","is_ca":true},{"name":"Xiucheng Wang","is_ca":false},{"name":"Ning Lu","is_ca":true},{"name":"Khalid Aldubaikhy","is_ca":false},{"name":"Abdullah Alqasir","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03446911279584057,"gpt":0.295279580669697,"spread":0.2608104678738564,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004318632,0.0005477687,0.0005019502,0.0003772874,0.0005879861,0.0008055358,0.001116922,0.0005432731,0.001274179],"category_scores_gemma":[0.0009384759,0.0002200322,0.0002759084,0.0005381148,0.0003737342,0.001637131,0.0007106324,0.0008314977,0.0001967217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001092389,"about_ca_system_score_gemma":0.0008836928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01053574,"about_ca_topic_score_gemma":0.01447345,"domain_scores_codex":[0.9997439,0.00003923747,0.00001725169,0.00007614256,0.00005943498,0.00006395272],"domain_scores_gemma":[0.9997874,0.00007085774,0.000026625,0.00001909021,0.00007814023,0.00001794233],"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":[0.0001597048,0.0001186837,0.001477325,0.0001022187,0.00004840307,0.0001196831,0.00006498876,0.8118973,0.006394446,0.008051362,0.004318225,0.1672477],"study_design_scores_gemma":[0.000002448213,0.00001094914,0.0001159717,0.000002910194,0.000005564145,0.00001117567,0.000008332944,0.9976555,0.0005481665,0.001247027,0.0003881186,0.000003892695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06381449,0.002521968,0.9224378,0.0007829728,0.0002700713,0.00007240131,0.0001226045,0.0009613048,0.009016363],"genre_scores_gemma":[0.9504385,0.0006906826,0.04539229,0.0002455006,0.00007685192,0.00006150704,0.0001122917,0.00004284897,0.00293956],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01053574,"threshold_uncertainty_score":0.02094883,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4404840325","doi":"10.1109/tccn.2024.3508777","title":"A Survey of Graph-Based Resource Management in Wireless Networks—Part II: Learning 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":23,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo; University of Victoria","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Wireless network; Resource management (computing); Wireless; Computer network; Data science; Telecommunications","authors":[{"name":"Yanpeng Dai","is_ca":false},{"name":"Ling Lyu","is_ca":false},{"name":"Nan Cheng","is_ca":false},{"name":"Min Sheng","is_ca":false},{"name":"Junyu Liu","is_ca":false},{"name":"Xiucheng Wang","is_ca":false},{"name":"Shuguang Cui","is_ca":false},{"name":"Lin Cai","is_ca":true},{"name":"Xuemin Shen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0891784165636157,"gpt":0.2779494257333164,"spread":0.1887710091697007,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001098128,0.001376705,0.001634473,0.001703988,0.0004407105,0.001736558,0.001957714,0.001271995,0.003841805],"category_scores_gemma":[0.002730099,0.0005780482,0.0009860116,0.003974093,0.0008018964,0.003541669,0.001095239,0.001724293,0.001306328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001231145,"about_ca_system_score_gemma":0.001127825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003056016,"about_ca_topic_score_gemma":0.002407372,"domain_scores_codex":[0.9993163,0.0002214835,0.00006215087,0.000173099,0.000170172,0.00005673486],"domain_scores_gemma":[0.9988058,0.0008539868,0.00005923221,0.00009020325,0.0001523393,0.00003856203],"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":[0.00006300931,0.0002073352,0.001247289,0.003576766,0.0001605244,0.0001133417,0.0001263267,0.1741249,0.001087824,0.1479488,0.0313201,0.6400237],"study_design_scores_gemma":[0.0000237017,0.0001809995,0.001188601,0.001227259,0.0001176828,0.0003733837,0.000175318,0.5667606,0.001139514,0.2670017,0.1617262,0.0000850706],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.005075782,0.2944644,0.6728402,0.003248808,0.0008597864,0.0001089316,0.0004251292,0.0005026848,0.0224743],"genre_scores_gemma":[0.1420746,0.6056072,0.2323761,0.002225224,0.004327084,0.0003257366,0.001481003,0.0004320962,0.01115093],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.003841805,"threshold_uncertainty_score":0.01285213,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3169712129","doi":"10.1109/tccn.2021.3085769","title":"Cooperative Sensing With Heterogeneous Spectrum Availability in Cognitive Radio","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Cognitive radio; Computer science; Overhead (engineering); Markov process; Markov chain; Reliability (semiconductor); Stochastic geometry; Distributed computing; Computer network; Shadow mapping; Fuse (electrical); Markov model; Telecommunications; Machine learning; Wireless; Artificial intelligence","authors":[{"name":"Keyu Wu","is_ca":false},{"name":"Hai Jiang","is_ca":true},{"name":"Chintha Tellambura","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03424303304759925,"gpt":0.2685219836211188,"spread":0.2342789505735196,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001998767,0.0006026597,0.0007828847,0.0005877936,0.0005136357,0.0007223822,0.001324001,0.0007374032,0.0004057296],"category_scores_gemma":[0.004639405,0.0004577808,0.0007022159,0.0005540443,0.001448256,0.001258544,0.001485036,0.0006301122,0.00009466985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008577589,"about_ca_system_score_gemma":0.001023144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003730228,"about_ca_topic_score_gemma":0.002727492,"domain_scores_codex":[0.9988971,0.0003829722,0.00003785113,0.0002900616,0.0002590254,0.0001329907],"domain_scores_gemma":[0.996572,0.002568831,0.0003291516,0.0002464258,0.0001842609,0.00009930682],"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":[0.0001277718,0.00006846197,0.0009447965,0.00006129253,0.00006572506,0.0001987295,0.0001958214,0.9305406,0.005861492,0.02994695,0.0004663063,0.0315221],"study_design_scores_gemma":[0.000006909896,0.00002498486,0.0001337137,0.000002955719,0.000007812871,0.00002729377,0.00001505448,0.9914357,0.0005899991,0.007638608,0.0001110304,0.000005949226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03937189,0.0002415158,0.9587495,0.0001029073,0.00002038688,0.00002902945,0.00001238389,0.0001287061,0.001343676],"genre_scores_gemma":[0.9588271,0.0001188537,0.04038613,0.00006187942,0.00002429645,0.00005266419,0.0000160153,0.00001305735,0.0004999401],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003730228,"threshold_uncertainty_score":0.01057065,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4414404571","doi":"10.1109/tccn.2025.3612760","title":"Toward Edge General Intelligence With Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo; Western University","funders":"","keywords":"Orchestration; Edge computing; Software deployment; Leverage (statistics); Robustness (evolution); Big data; Edge device; Adaptability","authors":[{"name":"Haoxiang Luo","is_ca":false},{"name":"Yinqiu Liu","is_ca":false},{"name":"Ruichen Zhang","is_ca":false},{"name":"Jiacheng Wang","is_ca":false},{"name":"Gang Sun","is_ca":false},{"name":"Dusit Niyato","is_ca":false},{"name":"Hongfang Yu","is_ca":false},{"name":"Zehui Xiong","is_ca":false},{"name":"Xianbin Wang","is_ca":true},{"name":"Xuemin Shen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1735528161070444,"gpt":0.3927301307171998,"spread":0.2191773146101554,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003361823,0.000820876,0.0009363287,0.0009561641,0.0008270536,0.005028419,0.002725508,0.001491975,0.001775343],"category_scores_gemma":[0.007663372,0.0006656823,0.00123213,0.001291471,0.001978291,0.00855399,0.006509331,0.00436492,0.001370546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001653784,"about_ca_system_score_gemma":0.003189689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004165987,"about_ca_topic_score_gemma":0.005096729,"domain_scores_codex":[0.9977247,0.0007540222,0.0002114964,0.000353968,0.0006806207,0.0002752944],"domain_scores_gemma":[0.9969038,0.0009795686,0.0002372724,0.001088728,0.0005421914,0.0002483545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002820583,0.000222588,0.003045329,0.000872181,0.000237714,0.0005536188,0.001603113,0.1086978,0.0101288,0.5877684,0.02053184,0.2660566],"study_design_scores_gemma":[0.00002376401,0.00008267982,0.0003401978,0.0001578486,0.00007641021,0.0002231628,0.0003209344,0.6520354,0.005533441,0.2941105,0.04703596,0.00005966932],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008606942,0.002817235,0.9755229,0.002475791,0.0001223405,0.0001209967,0.0001182839,0.003436892,0.006778638],"genre_scores_gemma":[0.309172,0.004995367,0.6761349,0.002115229,0.0002338122,0.0004080209,0.001073958,0.0009107,0.004955973],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005028419,"threshold_uncertainty_score":0.01777923,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4405488117","doi":"10.1109/tccn.2024.3519331","title":"A Prediction-Enhanced Physical-to-Virtual Twin Connectivity Framework for Human Digital Twin","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University; University of the Fraser Valley","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Distributed computing","authors":[{"name":"Samuel D. Okegbile","is_ca":true},{"name":"Jun Cai","is_ca":true},{"name":"Junjie Wu","is_ca":false},{"name":"Jiayuan Chen","is_ca":false},{"name":"Changyan Yi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04932612693934538,"gpt":0.2998110885057806,"spread":0.2504849615664352,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001166548,0.0006330669,0.0007861812,0.0003775943,0.0006898913,0.00112325,0.001802643,0.0008227109,0.002346838],"category_scores_gemma":[0.002589938,0.0002875186,0.0004858866,0.0006237535,0.001037031,0.002635227,0.002093979,0.001346254,0.0002810229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001019641,"about_ca_system_score_gemma":0.001858975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004717939,"about_ca_topic_score_gemma":0.003405511,"domain_scores_codex":[0.999123,0.0002188546,0.00003653355,0.0002400156,0.0002171587,0.0001643879],"domain_scores_gemma":[0.9991176,0.0003425786,0.0001106082,0.000152454,0.0001880943,0.00008868149],"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":[0.0001830398,0.00005552045,0.000992653,0.0001162268,0.00004032547,0.0003086374,0.0002101734,0.8069208,0.004207157,0.1212109,0.002679565,0.0630749],"study_design_scores_gemma":[0.000004418381,0.00003091228,0.00006310467,0.000003918462,0.000006509073,0.00004608206,0.00001794181,0.985115,0.0004833058,0.01338848,0.000832963,0.000007366691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007698988,0.0002098976,0.9904807,0.0001511762,0.0000423838,0.00002572454,0.00003681758,0.0001418875,0.001212339],"genre_scores_gemma":[0.8929695,0.0003623814,0.103243,0.0001213835,0.00007315355,0.0000764981,0.0001097552,0.00005020946,0.002994144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004717939,"threshold_uncertainty_score":0.009380996,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2996243818","doi":"10.1109/tccn.2019.2958639","title":"Experimental Results on the Impact of Memory in Neural Networks for Spectrum Prediction in Land Mobile Radio Bands","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Autoregressive integrated moving average; Computer science; Artificial neural network; Time series; Series (stratigraphy); Predictive modelling; Recurrent neural network; Term (time); Artificial intelligence; Set (abstract data type); Occupancy; Baseline (sea); Machine learning; Speech recognition","authors":[{"name":"Ozan Ozyegen","is_ca":true},{"name":"Sanaz Mohammadjafari","is_ca":true},{"name":"Emir Kavurmacioglu","is_ca":true},{"name":"John Maidens","is_ca":true},{"name":"Ayşe Bener","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02731477572718886,"gpt":0.2849183431651144,"spread":0.2576035674379255,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002168868,0.001913375,0.0006989651,0.0006052871,0.0006233245,0.0007951311,0.001181232,0.001448921,0.003170979],"category_scores_gemma":[0.008016842,0.0003388912,0.0006075794,0.0007673498,0.0004399564,0.001557997,0.0007159414,0.001537255,0.0006529783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001300881,"about_ca_system_score_gemma":0.000768725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03332906,"about_ca_topic_score_gemma":0.03281431,"domain_scores_codex":[0.9993216,0.0001636067,0.0000827085,0.0002067191,0.0001099321,0.0001155061],"domain_scores_gemma":[0.9964237,0.002236802,0.0002478283,0.0003591228,0.0006103395,0.0001222942],"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":[0.002573909,0.001919086,0.01486672,0.0007837047,0.0004839555,0.0003451998,0.0001773826,0.6864506,0.009211615,0.0008410194,0.008866676,0.2734802],"study_design_scores_gemma":[0.00008124117,0.0003950543,0.003566351,0.00005995367,0.00008174892,0.00004065789,0.00009555657,0.986055,0.008049879,0.0009456575,0.0006088117,0.00002005264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9427135,0.004769755,0.03550758,0.001223253,0.0006932288,0.0001904573,0.002707105,0.003287181,0.008907951],"genre_scores_gemma":[0.9690799,0.0006398312,0.02464311,0.0002057123,0.00005421367,0.00009660872,0.003256689,0.00006699533,0.00195706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03332906,"threshold_uncertainty_score":0.06627017,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4405718500","doi":"10.1109/tccn.2024.3520958","title":"A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Models","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Pruning; Modulation (music); Layer (electronics); Deep learning; SIGNAL (programming language); Artificial intelligence; Signal processing; Pattern recognition (psychology); Speech recognition; Telecommunications; Materials science","authors":[{"name":"Yao Lu","is_ca":true},{"name":"Yutao Zhu","is_ca":true},{"name":"Yuqi Li","is_ca":false},{"name":"Dongwei Xu","is_ca":true},{"name":"Yun Lin","is_ca":false},{"name":"Qi Xuan","is_ca":false},{"name":"Xiaoniu Yang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1595047132439671,"gpt":0.3284108391812466,"spread":0.1689061259372796,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009780625,0.001449271,0.0009815155,0.0007506026,0.000455107,0.0008453654,0.001915215,0.001154908,0.002785876],"category_scores_gemma":[0.002572883,0.0004743811,0.001061072,0.0006998436,0.0005012278,0.0014346,0.001359438,0.002376631,0.001196604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006395574,"about_ca_system_score_gemma":0.001228979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004143366,"about_ca_topic_score_gemma":0.007211655,"domain_scores_codex":[0.999496,0.00009272308,0.00003987909,0.0001129604,0.0001862252,0.00007218623],"domain_scores_gemma":[0.999531,0.0001355315,0.00004967511,0.0001339573,0.0001215703,0.00002824067],"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":[0.0002142469,0.0001370418,0.0009179685,0.0001926399,0.0001700372,0.0002493244,0.00009676295,0.3807898,0.02382267,0.01917442,0.01137722,0.5628578],"study_design_scores_gemma":[0.00001289485,0.00003299953,0.0001491204,0.00001668254,0.00002128684,0.00008144671,0.000006634431,0.9863668,0.005339601,0.005520693,0.002443395,0.000008434278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01019321,0.0008274037,0.9849828,0.0002014326,0.00007667513,0.00005971419,0.0001964145,0.002001293,0.001461009],"genre_scores_gemma":[0.3098327,0.00122437,0.6771742,0.0005265342,0.0001997044,0.0003367932,0.001688894,0.0005563266,0.008460568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004143366,"threshold_uncertainty_score":0.009319723,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"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","authors":[{"name":"Yanpeng Dai","is_ca":false},{"name":"Ling Lyu","is_ca":false},{"name":"Nan Cheng","is_ca":false},{"name":"Min Sheng","is_ca":false},{"name":"Junyu Liu","is_ca":false},{"name":"Xiucheng Wang","is_ca":false},{"name":"Shuguang Cui","is_ca":false},{"name":"Lin Cai","is_ca":true},{"name":"Xuemin Shen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09058678297912597,"gpt":0.276203392835273,"spread":0.185616609856147,"validation_status":"score_only:v0-immature-baseline"},"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"},"labels":[],"label_agreement":null},{"id":"W4390603600","doi":"10.1109/tccn.2024.3350596","title":"Joint Broadcast and Unicast Transmission Based on RSMA and Spectrum Sharing for Integrated Satellite–Terrestrial Network","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Satellite Communication Systems","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Unicast; Computer network; Telecommunications link; Transmission (telecommunications); Minimum mean square error; Broadcasting (networking); Optimization problem; Interference (communication); Distributed computing; Multicast; Telecommunications; Algorithm; Channel (broadcasting); Mathematics","authors":[{"name":"Shuai Han","is_ca":false},{"name":"Zhiqiang Li","is_ca":false},{"name":"Qiang Xue","is_ca":false},{"name":"Weixiao Meng","is_ca":false},{"name":"Cheng Li","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06430947336793902,"gpt":0.2806811125297596,"spread":0.2163716391618206,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007841397,0.000707607,0.0006788058,0.0003780895,0.0005982319,0.0006144983,0.001022876,0.0004567739,0.0008441521],"category_scores_gemma":[0.001459084,0.0002788805,0.0005045983,0.0005331613,0.0007706261,0.001003923,0.001038409,0.0006386845,0.0001517004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007041841,"about_ca_system_score_gemma":0.0009628008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003957392,"about_ca_topic_score_gemma":0.005442196,"domain_scores_codex":[0.9991819,0.0003206157,0.00003447887,0.0001336576,0.0002106653,0.0001186836],"domain_scores_gemma":[0.9993199,0.0003016499,0.0001165235,0.00007596423,0.0001387483,0.00004720812],"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":[0.0003429078,0.0001350623,0.001365051,0.0002294524,0.0001097513,0.0003418121,0.0003180643,0.8386626,0.02628235,0.04244888,0.002184667,0.08757941],"study_design_scores_gemma":[0.000008445839,0.00006248151,0.00006927804,0.000003312482,0.00001461887,0.00004928851,0.00001923033,0.9964544,0.001377819,0.001648846,0.00028405,0.000008208024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08583797,0.0007159009,0.9086232,0.0002131749,0.00006548124,0.00005355817,0.00003253505,0.0002297366,0.004228547],"genre_scores_gemma":[0.9521618,0.0003088062,0.04599596,0.00005154142,0.0000311943,0.00005456469,0.00003283216,0.00001435732,0.001348867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003957392,"threshold_uncertainty_score":0.007868707,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3016642727","doi":"10.1109/tccn.2020.2988480","title":"Intelligent Spectrum Assignment Based on Dynamical Cooperation for 5G-Satellite Integrated Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Satellite Communication Systems","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"St. Francis Xavier University","funders":"National Key Research and Development Program of China; Science and Technology Commission of Shanghai Municipality; Huawei Technologies; National Natural Science Foundation of China","keywords":"Computer science; Throughput; Cognitive radio; Greedy algorithm; Transmission (telecommunications); Computer network; Spectrum (functional analysis); Satellite; Frequency allocation; Matching (statistics); Distributed computing; Wireless; Algorithm; Telecommunications; Engineering","authors":[{"name":"Feilong Tang","is_ca":false},{"name":"Long Chen","is_ca":false},{"name":"Li Xu","is_ca":false},{"name":"Laurence T. Yang","is_ca":true},{"name":"Luoyi Fu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05675054567968781,"gpt":0.2688427329071628,"spread":0.2120921872274749,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000743463,0.0004885002,0.0004123344,0.000348582,0.000516988,0.0005028247,0.0008832592,0.0005286106,0.0004789649],"category_scores_gemma":[0.001851293,0.000220419,0.0003815892,0.0005016417,0.0008562898,0.001139131,0.0008935525,0.0004841856,0.00008214563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005501044,"about_ca_system_score_gemma":0.0007669415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001862723,"about_ca_topic_score_gemma":0.001703089,"domain_scores_codex":[0.9993311,0.0002252143,0.00002160216,0.0001283465,0.0001723735,0.0001214421],"domain_scores_gemma":[0.9993299,0.0003226916,0.0001332635,0.000066435,0.00009604193,0.00005163769],"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":[0.0001481944,0.0001257345,0.001648127,0.00007103696,0.00005245721,0.000196755,0.0002775859,0.869595,0.01788511,0.05008458,0.0006883941,0.05922708],"study_design_scores_gemma":[0.00000675051,0.00004955619,0.0001325425,0.000001821775,0.000008276136,0.00003795184,0.00002447029,0.9930708,0.0010647,0.005367639,0.0002302733,0.00000530037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05953792,0.000159489,0.9386566,0.0001247978,0.00001953085,0.00002971764,0.000006156161,0.00009621264,0.001369574],"genre_scores_gemma":[0.9675478,0.00008624541,0.03176071,0.00004540638,0.00001382961,0.00002941593,0.000008861606,0.000006675855,0.0005011566],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001862723,"threshold_uncertainty_score":0.003991306,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4401687402","doi":"10.1109/tccn.2024.3445380","title":"Joint Design for RIS-Aided ISAC via Deep Unfolding Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Computer science; Joint (building); Computer architecture; Engineering","authors":[{"name":"Jifa Zhang","is_ca":false},{"name":"Mingqian Liu","is_ca":false},{"name":"Jie Tang","is_ca":false},{"name":"Nan Zhao","is_ca":false},{"name":"Dusit Niyato","is_ca":false},{"name":"Xianbin Wang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06272331062864697,"gpt":0.2985708229254416,"spread":0.2358475122967947,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006480594,0.0008332172,0.0005801932,0.0003180475,0.0003022933,0.0006594025,0.0009868377,0.0007633552,0.001985154],"category_scores_gemma":[0.001640101,0.0003130719,0.0004039761,0.0003818989,0.000713992,0.001006536,0.001074519,0.001394049,0.0005738687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005589733,"about_ca_system_score_gemma":0.001207749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001508003,"about_ca_topic_score_gemma":0.00241914,"domain_scores_codex":[0.9996532,0.00006400439,0.0000159769,0.00008368567,0.00013484,0.00004818674],"domain_scores_gemma":[0.999476,0.0001602039,0.00005935652,0.00008406435,0.0001835709,0.00003684936],"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":[0.0001264353,0.00007543452,0.0008890003,0.0001026263,0.00004210488,0.00009506849,0.0001004197,0.7627381,0.02574953,0.01619076,0.002174529,0.1917159],"study_design_scores_gemma":[0.000003481118,0.00002896861,0.00004646567,0.000002793884,0.000003146699,0.00001606287,0.000004799758,0.9956667,0.002282773,0.001381611,0.0005592075,0.000003832957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005392585,0.00004500481,0.9929396,0.00005861869,0.0000170818,0.00001704613,0.00001481316,0.0002934436,0.001221773],"genre_scores_gemma":[0.5372826,0.0001359853,0.4586527,0.0002559814,0.00003696201,0.0001848365,0.0001628989,0.0001302708,0.003157714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001985154,"threshold_uncertainty_score":0.00664103,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4387415306","doi":"10.1109/tccn.2023.3320879","title":"Adaptive Network Configuration for Efficient and Accurate Neural Video Inference","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"Fundamental Research Funds for the Central Universities; Young Elite Scientists Sponsorship Program by Tianjin; Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial neural network; Inference; Artificial intelligence; Computer network","authors":[{"name":"Peng Yang","is_ca":false},{"name":"Yan Cheng","is_ca":false},{"name":"Ning Zhang","is_ca":true},{"name":"Qimin Cheng","is_ca":false},{"name":"Li Yu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08245911716342078,"gpt":0.3443223480146576,"spread":0.2618632308512369,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007217237,0.0009693718,0.0005847059,0.0005439887,0.0005295867,0.0007963956,0.001952712,0.0008060318,0.001398818],"category_scores_gemma":[0.004382578,0.0004128256,0.000236064,0.0004353904,0.0006253377,0.00226824,0.0009931958,0.001317819,0.0003464382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00123851,"about_ca_system_score_gemma":0.0008939744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005565577,"about_ca_topic_score_gemma":0.007410604,"domain_scores_codex":[0.9994009,0.0000981144,0.00003483887,0.0002211696,0.0001439274,0.0001011131],"domain_scores_gemma":[0.9990857,0.0003225819,0.000133175,0.0001810921,0.0002236195,0.0000538322],"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":[0.0003486685,0.0002341779,0.003368052,0.00008965417,0.00005234017,0.0002561962,0.0001617562,0.7008901,0.02827952,0.006283022,0.00356222,0.2564743],"study_design_scores_gemma":[0.000006198481,0.00002984171,0.0002874378,0.000004002788,0.000006575709,0.00003585447,0.00001302102,0.9928388,0.004803115,0.001707289,0.0002608977,0.000007022051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08355258,0.0004959351,0.9100977,0.0003251772,0.00009529902,0.00009476414,0.00008629015,0.002593266,0.002659061],"genre_scores_gemma":[0.9559448,0.00008541618,0.04277065,0.0001059486,0.000028104,0.00006661665,0.00008554368,0.00006658947,0.0008462737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005565577,"threshold_uncertainty_score":0.01106638,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3084868265","doi":"10.1109/tccn.2020.3022671","title":"Learning-Based Proactive Resource Allocation for Delay-Sensitive Packet Transmission","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Provisioning; Resource allocation; Computer network; Network packet; Resource management (computing); Resource (disambiguation); Shared resource; Quality of service; Distributed computing","authors":[{"name":"Jiayin Chen","is_ca":true},{"name":"Peng Yang","is_ca":false},{"name":"Qiang Ye","is_ca":false},{"name":"Weihua Zhuang","is_ca":true},{"name":"Xuemin Shen","is_ca":true},{"name":"Xu Li","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03182727512979351,"gpt":0.2506074120526665,"spread":0.218780136922873,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009627417,0.000794983,0.000930406,0.0004298351,0.0006453816,0.0007296629,0.00162374,0.0006608769,0.0009381732],"category_scores_gemma":[0.002149295,0.000284996,0.0003098034,0.0005405523,0.0007862693,0.001119113,0.000962971,0.00101502,0.0002296129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008211237,"about_ca_system_score_gemma":0.001225051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00305205,"about_ca_topic_score_gemma":0.002604497,"domain_scores_codex":[0.9993694,0.0001386876,0.00003511423,0.0001600854,0.0001497881,0.0001468787],"domain_scores_gemma":[0.999138,0.0004097895,0.0001315903,0.00009261291,0.0001706093,0.00005734245],"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":[0.0001960521,0.0001932192,0.000636227,0.00009119059,0.00004838733,0.0001149471,0.0001257505,0.8480192,0.01110978,0.0149351,0.001479583,0.1230506],"study_design_scores_gemma":[0.000004481396,0.00002723708,0.00004154549,0.000001912671,0.000004825295,0.00001700679,0.000004812368,0.9978287,0.0008363157,0.001086167,0.0001431327,0.000003877896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02502801,0.0002962753,0.9729083,0.00009854152,0.00004585409,0.00003759476,0.00001431045,0.0002735748,0.001297653],"genre_scores_gemma":[0.9503475,0.000168234,0.04795508,0.0001055429,0.00004343039,0.00007322714,0.00003022432,0.00002178265,0.001255082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00305205,"threshold_uncertainty_score":0.006068587,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4391216112","doi":"10.1109/tccn.2024.3358565","title":"Priority-Aware Deployment of Autoscaling Service Function Chains Based on Deep Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Software deployment; Quality of service; Reinforcement learning; Scheduling (production processes); Computer network; Distributed computing; Cloud computing; Artificial intelligence; Operating system; Engineering","authors":[{"name":"Yu Xue","is_ca":false},{"name":"Ran Wang","is_ca":false},{"name":"Jie Hao","is_ca":false},{"name":"Qiang Wu","is_ca":false},{"name":"Changyan Yi","is_ca":false},{"name":"Ping Wang","is_ca":true},{"name":"Dusit Niyato","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.032581938283152,"gpt":0.2710723976259306,"spread":0.2384904593427786,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009406242,0.0008673258,0.0008408268,0.0004320969,0.0003412511,0.0006596957,0.001235234,0.0007548723,0.001215868],"category_scores_gemma":[0.002512831,0.0003885,0.0004108593,0.0003000906,0.0007754741,0.0007982795,0.0009144321,0.001242658,0.0001674615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001213676,"about_ca_system_score_gemma":0.001639957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009276318,"about_ca_topic_score_gemma":0.007450771,"domain_scores_codex":[0.9995754,0.00009206727,0.00002423216,0.00009504653,0.0001030833,0.0001101638],"domain_scores_gemma":[0.9988756,0.0004997457,0.0001702888,0.00006696928,0.0002543404,0.0001330487],"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":[0.00004959778,0.00006211839,0.001036874,0.00002991701,0.00002046693,0.00004237031,0.00003658678,0.9646645,0.001600575,0.002651346,0.0004984001,0.02930733],"study_design_scores_gemma":[0.000002912403,0.00001040238,0.00002696683,0.000001132825,0.000001664732,0.000002420233,0.000001778314,0.9994276,0.0001100616,0.000372806,0.00004108759,0.00000110788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09929504,0.0003926253,0.8954283,0.0003726893,0.00006765882,0.00008223613,0.00004068232,0.0008700437,0.003450738],"genre_scores_gemma":[0.9598713,0.0001069824,0.03857833,0.0001378465,0.00002010062,0.00006486729,0.00005406958,0.00003195559,0.001134503],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009276318,"threshold_uncertainty_score":0.01844466,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963911302","doi":"10.1109/tccn.2019.2930253","title":"Artificial Intelligence Communicates With Cognitive Dynamic System for Cybersecurity","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Computer security; Cognition; Cognitive systems; Computer network","authors":[{"name":"S. Haykin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03284474114543074,"gpt":0.2776057063855998,"spread":0.2447609652401691,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009457141,0.0005088094,0.0002644397,0.001407732,0.001265122,0.00331473,0.0005624983,0.001658698,0.005027128],"category_scores_gemma":[0.001254077,0.0001744084,0.0003685935,0.00118148,0.008169946,0.005501311,0.001266441,0.003699362,0.00123724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001356259,"about_ca_system_score_gemma":0.000576215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008432889,"about_ca_topic_score_gemma":0.0006792992,"domain_scores_codex":[0.9994736,0.0001912878,0.00004577651,0.0001198239,0.0001269443,0.00004267532],"domain_scores_gemma":[0.9991832,0.0004809379,0.00007316568,0.0001305121,0.00009728189,0.00003479271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005360486,0.000005346957,0.00007568049,0.00004884986,0.000004813076,0.00003934896,0.0002508576,0.0003715819,0.0001959262,0.9853482,0.003384985,0.01026898],"study_design_scores_gemma":[0.000003710882,0.00002729932,0.0002903356,0.0001248187,0.000007746728,0.0001967689,0.000177711,0.001662849,0.0003939261,0.8012309,0.1958606,0.00002332012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01295537,0.05404448,0.4088496,0.04234796,0.008685483,0.0001020903,0.0002971766,0.0007085633,0.4720095],"genre_scores_gemma":[0.6654644,0.06787042,0.150466,0.01297874,0.01460999,0.0005188726,0.0003171037,0.0002875476,0.08748689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005027128,"threshold_uncertainty_score":0.01681739,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4394966992","doi":"10.1109/tccn.2024.3391318","title":"Toward Intelligent and Adaptive Task Scheduling for 6G: An Intent-Driven Framework","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Scheduling (production processes); Distributed computing; Processor scheduling; Computer network; Resource (disambiguation); Mathematical optimization","authors":[{"name":"Wang Qingqing","is_ca":false},{"name":"Sai Zou","is_ca":false},{"name":"Yanglong Sun","is_ca":false},{"name":"Minghui Liwang","is_ca":false},{"name":"Xianbin Wang","is_ca":true},{"name":"Wei Ni","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.116910592010156,"gpt":0.3333393902892066,"spread":0.2164287982790506,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001278165,0.0009213168,0.0007799363,0.0005479907,0.0005415458,0.0009873427,0.001363607,0.0008628655,0.001354877],"category_scores_gemma":[0.001608217,0.0004292711,0.0007651118,0.0005835406,0.0008574487,0.0009675684,0.001316698,0.001377324,0.0002280129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009348165,"about_ca_system_score_gemma":0.002742402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005635009,"about_ca_topic_score_gemma":0.006275387,"domain_scores_codex":[0.9994062,0.0001642715,0.00002605446,0.00009927281,0.000175294,0.0001288853],"domain_scores_gemma":[0.9993213,0.0002865623,0.000106586,0.00004619269,0.0001236999,0.0001156062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005839374,0.00006512952,0.0005745422,0.0000698787,0.00002911184,0.00008431698,0.0001083778,0.9467565,0.002479959,0.0278888,0.001085811,0.02079926],"study_design_scores_gemma":[0.000006053367,0.00002016846,0.00005966966,0.000003606154,0.000004592813,0.000009568127,0.00001459259,0.9920738,0.0001631608,0.007209441,0.0004316264,0.000003721268],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01333463,0.0002347453,0.9833557,0.0002964273,0.00005337018,0.00006178014,0.00004367927,0.0001286671,0.002490913],"genre_scores_gemma":[0.7425793,0.0004837825,0.2538391,0.0002354844,0.0001271536,0.0001975637,0.000125694,0.00007724961,0.002334712],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005635009,"threshold_uncertainty_score":0.01120442,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2900117962","doi":"10.1109/tccn.2018.2880232","title":"Channel Estimation for Sparse Massive MIMO Channels in Low SNR Regime","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"MIMO; Computer science; Channel (broadcasting); Estimator; Precoding; Signal-to-noise ratio (imaging); Algorithm; Channel state information; Noise (video); Filter (signal processing); Multi-user MIMO; Wireless; Energy (signal processing); Telecommunications; Mathematics; Statistics; Artificial intelligence","authors":[{"name":"Zijun Gong","is_ca":true},{"name":"Cheng Li","is_ca":true},{"name":"Fan Jiang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03802348258900243,"gpt":0.2782650399327954,"spread":0.240241557343793,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005879665,0.0005649102,0.0006390227,0.000368787,0.000343776,0.0005472528,0.0003748544,0.0005437104,0.0008378229],"category_scores_gemma":[0.002644837,0.0002533797,0.0003123757,0.0004217697,0.0006114551,0.0008441298,0.0008193342,0.0006214292,0.0002794514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002683853,"about_ca_system_score_gemma":0.0007333471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002145596,"about_ca_topic_score_gemma":0.002437581,"domain_scores_codex":[0.9996588,0.0001045717,0.00001301559,0.00006362342,0.0001058877,0.00005403163],"domain_scores_gemma":[0.9989843,0.0006246814,0.0001210142,0.0001008501,0.0001326636,0.0000365473],"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":[0.0001883254,0.00004079776,0.0009912587,0.0001348465,0.00003592961,0.0001428605,0.00007658114,0.9125897,0.01277102,0.01269416,0.001005091,0.05932955],"study_design_scores_gemma":[0.000007389853,0.00002204522,0.0001793041,0.000004233833,0.000004692942,0.00003605615,0.00001265193,0.9949881,0.001696751,0.002872493,0.0001700093,0.000006313172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02142872,0.0001761431,0.9770921,0.0001077491,0.00002338365,0.00001760201,0.00004698182,0.0001735145,0.0009338293],"genre_scores_gemma":[0.8173118,0.0005548695,0.1800978,0.0001242146,0.00008211337,0.00006547885,0.0001894039,0.00002638836,0.00154788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002145596,"threshold_uncertainty_score":0.004266202,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3134370868","doi":"10.1109/tccn.2021.3063132","title":"Hybrid Radio Resource Management for Time-Varying 5G Heterogeneous Wireless Access Network","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Computer network; Radio resource management; Resource allocation; Throughput; Lyapunov optimization; Wireless network; Network congestion; Power control; Resource management (computing); Overhead (engineering); UMTS Terrestrial Radio Access Network; Utility maximization problem; Heterogeneous network; Wireless; Radio access network; Network packet; Distributed computing; Base station; Utility maximization; Power (physics); Telecommunications","authors":[{"name":"Nagina Zarin","is_ca":true},{"name":"Anjali Agarwal","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02784054496006096,"gpt":0.2666328920579298,"spread":0.2387923470978689,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007605837,0.0005531409,0.000526729,0.0002872049,0.0004728389,0.000756132,0.000949408,0.0003640606,0.0006658349],"category_scores_gemma":[0.0007010912,0.0001292406,0.0003039687,0.0003872178,0.0005065505,0.0009330191,0.0006736324,0.0003770615,0.00009109067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005349047,"about_ca_system_score_gemma":0.0003540237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001707129,"about_ca_topic_score_gemma":0.002559841,"domain_scores_codex":[0.9995599,0.0001426101,0.00001510699,0.00009939817,0.00008764005,0.00009515715],"domain_scores_gemma":[0.9996998,0.0001299941,0.00006059092,0.00003420257,0.00004631016,0.00002909339],"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":[0.0001858682,0.0001472169,0.001212117,0.0000821944,0.000119906,0.0003080423,0.00009944956,0.8670176,0.02126712,0.01667204,0.0008653876,0.09202299],"study_design_scores_gemma":[0.000007583607,0.00008153247,0.0002446043,0.000002121269,0.00001814117,0.00003940484,0.00002183686,0.9962379,0.001069496,0.00192143,0.0003490358,0.000006956707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08452827,0.000936061,0.9115302,0.0001270392,0.00005778013,0.00004434759,0.00002314864,0.0002016438,0.002551535],"genre_scores_gemma":[0.9768988,0.0001890738,0.02218265,0.0000475449,0.00003870323,0.00002475505,0.00001586718,0.00001039932,0.000592195],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001707129,"threshold_uncertainty_score":0.004022419,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4415368556","doi":"10.1109/tccn.2025.3623369","title":"Internet of Agents: Fundamentals, Applications, and Challenges","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Mobile Agent-Based Network Management","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Orchestration; The Internet; Key (lock); Task (project management); Incentive; Trustworthiness; Virtual network","authors":[{"name":"Yuntao Wang","is_ca":false},{"name":"Shaolong Guo","is_ca":false},{"name":"Yanghe Pan","is_ca":false},{"name":"Zhou Su","is_ca":false},{"name":"Fahao Chen","is_ca":true},{"name":"Tom H. Luan","is_ca":false},{"name":"Peng Li","is_ca":false},{"name":"Jiawen Kang","is_ca":false},{"name":"Dusit Niyato","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08745237762335628,"gpt":0.3008252907150247,"spread":0.2133729130916684,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002443988,0.0007362927,0.001141697,0.00230253,0.001671593,0.00752263,0.002406904,0.003367361,0.002252023],"category_scores_gemma":[0.003880276,0.0009379028,0.000491115,0.004509084,0.003657036,0.01148903,0.003306597,0.005550417,0.001185197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001970778,"about_ca_system_score_gemma":0.001913793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001899005,"about_ca_topic_score_gemma":0.001273294,"domain_scores_codex":[0.9979849,0.000665823,0.0001532835,0.0003120224,0.0007392366,0.0001447386],"domain_scores_gemma":[0.9975433,0.001298896,0.0001491565,0.0003064976,0.0004289629,0.0002732222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002863942,0.00007111048,0.001189263,0.001101557,0.00002853032,0.0002876368,0.0005406543,0.007407466,0.0006509431,0.7474416,0.03210649,0.2091462],"study_design_scores_gemma":[0.000008813124,0.00003942452,0.0005729075,0.0007587288,0.00001765802,0.0008225663,0.0006476659,0.02844818,0.0002535855,0.5310516,0.4373395,0.00003943231],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.01104102,0.6229534,0.2047894,0.0687087,0.00334513,0.0002667145,0.0004077885,0.0008489371,0.08763891],"genre_scores_gemma":[0.1741409,0.6823425,0.1175182,0.004334083,0.007045023,0.0005566553,0.0005258158,0.0002166776,0.01332024],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00752263,"threshold_uncertainty_score":0.01429909,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3201426489","doi":"10.1109/tccn.2021.3111981","title":"An Intelligent Detection Based on Deep Learning for Multilevel Code Shifted Differential Chaos Shift Keying System With <i>M</i>-ary Modulation","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Chaos control and synchronization","field":"Physics and Astronomy","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Key Research and Development Program of China","keywords":"Demodulation; Computer science; Chaotic; Algorithm; Bit error rate; Modulation (music); Keying; Detector; Fading; Artificial neural network; Theoretical computer science; Artificial intelligence; Decoding methods; Telecommunications; Channel (broadcasting)","authors":[{"name":"Haotian Zhang","is_ca":false},{"name":"Lin Zhang","is_ca":false},{"name":"Julian Cheng","is_ca":true},{"name":"Yuan Jiang","is_ca":false},{"name":"Zhiqiang Wu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02546307069768482,"gpt":0.261572523921188,"spread":0.2361094532235032,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003573336,0.0005385908,0.0005351785,0.0002719191,0.0003456324,0.0004756584,0.0009583936,0.0007796554,0.001137734],"category_scores_gemma":[0.0007641554,0.0002836618,0.0003842509,0.0002575687,0.0003585517,0.0007976252,0.0006038034,0.0008008548,0.0002710101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006125438,"about_ca_system_score_gemma":0.0008520051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002789963,"about_ca_topic_score_gemma":0.004419805,"domain_scores_codex":[0.9997568,0.00002961216,0.00001825309,0.00006816382,0.00007711504,0.00005011286],"domain_scores_gemma":[0.9997715,0.0000769456,0.00003218865,0.0000212795,0.00007971602,0.00001838516],"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":[0.0003520811,0.0003238381,0.003342576,0.0002323672,0.000160411,0.0002821422,0.0001907798,0.220878,0.121194,0.01251243,0.003670823,0.6368605],"study_design_scores_gemma":[0.000007974171,0.00005901253,0.0002633515,0.000005031031,0.00001200025,0.00003025089,0.000004513169,0.9901883,0.008421645,0.0006738627,0.0003255939,0.000008377814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08141541,0.0004195314,0.9139881,0.0004199271,0.000107315,0.00007306688,0.00006907615,0.0009057139,0.002601906],"genre_scores_gemma":[0.8231716,0.0002063164,0.1720511,0.0003152221,0.00004216993,0.0001043996,0.0001539886,0.00002615328,0.003929126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002789963,"threshold_uncertainty_score":0.005547464,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4402916374","doi":"10.1109/tccn.2024.3469234","title":"Intelligent Digital Twin Communication Framework for Addressing Accuracy and Timeliness Tradeoff in Resource-Constrained Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; Canada Excellence Research Chairs, Government of Canada; Royal Academy of Engineering","keywords":"Computer science; Computer network; Distributed computing; Resource management (computing); Resource (disambiguation)","authors":[{"name":"Lal Verda Çakır","is_ca":false},{"name":"Craig Thomson","is_ca":false},{"name":"Mehmet Özdem","is_ca":false},{"name":"Berk Canberk","is_ca":false},{"name":"Van-Linh Nguyen","is_ca":false},{"name":"Trung Q. Duong","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06700995890453473,"gpt":0.3114103343669427,"spread":0.2444003754624079,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005139346,0.001095831,0.001114626,0.001282918,0.001236501,0.00316395,0.002908957,0.00163863,0.003884824],"category_scores_gemma":[0.01074778,0.0004596031,0.0007081493,0.002033805,0.002070248,0.005973973,0.003634176,0.002208462,0.0004683544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002953808,"about_ca_system_score_gemma":0.003563523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00465117,"about_ca_topic_score_gemma":0.004238604,"domain_scores_codex":[0.9963872,0.001388778,0.0002211317,0.0006761176,0.001010323,0.0003163219],"domain_scores_gemma":[0.9970359,0.001458182,0.0003112946,0.000414219,0.0005939188,0.0001865581],"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":[0.0001054878,0.00005562136,0.0004441677,0.00009831944,0.00003089566,0.000151055,0.0002671509,0.4701359,0.002264007,0.4586005,0.00323672,0.06461029],"study_design_scores_gemma":[0.00001056633,0.00003212174,0.00004530928,0.000009795456,0.00001289673,0.00003928786,0.00004592371,0.9242302,0.0008418714,0.07166112,0.003057791,0.00001313903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002924028,0.000103212,0.9941159,0.0002279059,0.00003472948,0.00003387137,0.00002804364,0.0001262932,0.0024059],"genre_scores_gemma":[0.3744791,0.000562513,0.6175244,0.0002925245,0.0002094544,0.0003371962,0.0001816146,0.0001615378,0.00625176],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005139346,"threshold_uncertainty_score":0.02717984,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4386113977","doi":"10.1109/tccn.2023.3307929","title":"AI-Assisted Slicing-Based Resource Management for Two-Tier Radio Access Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Huawei Technologies (Canada); Toronto Metropolitan University; Carleton University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Computer science; Quality of service; Base station; Computer network; Distributed computing; Slicing; Resource management (computing); Overhead (engineering); Benchmark (surveying); Flexibility (engineering); Resource allocation; Radio access network","authors":[{"name":"Conghao Zhou","is_ca":true},{"name":"Jie Gao","is_ca":true},{"name":"Mushu Li","is_ca":true},{"name":"Xuemin Shen","is_ca":true},{"name":"Weihua Zhuang","is_ca":true},{"name":"Xu Li","is_ca":true},{"name":"Weisen Shi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06524265872399744,"gpt":0.3295239321227362,"spread":0.2642812733987387,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006449263,0.0006570992,0.0005399617,0.0003364758,0.0003602361,0.0006666927,0.001014883,0.0004114255,0.001067824],"category_scores_gemma":[0.0009837797,0.0002274633,0.0003533363,0.0004171416,0.0005614185,0.0009817951,0.0007818181,0.0006895977,0.000135666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009062367,"about_ca_system_score_gemma":0.001071841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005610518,"about_ca_topic_score_gemma":0.006157134,"domain_scores_codex":[0.9996743,0.00007633079,0.00001754639,0.00006577064,0.00009946804,0.00006657957],"domain_scores_gemma":[0.9995732,0.0001819604,0.00006149141,0.00005081144,0.00008785612,0.00004473109],"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":[0.00006765146,0.0000376665,0.0004454394,0.00004106835,0.00002422434,0.0000772148,0.0000502872,0.916793,0.007803005,0.01094905,0.0008255528,0.06288581],"study_design_scores_gemma":[0.000001348499,0.00001048471,0.00003695067,0.000001340673,0.000002384969,0.000008308903,0.00000382025,0.9976798,0.0005878689,0.00147696,0.0001882727,0.000002588532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01526088,0.000412394,0.9820472,0.00007328991,0.00002737145,0.00003022021,0.00003431741,0.0003426596,0.001771665],"genre_scores_gemma":[0.79401,0.0003423529,0.204051,0.00009768556,0.00004302982,0.00006195041,0.00011195,0.00005037272,0.001231572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005610518,"threshold_uncertainty_score":0.01115572,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4408780256","doi":"10.1109/tccn.2025.3554003","title":"QoE-Guaranteed Optimization in MEC-Enabled Metaverse: An Active Inference Deep Reinforcement Learning Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Inference; Artificial intelligence; Computer network","authors":[{"name":"Jianbo Du","is_ca":false},{"name":"Xiaoli Chu","is_ca":false},{"name":"Zehui Xiong","is_ca":false},{"name":"Xianfu Chen","is_ca":false},{"name":"Mianxiong Dong","is_ca":false},{"name":"F. Richard Yu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05062850571869744,"gpt":0.3321089659858775,"spread":0.2814804602671801,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001140239,0.0009206338,0.001381465,0.0003957385,0.000340066,0.0009840112,0.001686887,0.001466218,0.00228979],"category_scores_gemma":[0.00292812,0.0004864584,0.0004324942,0.0003598116,0.0009171143,0.001124233,0.001141329,0.001615556,0.0002383173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00110276,"about_ca_system_score_gemma":0.001425398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008369179,"about_ca_topic_score_gemma":0.007160154,"domain_scores_codex":[0.999581,0.0001051453,0.00001547351,0.0001023099,0.00007863298,0.0001174206],"domain_scores_gemma":[0.9983581,0.001091369,0.0001336914,0.00006130763,0.0002518633,0.0001036923],"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":[0.00008232054,0.000061953,0.0007761305,0.00003640231,0.00002924293,0.00006699707,0.00002760842,0.977509,0.0007504014,0.003875931,0.0005476158,0.0162364],"study_design_scores_gemma":[0.000004114779,0.00001007996,0.00003682529,0.000002072096,0.00000258254,0.000003869653,0.000002413104,0.9989176,0.00007238994,0.000897533,0.00004922438,0.000001416957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05685796,0.0006365834,0.9367092,0.0006361828,0.00006917999,0.00004939467,0.00008272973,0.0004324802,0.004526299],"genre_scores_gemma":[0.9711128,0.0001268475,0.02613196,0.0002008691,0.00003547894,0.0000510145,0.00006338706,0.00003821356,0.002239516],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008369179,"threshold_uncertainty_score":0.0166409,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2293707299","doi":"10.1109/tccn.2015.2498615","title":"Cognitive Beamforming in Underlay Two-Way Relay Networks With Multiantenna Terminals","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Beamforming; Computer science; Underlay; Relay; Cognitive radio; Interference (communication); Algorithm; Power (physics); Topology (electrical circuits); Signal-to-noise ratio (imaging); Computer network; Mathematics; Telecommunications; Wireless; Combinatorics; Physics","authors":[{"name":"Yun Cao","is_ca":true},{"name":"Chintha Tellambura","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09257484298015302,"gpt":0.3227802822652531,"spread":0.2302054392851001,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006825497,0.001002685,0.0007114337,0.0003114592,0.0004575592,0.001038969,0.0006981282,0.001030341,0.0006857605],"category_scores_gemma":[0.001595571,0.0003842635,0.0005501683,0.0005581415,0.0009480481,0.001382314,0.0007324796,0.0005780248,0.0002014746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007078588,"about_ca_system_score_gemma":0.0004732657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004221172,"about_ca_topic_score_gemma":0.003722787,"domain_scores_codex":[0.9994169,0.0001961307,0.00001660549,0.0001163235,0.0001064422,0.0001477108],"domain_scores_gemma":[0.9990815,0.0005742872,0.0001061832,0.00004322181,0.0001472612,0.00004751715],"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":[0.0002853495,0.0000637734,0.001478012,0.0001757996,0.0001337306,0.001071072,0.0002522903,0.9239761,0.01624664,0.0333759,0.0007198495,0.02222146],"study_design_scores_gemma":[0.00002041797,0.0001822587,0.0006592149,0.00001642349,0.00006005813,0.0002379304,0.0001316447,0.988084,0.001807747,0.00788505,0.0008865839,0.00002861929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2017202,0.003751491,0.7815757,0.0004180334,0.0001623527,0.00005051835,0.0001112838,0.0001509235,0.01205955],"genre_scores_gemma":[0.9768389,0.001492921,0.01847198,0.00008592098,0.00008206328,0.00004253637,0.00002741816,0.00001174587,0.002946483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004221172,"threshold_uncertainty_score":0.008393228,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}