{"meta":{"query_hash":"a650894481b7","filters":{"venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference"},"cohort_total":70,"direct_labels_cover":0,"predictions_cover":70,"exported":70,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/a650894481b7","api":"https://metacan.xera.ac/api/v1/cohort?venue=GLOBECOM+2022+-+2022+IEEE+Global+Communications+Conference"},"results":[{"id":"W2898572688","doi":"10.1109/globecom48099.2022.10001500","title":"Real-time Context-aware learning System for IoT Applications","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Seneca Polytechnic; Université de Moncton","funders":"","keywords":"Computer science; Context (archaeology); Mobile device; Machine learning; Artificial intelligence; Mobile computing; Context switch; Context awareness; Server; Real-time computing; Context model; Internet of Things; Distributed computing; Embedded system; Computer network; Operating system","score_opus":0.04302834273524246,"score_gpt":0.2936228094687547,"score_spread":0.2505944667335122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898572688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07795904,0.0029241107,0.8865101,0.000476094,0.00051894307,0.00032663628,0.00029936462,0.019961651,0.011023966],"genre_scores_gemma":[0.8670901,0.0007444484,0.12685154,0.0004574957,0.00017233357,0.00025113276,0.00038159563,0.00016338498,0.0038879556],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966514,0.000035919842,0.000037885584,0.000098886776,0.000103074286,0.000059138445],"domain_scores_gemma":[0.9996772,0.00005707254,0.0000412471,0.00007304243,0.000105136576,0.00004634406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003386779,0.00065172894,0.00058871764,0.0005456866,0.00045847808,0.0007241064,0.0011877855,0.00062546233,0.002147076],"category_scores_gemma":[0.0009337114,0.00022882193,0.0002902754,0.0004460019,0.0001847925,0.0013786106,0.0010255075,0.0008367758,0.0010286189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010792894,0.0008479384,0.00767365,0.000457555,0.00015331041,0.0008855837,0.00033810883,0.057185136,0.08478289,0.009076232,0.019799132,0.8177211],"study_design_scores_gemma":[0.000083835024,0.00037580746,0.003843797,0.00006903168,0.00012002642,0.0006684718,0.00010818362,0.9271635,0.0381423,0.0069826324,0.022362772,0.000079784964],"about_ca_topic_score_codex":0.0014769933,"about_ca_topic_score_gemma":0.0018111306,"teacher_disagreement_score":0.002147076,"about_ca_system_score_codex":0.00032469083,"about_ca_system_score_gemma":0.0005340758,"threshold_uncertainty_score":0.0071827173},"labels":[],"label_agreement":null},{"id":"W4315605918","doi":"10.1109/globecom48099.2022.10001115","title":"Joint Low-Rank Factor and Sparsity for Detecting Access Jamming in Massive MTC Networks","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Jamming; Feature (linguistics); Rank (graph theory); Constraint (computer-aided design); Correctness; Telecommunications link; Factor (programming language); Joint (building); Exploit; Data mining; Algorithm; Computer network; Computer security; Engineering; Mathematics","score_opus":0.06556047803158377,"score_gpt":0.2938114253761599,"score_spread":0.22825094734457613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315605918","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02763325,0.00021299413,0.97052413,0.00022677756,0.000028741431,0.000025784151,0.000068416106,0.00024951194,0.0010304075],"genre_scores_gemma":[0.6909817,0.0005622815,0.3058282,0.00016792605,0.00015896873,0.000084136926,0.000393814,0.00006975921,0.0017531721],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999542,0.00014023448,0.000017891254,0.0000614488,0.00017622285,0.000062279374],"domain_scores_gemma":[0.99834406,0.0009821918,0.00023471277,0.00013312996,0.00022133728,0.000084521096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007015213,0.00081409584,0.00054854696,0.0005705299,0.0002725371,0.00048673118,0.00047445815,0.000561744,0.00089622085],"category_scores_gemma":[0.005210987,0.00027501653,0.00042604297,0.00067861524,0.0006806708,0.0010407278,0.000694112,0.000874699,0.00027947244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068040576,0.00013507657,0.005178446,0.0002923556,0.00008758071,0.00046468386,0.0002657578,0.6954772,0.03533622,0.023456555,0.0047076717,0.23391804],"study_design_scores_gemma":[0.000007630784,0.00003311205,0.0004352333,0.00000561124,0.0000049585083,0.000061457,0.000020288144,0.99465805,0.0017064621,0.0026873222,0.00037012756,0.000009653633],"about_ca_topic_score_codex":0.003065809,"about_ca_topic_score_gemma":0.0039993813,"teacher_disagreement_score":0.003065809,"about_ca_system_score_codex":0.00029722505,"about_ca_system_score_gemma":0.0007302855,"threshold_uncertainty_score":0.006095886},"labels":[],"label_agreement":null},{"id":"W4315605919","doi":"10.1109/globecom48099.2022.10001723","title":"Distributed RIS-Assisted FD Systems with Discrete Phase Shifts: A Reinforcement Learning Approach","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Beamforming; Reinforcement learning; Maximization; Computer science; Phase (matter); Wireless; Transmitter power output; Mathematical optimization; Optimization problem; Continuous phase modulation; Power (physics); Algorithm; Mathematics; Transmitter; Telecommunications; Artificial intelligence; Physics; Channel (broadcasting)","score_opus":0.03334916894112717,"score_gpt":0.28012021132287823,"score_spread":0.24677104238175107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315605919","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017709823,0.00023153132,0.9792423,0.00013843864,0.000022936425,0.000020845211,0.000011017157,0.00010524005,0.0025179654],"genre_scores_gemma":[0.9338038,0.00021849881,0.06250772,0.00009742003,0.00004006121,0.00007141493,0.000019208514,0.000022945205,0.0032188846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996711,0.00011880061,0.0000110752035,0.0000602998,0.00008626467,0.000052426658],"domain_scores_gemma":[0.9991358,0.00054139365,0.00011899136,0.000048516908,0.00012561285,0.000029670748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007265156,0.0006281799,0.0007678025,0.00021231044,0.00020905086,0.00060807157,0.00070048776,0.0007416727,0.0010905619],"category_scores_gemma":[0.0015457253,0.00029778748,0.0003250712,0.00027661482,0.00092273566,0.0005768245,0.0005952567,0.0008610582,0.00020372882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043866654,0.000024645526,0.00015417136,0.000029625246,0.00001813236,0.00003765858,0.000026271504,0.9790006,0.0019783783,0.0044773333,0.0001829647,0.014026344],"study_design_scores_gemma":[0.0000060081106,0.000018830873,0.000022342094,0.0000017212392,0.0000028839358,0.000007188868,0.0000029030116,0.9987789,0.00025141687,0.00079096295,0.000114636845,0.000002151863],"about_ca_topic_score_codex":0.0022444895,"about_ca_topic_score_gemma":0.0018829779,"teacher_disagreement_score":0.0022444895,"about_ca_system_score_codex":0.0005951449,"about_ca_system_score_gemma":0.0006029403,"threshold_uncertainty_score":0.004462838},"labels":[],"label_agreement":null},{"id":"W4315605964","doi":"10.1109/globecom48099.2022.10000913","title":"Heuristic-Based Proactive Service Migration Induced by Dynamic Computation Load in Edge Computing","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computation; Heuristic; Distributed computing; Latency (audio); Enhanced Data Rates for GSM Evolution; Edge computing; Service (business); Load balancing (electrical power); Response time; Mathematical optimization; Computer network; Algorithm; Artificial intelligence","score_opus":0.03680475511901938,"score_gpt":0.3012340949858866,"score_spread":0.26442933986686723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315605964","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29926184,0.00051204854,0.6942062,0.00046434609,0.00010487548,0.000076663884,0.000052791867,0.000758048,0.00456316],"genre_scores_gemma":[0.97268337,0.0000768539,0.026707025,0.0000430541,0.000009467766,0.0000221912,0.000021904529,0.000016949349,0.00041922388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996494,0.00011596841,0.0000152925,0.00006098519,0.000067410976,0.000090931644],"domain_scores_gemma":[0.9994929,0.0002685009,0.00006824768,0.00006513705,0.00006515979,0.00004005055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046778555,0.00047951064,0.0005162622,0.0003427323,0.0006090579,0.0007292774,0.0006904395,0.00049054704,0.0005098505],"category_scores_gemma":[0.0012769009,0.00028791523,0.00032794647,0.00035124985,0.00053352985,0.00070112955,0.00051024894,0.0004941803,0.00009165232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009267365,0.00005372698,0.00077857473,0.000028540935,0.000017886472,0.00007428995,0.00004691454,0.9661926,0.0033484965,0.0044774152,0.0005158437,0.02437299],"study_design_scores_gemma":[0.0000036411636,0.000016206106,0.00011836312,0.0000018324711,0.0000033904018,0.000011244918,0.00001066439,0.99793124,0.0005555883,0.0012281459,0.00011662901,0.0000030734013],"about_ca_topic_score_codex":0.005647144,"about_ca_topic_score_gemma":0.0056166374,"teacher_disagreement_score":0.005647144,"about_ca_system_score_codex":0.0008147443,"about_ca_system_score_gemma":0.0012083396,"threshold_uncertainty_score":0.011228561},"labels":[],"label_agreement":null},{"id":"W4315605978","doi":"10.1109/globecom48099.2022.10001142","title":"A Novel LFM Waveform for Terahertz-Band Joint Radar and Communications over Inter-Satellite Links","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Waveform; Computer science; Satellite; Communications satellite; Radar; Ambiguity function; Wireless; Electronic engineering; Terahertz radiation; Orbit (dynamics); Communications system; Bit error rate; Remote sensing; Telecommunications; Engineering; Physics; Geography; Channel (broadcasting); Optics; Aerospace engineering","score_opus":0.052298383375993115,"score_gpt":0.27663747473796746,"score_spread":0.22433909136197433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315605978","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08940237,0.0017570113,0.897223,0.0008055999,0.0002601854,0.000059708334,0.000110088455,0.00019526815,0.010186777],"genre_scores_gemma":[0.7955488,0.0012227542,0.19697091,0.0003453775,0.00020255579,0.00009511533,0.00018059238,0.000021724669,0.0054121586],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986935,0.000026417145,0.000006160435,0.000017020215,0.000068742476,0.000012189836],"domain_scores_gemma":[0.99984,0.000028360364,0.000034203782,0.000021052292,0.000064242944,0.000012144225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015697382,0.00022724002,0.00013968415,0.0002544396,0.00019564746,0.00038814082,0.0003355588,0.0005218086,0.00086523534],"category_scores_gemma":[0.0004461688,0.00006892174,0.00014978612,0.00037416213,0.00020074216,0.00047510825,0.00031490237,0.0004307798,0.00041523145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035798893,0.00007536811,0.0024508052,0.00032481083,0.000036898426,0.00071597035,0.00022431534,0.021673618,0.56996197,0.05193362,0.0030380983,0.3492066],"study_design_scores_gemma":[0.000110078254,0.0012746663,0.0033886672,0.00013501135,0.00007237768,0.0061087175,0.00019432402,0.74390185,0.18250202,0.009506004,0.05272761,0.00007857535],"about_ca_topic_score_codex":0.00018173926,"about_ca_topic_score_gemma":0.00022528142,"teacher_disagreement_score":0.00086523534,"about_ca_system_score_codex":0.0001844835,"about_ca_system_score_gemma":0.00025956923,"threshold_uncertainty_score":0.0028944612},"labels":[],"label_agreement":null},{"id":"W4315605992","doi":"10.1109/globecom48099.2022.10001380","title":"Age-oriented Access Control in GEO/LEO Heterogeneous Network for Marine IoRT","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Age of Information Optimization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Markov decision process; Satellite; Transmission (telecommunications); Throughput; Computer network; Heterogeneous network; Communications satellite; The Internet; Markov process; Distributed computing; Telecommunications; Engineering; Wireless; Wireless network","score_opus":0.031225629256287284,"score_gpt":0.2956600533729006,"score_spread":0.2644344241166133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315605992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06271321,0.0005185187,0.9323396,0.00038995693,0.00009707804,0.00006046147,0.00007016959,0.00024690863,0.00356416],"genre_scores_gemma":[0.9888993,0.00011648912,0.009744278,0.00007902611,0.00003220015,0.00002817803,0.000026556098,0.000009372593,0.0010646207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921525,0.00014130943,0.00003729708,0.00025213894,0.00015696071,0.0001969515],"domain_scores_gemma":[0.99866045,0.0005762638,0.00030321855,0.00010222414,0.00021821442,0.00013961778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095728657,0.000622122,0.00074774004,0.00036422184,0.0005364538,0.0009514771,0.0013326711,0.00072378176,0.00123355],"category_scores_gemma":[0.0025401902,0.00022104767,0.00037542338,0.000385405,0.00083119504,0.0012224999,0.001364805,0.0012040059,0.00016190819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001978904,0.00011518553,0.0027713478,0.000108719956,0.00005028471,0.0004164956,0.00022302315,0.90058136,0.01144847,0.024848333,0.0016144353,0.057624515],"study_design_scores_gemma":[0.000006440687,0.000032632353,0.00020514101,0.0000033625236,0.000011175303,0.00004142767,0.00001533223,0.99509,0.0006544893,0.00361115,0.00032185396,0.000006926038],"about_ca_topic_score_codex":0.0051538693,"about_ca_topic_score_gemma":0.005219841,"teacher_disagreement_score":0.0051538693,"about_ca_system_score_codex":0.0009170404,"about_ca_system_score_gemma":0.0008444545,"threshold_uncertainty_score":0.010247707},"labels":[],"label_agreement":null},{"id":"W4315605997","doi":"10.1109/globecom48099.2022.10000768","title":"Energy-Efficient Secure Data Collection and Transmission via UAV","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Base station; Eavesdropping; Transmitter power output; Transmission (telecommunications); Network packet; Scheduling (production processes); Data transmission; Convex optimization; Efficient energy use; Mathematical optimization; Real-time computing; Secure transmission; Computer network; Regular polygon; Transmitter; Telecommunications; Engineering; Mathematics; Electrical engineering","score_opus":0.0262809338233045,"score_gpt":0.2503568359805436,"score_spread":0.2240759021572391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315605997","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054828927,0.00075835123,0.94152665,0.00020297308,0.000083518506,0.00005290271,0.00007833561,0.000304726,0.0021636013],"genre_scores_gemma":[0.93007183,0.0005373512,0.06721175,0.00008414365,0.00004223365,0.00006201997,0.000103786086,0.000017536466,0.0018692786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955505,0.00009408665,0.000031238087,0.000080762824,0.0001578913,0.00008097537],"domain_scores_gemma":[0.9995846,0.00012122479,0.000083688064,0.000109350694,0.000079487814,0.000021639455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036059675,0.0006299417,0.00057011214,0.00030882764,0.00047736373,0.0004691433,0.0006995625,0.0003741994,0.0006645417],"category_scores_gemma":[0.0008024142,0.00020085167,0.0003486757,0.0004899923,0.00039319138,0.0009802463,0.0010312722,0.00054363575,0.00024742953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00088940037,0.00017053935,0.0029090303,0.00046762516,0.00012605882,0.00092469907,0.00046268123,0.5145389,0.17711842,0.06631186,0.0050222804,0.23105846],"study_design_scores_gemma":[0.000025465952,0.00024531264,0.00046863337,0.000013774504,0.00002282061,0.00033097484,0.00006902257,0.96750325,0.022888301,0.0053631407,0.0030466388,0.000022674589],"about_ca_topic_score_codex":0.0011405814,"about_ca_topic_score_gemma":0.0012279055,"teacher_disagreement_score":0.0011405814,"about_ca_system_score_codex":0.00034771126,"about_ca_system_score_gemma":0.00054880243,"threshold_uncertainty_score":0.0025228858},"labels":[],"label_agreement":null},{"id":"W4315606013","doi":"10.1109/globecom48099.2022.10001247","title":"Scheduler Design for Mobility-aware Multipath QUIC","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Goodput; Multihoming; Computer science; Computer network; Mobility model; Multipath propagation; Network packet; Multipath TCP; Scheduling (production processes); Mobility management; Distributed computing; Wireless; The Internet; Internet Protocol; Telecommunications; Throughput; Channel (broadcasting)","score_opus":0.09581041099166503,"score_gpt":0.3340087284376369,"score_spread":0.23819831744597186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315606013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03768417,0.0008528188,0.95455325,0.00022064948,0.0002615155,0.00030600803,0.00017033439,0.0024800412,0.0034713221],"genre_scores_gemma":[0.7307906,0.0005020911,0.26475665,0.00015613854,0.00014024119,0.00025168993,0.00024280939,0.0002103582,0.0029494679],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994991,0.00010095461,0.000045105284,0.00007877905,0.0001799031,0.0000961216],"domain_scores_gemma":[0.9986999,0.00029966977,0.00014674559,0.00013202312,0.0005697968,0.00015179777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011440794,0.0004967,0.00055437896,0.00057237386,0.0007479496,0.0009814585,0.0014939728,0.00032872558,0.0019069423],"category_scores_gemma":[0.002588447,0.00030028407,0.00021444604,0.0005999188,0.0003084798,0.0005898682,0.0008215256,0.0006564285,0.0005257108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006219209,0.0002107347,0.005141984,0.0003276566,0.000118662,0.0004867365,0.00030448183,0.64690524,0.059509695,0.04362714,0.0111428,0.23160289],"study_design_scores_gemma":[0.000044719043,0.0000935753,0.00028761383,0.0000075330654,0.000022261636,0.00007609367,0.00002743885,0.98539716,0.006874156,0.0018355714,0.0053165816,0.000017409617],"about_ca_topic_score_codex":0.0060496866,"about_ca_topic_score_gemma":0.00836732,"teacher_disagreement_score":0.0060496866,"about_ca_system_score_codex":0.0013356189,"about_ca_system_score_gemma":0.0040507815,"threshold_uncertainty_score":0.012028933},"labels":[],"label_agreement":null},{"id":"W4315606032","doi":"10.1109/globecom48099.2022.10001586","title":"Reinforcement Learning-based Dynamic Resource Allocation For Grant-Free Access","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Age of Information Optimization","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); University of Calgary; Queen's University","funders":"","keywords":"Reinforcement learning; Computer science; Telecommunications link; 3rd Generation Partnership Project 2; Latency (audio); Computer network; Cellular network; Broadband; Distributed computing; Telecommunications; Artificial intelligence","score_opus":0.03596270922348667,"score_gpt":0.2998518492864165,"score_spread":0.26388914006292985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315606032","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09832557,0.0006996464,0.8954373,0.00047814407,0.00010541152,0.000101948,0.00003863057,0.00057887664,0.0042344197],"genre_scores_gemma":[0.9818503,0.00007093901,0.016732141,0.00008548964,0.00001634137,0.000048438425,0.000020279871,0.000014781848,0.0011611661],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994764,0.00018626003,0.000023780382,0.00009455525,0.000086690205,0.00013232474],"domain_scores_gemma":[0.9983564,0.0010637328,0.00016193518,0.00006889751,0.0002251468,0.00012373613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012486867,0.00055390864,0.0008204788,0.00025498477,0.0003283319,0.0005934341,0.001041321,0.0005979454,0.0017781681],"category_scores_gemma":[0.003571376,0.00022925172,0.00024274911,0.00024126614,0.0007929293,0.00074999797,0.0007743646,0.0011654367,0.00023405324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015412491,0.00012887263,0.00070992217,0.000031538286,0.000028695624,0.00006254015,0.000045887497,0.9569854,0.0015338715,0.0042621703,0.0008074952,0.03524944],"study_design_scores_gemma":[0.0000089031955,0.000021267391,0.000048233698,0.0000014326132,0.0000021367198,0.000005215209,0.0000029736088,0.99863774,0.0001500228,0.0010355192,0.00008450645,0.000001961604],"about_ca_topic_score_codex":0.00574766,"about_ca_topic_score_gemma":0.0043187356,"teacher_disagreement_score":0.00574766,"about_ca_system_score_codex":0.0008177926,"about_ca_system_score_gemma":0.0010743657,"threshold_uncertainty_score":0.011428356},"labels":[],"label_agreement":null},{"id":"W4315606042","doi":"10.1109/globecom48099.2022.10001253","title":"Handling big tabular data of ICT supply chains: a multi-task, machine-interpretable approach","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Header; Table (database); Task (project management); Modal; Big data; Information extraction; Information retrieval; Artificial intelligence; Data mining; Natural language processing","score_opus":0.1309356717956981,"score_gpt":0.3231001213748782,"score_spread":0.1921644495791801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315606042","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07490012,0.0060136574,0.8765306,0.0031670837,0.0005121617,0.00086696364,0.018569471,0.015869223,0.003570752],"genre_scores_gemma":[0.30111772,0.0018911767,0.63532686,0.0008421895,0.00052922237,0.0007391504,0.053722538,0.00046250614,0.0053686686],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982609,0.00035024155,0.00021704497,0.0006130743,0.0003781204,0.00018064516],"domain_scores_gemma":[0.9968803,0.0014480202,0.00036433106,0.0005296468,0.0006199059,0.0001578489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00203978,0.0021498501,0.0020021126,0.0052901637,0.0009018428,0.0028254515,0.003144517,0.0022698368,0.0031062209],"category_scores_gemma":[0.0052064653,0.0005843048,0.0021434908,0.0068651955,0.00071979704,0.004766551,0.0018624895,0.0019487786,0.0023917696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010041327,0.0010604956,0.009786477,0.001403545,0.00025444807,0.0010918098,0.00057654484,0.09666922,0.017077286,0.0045850063,0.059370175,0.80712086],"study_design_scores_gemma":[0.000072097624,0.00018870198,0.0024143846,0.00007527817,0.00008449994,0.00026852486,0.00058423594,0.9634822,0.0060656276,0.014667584,0.012050316,0.000046601344],"about_ca_topic_score_codex":0.008118694,"about_ca_topic_score_gemma":0.009999016,"teacher_disagreement_score":0.008118694,"about_ca_system_score_codex":0.0011930207,"about_ca_system_score_gemma":0.0019056803,"threshold_uncertainty_score":0.016142845},"labels":[],"label_agreement":null},{"id":"W4315629526","doi":"10.1109/globecom48099.2022.10000950","title":"An IoT Traffic Modeling Framework and its Application to Autonomous Edge Scaling","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Computer network; Enhanced Data Rates for GSM Evolution; Flexibility (engineering); Distributed computing; Scope (computer science); Internet of Things; Computer security; Telecommunications","score_opus":0.041586019142135264,"score_gpt":0.3086542745226381,"score_spread":0.26706825538050283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006393362,0.00010441684,0.990732,0.0001971467,0.000041046893,0.00003277485,0.00007836056,0.00022445292,0.0021964924],"genre_scores_gemma":[0.6087857,0.0010810347,0.38255653,0.0002264045,0.00027580664,0.00036565302,0.0005534897,0.00021006916,0.0059453994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970204,0.00010286116,0.000019717725,0.000053206342,0.000088374276,0.00003383052],"domain_scores_gemma":[0.99950135,0.00019183788,0.00007295879,0.00004960036,0.0001460229,0.000038241764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008856509,0.00077473995,0.00036291257,0.00079161674,0.00057707273,0.0010600362,0.0011244328,0.0010497326,0.0011137271],"category_scores_gemma":[0.0018537042,0.00033469856,0.0008866591,0.00085678074,0.00044315401,0.0010243342,0.00083802815,0.0009360081,0.000320749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018758383,0.000054950706,0.0008046085,0.000025211175,0.0000148737445,0.0001604322,0.00007839688,0.8599087,0.0023317162,0.12149529,0.0015048498,0.013602156],"study_design_scores_gemma":[6.8136154e-7,0.000003867317,0.00003343357,0.0000018537725,0.0000012786097,0.000015891315,0.0000045690713,0.9928524,0.000081047605,0.006482226,0.0005207251,0.00000206807],"about_ca_topic_score_codex":0.0070168367,"about_ca_topic_score_gemma":0.003443729,"teacher_disagreement_score":0.0070168367,"about_ca_system_score_codex":0.0008194845,"about_ca_system_score_gemma":0.000871254,"threshold_uncertainty_score":0.013952017},"labels":[],"label_agreement":null},{"id":"W4315629538","doi":"10.1109/globecom48099.2022.10001155","title":"Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Codebook; Beamforming; Computer science; MIMO; Channel state information; Electronic engineering; Phase shift module; Artificial neural network; Computer engineering; Wireless; Algorithm; Artificial intelligence; Telecommunications; Engineering","score_opus":0.04602183009601995,"score_gpt":0.2725881820614758,"score_spread":0.22656635196545583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629538","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008332711,0.00007676749,0.9898837,0.00006374818,0.000013874486,0.000010264277,0.000031512405,0.00026561032,0.0013217941],"genre_scores_gemma":[0.7226904,0.00020821589,0.271194,0.00024252255,0.000058790843,0.00014942678,0.0002322816,0.000088641755,0.0051357816],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998305,0.000039309783,0.0000071493732,0.000043628603,0.000051513256,0.000027857588],"domain_scores_gemma":[0.99969125,0.00013343948,0.00003702715,0.00004172096,0.000075234064,0.000021337086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029741408,0.0005915544,0.0004292169,0.00018645603,0.0001953697,0.00034806994,0.0007124474,0.00047259894,0.0014871653],"category_scores_gemma":[0.00084740255,0.00027845593,0.0003610861,0.0003375848,0.0004810232,0.00062730163,0.0007592519,0.0007908413,0.00049698487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009066883,0.00007389216,0.000632384,0.00005669876,0.000054236854,0.00006589306,0.000055504777,0.8152609,0.01644018,0.013287049,0.0021317159,0.15185092],"study_design_scores_gemma":[0.0000028379209,0.000013557401,0.000041905987,0.000001515389,0.0000021195697,0.000008103388,0.0000027616413,0.9966365,0.000896584,0.0021384654,0.00025293208,0.0000027015296],"about_ca_topic_score_codex":0.0012047791,"about_ca_topic_score_gemma":0.003039677,"teacher_disagreement_score":0.0014871653,"about_ca_system_score_codex":0.00034480603,"about_ca_system_score_gemma":0.0004348151,"threshold_uncertainty_score":0.0049750805},"labels":[],"label_agreement":null},{"id":"W4315629593","doi":"10.1109/globecom48099.2022.10001721","title":"FedRL: Improving the Performance of Federated Learning with Non-IID Data","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); University of Toronto","funders":"","keywords":"Computer science; Reinforcement learning; Federated learning; Convergence (economics); Machine learning; Artificial intelligence; Data aggregator; Homogeneous; Training set; Data mining; Computer network","score_opus":0.05647577360395995,"score_gpt":0.2909231474283444,"score_spread":0.23444737382438446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629593","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09914438,0.0006320688,0.8923484,0.0005971637,0.00015030264,0.00011701093,0.00011229194,0.0049273428,0.0019709682],"genre_scores_gemma":[0.8715833,0.00013790718,0.12623192,0.00028929402,0.000046267272,0.00008629368,0.00019722835,0.00014166928,0.0012862106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808466,0.0006090892,0.00013750925,0.00046144574,0.00044826785,0.0002589076],"domain_scores_gemma":[0.993614,0.0029577122,0.00047726356,0.0016999524,0.0010036676,0.00024748256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006134666,0.0011808696,0.0015648319,0.0007331057,0.00085253495,0.0014507571,0.0024331387,0.0015618386,0.0010919705],"category_scores_gemma":[0.019269614,0.00040322775,0.00059895153,0.0007896391,0.0013522729,0.0033204106,0.0026325677,0.0024545628,0.0004727046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005300289,0.0003741866,0.0035404125,0.000065181324,0.00011049432,0.0001498521,0.00013604235,0.82469267,0.0024947438,0.0056570526,0.0033985039,0.15885086],"study_design_scores_gemma":[0.000019490186,0.00004022478,0.000097063144,0.000004102587,0.000004294827,0.000019773528,0.000008491852,0.99655724,0.00086367014,0.0022255797,0.00015560903,0.0000044289454],"about_ca_topic_score_codex":0.005894007,"about_ca_topic_score_gemma":0.0045711924,"teacher_disagreement_score":0.006134666,"about_ca_system_score_codex":0.0012216077,"about_ca_system_score_gemma":0.0020915929,"threshold_uncertainty_score":0.032443583},"labels":[],"label_agreement":null},{"id":"W4315629600","doi":"10.1109/globecom48099.2022.10001275","title":"LASUA: A Lightweight Authentication Scheme with User Anonymity for IoT-Enabled Mobile Cloud","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Cloud computing; Mutual authentication; Computer network; Authentication (law); Anonymity; Computer security; Mobile device; Operating system","score_opus":0.028954360593177755,"score_gpt":0.2808250768288596,"score_spread":0.25187071623568186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629600","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057808142,0.0011065182,0.9278744,0.0006228606,0.00045916592,0.00045052022,0.00016921833,0.004013412,0.007495667],"genre_scores_gemma":[0.9094534,0.00036134705,0.08042223,0.00028998996,0.00014828848,0.00026168767,0.00021607701,0.00008254261,0.008764446],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983388,0.00040991587,0.00016288021,0.00018368388,0.00050278025,0.00040182375],"domain_scores_gemma":[0.9982666,0.00024459988,0.0003052738,0.000600598,0.0004101308,0.00017283503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009667579,0.0005249235,0.00081505335,0.0010595669,0.0016695799,0.00136293,0.0013597562,0.0009631512,0.0022390892],"category_scores_gemma":[0.0021075227,0.00023575069,0.0007238342,0.0009790214,0.0012427754,0.0031821476,0.0033277913,0.0013155449,0.001317732],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036502914,0.00047812,0.0025365714,0.00066896994,0.00021973663,0.0016750946,0.0011493952,0.035546266,0.19973235,0.4058567,0.020148853,0.32833776],"study_design_scores_gemma":[0.00037033836,0.0014153975,0.0016176762,0.00016020784,0.00021225822,0.0031046383,0.00041440997,0.62343824,0.19017231,0.07241506,0.10627157,0.00040798646],"about_ca_topic_score_codex":0.00086329033,"about_ca_topic_score_gemma":0.0005430505,"teacher_disagreement_score":0.0022390892,"about_ca_system_score_codex":0.0010939988,"about_ca_system_score_gemma":0.0016417493,"threshold_uncertainty_score":0.007937551},"labels":[],"label_agreement":null},{"id":"W4315629602","doi":"10.1109/globecom48099.2022.10001580","title":"QoS-based Task Replication for Alleviating Uncertainty in Edge Computing","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Karush–Kuhn–Tucker conditions; Enhanced Data Rates for GSM Evolution; Reliability (semiconductor); Replication (statistics); Context (archaeology); Task (project management); Edge device; Distributed computing; Edge computing; Maximization; Computational complexity theory; Quality of service; Replica; Mathematical optimization; Computer network; Cloud computing; Algorithm; Artificial intelligence; Mathematics; Engineering","score_opus":0.058256315387625275,"score_gpt":0.3244304618272186,"score_spread":0.2661741464395933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629602","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.094269425,0.001606892,0.89726424,0.0005216954,0.00019536486,0.000120656165,0.000114809984,0.0007383724,0.0051685],"genre_scores_gemma":[0.92997664,0.00040628458,0.06750156,0.00011269684,0.00007876036,0.000078644545,0.0000947533,0.00007963778,0.001671022],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990933,0.0003127806,0.000051236417,0.00016957456,0.00020368594,0.00016948418],"domain_scores_gemma":[0.99819916,0.00086407276,0.00018047023,0.00030388657,0.00031538747,0.00013701344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014277908,0.0007312378,0.00083155325,0.00041276682,0.00081637816,0.001115418,0.001509213,0.0006417258,0.0018238167],"category_scores_gemma":[0.003813086,0.00025257294,0.0004888922,0.00065431144,0.00060221116,0.0017119446,0.0015368515,0.0010479974,0.00036607686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065595406,0.00017463247,0.0015301548,0.0003165192,0.00005710145,0.0004363747,0.00036051386,0.8103403,0.03540962,0.028489096,0.00512024,0.11710953],"study_design_scores_gemma":[0.0000150753185,0.000080447535,0.00016762229,0.000008868884,0.000010554412,0.00008856574,0.000047963164,0.98734236,0.0029091132,0.007964698,0.0013527762,0.00001192184],"about_ca_topic_score_codex":0.0017091223,"about_ca_topic_score_gemma":0.0019789652,"teacher_disagreement_score":0.0018238167,"about_ca_system_score_codex":0.00068677886,"about_ca_system_score_gemma":0.0010321499,"threshold_uncertainty_score":0.007550955},"labels":[],"label_agreement":null},{"id":"W4315629604","doi":"10.1109/globecom48099.2022.10001152","title":"LidNet: Boosting Perception and Motion Prediction from a Sequence of LIDAR Point Clouds for Autonomous Driving","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Pooling; Computer science; Encoder; Convolution (computer science); Boosting (machine learning); Artificial intelligence; Computer vision; Lidar; Point cloud; Residual; Algorithm; Artificial neural network; Remote sensing; Geography","score_opus":0.04719044268613398,"score_gpt":0.2982042658041004,"score_spread":0.25101382311796644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629604","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28157234,0.0039227954,0.6685287,0.0013294474,0.0014045412,0.00036058272,0.005766221,0.027257059,0.009858376],"genre_scores_gemma":[0.8186328,0.000650764,0.1614904,0.00046023613,0.00018379473,0.00012597536,0.010512729,0.00030524572,0.0076381587],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997063,0.00003072006,0.000009911372,0.00012675175,0.00007312263,0.000053076168],"domain_scores_gemma":[0.99973685,0.000058418376,0.000022654514,0.000045757006,0.000108192806,0.000028083145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005808519,0.0017193117,0.0007317338,0.00091492134,0.00037629963,0.0006412856,0.0019321422,0.0008956681,0.002230409],"category_scores_gemma":[0.001529157,0.00051335647,0.0005420783,0.00065298175,0.0003023752,0.0015098613,0.0010966934,0.0012022371,0.0013449044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058364694,0.00056009425,0.0071072993,0.00027331928,0.00025072315,0.0002627824,0.00009202994,0.25121364,0.02896989,0.0023653307,0.036214124,0.67210716],"study_design_scores_gemma":[0.000018703024,0.00007347447,0.0010975972,0.000013768693,0.00002244365,0.000046327703,0.000019646413,0.9891684,0.006096964,0.0014491463,0.0019823345,0.000011186291],"about_ca_topic_score_codex":0.015149449,"about_ca_topic_score_gemma":0.02537104,"teacher_disagreement_score":0.015149449,"about_ca_system_score_codex":0.0008279825,"about_ca_system_score_gemma":0.0011141835,"threshold_uncertainty_score":0.030122519},"labels":[],"label_agreement":null},{"id":"W4315629608","doi":"10.1109/globecom48099.2022.10000921","title":"Uplink Cluster-Based Radio Resource Scheduling for HetNet mMTC Scenarios","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Qatar University","keywords":"Telecommunications link; Computer science; Heterogeneous network; Scheduling (production processes); Noma; Spectral efficiency; Computer network; User equipment; Orthogonality; Radio access technology; Orthogonal frequency-division multiplexing; Radio resource management; Single antenna interference cancellation; Proportionally fair; Cellular network; Distributed computing; Base station; Dynamic priority scheduling; Channel (broadcasting); Wireless; Telecommunications; Round-robin scheduling; Wireless network; Engineering; Quality of service","score_opus":0.03619298857933601,"score_gpt":0.2820206379541715,"score_spread":0.24582764937483548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629608","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6495785,0.0005768565,0.29340154,0.0007274395,0.00016439625,0.00022543958,0.0005297358,0.00043249424,0.05436359],"genre_scores_gemma":[0.9900023,0.00006379925,0.008532061,0.00004220292,0.000009731224,0.000036547903,0.000051666593,0.000017345234,0.0012443792],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972135,0.00011340074,0.000005918101,0.000028287312,0.000043493485,0.00008746724],"domain_scores_gemma":[0.999511,0.00021968392,0.00006581524,0.00003200862,0.000111577945,0.000059964786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005810523,0.00035888594,0.00042141744,0.00034005794,0.00056855445,0.00069279026,0.0006017184,0.00066794694,0.0021406421],"category_scores_gemma":[0.0010647122,0.00019024871,0.00030607788,0.00049463764,0.0005433168,0.00047098895,0.0005641838,0.0004353381,0.00016961587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025091604,0.000012861623,0.00026859317,0.000006453657,0.0000042530833,0.000026622823,0.000011441841,0.9956306,0.00027346972,0.0027211236,0.00024620668,0.00077330525],"study_design_scores_gemma":[0.0000051500083,0.000013661953,0.00010233028,0.0000010492824,0.0000019998529,0.000005646848,0.000012827181,0.99899405,0.00009655369,0.00063293055,0.00013172429,0.0000020369366],"about_ca_topic_score_codex":0.015281572,"about_ca_topic_score_gemma":0.015149363,"teacher_disagreement_score":0.015281572,"about_ca_system_score_codex":0.0013022001,"about_ca_system_score_gemma":0.0013551706,"threshold_uncertainty_score":0.030385196},"labels":[],"label_agreement":null},{"id":"W4315629610","doi":"10.1109/globecom48099.2022.10000941","title":"SFL: A High-precision Traffic Flow Predictor for Supporting Intelligent Transportation Systems","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Dankook University","keywords":"Computer science; Intelligent transportation system; Advanced Traffic Management System; Traffic flow (computer networking); Noise (video); Artificial neural network; Filter (signal processing); Flow network; Time series; Deep learning; Machine learning; Artificial intelligence; Data mining; Real-time computing; Transport engineering; Engineering","score_opus":0.027337523608036898,"score_gpt":0.2726505909286824,"score_spread":0.24531306732064548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05978586,0.00040290706,0.91538936,0.00053043803,0.00044015743,0.000120136894,0.0020305582,0.017501144,0.0037994839],"genre_scores_gemma":[0.673111,0.00044118983,0.31489116,0.00026766,0.00020609787,0.00022116725,0.0042398074,0.0002476639,0.006374211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998529,0.000018849416,0.0000067953024,0.000033504093,0.000064254134,0.000023712033],"domain_scores_gemma":[0.9997532,0.00005156358,0.000023718148,0.000029729088,0.00011369706,0.000028148626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005983912,0.0008646008,0.0006591069,0.0011190893,0.00040841496,0.00048297996,0.0008987906,0.00056410395,0.0029795577],"category_scores_gemma":[0.00096373283,0.00026186556,0.00029964748,0.00073699723,0.00027884595,0.0010108969,0.0006739361,0.0010330811,0.0011730778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003473844,0.00028711982,0.009444077,0.00011817017,0.00005676543,0.00013782829,0.000079233774,0.5324073,0.011638854,0.0040771277,0.031234782,0.41017127],"study_design_scores_gemma":[0.0000071215673,0.000023293138,0.00040658933,0.00000333332,0.0000036304825,0.000007760178,0.000004317159,0.99667346,0.0010473185,0.0005164855,0.0013012416,0.0000054910315],"about_ca_topic_score_codex":0.01410982,"about_ca_topic_score_gemma":0.016708095,"teacher_disagreement_score":0.01410982,"about_ca_system_score_codex":0.00069239916,"about_ca_system_score_gemma":0.0014102706,"threshold_uncertainty_score":0.02805537},"labels":[],"label_agreement":null},{"id":"W4315629621","doi":"10.1109/globecom48099.2022.10001526","title":"Evaluation of sensors impact on information redundancy in cooperative perception system","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Perception; Redundancy (engineering); Real-time computing; Field of view; Global Positioning System; Communications system; Computer vision; Simulation; Telecommunications","score_opus":0.030148930143031917,"score_gpt":0.29453774741383654,"score_spread":0.2643888172708046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629621","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8997619,0.0011064388,0.095899016,0.00014324418,0.00007520501,0.00009477438,0.00009394416,0.0005529936,0.0022724755],"genre_scores_gemma":[0.9946532,0.00010631961,0.004944696,0.000015091613,0.0000055435985,0.00001598021,0.000040206323,0.000013589299,0.00020540542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791247,0.0005067676,0.00011630993,0.00027417252,0.0009204239,0.00026982895],"domain_scores_gemma":[0.99248135,0.003775599,0.00084739504,0.000749713,0.0019483842,0.00019761501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017463795,0.00090442813,0.00079214835,0.0008042415,0.00048270015,0.00079416414,0.001048725,0.0007398514,0.0008129443],"category_scores_gemma":[0.008802176,0.0002828489,0.00041059582,0.0005550729,0.0004764752,0.0012377623,0.00094726885,0.00044490278,0.00016456224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029502618,0.00044965296,0.03209862,0.0011964934,0.0004443495,0.0017626884,0.0009358581,0.6716293,0.1353683,0.0019999838,0.0009537682,0.15021068],"study_design_scores_gemma":[0.00006575614,0.0038646495,0.025846124,0.000086810614,0.0004118395,0.00102282,0.0006733586,0.82992244,0.13524367,0.0011716963,0.0015938306,0.000097040145],"about_ca_topic_score_codex":0.0016767653,"about_ca_topic_score_gemma":0.0009921172,"teacher_disagreement_score":0.0017463795,"about_ca_system_score_codex":0.00062280986,"about_ca_system_score_gemma":0.00048624678,"threshold_uncertainty_score":0.009235859},"labels":[],"label_agreement":null},{"id":"W4315629633","doi":"10.1109/globecom48099.2022.10000980","title":"Federated Deep Reinforcement Learning for Task Scheduling in Heterogeneous Autonomous Robotic System","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Reinforcement learning; Computer science; Distributed computing; Queue; Scheduling (production processes); Queueing theory; Dynamic priority scheduling; Robot; Task (project management); Two-level scheduling; Fair-share scheduling; Job shop scheduling; Artificial intelligence; Real-time computing; Mathematical optimization; Computer network; Quality of service; Engineering","score_opus":0.03608818262905673,"score_gpt":0.2768275039875015,"score_spread":0.24073932135844478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629633","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.105415575,0.0004544826,0.89077425,0.0004993696,0.000078825244,0.000050235372,0.000039797935,0.0005053567,0.00218207],"genre_scores_gemma":[0.9769504,0.000065211185,0.02164375,0.000104994135,0.000017316574,0.00003931097,0.000029461711,0.00001862868,0.001130889],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953675,0.00013718015,0.00001920474,0.00010846028,0.00008264871,0.00011578627],"domain_scores_gemma":[0.99911886,0.0004603021,0.000117239586,0.000058853242,0.0001581768,0.0000866325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011976338,0.0006688521,0.0009489688,0.00026196643,0.00040314186,0.00059140724,0.0010337239,0.0008094604,0.0010217285],"category_scores_gemma":[0.0023750826,0.00031041127,0.00031610072,0.00031291103,0.0008766764,0.0008157184,0.00089014915,0.0012313841,0.00013193714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054523134,0.000052212432,0.0003944625,0.000018927736,0.000017024802,0.00003181313,0.000022655737,0.98468196,0.0006729782,0.001732854,0.00028574452,0.012034914],"study_design_scores_gemma":[0.0000039979777,0.000010484781,0.00003039383,6.790203e-7,0.0000013810068,0.000001616309,0.0000020459515,0.99912304,0.00008387613,0.0007108628,0.00003072812,9.2883766e-7],"about_ca_topic_score_codex":0.009043317,"about_ca_topic_score_gemma":0.006004398,"teacher_disagreement_score":0.009043317,"about_ca_system_score_codex":0.0013286688,"about_ca_system_score_gemma":0.0017884243,"threshold_uncertainty_score":0.01798135},"labels":[],"label_agreement":null},{"id":"W4315629645","doi":"10.1109/globecom48099.2022.10001116","title":"Optimizing Information Freshness Leveraging Multi-RISs in NOMA-based IoT Networks","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Age of Information Optimization","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Base station; Telecommunications link; Cluster analysis; Optimization problem; Wireless; Relaxation (psychology); Transmitter power output; Wireless sensor network; Mathematical optimization; Upload; Codebook; Computer network; Distributed computing; Algorithm; Channel (broadcasting); Telecommunications; Transmitter; Mathematics; Artificial intelligence","score_opus":0.03479680152394302,"score_gpt":0.2695380746530084,"score_spread":0.23474127312906537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629645","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058173373,0.0010749811,0.9364483,0.0003226718,0.000115982795,0.00005588364,0.00009166538,0.00025396084,0.0034632315],"genre_scores_gemma":[0.9455596,0.00043015074,0.05202692,0.00012996422,0.00005657118,0.00006495494,0.000089481764,0.000045140216,0.001597173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908924,0.00031410536,0.00003750116,0.00018876928,0.00016840194,0.00020196382],"domain_scores_gemma":[0.99823296,0.0010798546,0.0002487988,0.00013761154,0.00018834452,0.000112364774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015531109,0.0013929437,0.0012747536,0.000543079,0.0007474124,0.0014946451,0.0015968052,0.0010138343,0.0011034781],"category_scores_gemma":[0.003370171,0.00063674897,0.0006504302,0.001048636,0.0012015852,0.001823624,0.0015739797,0.001040476,0.00027857808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000825802,0.000039792514,0.0003890994,0.0000640846,0.00003378529,0.000121418496,0.000055471126,0.9749834,0.0022811608,0.006128137,0.00062891387,0.015192112],"study_design_scores_gemma":[0.000003986496,0.000037259804,0.00009918311,0.0000041849894,0.000009284549,0.0000240303,0.000021042857,0.9968353,0.00037990435,0.0023734106,0.0002060886,0.0000062180393],"about_ca_topic_score_codex":0.0033283606,"about_ca_topic_score_gemma":0.0042437385,"teacher_disagreement_score":0.0033283606,"about_ca_system_score_codex":0.0010331371,"about_ca_system_score_gemma":0.0011118678,"threshold_uncertainty_score":0.0082137585},"labels":[],"label_agreement":null},{"id":"W4315629677","doi":"10.1109/globecom48099.2022.10000905","title":"PPsky: Privacy-Preserving Skyline Queries with Secret Sharing in eHealthcare","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Skyline; Computer science; Secret sharing; Scheme (mathematics); Information privacy; Security analysis; Computer security; Cryptography; Data mining","score_opus":0.04884211409982258,"score_gpt":0.3001426850261781,"score_spread":0.2513005709263555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629677","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074335456,0.0010938119,0.9146144,0.0014969675,0.000118835575,0.00058566994,0.000471892,0.0021774487,0.0051054917],"genre_scores_gemma":[0.9033137,0.00042800436,0.09246266,0.00040452695,0.00008205779,0.0003178775,0.00044984612,0.00007863692,0.0024626192],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99384165,0.0026401822,0.0004792797,0.00063355034,0.0016820265,0.0007234175],"domain_scores_gemma":[0.99391323,0.0019375301,0.0006992061,0.002533317,0.00062781246,0.00028885386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058584246,0.00051880296,0.00088257773,0.00077454315,0.0013542037,0.0019858046,0.0017730176,0.0015789542,0.002473223],"category_scores_gemma":[0.007798414,0.0003666883,0.00073263387,0.0016963094,0.0023349305,0.008493872,0.007501136,0.0017099589,0.0007468937],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005049639,0.00051722943,0.00804975,0.0010616336,0.0003867432,0.0021241473,0.0038861658,0.11744895,0.09001067,0.4146507,0.022240665,0.3345736],"study_design_scores_gemma":[0.0006445423,0.0013442815,0.0030485392,0.00011812749,0.00016322984,0.002455265,0.001766301,0.62248,0.08404163,0.23655836,0.047111645,0.0002681327],"about_ca_topic_score_codex":0.0012606664,"about_ca_topic_score_gemma":0.0005294051,"teacher_disagreement_score":0.0058584246,"about_ca_system_score_codex":0.0011953196,"about_ca_system_score_gemma":0.0017913661,"threshold_uncertainty_score":0.030982673},"labels":[],"label_agreement":null},{"id":"W4315629690","doi":"10.1109/globecom48099.2022.10001012","title":"Energy Saving on Constrained 12-Leads Real-Time ECG Monitoring","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; Queen's University","funders":"","keywords":"Computer science; Real-time computing; Microcontroller; Embedded system; Energy (signal processing); Bluetooth; Wireless; Telecommunications","score_opus":0.04212716863010765,"score_gpt":0.3115039209828985,"score_spread":0.2693767523527908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629690","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91629785,0.0009133527,0.07821497,0.00021761331,0.000044055938,0.00004897801,0.00019521358,0.00084941916,0.0032184743],"genre_scores_gemma":[0.9925413,0.00010669453,0.0066134427,0.000034215573,0.000008265636,0.000013209583,0.000067848734,0.000018466368,0.0005964508],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996618,0.000093448885,0.000020147927,0.000058602644,0.000117834315,0.00004818201],"domain_scores_gemma":[0.9994393,0.0003116938,0.00006444906,0.000074792304,0.00008269332,0.000027090719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020946468,0.00054777187,0.00039132393,0.0003631335,0.00015494632,0.0004041876,0.00066731195,0.00026203663,0.0015133007],"category_scores_gemma":[0.0011350721,0.0000943778,0.00014242099,0.00042925202,0.00015010108,0.0005457755,0.00026228908,0.00014862744,0.00023273476],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0054307776,0.0006548401,0.009472429,0.00042852064,0.00012980922,0.0015775273,0.00017891222,0.23393364,0.2431446,0.0016304543,0.002824123,0.5005943],"study_design_scores_gemma":[0.00015318379,0.0018909886,0.02676538,0.000050383354,0.00010817119,0.000987608,0.00014383906,0.8293973,0.13646041,0.0014114206,0.0025825324,0.000048782113],"about_ca_topic_score_codex":0.0010646705,"about_ca_topic_score_gemma":0.001723019,"teacher_disagreement_score":0.0015133007,"about_ca_system_score_codex":0.00022422217,"about_ca_system_score_gemma":0.00022279735,"threshold_uncertainty_score":0.0050625205},"labels":[],"label_agreement":null},{"id":"W4315629725","doi":"10.1109/globecom48099.2022.10001495","title":"6G Intelligent Distributed Uplink Beamforming for Transport System in Highly Dynamic Environments","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Beamforming; Telecommunications link; Computer science; WSDMA; MIMO; Transmission (telecommunications); Wireless; Base station; Multiplexing; Orthogonal frequency-division multiplexing; Wireless network; Computer network; Channel state information; Real-time computing; Electronic engineering; Channel (broadcasting); Engineering; Telecommunications; Precoding","score_opus":0.029772747848863475,"score_gpt":0.25609446572995637,"score_spread":0.2263217178810929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629725","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014359982,0.00039576192,0.9781225,0.0004115695,0.000113725684,0.000029443152,0.00006875042,0.001289991,0.005208192],"genre_scores_gemma":[0.83120316,0.0007369372,0.15939228,0.00041865904,0.00012829433,0.00008414486,0.00023427153,0.00009132009,0.0077109006],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971443,0.00008394723,0.000011815622,0.000047626403,0.00008714899,0.00005502995],"domain_scores_gemma":[0.99979454,0.000043663997,0.00002058748,0.000027724021,0.000094636234,0.000018883688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041018578,0.00075783837,0.00043002056,0.00026179515,0.00039725815,0.0005443852,0.0005704582,0.0006082052,0.0030216216],"category_scores_gemma":[0.0005589803,0.00019645406,0.0002941262,0.0003580437,0.00034513455,0.0010094282,0.00059620634,0.00071698707,0.0010913064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037148362,0.000115097595,0.0033467102,0.00018190629,0.00010068617,0.00032618621,0.0001778696,0.36325836,0.08778557,0.030044695,0.011737624,0.5025539],"study_design_scores_gemma":[0.000020678048,0.00013573721,0.00047052608,0.000020013946,0.00002627281,0.0001291614,0.00005462767,0.9695292,0.014143771,0.007866979,0.007584121,0.000018895931],"about_ca_topic_score_codex":0.0021428678,"about_ca_topic_score_gemma":0.004593177,"teacher_disagreement_score":0.0030216216,"about_ca_system_score_codex":0.0005331511,"about_ca_system_score_gemma":0.00050784834,"threshold_uncertainty_score":0.010108352},"labels":[],"label_agreement":null},{"id":"W4315629807","doi":"10.1109/globecom48099.2022.10001383","title":"Resource Management for Heterogeneous Aerial Networks with Backhaul Constraints","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; McGill University","funders":"National Key Research and Development Program of China; Aeronautical Science Foundation of China; National Natural Science Foundation of China","keywords":"Backhaul (telecommunications); Computer science; Base station; Computer network; Power control; Telecommunications link; Wireless network; Cellular network; Maximization; Wireless; Distributed computing; Mathematical optimization; Power (physics); Telecommunications","score_opus":0.017286260340243647,"score_gpt":0.2350462819268435,"score_spread":0.21776002158659985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10086905,0.0013093863,0.8863197,0.00047833056,0.00006917108,0.000093115996,0.00021788584,0.00015738499,0.010486006],"genre_scores_gemma":[0.96373725,0.0005007607,0.032641068,0.000093931354,0.000053772852,0.00008582517,0.00012109316,0.00003034182,0.0027359673],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996197,0.000103787475,0.000011351975,0.000092602255,0.00007203059,0.00010055878],"domain_scores_gemma":[0.99951637,0.00028943838,0.000080966376,0.000025046882,0.000048324226,0.0000397458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000546769,0.0008819052,0.0007052201,0.00042612173,0.0005368162,0.001232787,0.0009779698,0.0006622255,0.0018910753],"category_scores_gemma":[0.0010791138,0.0002836223,0.0003087764,0.0008403238,0.00056539476,0.0014271906,0.0010923701,0.0005490201,0.000168278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004478498,0.00003075753,0.00034908348,0.000061778344,0.000032854692,0.00030511842,0.000045001703,0.9667702,0.0023239374,0.01297144,0.0010921579,0.01597295],"study_design_scores_gemma":[0.000011427861,0.000025390742,0.00018484645,0.000004346869,0.000009119578,0.00002885719,0.000043400407,0.9943212,0.0003884678,0.0043796697,0.00059850677,0.00000464738],"about_ca_topic_score_codex":0.0060001677,"about_ca_topic_score_gemma":0.005020328,"teacher_disagreement_score":0.0060001677,"about_ca_system_score_codex":0.0010985992,"about_ca_system_score_gemma":0.00054543803,"threshold_uncertainty_score":0.011930466},"labels":[],"label_agreement":null},{"id":"W4315629810","doi":"10.1109/globecom48099.2022.10000863","title":"An Accurate & Efficient Approach for Traffic Classification Inside Programmable Data Plane","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Traffic classification; Network packet; Forwarding plane; Classifier (UML); Data mining; The Internet; Artificial intelligence; Flow network; Computer network; World Wide Web; Mathematics","score_opus":0.1327742722399738,"score_gpt":0.3406184952423835,"score_spread":0.20784422300240973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629810","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02396462,0.000110541165,0.97237664,0.00013169534,0.000036000893,0.00007353875,0.000091663955,0.0020066276,0.0012085862],"genre_scores_gemma":[0.43718436,0.00022087547,0.5586856,0.0001323017,0.000056988007,0.000125643,0.00046254194,0.00015055807,0.0029811624],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999094,0.00009836962,0.00005105598,0.00019277295,0.00044594437,0.00011784322],"domain_scores_gemma":[0.9989838,0.00019120205,0.00016182415,0.00031364954,0.00029744412,0.000052031017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005270816,0.0008348014,0.0008051992,0.0010513258,0.0005373097,0.0018628282,0.0014290648,0.00066997594,0.0013381699],"category_scores_gemma":[0.0022603504,0.00030081734,0.00044491232,0.0009476854,0.0004545052,0.0019799524,0.0007536936,0.0010476192,0.001092509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003180042,0.0002850953,0.00544402,0.00011049839,0.00005127848,0.00021032138,0.00013551385,0.2661991,0.07782078,0.015282974,0.0069181835,0.62722415],"study_design_scores_gemma":[0.0000042794522,0.000027036864,0.00040932646,0.000003736911,0.000006125252,0.000042058255,0.000025388514,0.9830641,0.012784174,0.0019409084,0.0016848035,0.000007970358],"about_ca_topic_score_codex":0.004470782,"about_ca_topic_score_gemma":0.0044356957,"teacher_disagreement_score":0.004470782,"about_ca_system_score_codex":0.0011197318,"about_ca_system_score_gemma":0.0014557623,"threshold_uncertainty_score":0.008889556},"labels":[],"label_agreement":null},{"id":"W4315629825","doi":"10.1109/globecom48099.2022.10001016","title":"AI-Enabled Health 4.0: An IoT-Based COVID-19 Diagnosis Use-Case","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Carleton University","funders":"","keywords":"Computer science; Provisioning; Cloud computing; Edge computing; Edge device; Enhanced Data Rates for GSM Evolution; Process (computing); Service (business); Internet of Things; Artificial intelligence; Proof of concept; Computer security; Computer network","score_opus":0.14805765332870802,"score_gpt":0.4162358441785033,"score_spread":0.2681781908497953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31252223,0.0030020175,0.5564,0.030816065,0.00085825013,0.0015075463,0.0023657698,0.008755713,0.083772324],"genre_scores_gemma":[0.82110435,0.0009942413,0.166212,0.0019136633,0.00012952766,0.00025097717,0.0010371635,0.00011661209,0.008241497],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847776,0.0004994579,0.00010830002,0.00016664647,0.0005687616,0.00017910963],"domain_scores_gemma":[0.99902546,0.0004158005,0.00004586197,0.00012554902,0.00020415634,0.00018324715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018822926,0.0004913967,0.00042105943,0.0007382143,0.0006426766,0.0020620457,0.0015100181,0.002981196,0.0024379953],"category_scores_gemma":[0.00272316,0.00022535595,0.0005015623,0.00060500164,0.0009917832,0.0015258393,0.0017585786,0.001042519,0.00082928844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035593284,0.001732015,0.06721869,0.0012758283,0.00025657838,0.036796574,0.0024862683,0.10652655,0.052447025,0.17625116,0.09256942,0.4588806],"study_design_scores_gemma":[0.0003160692,0.00086202624,0.010313886,0.00031814317,0.00010099894,0.011614521,0.0011456481,0.7532159,0.07375703,0.028217066,0.12002355,0.000115194365],"about_ca_topic_score_codex":0.0038019037,"about_ca_topic_score_gemma":0.0035479646,"teacher_disagreement_score":0.0038019037,"about_ca_system_score_codex":0.001659805,"about_ca_system_score_gemma":0.00093561714,"threshold_uncertainty_score":0.01204282},"labels":[],"label_agreement":null},{"id":"W4315629837","doi":"10.1109/globecom48099.2022.10001135","title":"Outage of Multi-Antenna NOMA-based Cooperative Underlay Satellite-Terrestrial Networks","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Satellite Communication Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Underlay; Noma; Computer science; Relay; Satellite; Communications satellite; Residual; Computer network; Key (lock); Antenna (radio); Telecommunications; Electronic engineering; Telecommunications link; Signal-to-noise ratio (imaging); Power (physics); Engineering; Algorithm; Computer security","score_opus":0.07554453413508086,"score_gpt":0.2988252712458571,"score_spread":0.22328073711077626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629837","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.643483,0.0017268243,0.34503767,0.00026635107,0.0000425515,0.000037319223,0.00024495373,0.00033431686,0.008827002],"genre_scores_gemma":[0.9965714,0.0001871478,0.0027999566,0.000014042377,0.000010615454,0.000010135975,0.000027551963,0.0000061928076,0.00037301454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992919,0.00032377298,0.00002255787,0.00008324398,0.00018264951,0.00009590328],"domain_scores_gemma":[0.99792093,0.0014022832,0.00023536515,0.00014866366,0.00023003398,0.00006279682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010538533,0.0006180813,0.00061701896,0.00037750637,0.00054591784,0.0006974732,0.00063519186,0.0004950442,0.00059169374],"category_scores_gemma":[0.0031752898,0.00021451987,0.00023381681,0.00066671276,0.00085439894,0.0008517523,0.0011117837,0.00032637105,0.000112299516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018498691,0.000020710648,0.0026051311,0.00009633261,0.000031238338,0.00044068255,0.0001723975,0.9664651,0.0057523274,0.011162147,0.00058793376,0.012480921],"study_design_scores_gemma":[0.0000042172132,0.00003701846,0.00058248517,0.0000042647275,0.000008931758,0.000121946345,0.000044027845,0.9962412,0.0006255474,0.0021576898,0.00016640198,0.000006244292],"about_ca_topic_score_codex":0.0029171894,"about_ca_topic_score_gemma":0.00329965,"teacher_disagreement_score":0.0029171894,"about_ca_system_score_codex":0.0010547236,"about_ca_system_score_gemma":0.00045012566,"threshold_uncertainty_score":0.0076525807},"labels":[],"label_agreement":null},{"id":"W4315629866","doi":"10.1109/globecom48099.2022.10001017","title":"Intelligent Ensemble based System for Rare Attacks Dectection in IoT Networks","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Intrusion detection system; Robustness (evolution); Internet of Things; Classifier (UML); Data mining; Computer security; Artificial intelligence; Pattern recognition (psychology); Machine learning","score_opus":0.040259207527575024,"score_gpt":0.28429674216299433,"score_spread":0.2440375346354193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26244137,0.0016692689,0.72020507,0.0005148206,0.00038284558,0.00020732221,0.00028747285,0.0075902822,0.0067016045],"genre_scores_gemma":[0.93968177,0.00045194966,0.05615169,0.00020345824,0.00008668029,0.00009099684,0.00031360378,0.000045527435,0.0029741942],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992005,0.00011040611,0.00007268692,0.00021884461,0.00027812374,0.00011945571],"domain_scores_gemma":[0.9993631,0.00012267138,0.00008487179,0.00008569356,0.00029832564,0.00004533321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010473597,0.0006804606,0.0011292143,0.0010036597,0.0006055759,0.0008649423,0.00080621574,0.0006504789,0.00089916756],"category_scores_gemma":[0.0014886594,0.0002483487,0.0005734786,0.0004175579,0.00018323935,0.0010939174,0.0007992873,0.0006825806,0.00050217996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006950549,0.00054799626,0.021690907,0.00014207537,0.00034822532,0.0006507297,0.00031715923,0.14796051,0.052127827,0.0017017905,0.0070566116,0.766761],"study_design_scores_gemma":[0.000010426517,0.00024208566,0.004540297,0.000015045114,0.0000974672,0.00022255405,0.00004722398,0.9778331,0.013945811,0.00094637123,0.0020726027,0.000027085263],"about_ca_topic_score_codex":0.0029407793,"about_ca_topic_score_gemma":0.0034530691,"teacher_disagreement_score":0.0029407793,"about_ca_system_score_codex":0.00050330535,"about_ca_system_score_gemma":0.0004643405,"threshold_uncertainty_score":0.005847335},"labels":[],"label_agreement":null},{"id":"W4315629868","doi":"10.1109/globecom48099.2022.10001297","title":"CNN-Enabled Multiple Power-Levels Identification in Cognitive Radio Networks","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Cognitive radio; Preprocessor; Pattern recognition (psychology); Test statistic; Residual; Likelihood-ratio test; Noise power; Statistic; Algorithm; Artificial intelligence; Data mining; Statistical hypothesis testing; Power (physics); Statistics; Mathematics; Wireless; Telecommunications","score_opus":0.04119910875852743,"score_gpt":0.2879645396365639,"score_spread":0.24676543087803646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20999196,0.00053554995,0.7805375,0.00025646243,0.00013363746,0.000050815048,0.0001230769,0.0014664534,0.0069046244],"genre_scores_gemma":[0.95940924,0.000089957946,0.038868476,0.000075777396,0.000017156792,0.000028696564,0.00006667902,0.000016501446,0.001427525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998252,0.00002443414,0.000007318403,0.00004842581,0.00004935079,0.000045365505],"domain_scores_gemma":[0.99982256,0.000054289405,0.000030556872,0.000027356677,0.0000504074,0.000014881483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035656246,0.0004081762,0.00032302018,0.00026087076,0.00020245308,0.00043667728,0.00076065795,0.00034687706,0.00059443206],"category_scores_gemma":[0.00088843453,0.00020035532,0.00025288618,0.00025733377,0.00028636114,0.0006775848,0.00062792224,0.0004306361,0.00013437965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002960806,0.00017722422,0.003679668,0.000076213015,0.000075997305,0.00023935597,0.00011512312,0.6322441,0.035255216,0.0074235103,0.002024974,0.31839252],"study_design_scores_gemma":[0.0000023875075,0.000023241979,0.00033138474,0.0000017338091,0.0000059615295,0.000016200453,0.0000053240524,0.9955491,0.003050454,0.00081928604,0.00019116243,0.00000375814],"about_ca_topic_score_codex":0.006752437,"about_ca_topic_score_gemma":0.0067132874,"teacher_disagreement_score":0.006752437,"about_ca_system_score_codex":0.0004619133,"about_ca_system_score_gemma":0.0005126401,"threshold_uncertainty_score":0.013426244},"labels":[],"label_agreement":null},{"id":"W4315629879","doi":"10.1109/globecom48099.2022.10000811","title":"Prior Knowledge based Advanced Persistent Threats Detection for IoT in a Realistic Benchmark","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ciena (Canada); University of Ottawa","funders":"","keywords":"Computer science; Benchmark (surveying); Machine learning; Cluster analysis; Baseline (sea); Artificial intelligence; Intrusion detection system; Data mining; Internet of Things; Computer security","score_opus":0.04486782102638437,"score_gpt":0.30523622346379514,"score_spread":0.2603684024374108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629879","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8514483,0.0048025367,0.11894995,0.0020260063,0.000437671,0.0003905185,0.006027871,0.0035586,0.0123586245],"genre_scores_gemma":[0.94135404,0.00064240536,0.04295669,0.00026034133,0.00009176603,0.0001272377,0.011644612,0.00007158029,0.0028513514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983779,0.00040857957,0.00009237078,0.00051959534,0.00039847838,0.00020308195],"domain_scores_gemma":[0.9969023,0.0017272851,0.00023365227,0.0004794069,0.00051182474,0.00014554501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028185665,0.0015775904,0.0012142123,0.0015530718,0.0006722708,0.0014337813,0.0014146854,0.0017803267,0.0011393044],"category_scores_gemma":[0.0074932016,0.00028920636,0.0009011427,0.0009894765,0.0006903076,0.001988444,0.0011239542,0.0015812726,0.0005710682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014214613,0.0014807595,0.018588152,0.00043192314,0.00021840307,0.00036111678,0.00011673362,0.75841326,0.0047262856,0.0027953554,0.01492684,0.19651967],"study_design_scores_gemma":[0.000023861054,0.00022375524,0.0052966806,0.00003093285,0.000027024611,0.000115228875,0.000041392454,0.98820835,0.0027580592,0.001904359,0.0013455192,0.000024902554],"about_ca_topic_score_codex":0.016106296,"about_ca_topic_score_gemma":0.016036768,"teacher_disagreement_score":0.016106296,"about_ca_system_score_codex":0.0016181283,"about_ca_system_score_gemma":0.0011615681,"threshold_uncertainty_score":0.0320251},"labels":[],"label_agreement":null},{"id":"W4315629933","doi":"10.1109/globecom48099.2022.10000757","title":"Adversarial Machine Learning-Based Anticipation of Threats Against Vehicle-to-Microgrid Services","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Adversary; Adversarial machine learning; Artificial intelligence; Machine learning; Adversarial system; Classifier (UML); Attack model; Inference; Computer security","score_opus":0.028662259857975204,"score_gpt":0.29763317429265296,"score_spread":0.26897091443467774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629933","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19297954,0.0004479937,0.7986402,0.00097504054,0.00011670587,0.000103558494,0.00008712594,0.00047540196,0.0061745467],"genre_scores_gemma":[0.99125296,0.00008016523,0.007529671,0.00009368485,0.000019466761,0.00002231596,0.000024077313,0.000013750001,0.0009638566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998765,0.00046309127,0.00003660554,0.00022393907,0.00027209066,0.00023944343],"domain_scores_gemma":[0.99368423,0.0047181495,0.0006526401,0.0004016832,0.00037748358,0.00016572184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002121822,0.00086870865,0.0006959862,0.00037427558,0.00034919332,0.0007936486,0.0008165521,0.00092652696,0.0013041364],"category_scores_gemma":[0.008680999,0.0002589061,0.0005247066,0.00020158899,0.0014019975,0.0018333234,0.0016246298,0.0013886066,0.00019132749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008724072,0.000020337817,0.00086461863,0.000021965043,0.000020011496,0.00006869135,0.000025210295,0.9844333,0.0015328494,0.0062935078,0.00024610537,0.006386224],"study_design_scores_gemma":[0.0000023778914,0.00005050512,0.00017875386,0.0000034116338,0.0000033179208,0.00002485649,0.000010313361,0.99646497,0.00072247034,0.0024104125,0.00012480149,0.0000038204334],"about_ca_topic_score_codex":0.0013841011,"about_ca_topic_score_gemma":0.000752849,"teacher_disagreement_score":0.002121822,"about_ca_system_score_codex":0.0008885425,"about_ca_system_score_gemma":0.0006117646,"threshold_uncertainty_score":0.011221409},"labels":[],"label_agreement":null},{"id":"W4315629947","doi":"10.1109/globecom48099.2022.10001126","title":"Analyzing Age Performance of Hybrid-ARQ: A Unified Explicit Result","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Age of Information Optimization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Hybrid automatic repeat request; Computer science; Decoding methods; Robustness (evolution); Minification; Automatic repeat request; Coding (social sciences); Block (permutation group theory); Algorithm; Real-time computing; Transmission (telecommunications); Mathematics; Telecommunications","score_opus":0.036459702392934216,"score_gpt":0.27186259832829757,"score_spread":0.23540289593536334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032322448,0.0020056856,0.9516764,0.0004356728,0.000102690356,0.000051667823,0.000106096304,0.0002650257,0.013034347],"genre_scores_gemma":[0.9342905,0.0020626984,0.05753727,0.00032420445,0.00018711672,0.00009209203,0.00008113431,0.00014188194,0.0052829715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99852425,0.00046268874,0.000050518836,0.00018679275,0.0005042317,0.00027164398],"domain_scores_gemma":[0.99386036,0.0038991584,0.0005943273,0.00049604935,0.0010194025,0.00013075338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035453646,0.0012028825,0.00093002315,0.000966112,0.00057810027,0.0019250163,0.001341793,0.0010823847,0.0030250698],"category_scores_gemma":[0.013566705,0.00043152578,0.0004741279,0.0007736926,0.0020911938,0.0031687764,0.0019120269,0.0014168371,0.0007160307],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000747195,0.000046575806,0.0008518864,0.00018201875,0.00004119538,0.00013446035,0.00023377419,0.75240374,0.0063225008,0.21888688,0.001667499,0.019154774],"study_design_scores_gemma":[0.0000049094824,0.00004800676,0.00022779411,0.000028010883,0.00001309384,0.00009440961,0.00004690815,0.97213304,0.0015068597,0.025016328,0.0008609224,0.000019607709],"about_ca_topic_score_codex":0.0023262957,"about_ca_topic_score_gemma":0.0014096864,"teacher_disagreement_score":0.0035453646,"about_ca_system_score_codex":0.0017674447,"about_ca_system_score_gemma":0.0014915329,"threshold_uncertainty_score":0.018749893},"labels":[],"label_agreement":null},{"id":"W4315629974","doi":"10.1109/globecom48099.2022.10001483","title":"Joint Anti-Interference and Anti-Collision for ABS-Assisted Medical-Care Sensor Networks","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Key Research and Development Program of China; Canada Foundation for Innovation","keywords":"Interference (communication); Computer science; Collision; Transmission (telecommunications); Base station; Wireless sensor network; Computer network; Real-time computing; Telecommunications; Computer security","score_opus":0.03347971884117748,"score_gpt":0.27033587753169813,"score_spread":0.23685615869052065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06814939,0.0020758042,0.92665493,0.00026141474,0.000091807306,0.000052118216,0.0000146533075,0.00015528259,0.0025445246],"genre_scores_gemma":[0.94259024,0.0006954662,0.055581775,0.000086038344,0.00006783862,0.000038396985,0.000016661139,0.000008312724,0.0009152164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921167,0.00033325065,0.000030103225,0.000071101706,0.0002745397,0.00007921196],"domain_scores_gemma":[0.99923384,0.00038320577,0.000099920784,0.00006759148,0.00018081466,0.00003455044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086831395,0.00038947686,0.00032941042,0.00039060443,0.00045187218,0.00041663568,0.0006968135,0.00041356974,0.0004888347],"category_scores_gemma":[0.001466256,0.00015576713,0.00027061923,0.00044108662,0.00038887456,0.0007055461,0.0007148062,0.0003808874,0.000094705596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009980273,0.0002979035,0.00504571,0.0005796208,0.00027356733,0.000772963,0.0005284478,0.40704978,0.14345865,0.049763072,0.0027351973,0.38849705],"study_design_scores_gemma":[0.00001833971,0.00027949692,0.00056126335,0.000008937225,0.000032261254,0.00025910843,0.000059496073,0.9835231,0.009689408,0.003585047,0.0019715175,0.0000120942095],"about_ca_topic_score_codex":0.00069063663,"about_ca_topic_score_gemma":0.0012966698,"teacher_disagreement_score":0.00086831395,"about_ca_system_score_codex":0.00039315576,"about_ca_system_score_gemma":0.0005868789,"threshold_uncertainty_score":0.0045921803},"labels":[],"label_agreement":null},{"id":"W4315629987","doi":"10.1109/globecom48099.2022.10001658","title":"Federated Deep Reinforcement Learning for Resource Allocation in O-RAN Slicing","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Computer science; Radio access network; Latency (audio); Distributed computing; Cellular network; Flexibility (engineering); Computer network; Deep learning; Artificial intelligence; Base station; Telecommunications","score_opus":0.02583570512000805,"score_gpt":0.26949664299756537,"score_spread":0.24366093787755733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062044326,0.00028098622,0.9347037,0.00025711054,0.000049460643,0.00003937139,0.000025952611,0.0005213527,0.0020777518],"genre_scores_gemma":[0.96645975,0.000057013338,0.03217073,0.00011901314,0.000012571257,0.000040232546,0.000028791566,0.000022201533,0.001089642],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995214,0.00015473801,0.0000209263,0.00010415363,0.00008363323,0.00011507074],"domain_scores_gemma":[0.9990126,0.00050772977,0.000116450596,0.000068148176,0.00020134833,0.00009365434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012766583,0.0007268618,0.00094093,0.000257911,0.00030507336,0.0005459441,0.0008373205,0.0007772767,0.0012353054],"category_scores_gemma":[0.0027486307,0.00030744044,0.00030035822,0.00021585924,0.0008337708,0.00074122474,0.00087794155,0.0010402338,0.00015485962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010030205,0.00005541155,0.00060836656,0.000019723278,0.000023204331,0.00005162413,0.000031105064,0.9697193,0.0012449095,0.0029592777,0.00043587034,0.024750894],"study_design_scores_gemma":[0.0000052789824,0.000013572639,0.000027161448,0.0000012476537,0.0000020117134,0.000002790171,0.0000019827698,0.9989097,0.00016421445,0.0008235453,0.00004728844,0.0000012557517],"about_ca_topic_score_codex":0.0062421523,"about_ca_topic_score_gemma":0.0051315394,"teacher_disagreement_score":0.0062421523,"about_ca_system_score_codex":0.0008310743,"about_ca_system_score_gemma":0.0014084694,"threshold_uncertainty_score":0.012411654},"labels":[],"label_agreement":null},{"id":"W4315630011","doi":"10.1109/globecom48099.2022.10001081","title":"On block delivery time in Ethereum network","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Peer-to-Peer Network Technologies","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Exponential distribution; Block (permutation group theory); Node (physics); Computer science; Exponential function; Distribution (mathematics); Phase-type distribution; Probability distribution; Gossip; Queueing theory; Computer network; Mathematics; Statistics; Engineering; Combinatorics","score_opus":0.02678827295741041,"score_gpt":0.2675001353246644,"score_spread":0.24071186236725398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630011","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2334364,0.0031475767,0.75126266,0.0010915123,0.000094172916,0.000074004,0.00030624348,0.00044786153,0.010139535],"genre_scores_gemma":[0.98129374,0.0015707593,0.013676896,0.0001163962,0.00005157992,0.00006244826,0.00013101414,0.0000820989,0.0030150928],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988518,0.0003739986,0.00003778003,0.00016695868,0.00038363505,0.00018587081],"domain_scores_gemma":[0.994245,0.004440688,0.0005493358,0.00023161503,0.00042671114,0.000106699656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025743148,0.00063439814,0.00063996523,0.0014786666,0.0004764169,0.000681452,0.0011537925,0.00063477154,0.0018315293],"category_scores_gemma":[0.011375354,0.00029115513,0.0003685763,0.0013228328,0.0007657639,0.0024622926,0.0007615054,0.00083858205,0.00034952103],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019343439,0.00003665959,0.0022315476,0.0001174029,0.000027377639,0.000176324,0.0002037247,0.881867,0.0043644872,0.09265029,0.0012916075,0.0168402],"study_design_scores_gemma":[0.000003976063,0.000027999718,0.0002875175,0.000010366054,0.0000061470514,0.00005856159,0.000020209529,0.99038625,0.000639365,0.007951311,0.0006009379,0.0000072904672],"about_ca_topic_score_codex":0.0040378436,"about_ca_topic_score_gemma":0.0020252233,"teacher_disagreement_score":0.0040378436,"about_ca_system_score_codex":0.002759254,"about_ca_system_score_gemma":0.000751422,"threshold_uncertainty_score":0.02001983},"labels":[],"label_agreement":null},{"id":"W4315630017","doi":"10.1109/globecom48099.2022.10000989","title":"An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Flexibility (engineering); Cluster analysis; Core (optical fiber); Distributed computing; Core network; Cellular network; Artificial intelligence; Machine learning; Data modeling; Wireless network; Data science; Wireless; Computer network; Software engineering","score_opus":0.048837598202480195,"score_gpt":0.2870769206737382,"score_spread":0.238239322471258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630017","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10311064,0.00022722589,0.8890152,0.00050183147,0.0000573873,0.00012209381,0.00044347858,0.0010239377,0.005498164],"genre_scores_gemma":[0.86152774,0.0002869486,0.13456877,0.00010836875,0.000069241134,0.00013004027,0.0005958585,0.00009159952,0.0026214262],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99965334,0.00007145057,0.000013063517,0.000072316594,0.00015164404,0.00003812631],"domain_scores_gemma":[0.9992687,0.00028294264,0.00005483676,0.00012520756,0.00024127118,0.00002714047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066349085,0.00070859486,0.000360311,0.0016348509,0.00039367503,0.0010349847,0.00060886727,0.00052237517,0.0010299447],"category_scores_gemma":[0.002243567,0.00019207323,0.00034634388,0.0008162927,0.00044113834,0.0014131214,0.00045898094,0.0008354954,0.000270708],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016788114,0.00034554623,0.01554875,0.000097375596,0.00005299174,0.00028638923,0.00025286878,0.68722683,0.026590837,0.059994597,0.0038126195,0.20562328],"study_design_scores_gemma":[0.0000010130716,0.0000113888855,0.000638376,0.000002515198,9.938182e-7,0.000032356904,0.000013915522,0.99391985,0.0014463514,0.0032838602,0.0006450376,0.0000043693776],"about_ca_topic_score_codex":0.0058015944,"about_ca_topic_score_gemma":0.004017795,"teacher_disagreement_score":0.0058015944,"about_ca_system_score_codex":0.0012401792,"about_ca_system_score_gemma":0.0006981887,"threshold_uncertainty_score":0.0115356445},"labels":[],"label_agreement":null},{"id":"W4315630028","doi":"10.1109/globecom48099.2022.10001117","title":"Liquid State Machine-Empowered Reflection Tracking in RIS-Aided THz Communications","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; York University","funders":"Natural Sciences and Engineering Research Council of Canada; Oulun Yliopisto; Academy of Finland","keywords":"Initialization; Computer science; Reflection (computer programming); Variance (accounting); Artificial intelligence; Tracking (education); Algorithm; Machine learning","score_opus":0.050318242085102084,"score_gpt":0.324473120299483,"score_spread":0.2741548782143809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630028","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057945658,0.00057569606,0.9365473,0.00030597462,0.0000574633,0.000017894781,0.000067665,0.0011584667,0.0033238519],"genre_scores_gemma":[0.8686265,0.00039080338,0.12788899,0.00017903668,0.000036181907,0.000056556393,0.000118851975,0.00006660694,0.0026365835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998778,0.000030529925,0.0000054341695,0.000028694056,0.000041423053,0.000015962274],"domain_scores_gemma":[0.9998313,0.00007741312,0.000033369954,0.00002484207,0.000025520469,0.0000075463113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027268004,0.0003885753,0.00029769607,0.0001570087,0.00015188755,0.00041024564,0.0005463749,0.0005190357,0.00095215003],"category_scores_gemma":[0.00071135577,0.00018656984,0.00021228535,0.00022462466,0.00037079712,0.0007779225,0.00042586733,0.00074521295,0.00038538902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020741188,0.0000969071,0.0024312667,0.0001444953,0.00004764708,0.00019064886,0.000170913,0.63747483,0.14799659,0.019519674,0.0019542223,0.18976535],"study_design_scores_gemma":[0.0000028308475,0.000032891137,0.00014115611,0.000005507549,0.0000043020077,0.000022526381,0.000007117856,0.981278,0.016060766,0.0015368041,0.00090029644,0.000007858428],"about_ca_topic_score_codex":0.00070358213,"about_ca_topic_score_gemma":0.0011500563,"teacher_disagreement_score":0.00095215003,"about_ca_system_score_codex":0.00032978246,"about_ca_system_score_gemma":0.00037190714,"threshold_uncertainty_score":0.0031852722},"labels":[],"label_agreement":null},{"id":"W4315630035","doi":"10.1109/globecom48099.2022.10001554","title":"Hierarchical Reinforcement Learning for RIS-Assisted Energy-Efficient RAN","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); University of Ottawa","funders":"","keywords":"Reinforcement learning; Base station; Computer science; Efficient energy use; Energy consumption; Throughput; Controller (irrigation); Transmission (telecommunications); Computer network; Radio access network; Power control; Distributed computing; Wireless; Power (physics); Artificial intelligence; Telecommunications; Engineering; Electrical engineering","score_opus":0.03462045193845421,"score_gpt":0.27953207829496035,"score_spread":0.24491162635650615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630035","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.107177936,0.00031967694,0.8858471,0.0002826719,0.00006249967,0.00006494532,0.00003147894,0.00057677226,0.0056369086],"genre_scores_gemma":[0.98166776,0.000041677486,0.017044296,0.00006313623,0.000012984776,0.00004991073,0.000018685349,0.000013983727,0.0010875141],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997247,0.000083792285,0.000010554588,0.000059209462,0.000050379593,0.00007133284],"domain_scores_gemma":[0.99940753,0.00032446795,0.00008122661,0.000033275144,0.00010171914,0.000051715975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060771813,0.0005263867,0.00058573275,0.00021135148,0.0002642837,0.00039837463,0.00071334885,0.0004879983,0.0014946702],"category_scores_gemma":[0.001634713,0.00021525541,0.0002834758,0.00013689308,0.00061268243,0.00039592,0.00066755083,0.0007411184,0.00016226985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005460399,0.000050869916,0.00057665166,0.00002216084,0.000021482914,0.00004876968,0.00003340795,0.976895,0.0017178614,0.0038294715,0.00038076,0.016369024],"study_design_scores_gemma":[0.000005163487,0.000017321223,0.000041467294,8.8353863e-7,0.0000019356114,0.0000029637954,0.000001804572,0.9992453,0.00010334984,0.0005237737,0.000054909102,0.000001202783],"about_ca_topic_score_codex":0.004218291,"about_ca_topic_score_gemma":0.0037259809,"teacher_disagreement_score":0.004218291,"about_ca_system_score_codex":0.0006138272,"about_ca_system_score_gemma":0.00084656454,"threshold_uncertainty_score":0.008387446},"labels":[],"label_agreement":null},{"id":"W4315630052","doi":"10.1109/globecom48099.2022.10001461","title":"FTLIoT: A Federated Transfer Learning Framework for Securing IoT","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Transfer of learning; Artificial intelligence; Intrusion detection system; Deep learning; Convolutional neural network; Benchmark (surveying); Internet of Things; Machine learning; Artificial neural network; The Internet; Anonymity; Distributed computing; Computer network; Computer security; World Wide Web","score_opus":0.038979086854405265,"score_gpt":0.29180557052148975,"score_spread":0.2528264836670845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630052","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014485797,0.00037405975,0.97934204,0.00025883623,0.00007979476,0.000075982054,0.00010503217,0.0033630673,0.0019154031],"genre_scores_gemma":[0.7092226,0.0004875986,0.28328624,0.00036724095,0.00007289572,0.00025682122,0.0006455546,0.00016452248,0.0054963846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956423,0.00011136308,0.00002237005,0.00009959519,0.00013012663,0.0000722273],"domain_scores_gemma":[0.9996619,0.00010507072,0.000036559566,0.0000725496,0.000088745204,0.00003506794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00117819,0.0006023791,0.0005441671,0.00056062476,0.00044365154,0.0008919053,0.0016403573,0.0013004185,0.001646096],"category_scores_gemma":[0.0019299632,0.00019153446,0.0005845766,0.00049008155,0.00072055747,0.0020863446,0.0016668608,0.0015728237,0.0004989093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020588866,0.0002585076,0.0014708119,0.00008116303,0.000079177706,0.00026233675,0.00011145249,0.6811839,0.0051255487,0.017911855,0.006490318,0.28681904],"study_design_scores_gemma":[0.0000063156144,0.00004010273,0.00008705925,0.0000052244463,0.000004452362,0.00002922534,0.0000077103405,0.9914888,0.0010600401,0.006140075,0.0011244306,0.0000065376803],"about_ca_topic_score_codex":0.004836501,"about_ca_topic_score_gemma":0.0035087732,"teacher_disagreement_score":0.004836501,"about_ca_system_score_codex":0.0009164372,"about_ca_system_score_gemma":0.0012236002,"threshold_uncertainty_score":0.009616673},"labels":[],"label_agreement":null},{"id":"W4315630053","doi":"10.1109/globecom48099.2022.10000602","title":"Novel Low-complexity Neural Network Aided Detection for FTN Signalling in ISI Channel","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intersymbol interference; Computer science; Artificial neural network; Channel (broadcasting); Euclidean distance; Viterbi algorithm; Robustness (evolution); Metric (unit); Algorithm; Artificial intelligence; Decoding methods; Computer network; Engineering","score_opus":0.07699485586050042,"score_gpt":0.28909168088323417,"score_spread":0.21209682502273375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051829226,0.00038431963,0.94370764,0.00021409873,0.000066539855,0.000040349532,0.000049849925,0.00055662415,0.0031512794],"genre_scores_gemma":[0.77671105,0.00022272642,0.21809226,0.00014100558,0.000045299614,0.000062330415,0.000086980355,0.00003166557,0.0046066833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966764,0.00007767856,0.000015647378,0.000058209742,0.00013420371,0.00004665941],"domain_scores_gemma":[0.9994217,0.00032573723,0.00005325192,0.00004384634,0.0001384947,0.000017063128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064162916,0.00041382745,0.0003925096,0.00031783406,0.00032572856,0.00063931965,0.0009022752,0.0007429984,0.0012580802],"category_scores_gemma":[0.0022280368,0.00020400852,0.00018969082,0.00034175214,0.00033889737,0.0009164194,0.00057535927,0.00086027087,0.00025364562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039410338,0.00012883294,0.0016659513,0.00014472386,0.00006509742,0.00012897025,0.000103539125,0.6008472,0.033334523,0.017246218,0.001279857,0.344661],"study_design_scores_gemma":[0.0000039157435,0.000030367379,0.00009817688,0.000003407483,0.000004084458,0.000018750598,0.0000024578428,0.99519086,0.0037545336,0.0006482979,0.00024113688,0.0000039672705],"about_ca_topic_score_codex":0.0024446922,"about_ca_topic_score_gemma":0.0035826392,"teacher_disagreement_score":0.0024446922,"about_ca_system_score_codex":0.00078304735,"about_ca_system_score_gemma":0.000747157,"threshold_uncertainty_score":0.0056813955},"labels":[],"label_agreement":null},{"id":"W4315630070","doi":"10.1109/globecom48099.2022.10000855","title":"Multitiered Worker-Oriented Resource Allocation at the Extreme Edge","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Resource allocation; Profit (economics); Task (project management); Context (archaeology); Scheduling (production processes); Enhanced Data Rates for GSM Evolution; Resource management (computing); The Internet; Operations research; Distributed computing; Computer network; Operations management; Telecommunications; Microeconomics; Economics; World Wide Web; Engineering","score_opus":0.058596031377161684,"score_gpt":0.2777776698856355,"score_spread":0.21918163850847383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06517464,0.00092359254,0.92450786,0.0003415766,0.00015450788,0.00009480243,0.00007585033,0.0009929001,0.007734244],"genre_scores_gemma":[0.8971528,0.00030978682,0.09786877,0.00017593577,0.000059235146,0.000083965206,0.00007614325,0.00007616475,0.00419716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999273,0.00019907283,0.000025757554,0.00015520451,0.00013883853,0.0002081343],"domain_scores_gemma":[0.9993711,0.00021370538,0.000060521565,0.00015228418,0.000110188164,0.00009222911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080849446,0.0005167661,0.00067336985,0.00025356194,0.00060856674,0.0011806627,0.0016634183,0.0005358569,0.003280606],"category_scores_gemma":[0.0009618492,0.00018864613,0.00040609163,0.00045509893,0.00064612605,0.0012396466,0.0014908988,0.00081724563,0.00052590785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010416571,0.0003530746,0.001711019,0.00030996426,0.00012527486,0.00052104663,0.00046364462,0.6437226,0.06297034,0.06995146,0.014455396,0.20437442],"study_design_scores_gemma":[0.00003315717,0.000105456704,0.00030801204,0.00001220778,0.000017017917,0.000097185366,0.000061537874,0.9703536,0.005655279,0.017721623,0.0056137554,0.000021181422],"about_ca_topic_score_codex":0.0021340156,"about_ca_topic_score_gemma":0.0030105407,"teacher_disagreement_score":0.003280606,"about_ca_system_score_codex":0.0007019252,"about_ca_system_score_gemma":0.00074717624,"threshold_uncertainty_score":0.010974705},"labels":[],"label_agreement":null},{"id":"W4315630085","doi":"10.1109/globecom48099.2022.10001246","title":"Energy Efficiency and Power Allocation Optimization in Hardware-Impaired Full-Duplex Access Point","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Full-Duplex Wireless Communications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Karush–Kuhn–Tucker conditions; Computer science; Efficient energy use; Wireless; Transmission (telecommunications); Quality of service; Maximization; Spectral efficiency; Power budget; Optimization problem; Fractional programming; Fading; Transceiver; Nakagami distribution; Power (physics); Mathematical optimization; Power control; Electronic engineering; Computer network; Telecommunications; Engineering; Algorithm; Nonlinear programming; Mathematics; Electrical engineering; Channel (broadcasting)","score_opus":0.026382475333790786,"score_gpt":0.2644826580507568,"score_spread":0.23810018271696604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20699506,0.00044349287,0.78686535,0.00022506913,0.00002930346,0.000023126779,0.000040181552,0.00008083139,0.0052975053],"genre_scores_gemma":[0.9802639,0.0001613008,0.018082812,0.000023796429,0.000008605535,0.000018965147,0.000015214473,0.00001276763,0.0014125311],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976605,0.00009487808,0.0000065177437,0.00003498758,0.000053392185,0.00004417781],"domain_scores_gemma":[0.9996407,0.00023304617,0.000049620194,0.000016628625,0.00004404322,0.000015949176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004826252,0.0003363427,0.0005707521,0.0002014274,0.00018697785,0.00069604156,0.00039195322,0.000401327,0.00066918496],"category_scores_gemma":[0.0010525465,0.00024685977,0.0002389469,0.00040689993,0.000504423,0.0005478136,0.00038139842,0.00037129765,0.00007591815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055087334,0.000023943308,0.00035172977,0.000030952306,0.000014144481,0.000066687346,0.000024483796,0.98277736,0.0029584006,0.0054126824,0.00019609195,0.008088493],"study_design_scores_gemma":[0.0000033223464,0.000013361682,0.0001113592,0.0000011654705,0.0000030813512,0.000009179707,0.0000067240608,0.99809295,0.00046069064,0.0012127736,0.000083689425,0.0000016392112],"about_ca_topic_score_codex":0.0016053627,"about_ca_topic_score_gemma":0.0010641605,"teacher_disagreement_score":0.0016053627,"about_ca_system_score_codex":0.00059190043,"about_ca_system_score_gemma":0.00048514406,"threshold_uncertainty_score":0.0042945743},"labels":[],"label_agreement":null},{"id":"W4315630096","doi":"10.1109/globecom48099.2022.10001329","title":"Vehicle-Assisted Data Delivery Based on Trajectory Prediction","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Trajectory; Computer science; Heuristics; Network packet; Overhead (engineering); Scale (ratio); Real-time computing; Data mining; Computer network","score_opus":0.05267652090872337,"score_gpt":0.2609291995285517,"score_spread":0.20825267861982832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630096","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028789153,0.00034229853,0.96770895,0.00012270233,0.000061011557,0.000074387885,0.0002439699,0.00169628,0.0009612074],"genre_scores_gemma":[0.71716505,0.0005606657,0.27824423,0.000066355125,0.000041759235,0.00011678296,0.0013211473,0.000118750155,0.002365338],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955744,0.00006292356,0.000027970773,0.0001312543,0.00017184774,0.000048558588],"domain_scores_gemma":[0.99900645,0.0003437133,0.00013939738,0.00019079327,0.00026553037,0.00005399194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062750373,0.0007018085,0.0009006384,0.0010360971,0.00065016246,0.0007575055,0.001654417,0.00049970293,0.00064466265],"category_scores_gemma":[0.0024179607,0.000346845,0.00044388502,0.0013444684,0.00029114736,0.0014247274,0.0010766874,0.0007159973,0.0004275336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015884385,0.00006578067,0.00314263,0.00006465235,0.000051228082,0.00012237788,0.00008069884,0.75157434,0.005817433,0.004955274,0.002935536,0.2310312],"study_design_scores_gemma":[0.000006359346,0.000023468996,0.00022171537,0.00000374354,0.0000061149476,0.00004726539,0.000016918133,0.99485946,0.0023411412,0.0013792556,0.0010880255,0.0000066285907],"about_ca_topic_score_codex":0.009393988,"about_ca_topic_score_gemma":0.006175968,"teacher_disagreement_score":0.009393988,"about_ca_system_score_codex":0.00096803956,"about_ca_system_score_gemma":0.0015135219,"threshold_uncertainty_score":0.018678606},"labels":[],"label_agreement":null},{"id":"W4315630106","doi":"10.1109/globecom48099.2022.10000797","title":"UAV-Assisted Networks With Underlaid Ambient Backscattering: Modeling and Outage Analysis","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Backscatter (email); Computer science; Fading; Wireless; Monte Carlo method; Electronic engineering; Real-time computing; Remote sensing; Telecommunications; Computer network; Channel (broadcasting); Engineering; Geography; Statistics","score_opus":0.02594531621960327,"score_gpt":0.24211357983256537,"score_spread":0.2161682636129621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31778255,0.002153046,0.66320586,0.00064891257,0.00008295402,0.00006182577,0.00040069077,0.00035408046,0.015310166],"genre_scores_gemma":[0.99084526,0.0006404064,0.0060299444,0.000035088542,0.00003157965,0.00003072702,0.00007614917,0.000016651511,0.0022941863],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996444,0.00011047907,0.000014069379,0.000053076023,0.00008740906,0.00009045953],"domain_scores_gemma":[0.9992386,0.00036300818,0.00016263167,0.000052648127,0.00015595049,0.000027168215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005395933,0.00067530427,0.0005705258,0.00044082073,0.00038383662,0.0006269663,0.00075331447,0.0007913079,0.0010003013],"category_scores_gemma":[0.0013947584,0.00024571316,0.000377787,0.000618645,0.0006608076,0.0011774197,0.0006271432,0.0005996413,0.00020516098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002340121,0.0000130189965,0.0010726748,0.000029963332,0.0000121256,0.00019696147,0.000083852356,0.9834293,0.0014335342,0.009576185,0.00043642567,0.0036923992],"study_design_scores_gemma":[8.2093607e-7,0.000005710182,0.00014225865,0.0000015995712,0.0000028373731,0.000021766733,0.000013116452,0.9987122,0.00009524197,0.0009101662,0.0000922763,0.0000019592562],"about_ca_topic_score_codex":0.009830348,"about_ca_topic_score_gemma":0.0073902044,"teacher_disagreement_score":0.009830348,"about_ca_system_score_codex":0.0011353997,"about_ca_system_score_gemma":0.000483963,"threshold_uncertainty_score":0.01954627},"labels":[],"label_agreement":null},{"id":"W4315630116","doi":"10.1109/globecom48099.2022.10000610","title":"Transfer Learning with Input Reconstruction Loss","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Transfer of learning; Artificial intelligence; Artificial neural network; Machine learning; Exploit; Wireless; Deep learning; Feature (linguistics); Task (project management); Wireless network; Distributed computing; Engineering","score_opus":0.031227157364804945,"score_gpt":0.26212370735637963,"score_spread":0.2308965499915747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011997122,0.00036843258,0.9846853,0.0003266785,0.000053467313,0.000052619707,0.00006152049,0.0007366758,0.0017181019],"genre_scores_gemma":[0.7940658,0.0006313042,0.19442208,0.00061882305,0.00018228142,0.0004944849,0.00061606173,0.00027028332,0.008698735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896824,0.0003759462,0.00006196325,0.0002471866,0.00023235499,0.000114375944],"domain_scores_gemma":[0.9976023,0.0014483547,0.00016188572,0.0003591657,0.0003553398,0.00007307081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002577266,0.0018618336,0.0015495303,0.00057156896,0.0004383142,0.0011285874,0.0021514436,0.0023425682,0.0031146323],"category_scores_gemma":[0.008705625,0.0005325079,0.00097306294,0.0008659864,0.0014264338,0.0029283464,0.002518072,0.0031860222,0.00089799927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015039057,0.0001356753,0.00068991684,0.00012212532,0.00006771068,0.00011523425,0.000054924945,0.8740865,0.0017834362,0.014661034,0.0027648597,0.10536827],"study_design_scores_gemma":[0.000006043417,0.000028440276,0.000046710265,0.0000049503915,0.0000051212783,0.000012260168,0.0000036130177,0.9940361,0.0006745338,0.004949429,0.00022930221,0.0000035644127],"about_ca_topic_score_codex":0.0021554786,"about_ca_topic_score_gemma":0.0013190372,"teacher_disagreement_score":0.0031146323,"about_ca_system_score_codex":0.0011886616,"about_ca_system_score_gemma":0.0011791661,"threshold_uncertainty_score":0.013630033},"labels":[],"label_agreement":null},{"id":"W4315630157","doi":"10.1109/globecom48099.2022.10001422","title":"Joint Computation Offloading, UAV Trajectory, User Scheduling, and Resource Allocation in SAGIN","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Satellite Communication Systems","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Computation offloading; Trajectory optimization; Scheduling (production processes); Computation; Mathematical optimization; Optimization problem; Convex optimization; Resource allocation; Bandwidth allocation; Server; Real-time computing; Bandwidth (computing); Distributed computing; Edge computing; Regular polygon; Enhanced Data Rates for GSM Evolution; Optimal control; Computer network; Algorithm; Mathematics; Artificial intelligence","score_opus":0.05037661785141253,"score_gpt":0.2779998082432865,"score_spread":0.22762319039187398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630157","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14575624,0.00042628162,0.84625554,0.0002892492,0.00006422959,0.000079728845,0.00007158293,0.0001747217,0.006882353],"genre_scores_gemma":[0.967845,0.00015208298,0.030223839,0.00005325546,0.000019516678,0.00005046691,0.000047960253,0.000030397892,0.0015774205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996152,0.00013655561,0.000009794214,0.000084455365,0.000058710502,0.00009530064],"domain_scores_gemma":[0.9996402,0.00019093581,0.0000635422,0.000027571992,0.000040775118,0.000036955553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046974717,0.00083364645,0.0006055145,0.0001770778,0.0005761102,0.00086569274,0.0005967171,0.00062504696,0.001169156],"category_scores_gemma":[0.00097645307,0.0002546325,0.00028219816,0.0004084613,0.0007436373,0.00088822626,0.000791951,0.0006409474,0.00012392153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000956033,0.000044915454,0.00065406406,0.000046576228,0.000014441789,0.00012020504,0.000046353427,0.98065484,0.0024265267,0.0051039956,0.00047961163,0.010312945],"study_design_scores_gemma":[0.0000047061135,0.000041595817,0.0001744217,0.000002708495,0.000003959975,0.000020565358,0.00003554175,0.99722785,0.00052071514,0.0015982308,0.00036698885,0.0000027113738],"about_ca_topic_score_codex":0.00520272,"about_ca_topic_score_gemma":0.0061048716,"teacher_disagreement_score":0.00520272,"about_ca_system_score_codex":0.00071591843,"about_ca_system_score_gemma":0.0008495633,"threshold_uncertainty_score":0.010344863},"labels":[],"label_agreement":null},{"id":"W4315630158","doi":"10.1109/globecom48099.2022.10000691","title":"On the Average Cost and Latency of Migration to the Next Generation of Networks","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Solver; Latency (audio); Mathematical optimization; Optimization problem; Exploit; Node (physics); Binary number; Network topology; Distributed computing; Algorithm; Computer network; Engineering; Mathematics","score_opus":0.09420029539361095,"score_gpt":0.27829583375132627,"score_spread":0.1840955383577153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630158","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55859643,0.0038720153,0.4064723,0.0024567705,0.00028991798,0.00011701108,0.00044350539,0.00056426803,0.027187722],"genre_scores_gemma":[0.9555756,0.0010660957,0.040373214,0.00009270785,0.000038084258,0.000054925702,0.00020856742,0.00011478393,0.002476116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993881,0.00023323005,0.00001983709,0.00007202674,0.00014955418,0.0001372096],"domain_scores_gemma":[0.9983032,0.0011383105,0.0001526376,0.00008991039,0.00024217859,0.000073675845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010776154,0.00076887815,0.00053692365,0.00058201567,0.000768962,0.0011240564,0.000676145,0.00060589146,0.0028920937],"category_scores_gemma":[0.0042924816,0.0002437041,0.00041601533,0.0012759981,0.00047555726,0.0018335014,0.0005410882,0.0006475303,0.00018013704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009143508,0.000027796541,0.00084682426,0.000070406146,0.000016518014,0.000060610655,0.00001889805,0.96964353,0.0013417081,0.0066946554,0.0009441919,0.020243512],"study_design_scores_gemma":[0.000004801381,0.000046638164,0.0006002212,0.0000072358116,0.000014355957,0.00003045914,0.000045583172,0.9954478,0.0008962464,0.002388335,0.00051315635,0.0000050736],"about_ca_topic_score_codex":0.010173907,"about_ca_topic_score_gemma":0.012190474,"teacher_disagreement_score":0.010173907,"about_ca_system_score_codex":0.001713514,"about_ca_system_score_gemma":0.0016000986,"threshold_uncertainty_score":0.0202294},"labels":[],"label_agreement":null},{"id":"W4315630164","doi":"10.1109/globecom48099.2022.10001418","title":"Computation Offloading and Energy Harvesting Schemes for Sum Rate Maximization in Space-Air-Ground Networks","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Computation offloading; Lyapunov optimization; Energy consumption; Base station; Edge device; Distributed computing; Computation; Maximization; Real-time computing; Server; Cloud computing; Computer network; Edge computing; Enhanced Data Rates for GSM Evolution; Mathematical optimization; Telecommunications; Electrical engineering","score_opus":0.024030145750213568,"score_gpt":0.24959637294106612,"score_spread":0.22556622719085256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03954469,0.00055314816,0.9542927,0.00022634174,0.000055429988,0.000031480573,0.000036506128,0.00015062805,0.0051090834],"genre_scores_gemma":[0.9679676,0.0004448677,0.02896468,0.00006322884,0.000029137309,0.00005503826,0.000034915585,0.00003379429,0.0024065485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974984,0.0000837693,0.0000090113235,0.00004413612,0.000054028613,0.000059236932],"domain_scores_gemma":[0.999726,0.0001535228,0.00003467166,0.000021520877,0.000041622974,0.000022658916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000493509,0.0007800834,0.0008454927,0.0002932962,0.00045373186,0.0008230528,0.0008369887,0.0004838818,0.0013252281],"category_scores_gemma":[0.0008935291,0.00025370874,0.00041234423,0.0005749037,0.00063935353,0.0007924074,0.0010190075,0.00065512856,0.00018847443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036232293,0.000019098212,0.00014518136,0.000032007807,0.000011699067,0.000041835683,0.000036762776,0.97992116,0.0013289598,0.006918818,0.0005715398,0.010936758],"study_design_scores_gemma":[0.0000014338804,0.000007220411,0.00002153642,0.0000010175253,0.0000015982278,0.000003584939,0.0000049143036,0.9986369,0.0001027994,0.0011387687,0.000079171536,0.0000011229912],"about_ca_topic_score_codex":0.0033489892,"about_ca_topic_score_gemma":0.0033720955,"teacher_disagreement_score":0.0033489892,"about_ca_system_score_codex":0.0006968151,"about_ca_system_score_gemma":0.0007093945,"threshold_uncertainty_score":0.0066589713},"labels":[],"label_agreement":null},{"id":"W4315630181","doi":"10.1109/globecom48099.2022.10000623","title":"Toward Secure and Private Federated Learning for IoT using Blockchain","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Blockchain; Oracle; Computer security; Reliability (semiconductor); Internet of Things; Distributed computing; Computer network","score_opus":0.08711412784704074,"score_gpt":0.3200816354292165,"score_spread":0.23296750758217577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630181","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02644338,0.00018574818,0.96818584,0.00058487064,0.00003622762,0.00012821874,0.000062810024,0.00043463564,0.0039382097],"genre_scores_gemma":[0.8413123,0.00040538484,0.153577,0.00012833226,0.000042961496,0.00032129622,0.00019085706,0.000058826106,0.0039630085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982167,0.0006500839,0.000112300586,0.0002738103,0.000505968,0.00024104545],"domain_scores_gemma":[0.99731547,0.001182266,0.000225427,0.00074186345,0.00033781302,0.00019718483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028172373,0.00049877545,0.0009560502,0.00055723864,0.0011790858,0.001863793,0.001455207,0.0014827027,0.0024581407],"category_scores_gemma":[0.0049233236,0.00037986462,0.0006089322,0.0010371405,0.0018273506,0.0037759552,0.0037040524,0.001543367,0.0005130742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029906371,0.00016255635,0.0008070206,0.000114636605,0.00003754302,0.00034548662,0.00028028307,0.6434483,0.0054356325,0.2725988,0.0019014189,0.074569285],"study_design_scores_gemma":[0.00003523882,0.000028212598,0.000043070926,0.000011382929,0.0000042035103,0.00002795831,0.000020718997,0.8922479,0.0012371447,0.104600996,0.0017352792,0.000007882529],"about_ca_topic_score_codex":0.0023101268,"about_ca_topic_score_gemma":0.0026432676,"teacher_disagreement_score":0.0028172373,"about_ca_system_score_codex":0.001272787,"about_ca_system_score_gemma":0.0026486358,"threshold_uncertainty_score":0.014899194},"labels":[],"label_agreement":null},{"id":"W4315630184","doi":"10.1109/globecom48099.2022.10001254","title":"BlueTiSCH: A Multi-PHY Simulation of Low-Power 6TiSCH IoT Networks","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Bluetooth and Wireless Communication Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"PHY; Computer science; IEEE 802.15; Computer network; Scalability; Physical layer; Wireless; Reliability (semiconductor); Bluetooth; Wireless network; Throughput; Wireless sensor network; Power (physics); Telecommunications","score_opus":0.04367279063221668,"score_gpt":0.3001646584434833,"score_spread":0.2564918678112666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630184","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7367009,0.00048835296,0.1999495,0.00074865436,0.00044919903,0.00036499582,0.0044705626,0.005368534,0.051459346],"genre_scores_gemma":[0.9396118,0.00022421656,0.053281598,0.00018353645,0.00002838422,0.00027123353,0.0023405172,0.00034947012,0.0037092469],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965465,0.00013544274,0.000018962195,0.000035528115,0.00008861567,0.000066753346],"domain_scores_gemma":[0.9988158,0.00079016923,0.00006639057,0.00009612058,0.0001435113,0.000088033936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007642789,0.0006146292,0.0005229835,0.00052444014,0.00047805364,0.0007279071,0.0015846429,0.000923437,0.004263284],"category_scores_gemma":[0.002041455,0.00035059178,0.00067785446,0.0005393331,0.00047147876,0.0007663227,0.00084298325,0.0011053581,0.0003771206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005437444,0.00005902076,0.001089654,0.000034287743,0.000021926875,0.00003950264,0.00004010249,0.9924489,0.0006532598,0.00244287,0.0010224497,0.0020936434],"study_design_scores_gemma":[0.000013456824,0.000023101691,0.00012820127,0.0000026815496,0.000003741514,0.0000055774294,0.00001221233,0.9984736,0.0003086283,0.00044822745,0.0005769208,0.0000036286242],"about_ca_topic_score_codex":0.009240967,"about_ca_topic_score_gemma":0.0085607795,"teacher_disagreement_score":0.009240967,"about_ca_system_score_codex":0.0007685522,"about_ca_system_score_gemma":0.0010800167,"threshold_uncertainty_score":0.018374324},"labels":[],"label_agreement":null},{"id":"W4315630225","doi":"10.1109/globecom48099.2022.10000715","title":"Secure and Distributed Access Control for Dynamic Pervasive Edge Computing Services","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Waterloo","funders":"","keywords":"Computer science; Edge computing; Ubiquitous computing; Distributed computing; Access control; Context-aware pervasive systems; Enhanced Data Rates for GSM Evolution; Computer network; Computer security; Operating system; Telecommunications","score_opus":0.02364015779418623,"score_gpt":0.3061354620481124,"score_spread":0.28249530425392616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630225","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035813037,0.00033971737,0.95976037,0.00022323563,0.00007538447,0.00011660085,0.000030693303,0.00074280245,0.0028981739],"genre_scores_gemma":[0.9527858,0.00016978584,0.044831008,0.00007347441,0.000056765417,0.00009517212,0.0000511353,0.000029100136,0.0019077524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981516,0.00034003184,0.000111307025,0.00033697698,0.00071980763,0.00034032742],"domain_scores_gemma":[0.99856037,0.0003864685,0.00018321477,0.000407553,0.00035749484,0.00010496565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014135393,0.00049283775,0.0005661247,0.0006703441,0.0011144112,0.0014419786,0.0012218223,0.00063568,0.0015496329],"category_scores_gemma":[0.0028239256,0.00020044264,0.00042721056,0.0005361637,0.0012366459,0.002278447,0.0018467446,0.0011931435,0.00037309979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011208488,0.00037400413,0.0022529678,0.00023419809,0.00009542677,0.00061097275,0.00059111184,0.34719038,0.06549934,0.32632497,0.008506362,0.24719931],"study_design_scores_gemma":[0.000053840045,0.000096868294,0.00028150747,0.00000891617,0.000015374264,0.00009081251,0.000056560944,0.9684254,0.006376493,0.02010837,0.004467564,0.000018315168],"about_ca_topic_score_codex":0.002786649,"about_ca_topic_score_gemma":0.0022302861,"teacher_disagreement_score":0.002786649,"about_ca_system_score_codex":0.0012644461,"about_ca_system_score_gemma":0.0012688682,"threshold_uncertainty_score":0.009174287},"labels":[],"label_agreement":null},{"id":"W4315630240","doi":"10.1109/globecom48099.2022.10001450","title":"Optimal Job Scheduling and Bandwidth Augmentation in Hybrid Data Center Networks","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Scheduling (production processes); Bottleneck; Distributed computing; Job shop scheduling; Data center; Bandwidth (computing); Mathematical optimization; Integer programming; Computer network; Algorithm; Embedded system; Mathematics","score_opus":0.04667136907575386,"score_gpt":0.29419211170541315,"score_spread":0.24752074262965928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1387,0.00041565357,0.8568999,0.00019673043,0.000065098415,0.00005639857,0.000057294965,0.0005210534,0.0030878664],"genre_scores_gemma":[0.9333345,0.000110052766,0.06516459,0.000043752174,0.00002880892,0.00006164529,0.0000529148,0.00005375803,0.0011498512],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942327,0.00020268113,0.000014534705,0.00011228631,0.000106801665,0.00014048512],"domain_scores_gemma":[0.99932575,0.00038766724,0.00008628407,0.000056852492,0.00007075468,0.00007269298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001018161,0.0008323784,0.00088721345,0.00044285064,0.00057893374,0.0008543159,0.0009085048,0.0005926323,0.0010346101],"category_scores_gemma":[0.0014303597,0.00050207175,0.00042736248,0.00060740375,0.00080696243,0.0010528619,0.00089229626,0.00069469697,0.00013632435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001773619,0.00004865132,0.00022447281,0.000039302933,0.000013753419,0.000036601567,0.000028472401,0.97845584,0.003295618,0.004043469,0.00038151132,0.013254991],"study_design_scores_gemma":[0.000005111154,0.000012958052,0.000041589767,9.681231e-7,0.0000019233316,0.000003420883,0.0000061197075,0.9984102,0.00041447955,0.0010142802,0.000086712265,0.00000214405],"about_ca_topic_score_codex":0.00459512,"about_ca_topic_score_gemma":0.0033130243,"teacher_disagreement_score":0.00459512,"about_ca_system_score_codex":0.0008833404,"about_ca_system_score_gemma":0.0011547226,"threshold_uncertainty_score":0.009136796},"labels":[],"label_agreement":null},{"id":"W4315630242","doi":"10.1109/globecom48099.2022.10001192","title":"Learning-Based Load-Aware Heterogeneous Vehicular Edge Computing","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Edge computing; Bottleneck; Distributed computing; Relay; Reinforcement learning; Computation offloading; Edge device; Enhanced Data Rates for GSM Evolution; Computation; Wireless; Latency (audio); Computer network; Task (project management); Load balancing (electrical power); Vehicular ad hoc network; Cloudlet; Cloud computing; Embedded system; Wireless ad hoc network; Artificial intelligence; Engineering; Telecommunications","score_opus":0.04371974259148603,"score_gpt":0.295808043476172,"score_spread":0.252088300884686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630242","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.099527694,0.0003446122,0.89308804,0.0003532435,0.0000832239,0.0000800781,0.00005041329,0.00046161862,0.0060109748],"genre_scores_gemma":[0.98663956,0.00006197628,0.011971886,0.000056251716,0.00001930259,0.00002791812,0.000033655037,0.000020515152,0.0011689288],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999622,0.000052202973,0.000014888987,0.000102023136,0.000081156446,0.00012775387],"domain_scores_gemma":[0.99947363,0.00018948376,0.000075872515,0.00004805066,0.0001280419,0.00008492944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047076223,0.00070262124,0.0009443437,0.00035135786,0.00064935006,0.0009444823,0.0016368546,0.0005471457,0.0011767033],"category_scores_gemma":[0.0013201237,0.00027364184,0.00031769258,0.0004257969,0.00060585944,0.0008217664,0.0014414197,0.0006263422,0.00020783632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006496158,0.000046715173,0.0004824243,0.00002025714,0.000011761269,0.000050607097,0.000028702267,0.9746927,0.0020681426,0.0028416384,0.00073277764,0.018959364],"study_design_scores_gemma":[0.0000020393984,0.000006535479,0.000027604074,5.3884287e-7,0.0000012217354,0.000003084911,0.0000035927744,0.9992126,0.00014562321,0.0005342625,0.00006157901,0.0000012045482],"about_ca_topic_score_codex":0.0063511156,"about_ca_topic_score_gemma":0.005816686,"teacher_disagreement_score":0.0063511156,"about_ca_system_score_codex":0.0009872632,"about_ca_system_score_gemma":0.0010275074,"threshold_uncertainty_score":0.012628317},"labels":[],"label_agreement":null},{"id":"W4315630289","doi":"10.1109/globecom48099.2022.10000743","title":"FLAC: Federated Learning with Autoencoder Compression and Convergence Guarantee","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Autoencoder; Computer science; Decoding methods; Quantization (signal processing); Data compression; Bottleneck; Computer engineering; Codec; Convergence (economics); Artificial intelligence; Deep learning; Algorithm; Real-time computing; Machine learning; Embedded system; Computer hardware","score_opus":0.037877586660457783,"score_gpt":0.2856852426912725,"score_spread":0.2478076560308147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630289","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012241202,0.00041342218,0.98420185,0.00029924492,0.00004691055,0.000050966155,0.000083025545,0.0009305992,0.0017328344],"genre_scores_gemma":[0.8289519,0.00046417592,0.16499925,0.00049736677,0.00012509029,0.00024920653,0.00037669562,0.00020455386,0.0041317134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977077,0.00059849495,0.00015310959,0.00045609535,0.0007899493,0.0002945627],"domain_scores_gemma":[0.9906657,0.0054972298,0.0007235098,0.0010766588,0.0018187126,0.00021834261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033581147,0.0014600368,0.0016258848,0.0008458868,0.0008359262,0.0015664413,0.0020200363,0.0019031932,0.0024319051],"category_scores_gemma":[0.015438542,0.00058644864,0.0007682754,0.0009140156,0.0017966031,0.0023038962,0.0026551208,0.0030420637,0.00070056156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018970511,0.00008562838,0.000850862,0.0001172932,0.000054754568,0.0001380841,0.00009202096,0.90043235,0.002057406,0.023962548,0.0024202317,0.069599055],"study_design_scores_gemma":[0.0000053185113,0.00002339266,0.000048857673,0.000007705881,0.0000026260684,0.00002276687,0.0000047728327,0.993891,0.0006450721,0.0051659243,0.00017779546,0.000004778129],"about_ca_topic_score_codex":0.005162669,"about_ca_topic_score_gemma":0.003374787,"teacher_disagreement_score":0.005162669,"about_ca_system_score_codex":0.001386821,"about_ca_system_score_gemma":0.002791881,"threshold_uncertainty_score":0.017759621},"labels":[],"label_agreement":null},{"id":"W4315630313","doi":"10.1109/globecom48099.2022.10001396","title":"Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Markov decision process; Computer science; Acceleration; Base station; Wireless; Reliability (semiconductor); Markov process; Process (computing); Vehicle dynamics; Q-learning; Wireless sensor network; Real-time computing; Network dynamics; Computer network; Artificial intelligence; Telecommunications; Engineering; Automotive engineering","score_opus":0.02395971897213769,"score_gpt":0.25172716239240983,"score_spread":0.22776744342027214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630313","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.087895304,0.00049482274,0.90626293,0.00071818184,0.00006506075,0.00008835586,0.000100352125,0.0003467769,0.004028111],"genre_scores_gemma":[0.9787266,0.00011436459,0.019100402,0.00011618065,0.000022835759,0.00009787394,0.000061815634,0.000027012102,0.0017329423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936527,0.00021418177,0.000024196954,0.000143397,0.00009219304,0.00016072021],"domain_scores_gemma":[0.99690384,0.0020994395,0.00036981117,0.00008337882,0.00036416462,0.00017931829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001733108,0.00096267567,0.0012349943,0.00045522305,0.00041846707,0.0007653014,0.0011469355,0.0011675173,0.001996937],"category_scores_gemma":[0.005325591,0.0005256798,0.0004585941,0.00030981997,0.0013166305,0.0008971718,0.0010144463,0.0015200183,0.00020407175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003778105,0.000024444047,0.0005753511,0.000016840411,0.000013725042,0.00003156954,0.000021966654,0.99021834,0.00025961746,0.0033700103,0.00023008906,0.0052002883],"study_design_scores_gemma":[0.0000045960437,0.000008921294,0.000047384186,0.0000017381244,0.0000019634167,0.00000199826,0.0000030239426,0.99874675,0.00004370205,0.0010953535,0.000043042568,0.0000014755379],"about_ca_topic_score_codex":0.012103721,"about_ca_topic_score_gemma":0.007492812,"teacher_disagreement_score":0.012103721,"about_ca_system_score_codex":0.0016933767,"about_ca_system_score_gemma":0.0019311126,"threshold_uncertainty_score":0.024066508},"labels":[],"label_agreement":null},{"id":"W4315630319","doi":"10.1109/globecom48099.2022.10001555","title":"Agile Design and Dimensioning of MEC-NFV Infrastructure to Support Heterogeneous Service Chains","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Virtual network; Dimensioning; Software deployment; Cloud computing; Virtualization; Distributed computing; Mobile edge computing; Service (business); Computer network; Agile software development; Server; Software engineering; Operating system; Engineering","score_opus":0.04017603646917898,"score_gpt":0.2809988512533391,"score_spread":0.24082281478416015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630319","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.097803734,0.00026764435,0.89397794,0.00026250447,0.00005100761,0.000107469845,0.00005013651,0.00018735997,0.0072922497],"genre_scores_gemma":[0.91784906,0.00015970149,0.08089868,0.000038132825,0.0000072707703,0.00007381639,0.000041164778,0.00001684759,0.000915396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996618,0.00009936062,0.000010702646,0.00005148335,0.00006753929,0.000109010834],"domain_scores_gemma":[0.9998078,0.000050391372,0.000042737407,0.000024684652,0.000038768685,0.000035653997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004146682,0.00046759474,0.00029438568,0.00030084688,0.00036578593,0.0007096643,0.0007175263,0.00052967254,0.000790245],"category_scores_gemma":[0.0006419438,0.00022868453,0.0002960872,0.00022417257,0.0004676012,0.000850753,0.0010075758,0.00055910385,0.00011941624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035283894,0.0000392787,0.00095789944,0.0000514141,0.00001821185,0.00021692144,0.00006467329,0.94090223,0.016355198,0.014392376,0.00060113095,0.026365386],"study_design_scores_gemma":[0.000004291645,0.000053221826,0.0001914969,0.0000073442507,0.00000491123,0.00005435772,0.00005316962,0.99355835,0.0017590618,0.0030328701,0.0012760278,0.0000048050347],"about_ca_topic_score_codex":0.0018170876,"about_ca_topic_score_gemma":0.00220651,"teacher_disagreement_score":0.0018170876,"about_ca_system_score_codex":0.000748592,"about_ca_system_score_gemma":0.00082411064,"threshold_uncertainty_score":0.0054314137},"labels":[],"label_agreement":null},{"id":"W4315630387","doi":"10.1109/globecom48099.2022.10000829","title":"Object-Based Resolution Selection for Efficient Edge-Assisted Multi-Task Video Analytics","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Analytics; Video tracking; Bandwidth (computing); Artificial intelligence; Enhanced Data Rates for GSM Evolution; Real-time computing; Computer vision; Latency (audio); Task (project management); Video processing; Computation; Data mining; Algorithm; Computer network; Telecommunications","score_opus":0.05635500627788053,"score_gpt":0.32118535474640997,"score_spread":0.26483034846852943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02835276,0.0002652538,0.9693996,0.000059445207,0.000022081951,0.000033728644,0.000025661895,0.0005044864,0.0013370153],"genre_scores_gemma":[0.47651204,0.00025136585,0.5207119,0.000107870634,0.000046404228,0.000057467067,0.00013707342,0.0001142882,0.002061604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997094,0.000049979622,0.000016598826,0.00006606546,0.00011282949,0.000045026747],"domain_scores_gemma":[0.9996166,0.00012920574,0.000052197414,0.000072634015,0.000098838704,0.000030580984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004173896,0.0005293614,0.00041707058,0.0005140849,0.0002630908,0.0006739981,0.0008758178,0.00047019825,0.0010699965],"category_scores_gemma":[0.0013107137,0.00020380771,0.00026605977,0.00045940836,0.0002456888,0.0011430864,0.0007460635,0.00061774853,0.00048251063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053920725,0.00022732018,0.002855702,0.000125514,0.000063797735,0.0002933098,0.00021352281,0.14266025,0.1729414,0.0084188925,0.0036942353,0.6679669],"study_design_scores_gemma":[0.000017684239,0.00007766802,0.0007752719,0.00000805892,0.000014569482,0.00017317083,0.0000378336,0.9524194,0.04207106,0.002449638,0.0019379193,0.000017767681],"about_ca_topic_score_codex":0.000766399,"about_ca_topic_score_gemma":0.0011187887,"teacher_disagreement_score":0.0010699965,"about_ca_system_score_codex":0.00025269482,"about_ca_system_score_gemma":0.00033237,"threshold_uncertainty_score":0.0035794973},"labels":[],"label_agreement":null},{"id":"W4315630404","doi":"10.1109/globecom48099.2022.10000647","title":"Efficient Server-Aided Personalized Treatment Recommendation with Privacy Preservation","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; University of Waterloo","funders":"","keywords":"Computer science; Homomorphic encryption; Upload; Encryption; Protocol (science); Path (computing); Hash function; Scheme (mathematics); Graph; Computer security; Computer network; Theoretical computer science; World Wide Web; Medicine; Mathematics","score_opus":0.07553104229276386,"score_gpt":0.31271840808868046,"score_spread":0.2371873657959166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630404","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051928,0.0005453123,0.93789816,0.0007752583,0.000091585185,0.00027370252,0.0009968493,0.0031747855,0.004316345],"genre_scores_gemma":[0.8529557,0.00032218677,0.13925058,0.00027222125,0.000074772586,0.000125746,0.0013435642,0.00008720693,0.005568075],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797684,0.00054872705,0.00017286035,0.00043508364,0.0006323499,0.00023421664],"domain_scores_gemma":[0.9961628,0.0007982224,0.00032768148,0.0022078976,0.0003713603,0.0001321657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012316861,0.00060964335,0.0011395762,0.00076362805,0.000955812,0.0013985729,0.001903658,0.0011596688,0.0029152231],"category_scores_gemma":[0.00346645,0.00038793316,0.00080681284,0.0017506417,0.00052911544,0.0033776523,0.0023108358,0.0011961181,0.0014710166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022610736,0.0006188258,0.0062370193,0.000411468,0.00030562634,0.00091696385,0.00078234635,0.23877619,0.04604355,0.084587984,0.02040811,0.5986509],"study_design_scores_gemma":[0.00014288472,0.00016913116,0.0012740376,0.00001720204,0.00010404417,0.00089606066,0.00017575656,0.91435945,0.024433427,0.051372238,0.006979446,0.00007635736],"about_ca_topic_score_codex":0.002261271,"about_ca_topic_score_gemma":0.0023536617,"teacher_disagreement_score":0.0029152231,"about_ca_system_score_codex":0.0010016756,"about_ca_system_score_gemma":0.0021575287,"threshold_uncertainty_score":0.009752452},"labels":[],"label_agreement":null},{"id":"W4315630455","doi":"10.1109/globecom48099.2022.10001321","title":"Parallel Computing at the Extreme Edge: Spatiotemporal Analysis","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Edge computing; Task (project management); Distributed computing; Enhanced Data Rates for GSM Evolution; Context (archaeology); Edge device; Wireless; Computation; Cloud computing; Algorithm; Artificial intelligence; Telecommunications","score_opus":0.07264076839525885,"score_gpt":0.2986818784623422,"score_spread":0.22604111006708333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630455","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0679784,0.0007918145,0.92379415,0.0010243837,0.00007461304,0.000053794363,0.00017848572,0.00014409662,0.0059602545],"genre_scores_gemma":[0.97250926,0.0011268319,0.023107545,0.00013295095,0.00008939935,0.00006677643,0.00013552832,0.00005963032,0.0027721066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996556,0.00009234419,0.000017187174,0.000073063566,0.00008604289,0.000075770186],"domain_scores_gemma":[0.99882394,0.0006108619,0.00021135139,0.00008768399,0.00017372513,0.000092418566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073103403,0.0006856067,0.0005989798,0.00048372295,0.00041072362,0.0010615162,0.000855473,0.0006542572,0.0017110582],"category_scores_gemma":[0.0031992474,0.00031963905,0.0005815496,0.0005680962,0.0009976398,0.0014347606,0.0009507246,0.0009937244,0.00021183204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037887083,0.00001971445,0.0014551479,0.000041814696,0.000024983607,0.00015182984,0.00004840479,0.9094112,0.0023023265,0.08161106,0.00077575765,0.004119833],"study_design_scores_gemma":[0.0000010604762,0.000003779047,0.000130228,0.0000015231484,0.0000018685477,0.000009587025,0.0000059076456,0.9935318,0.000093717725,0.006055663,0.00016304433,0.0000017833496],"about_ca_topic_score_codex":0.0067548077,"about_ca_topic_score_gemma":0.0028581726,"teacher_disagreement_score":0.0067548077,"about_ca_system_score_codex":0.0010979123,"about_ca_system_score_gemma":0.0010819142,"threshold_uncertainty_score":0.013431013},"labels":[],"label_agreement":null},{"id":"W4315630458","doi":"10.1109/globecom48099.2022.10001041","title":"Efficient Information Sharing in ICT Supply Chain Social Network via Table Structure Recognition","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Mitacs","keywords":"Computer science; Table (database); Supply chain; Information sharing; Representation (politics); Structuring; Process (computing); Data mining; Artificial intelligence; World Wide Web","score_opus":0.11417516000948796,"score_gpt":0.3601949135115256,"score_spread":0.24601975350203764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630458","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1599367,0.0009646874,0.8234101,0.0008045455,0.00016610988,0.00028241234,0.0015797232,0.006297227,0.00655851],"genre_scores_gemma":[0.75574577,0.00039774002,0.23540425,0.00022090621,0.00008797498,0.00017132479,0.0032961152,0.00011340931,0.0045624063],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987149,0.0002303389,0.000098417106,0.00041233972,0.00040335488,0.00014059093],"domain_scores_gemma":[0.9987765,0.00040533135,0.00021007359,0.00022584209,0.00031426467,0.00006814311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007588193,0.0007634654,0.0010951415,0.0023828652,0.0008003676,0.0014103121,0.0013135931,0.0009443266,0.0018579733],"category_scores_gemma":[0.0034330937,0.0003490201,0.00096879294,0.0025416273,0.00038648248,0.002841176,0.001344119,0.0007244867,0.0007951641],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048646564,0.0003275555,0.008280455,0.00022842146,0.00015005512,0.0003784774,0.0004879006,0.16771373,0.015190009,0.008323363,0.011839868,0.7865938],"study_design_scores_gemma":[0.000009744993,0.00003517553,0.0012255573,0.000007937318,0.000024418461,0.00006898242,0.00010977598,0.9862599,0.0052810595,0.0053474754,0.0016157815,0.000014195144],"about_ca_topic_score_codex":0.010160428,"about_ca_topic_score_gemma":0.006896411,"teacher_disagreement_score":0.010160428,"about_ca_system_score_codex":0.0011750578,"about_ca_system_score_gemma":0.0012004211,"threshold_uncertainty_score":0.020202577},"labels":[],"label_agreement":null},{"id":"W4320029268","doi":"10.1109/globecom48099.2022.10000619","title":"LoS-Aware Handover Uplink NOMA Transmissions for Multi-Layer LEO Satellite Constellation","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Satellite Communication Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Noma; Telecommunications link; Computer science; Transmission (telecommunications); Computer network; Handover; Constellation; Ergodic theory; Physical layer; Single antenna interference cancellation; Throughput; Satellite; Topology (electrical circuits); Real-time computing; Telecommunications; Wireless; Engineering; Mathematics; Electrical engineering; Channel (broadcasting)","score_opus":0.12818146823233217,"score_gpt":0.3336299684810931,"score_spread":0.20544850024876093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320029268","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20037517,0.0016269123,0.7871423,0.0003299278,0.00013045904,0.00005587436,0.00012337888,0.00042378358,0.009792238],"genre_scores_gemma":[0.9843089,0.0003699561,0.0142580075,0.00004442588,0.000050021154,0.00002945234,0.000045432593,0.000012429549,0.0008814993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995859,0.0001578745,0.000016279551,0.000038192222,0.000098134544,0.0001034998],"domain_scores_gemma":[0.99940276,0.0002826545,0.000090690985,0.000079664576,0.00010776592,0.00003640027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049752305,0.0005579412,0.00034598468,0.0003125151,0.0006308032,0.0006010904,0.00039418272,0.0002808716,0.0010339346],"category_scores_gemma":[0.0013955921,0.00019828172,0.00036401025,0.0004840373,0.00056161097,0.0006483757,0.0008589117,0.00049305905,0.00023010465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035415465,0.00009715801,0.0054317876,0.00026469867,0.000081384045,0.0008640139,0.00039094454,0.8115094,0.03591624,0.03858332,0.0032701765,0.103236794],"study_design_scores_gemma":[0.000010163279,0.00008317417,0.00060956914,0.000008997247,0.000022205988,0.00015427616,0.00007963457,0.9924039,0.0028183958,0.0028610013,0.00092952227,0.000019025298],"about_ca_topic_score_codex":0.0024946148,"about_ca_topic_score_gemma":0.0043078843,"teacher_disagreement_score":0.0024946148,"about_ca_system_score_codex":0.0007098057,"about_ca_system_score_gemma":0.00073523645,"threshold_uncertainty_score":0.00515002},"labels":[],"label_agreement":null},{"id":"W4320029327","doi":"10.1109/globecom48099.2022.10001198","title":"Community-Oriented Resource Allocation at the Extreme Edge","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Job shop scheduling; Bipartite graph; Resource allocation; Resource management (computing); Enhanced Data Rates for GSM Evolution; Distributed computing; Exploit; Context (archaeology); Edge computing; Computer network; Graph; Computer security; Theoretical computer science; Routing (electronic design automation); Artificial intelligence","score_opus":0.07502582090834353,"score_gpt":0.2864199305602017,"score_spread":0.21139410965185818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320029327","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11603645,0.00072842045,0.8693258,0.0006827321,0.000119438395,0.00020066477,0.00014258863,0.0008478716,0.011915977],"genre_scores_gemma":[0.8660058,0.00019687755,0.13061245,0.0001787193,0.000025436273,0.00010055829,0.000104273786,0.0001001927,0.0026757834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909985,0.00028713007,0.000024848741,0.00017336989,0.00014094939,0.00027391769],"domain_scores_gemma":[0.99890554,0.00040534604,0.000095873234,0.00019216139,0.00018775133,0.00021333627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012439667,0.0005143971,0.0006774828,0.00051107834,0.0011904065,0.0013468137,0.0017954825,0.000758627,0.0030578063],"category_scores_gemma":[0.0021269217,0.00026638794,0.00046977133,0.00062518165,0.00078267546,0.0019935092,0.0022119936,0.00084175076,0.00042611282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005875224,0.0003422118,0.0012776119,0.00021592705,0.00008983658,0.00033222203,0.00033473814,0.7688464,0.017623814,0.096644424,0.009529188,0.1041761],"study_design_scores_gemma":[0.00003411489,0.00006194536,0.0002491667,0.000011681254,0.000013169069,0.00008106874,0.000099602774,0.95966285,0.0020164312,0.034239013,0.0035153423,0.00001566612],"about_ca_topic_score_codex":0.0035653098,"about_ca_topic_score_gemma":0.0047003124,"teacher_disagreement_score":0.0035653098,"about_ca_system_score_codex":0.000990598,"about_ca_system_score_gemma":0.0015808045,"threshold_uncertainty_score":0.010229409},"labels":[],"label_agreement":null},{"id":"W4320029339","doi":"10.1109/globecom48099.2022.10000605","title":"A Novel SDN-enabled Edge Computing Load Balancing Scheme for IoT Video Analytics","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Server; Computer network; Edge computing; Software-defined networking; Enhanced Data Rates for GSM Evolution; Latency (audio); Load balancing (electrical power); OpenFlow; Distributed computing","score_opus":0.05684041176808003,"score_gpt":0.3043176806277127,"score_spread":0.24747726885963267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320029339","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10857422,0.0006103804,0.8815523,0.00034204833,0.00031812585,0.00014443224,0.00013176553,0.0012375317,0.0070892153],"genre_scores_gemma":[0.90572846,0.00018273144,0.09149462,0.00021629297,0.00009652467,0.00005719803,0.00016497832,0.000038454666,0.002020638],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975294,0.000031934214,0.000016420901,0.000068072484,0.00008111198,0.000049455302],"domain_scores_gemma":[0.9997296,0.000041690102,0.000028746548,0.000043057444,0.00012602798,0.000031022875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034058225,0.0004070037,0.00034052623,0.00046656464,0.00064912136,0.0005476249,0.0011188836,0.0003315143,0.000902724],"category_scores_gemma":[0.0005155113,0.00012072731,0.00020839948,0.0005569681,0.0002870155,0.0009804188,0.00061192765,0.0004314863,0.00016970419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010677753,0.0006695351,0.0034219765,0.00026216195,0.00012987862,0.0004312216,0.0002492325,0.21378827,0.21168306,0.038002923,0.012336392,0.5179575],"study_design_scores_gemma":[0.000032675835,0.0001237191,0.00056204404,0.000009445274,0.000020901147,0.00012091345,0.00003566721,0.9701283,0.020358393,0.0040301625,0.0045590396,0.00001872138],"about_ca_topic_score_codex":0.0016877997,"about_ca_topic_score_gemma":0.0034104136,"teacher_disagreement_score":0.0016877997,"about_ca_system_score_codex":0.000782945,"about_ca_system_score_gemma":0.0006465061,"threshold_uncertainty_score":0.00568074},"labels":[],"label_agreement":null},{"id":"W4320029372","doi":"10.1109/globecom48099.2022.10001232","title":"A Miniaturized 8x8 Dual-layer EBG Slotted Circularly Polarized Patch Antenna Array for mMIMO","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Beamwidth; Return loss; Directivity; Patch antenna; Microstrip antenna; Antenna array; Optics; Radiation pattern; Antenna measurement; Antenna (radio); Antenna efficiency; Coaxial antenna; Materials science; Physics; Acoustics; Computer science; Telecommunications","score_opus":0.03778598525954694,"score_gpt":0.2675650931930417,"score_spread":0.2297791079334948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320029372","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16624965,0.00055157894,0.8176597,0.00030163449,0.0002305491,0.000037983307,0.000109127104,0.0014316178,0.013428153],"genre_scores_gemma":[0.70545053,0.00028972022,0.28795514,0.00027443835,0.00008793265,0.000065512846,0.00018856933,0.00006169323,0.0056265006],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99985254,0.000035881716,0.000005263736,0.000028887422,0.00005190694,0.000025597352],"domain_scores_gemma":[0.9999089,0.000011411842,0.00002200374,0.000021174033,0.000025591426,0.000010743769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008758966,0.00043416113,0.00031999702,0.00018183546,0.000113978065,0.00035675854,0.0005427131,0.0005119843,0.0012401685],"category_scores_gemma":[0.00016122096,0.00020263849,0.00027878798,0.00021588641,0.00011860981,0.00036292864,0.00031965523,0.00031712037,0.0016143773],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021902924,0.00006704267,0.0013512318,0.00010665484,0.00008149881,0.00037064735,0.000055427976,0.022476343,0.8513727,0.003840051,0.0025488995,0.11751047],"study_design_scores_gemma":[0.000067743706,0.0012694794,0.0034623505,0.000028176559,0.00008941845,0.0028001023,0.00008877574,0.45020652,0.5054047,0.0019303715,0.034584012,0.000068298854],"about_ca_topic_score_codex":0.00009669357,"about_ca_topic_score_gemma":0.00016591977,"teacher_disagreement_score":0.0012401685,"about_ca_system_score_codex":0.00014404791,"about_ca_system_score_gemma":0.00009779044,"threshold_uncertainty_score":0.0041487813},"labels":[],"label_agreement":null},{"id":"W4320029381","doi":"10.1109/globecom48099.2022.10000894","title":"Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); University of Ottawa","funders":"Mitacs","keywords":"Reinforcement learning; Computer science; Handover; Latency (audio); Context (archaeology); Beamforming; Wireless; Wireless network; Real-time computing; Computer network; Artificial intelligence; Telecommunications","score_opus":0.014160459551019489,"score_gpt":0.23856615315079355,"score_spread":0.22440569359977405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320029381","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09723919,0.0006353819,0.89850825,0.00043062362,0.00006856642,0.000053926397,0.000038216065,0.0004465004,0.002579373],"genre_scores_gemma":[0.9674305,0.00011574848,0.030823069,0.00015766131,0.000022564467,0.000039371225,0.00004139193,0.000015685484,0.001354071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995548,0.00013175976,0.000020442461,0.000085331085,0.000073880576,0.00013371297],"domain_scores_gemma":[0.99878925,0.0008027068,0.000104419545,0.000051356066,0.00016488525,0.0000874197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012660808,0.0005831695,0.00086090807,0.0002728868,0.00035800535,0.00063371373,0.0013483326,0.0008141897,0.0014655378],"category_scores_gemma":[0.002658354,0.00028024198,0.0003212993,0.0002818682,0.0007519779,0.00094181934,0.0010404988,0.0010723359,0.00017893838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017094029,0.00011951152,0.0011805035,0.000045907316,0.000028702705,0.000088827896,0.00007781102,0.9248594,0.0017141434,0.004334019,0.00082161656,0.066558495],"study_design_scores_gemma":[0.000006743104,0.000021701011,0.000056766523,0.0000013606897,0.000002557193,0.0000048436377,0.000004389387,0.9988657,0.00014617364,0.0008297895,0.000058392863,0.0000015044307],"about_ca_topic_score_codex":0.008264361,"about_ca_topic_score_gemma":0.006264497,"teacher_disagreement_score":0.008264361,"about_ca_system_score_codex":0.000925049,"about_ca_system_score_gemma":0.0011281322,"threshold_uncertainty_score":0.016432524},"labels":[],"label_agreement":null},{"id":"W4320029434","doi":"10.1109/globecom48099.2022.10000957","title":"Real-Time Jaywalking Detection and Notification System using Deep Learning and Multi-Object Tracking","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Object detection; Artificial intelligence; Computer vision; Deep learning; Trajectory; Segmentation; Video tracking; Real-time computing; Object (grammar); Tracking system; Tracking (education); Path (computing); Component (thermodynamics); Motion (physics); Computer network; Kalman filter","score_opus":0.05396969457280422,"score_gpt":0.32151514583903307,"score_spread":0.26754545126622886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320029434","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.157338,0.0005522793,0.8269091,0.00031696286,0.00029324886,0.00013437362,0.00035872028,0.010476382,0.0036208706],"genre_scores_gemma":[0.863006,0.0002744385,0.13101329,0.00028707858,0.000058409325,0.000102112,0.0007237459,0.0000853933,0.0044494756],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99958974,0.00002779487,0.000026891608,0.0001375932,0.00013761566,0.00008032847],"domain_scores_gemma":[0.9996277,0.000052286177,0.000054749056,0.000051177118,0.00015054765,0.00006362283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054101314,0.0007296705,0.00090251287,0.0008663235,0.00035822517,0.0005944553,0.0012960043,0.0008087002,0.0010817375],"category_scores_gemma":[0.0008834036,0.00037573918,0.00047020992,0.000579054,0.00018618225,0.0009992317,0.0010189057,0.0009287264,0.0006287927],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075014186,0.00082497066,0.011394046,0.00019378254,0.00011784607,0.0006166883,0.00014417461,0.072951786,0.07848873,0.0012976235,0.0084730955,0.8247471],"study_design_scores_gemma":[0.000024301477,0.000121381025,0.0026816602,0.000009041851,0.00002788573,0.00013116273,0.000021489483,0.980646,0.014850011,0.0005520604,0.00091175316,0.000023246908],"about_ca_topic_score_codex":0.0068814475,"about_ca_topic_score_gemma":0.007003159,"teacher_disagreement_score":0.0068814475,"about_ca_system_score_codex":0.00068336364,"about_ca_system_score_gemma":0.0009898461,"threshold_uncertainty_score":0.013682783},"labels":[],"label_agreement":null},{"id":"W4320029510","doi":"10.1109/globecom48099.2022.10000854","title":"Impact of Users' Mobility on the Quality of Edge Sensing Systems","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Qatar University","keywords":"Waypoint; Incentive; Computer science; Enhanced Data Rates for GSM Evolution; Exploit; Scheme (mathematics); Computer network; Randomness; Mobility model; Quality (philosophy); Real-time computing; Computer security; Telecommunications","score_opus":0.08411425092889377,"score_gpt":0.3463369780461799,"score_spread":0.2622227271172861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320029510","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94633865,0.00093439996,0.047303412,0.0008456893,0.000056089557,0.00006673039,0.00018067223,0.00018105688,0.004093279],"genre_scores_gemma":[0.9991202,0.000050270777,0.0006745475,0.00001648052,0.000003730995,0.0000048494107,0.000011299782,0.0000029876296,0.00011577268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99651706,0.0015856848,0.00015192863,0.00033714334,0.0006445693,0.0007635792],"domain_scores_gemma":[0.9883541,0.006946038,0.0016039646,0.001031887,0.0015667974,0.0004971199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030497685,0.0007102496,0.000674195,0.00071355695,0.0007341782,0.0012473661,0.0009473875,0.0009743636,0.0009499382],"category_scores_gemma":[0.014156532,0.00022805633,0.0003644707,0.00092394307,0.001027592,0.0014052504,0.001754405,0.00054596574,0.00011297165],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016639311,0.0002893876,0.062910534,0.00018578582,0.00019208387,0.0007355846,0.00036955395,0.8649045,0.017422132,0.011174504,0.0010851886,0.039066754],"study_design_scores_gemma":[0.0000797267,0.000849622,0.019646937,0.000034128694,0.00010577796,0.00038724678,0.00049750676,0.96297807,0.01080167,0.0034745748,0.001080892,0.00006382583],"about_ca_topic_score_codex":0.006018913,"about_ca_topic_score_gemma":0.0032381804,"teacher_disagreement_score":0.006018913,"about_ca_system_score_codex":0.001505016,"about_ca_system_score_gemma":0.00077721785,"threshold_uncertainty_score":0.016128957},"labels":[],"label_agreement":null},{"id":"W4320029570","doi":"10.1109/globecom48099.2022.10000909","title":"Annotation Efficiency in Multimodal Emotion Recognition with Deep Learning","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Wearable computer; Artificial intelligence; Deep learning; Facial expression; Emotion recognition; Emotion classification; Machine learning; Pace; Convolutional neural network; Process (computing); Speech recognition; Human–computer interaction","score_opus":0.05213402463928806,"score_gpt":0.324930110147868,"score_spread":0.2727960855085799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320029570","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2628375,0.0034976024,0.7164518,0.0014597956,0.00035034493,0.00019408796,0.0005929041,0.006832441,0.0077835578],"genre_scores_gemma":[0.8667456,0.00064007245,0.12395695,0.00047613724,0.0000873266,0.00017806966,0.0012154726,0.00020216979,0.0064982176],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99868697,0.0004242919,0.00008602767,0.00037629422,0.00024394563,0.00018238234],"domain_scores_gemma":[0.9984363,0.0007658517,0.000105831255,0.00022318428,0.0004127095,0.000056165616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022998836,0.00101876,0.000868472,0.0007005779,0.0004557195,0.0011414302,0.0012623846,0.0011214713,0.0028216776],"category_scores_gemma":[0.006443274,0.00033225375,0.0005563375,0.00063336326,0.00039159524,0.0020220215,0.0016105758,0.0014848737,0.0014376034],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00096872984,0.00043781372,0.0039168214,0.0001595449,0.00011185622,0.00017072099,0.00027841635,0.04879548,0.03015985,0.0020705713,0.0062878462,0.9066424],"study_design_scores_gemma":[0.0000193928,0.000096387674,0.0017773972,0.000021139242,0.0000345937,0.000048848997,0.00008050197,0.9832412,0.010912343,0.0025254956,0.001226452,0.000016097907],"about_ca_topic_score_codex":0.0051441044,"about_ca_topic_score_gemma":0.005052279,"teacher_disagreement_score":0.0051441044,"about_ca_system_score_codex":0.0007853725,"about_ca_system_score_gemma":0.00052417576,"threshold_uncertainty_score":0.012163103},"labels":[],"label_agreement":null},{"id":"W4320029579","doi":"10.1109/globecom48099.2022.10001559","title":"JUNO: Jump-Start Reinforcement Learning-based Node Selection for UWB Indoor Localization","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Concordia University","funders":"","keywords":"Non-line-of-sight propagation; Reinforcement learning; Computer science; Beacon; Context (archaeology); Node (physics); Jump; Internet of Things; Real-time computing; Ultra-wideband; Selection (genetic algorithm); Artificial intelligence; Wireless; Embedded system; Engineering; Telecommunications","score_opus":0.026084999481815887,"score_gpt":0.2615785019724933,"score_spread":0.23549350249067744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320029579","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018796043,0.00026309074,0.97822964,0.00013963958,0.000083183884,0.000054957807,0.000025603373,0.00072852796,0.0016792296],"genre_scores_gemma":[0.9442807,0.00013141782,0.053010732,0.00018410628,0.000057342477,0.00012567823,0.000060133618,0.000050246006,0.0020997373],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956375,0.00012244411,0.00001905544,0.00010757092,0.000105371764,0.00008180279],"domain_scores_gemma":[0.99881315,0.00065935455,0.00016241985,0.00007081023,0.00017427932,0.00012003119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009677763,0.00072871643,0.0008694871,0.0003076545,0.0003429294,0.00053743494,0.0018948357,0.0008052932,0.0014119388],"category_scores_gemma":[0.0029440497,0.00027686628,0.00033846294,0.00026092003,0.00090930425,0.0006230619,0.0010742489,0.0011358535,0.0002797481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017484317,0.00009175567,0.0011900179,0.00006813983,0.00004052952,0.00011469447,0.00008757801,0.9340475,0.0030074266,0.004730512,0.0011631345,0.05528392],"study_design_scores_gemma":[0.0000115229195,0.000045833804,0.00006872121,0.000003003317,0.0000051475577,0.00001378437,0.000005920086,0.99845386,0.00036935828,0.0008451681,0.00017326836,0.0000043920395],"about_ca_topic_score_codex":0.0028132016,"about_ca_topic_score_gemma":0.0032446077,"teacher_disagreement_score":0.0028132016,"about_ca_system_score_codex":0.00052973593,"about_ca_system_score_gemma":0.0009514467,"threshold_uncertainty_score":0.005593717},"labels":[],"label_agreement":null}]}