{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":70,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":70,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"a650894481b7","filters":{"venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference"}},"results":[{"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Computer science; Radio access network; Latency (audio); Distributed computing; Cellular network; Flexibility (engineering); Computer network; Deep learning; Artificial intelligence; Base station; Telecommunications","authors":[{"name":"Han Zhang","is_ca":true},{"name":"Hao Zhou","is_ca":true},{"name":"Melike Erol‐Kantarci","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02583570512000805,"gpt":0.2694966429975654,"spread":0.2436609378775573,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001276658,0.0007268618,0.00094093,0.000257911,0.0003050734,0.0005459441,0.0008373205,0.0007772767,0.001235305],"category_scores_gemma":[0.002748631,0.0003074404,0.0003003582,0.0002158592,0.0008337708,0.0007412247,0.0008779416,0.001040234,0.0001548596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008310743,"about_ca_system_score_gemma":0.001408469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006242152,"about_ca_topic_score_gemma":0.005131539,"domain_scores_codex":[0.9995214,0.000154738,0.0000209263,0.0001041536,0.00008363323,0.0001150707],"domain_scores_gemma":[0.9990126,0.0005077298,0.0001164506,0.00006814818,0.0002013483,0.00009365434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001003021,0.00005541155,0.0006083666,0.00001972328,0.00002320433,0.00005162413,0.00003110506,0.9697193,0.001244909,0.002959278,0.0004358703,0.02475089],"study_design_scores_gemma":[0.000005278982,0.00001357264,0.00002716145,0.000001247654,0.000002011713,0.000002790171,0.00000198277,0.9989097,0.0001642144,0.0008235453,0.00004728844,0.000001255752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06204433,0.0002809862,0.9347037,0.0002571105,0.00004946064,0.00003937139,0.00002595261,0.0005213527,0.002077752],"genre_scores_gemma":[0.9664598,0.00005701334,0.03217073,0.0001190131,0.00001257126,0.00004023255,0.00002879157,0.00002220153,0.001089642],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006242152,"threshold_uncertainty_score":0.01241165,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Dimitrios Michael Manias","is_ca":true},{"name":"Ali Chouman","is_ca":true},{"name":"Abdallah Shami","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0488375982024802,"gpt":0.2870769206737382,"spread":0.238239322471258,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006634908,0.0007085949,0.000360311,0.001634851,0.000393675,0.001034985,0.0006088673,0.0005223752,0.001029945],"category_scores_gemma":[0.002243567,0.0001920732,0.0003463439,0.0008162927,0.0004411383,0.001413121,0.0004589809,0.0008354954,0.000270708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001240179,"about_ca_system_score_gemma":0.0006981887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005801594,"about_ca_topic_score_gemma":0.004017795,"domain_scores_codex":[0.9996533,0.00007145057,0.00001306352,0.00007231659,0.000151644,0.00003812631],"domain_scores_gemma":[0.9992687,0.0002829426,0.00005483676,0.0001252076,0.0002412712,0.00002714047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001678811,0.0003455462,0.01554875,0.0000973756,0.00005299174,0.0002863892,0.0002528688,0.6872268,0.02659084,0.0599946,0.003812619,0.2056233],"study_design_scores_gemma":[0.000001013072,0.00001138889,0.000638376,0.000002515198,9.938182e-7,0.0000323569,0.00001391552,0.9939198,0.001446351,0.00328386,0.0006450376,0.000004369378],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1031106,0.0002272259,0.8890152,0.0005018315,0.0000573873,0.0001220938,0.0004434786,0.001023938,0.005498164],"genre_scores_gemma":[0.8615277,0.0002869486,0.1345688,0.0001083688,0.00006924113,0.0001300403,0.0005958585,0.00009159952,0.002621426],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005801594,"threshold_uncertainty_score":0.01153564,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Zijiang Yan","is_ca":true},{"name":"Hina Tabassum","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02395971897213769,"gpt":0.2517271623924098,"spread":0.2277674434202721,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001733108,0.0009626757,0.001234994,0.0004552231,0.0004184671,0.0007653014,0.001146936,0.001167517,0.001996937],"category_scores_gemma":[0.005325591,0.0005256798,0.0004585941,0.00030982,0.001316631,0.0008971718,0.001014446,0.001520018,0.0002040718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001693377,"about_ca_system_score_gemma":0.001931113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01210372,"about_ca_topic_score_gemma":0.007492812,"domain_scores_codex":[0.9993653,0.0002141818,0.00002419695,0.000143397,0.00009219304,0.0001607202],"domain_scores_gemma":[0.9969038,0.00209944,0.0003698112,0.00008337882,0.0003641646,0.0001793183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003778105,0.00002444405,0.0005753511,0.00001684041,0.00001372504,0.00003156954,0.00002196665,0.9902183,0.0002596175,0.00337001,0.0002300891,0.005200288],"study_design_scores_gemma":[0.000004596044,0.000008921294,0.00004738419,0.000001738124,0.000001963417,0.00000199826,0.000003023943,0.9987468,0.00004370205,0.001095354,0.00004304257,0.000001475538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0878953,0.0004948227,0.9062629,0.0007181818,0.00006506075,0.00008835586,0.0001003521,0.0003467769,0.004028111],"genre_scores_gemma":[0.9787266,0.0001143646,0.0191004,0.0001161807,0.00002283576,0.00009787394,0.00006181563,0.0000270121,0.001732942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01210372,"threshold_uncertainty_score":0.02406651,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Gizem Sümen","is_ca":false},{"name":"Güneş Karabulut Kurt","is_ca":true},{"name":"Ali Görçin","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05229838337599312,"gpt":0.2766374747379675,"spread":0.2243390913619743,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001569738,0.00022724,0.0001396842,0.0002544396,0.0001956475,0.0003881408,0.0003355588,0.0005218086,0.0008652353],"category_scores_gemma":[0.0004461688,0.00006892174,0.0001497861,0.0003741621,0.0002007422,0.0004751082,0.0003149024,0.0004307798,0.0004152315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001844835,"about_ca_system_score_gemma":0.0002595692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001817393,"about_ca_topic_score_gemma":0.0002252814,"domain_scores_codex":[0.9998693,0.00002641714,0.000006160435,0.00001702021,0.00006874248,0.00001218984],"domain_scores_gemma":[0.99984,0.00002836036,0.00003420378,0.00002105229,0.00006424294,0.00001214423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003579889,0.00007536811,0.002450805,0.0003248108,0.00003689843,0.0007159704,0.0002243153,0.02167362,0.569962,0.05193362,0.003038098,0.3492066],"study_design_scores_gemma":[0.0001100783,0.001274666,0.003388667,0.0001350113,0.00007237768,0.006108718,0.000194324,0.7439018,0.182502,0.009506004,0.05272761,0.00007857535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08940237,0.001757011,0.897223,0.0008055999,0.0002601854,0.00005970833,0.0001100885,0.0001952681,0.01018678],"genre_scores_gemma":[0.7955488,0.001222754,0.1969709,0.0003453775,0.0002025558,0.00009511533,0.0001805924,0.00002172467,0.005412159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008652353,"threshold_uncertainty_score":0.002894461,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Hamed Hojatian","is_ca":true},{"name":"Jérémy Nadal","is_ca":true},{"name":"Jean‐François Frigon","is_ca":true},{"name":"François Leduc-Primeau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04602183009601995,"gpt":0.2725881820614758,"spread":0.2265663519654558,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002974141,0.0005915544,0.0004292169,0.000186456,0.0001953697,0.0003480699,0.0007124474,0.0004725989,0.001487165],"category_scores_gemma":[0.0008474026,0.0002784559,0.0003610861,0.0003375848,0.0004810232,0.0006273016,0.0007592519,0.0007908413,0.0004969849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000344806,"about_ca_system_score_gemma":0.0004348151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001204779,"about_ca_topic_score_gemma":0.003039677,"domain_scores_codex":[0.9998305,0.00003930978,0.000007149373,0.0000436286,0.00005151326,0.00002785759],"domain_scores_gemma":[0.9996912,0.0001334395,0.00003702715,0.00004172096,0.00007523406,0.00002133709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009066883,0.00007389216,0.000632384,0.00005669876,0.00005423685,0.00006589306,0.00005550478,0.8152609,0.01644018,0.01328705,0.002131716,0.1518509],"study_design_scores_gemma":[0.000002837921,0.0000135574,0.00004190599,0.000001515389,0.00000211957,0.000008103388,0.000002761641,0.9966365,0.000896584,0.002138465,0.0002529321,0.00000270153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008332711,0.00007676749,0.9898837,0.00006374818,0.00001387449,0.00001026428,0.00003151241,0.0002656103,0.001321794],"genre_scores_gemma":[0.7226904,0.0002082159,0.271194,0.0002425226,0.00005879084,0.0001494268,0.0002322816,0.00008864175,0.005135782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001487165,"threshold_uncertainty_score":0.00497508,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Minh Dat Nguyen","is_ca":true},{"name":"Long Bao Le","is_ca":true},{"name":"Andre Girard","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05037661785141253,"gpt":0.2779998082432865,"spread":0.227623190391874,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004697472,0.0008336465,0.0006055145,0.0001770778,0.0005761102,0.0008656927,0.0005967171,0.000625047,0.001169156],"category_scores_gemma":[0.0009764531,0.0002546325,0.0002821982,0.0004084613,0.0007436373,0.0008882263,0.000791951,0.0006409474,0.0001239215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007159184,"about_ca_system_score_gemma":0.0008495633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00520272,"about_ca_topic_score_gemma":0.006104872,"domain_scores_codex":[0.9996152,0.0001365556,0.000009794214,0.00008445537,0.0000587105,0.00009530064],"domain_scores_gemma":[0.9996402,0.0001909358,0.0000635422,0.00002757199,0.00004077512,0.00003695555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000956033,0.00004491545,0.0006540641,0.00004657623,0.00001444179,0.000120205,0.00004635343,0.9806548,0.002426527,0.005103996,0.0004796116,0.01031294],"study_design_scores_gemma":[0.000004706113,0.00004159582,0.0001744217,0.000002708495,0.000003959975,0.00002056536,0.00003554175,0.9972278,0.0005207151,0.001598231,0.0003669889,0.000002711374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1457562,0.0004262816,0.8462555,0.0002892492,0.00006422959,0.00007972884,0.00007158293,0.0001747217,0.006882353],"genre_scores_gemma":[0.967845,0.000152083,0.03022384,0.00005325546,0.00001951668,0.00005046691,0.00004796025,0.00003039789,0.001577421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00520272,"threshold_uncertainty_score":0.01034486,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Yazan Otoum","is_ca":true},{"name":"Sai Krishna Yadlapalli","is_ca":true},{"name":"Amiya Nayak","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03897908685440526,"gpt":0.2918055705214898,"spread":0.2528264836670845,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00117819,0.0006023791,0.0005441671,0.0005606248,0.0004436515,0.0008919053,0.001640357,0.001300419,0.001646096],"category_scores_gemma":[0.001929963,0.0001915345,0.0005845766,0.0004900816,0.0007205575,0.002086345,0.001666861,0.001572824,0.0004989093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009164372,"about_ca_system_score_gemma":0.0012236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004836501,"about_ca_topic_score_gemma":0.003508773,"domain_scores_codex":[0.9995642,0.0001113631,0.00002237005,0.00009959519,0.0001301266,0.0000722273],"domain_scores_gemma":[0.9996619,0.0001050707,0.00003655957,0.0000725496,0.0000887452,0.00003506794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002058887,0.0002585076,0.001470812,0.00008116303,0.00007917771,0.0002623367,0.0001114525,0.6811839,0.005125549,0.01791186,0.006490318,0.286819],"study_design_scores_gemma":[0.000006315614,0.00004010273,0.00008705925,0.000005224446,0.000004452362,0.00002922534,0.00000771034,0.9914888,0.00106004,0.006140075,0.001124431,0.00000653768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0144858,0.0003740597,0.979342,0.0002588362,0.00007979476,0.00007598205,0.0001050322,0.003363067,0.001915403],"genre_scores_gemma":[0.7092226,0.0004875986,0.2832862,0.000367241,0.00007289572,0.0002568212,0.0006455546,0.0001645225,0.005496385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004836501,"threshold_uncertainty_score":0.009616673,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Mahdi Beitollahi","is_ca":true},{"name":"Ning Lu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03787758666045778,"gpt":0.2856852426912725,"spread":0.2478076560308147,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003358115,0.001460037,0.001625885,0.0008458868,0.0008359262,0.001566441,0.002020036,0.001903193,0.002431905],"category_scores_gemma":[0.01543854,0.0005864486,0.0007682754,0.0009140156,0.001796603,0.002303896,0.002655121,0.003042064,0.0007005616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001386821,"about_ca_system_score_gemma":0.002791881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005162669,"about_ca_topic_score_gemma":0.003374787,"domain_scores_codex":[0.9977077,0.000598495,0.0001531096,0.0004560954,0.0007899493,0.0002945627],"domain_scores_gemma":[0.9906657,0.00549723,0.0007235098,0.001076659,0.001818713,0.0002183426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001897051,0.00008562838,0.000850862,0.0001172932,0.00005475457,0.0001380841,0.00009202096,0.9004323,0.002057406,0.02396255,0.002420232,0.06959905],"study_design_scores_gemma":[0.000005318511,0.00002339266,0.00004885767,0.000007705881,0.000002626068,0.00002276687,0.000004772833,0.993891,0.0006450721,0.005165924,0.0001777955,0.000004778129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0122412,0.0004134222,0.9842018,0.0002992449,0.00004691055,0.00005096615,0.00008302554,0.0009305992,0.001732834],"genre_scores_gemma":[0.8289519,0.0004641759,0.1649992,0.0004973668,0.0001250903,0.0002492065,0.0003766956,0.0002045539,0.004131713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005162669,"threshold_uncertainty_score":0.01775962,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Tai Manh Ho","is_ca":true},{"name":"Mohamed Cheriet","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03608818262905673,"gpt":0.2768275039875015,"spread":0.2407393213584448,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001197634,0.0006688521,0.0009489688,0.0002619664,0.0004031419,0.0005914072,0.001033724,0.0008094604,0.001021729],"category_scores_gemma":[0.002375083,0.0003104113,0.0003161007,0.000312911,0.0008766764,0.0008157184,0.0008901492,0.001231384,0.0001319371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001328669,"about_ca_system_score_gemma":0.001788424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009043317,"about_ca_topic_score_gemma":0.006004398,"domain_scores_codex":[0.9995368,0.0001371801,0.00001920474,0.0001084603,0.00008264871,0.0001157863],"domain_scores_gemma":[0.9991189,0.0004603021,0.0001172396,0.00005885324,0.0001581768,0.0000866325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005452313,0.00005221243,0.0003944625,0.00001892774,0.0000170248,0.00003181313,0.00002265574,0.984682,0.0006729782,0.001732854,0.0002857445,0.01203491],"study_design_scores_gemma":[0.000003997978,0.00001048478,0.00003039383,6.790203e-7,0.000001381007,0.000001616309,0.000002045952,0.999123,0.00008387613,0.0007108628,0.00003072812,9.288377e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1054156,0.0004544826,0.8907743,0.0004993696,0.00007882524,0.00005023537,0.00003979793,0.0005053567,0.00218207],"genre_scores_gemma":[0.9769504,0.00006521118,0.02164375,0.0001049941,0.00001731657,0.00003931097,0.00002946171,0.00001862868,0.001130889],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009043317,"threshold_uncertainty_score":0.01798135,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"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","authors":[{"name":"Pouria Pourrashidi Shahrbabaki","is_ca":true},{"name":"Rodolfo W. L. Coutinho","is_ca":true},{"name":"Yousef R. Shayan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05684041176808003,"gpt":0.3043176806277127,"spread":0.2474772688596327,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003405822,0.0004070037,0.0003405262,0.0004665646,0.0006491214,0.0005476249,0.001118884,0.0003315143,0.000902724],"category_scores_gemma":[0.0005155113,0.0001207273,0.0002083995,0.0005569681,0.0002870155,0.0009804188,0.0006119277,0.0004314863,0.0001697042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000782945,"about_ca_system_score_gemma":0.0006465061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0016878,"about_ca_topic_score_gemma":0.003410414,"domain_scores_codex":[0.9997529,0.00003193421,0.0000164209,0.00006807248,0.00008111198,0.0000494553],"domain_scores_gemma":[0.9997296,0.0000416901,0.00002874655,0.00004305744,0.000126028,0.00003102288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001067775,0.0006695351,0.003421976,0.0002621619,0.0001298786,0.0004312216,0.0002492325,0.2137883,0.2116831,0.03800292,0.01233639,0.5179575],"study_design_scores_gemma":[0.00003267583,0.0001237191,0.000562044,0.000009445274,0.00002090115,0.0001209134,0.00003566721,0.9701283,0.02035839,0.004030162,0.00455904,0.00001872138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1085742,0.0006103804,0.8815523,0.0003420483,0.0003181259,0.0001444322,0.0001317655,0.001237532,0.007089215],"genre_scores_gemma":[0.9057285,0.0001827314,0.09149462,0.000216293,0.00009652467,0.00005719803,0.0001649783,0.00003845467,0.002020638],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0016878,"threshold_uncertainty_score":0.00568074,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Yuanzhe Gong","is_ca":true},{"name":"Robert Morawski","is_ca":true},{"name":"Hak Hyun Lee","is_ca":true},{"name":"Tho Le‐Ngoc","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03778598525954694,"gpt":0.2675650931930417,"spread":0.2297791079334948,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008758966,0.0004341611,0.000319997,0.0001818355,0.0001139781,0.0003567585,0.0005427131,0.0005119843,0.001240168],"category_scores_gemma":[0.000161221,0.0002026385,0.000278788,0.0002158864,0.0001186098,0.0003629286,0.0003196552,0.0003171204,0.001614377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001440479,"about_ca_system_score_gemma":0.00009779044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009669357,"about_ca_topic_score_gemma":0.0001659198,"domain_scores_codex":[0.9998525,0.00003588172,0.000005263736,0.00002888742,0.00005190694,0.00002559735],"domain_scores_gemma":[0.9999089,0.00001141184,0.00002200374,0.00002117403,0.00002559143,0.00001074377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002190292,0.00006704267,0.001351232,0.0001066548,0.00008149881,0.0003706474,0.00005542798,0.02247634,0.8513727,0.003840051,0.0025489,0.1175105],"study_design_scores_gemma":[0.00006774371,0.001269479,0.00346235,0.00002817656,0.00008941845,0.002800102,0.00008877574,0.4502065,0.5054047,0.001930372,0.03458401,0.00006829885],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1662496,0.0005515789,0.8176597,0.0003016345,0.0002305491,0.00003798331,0.0001091271,0.001431618,0.01342815],"genre_scores_gemma":[0.7054505,0.0002897202,0.2879551,0.0002744384,0.00008793265,0.00006551285,0.0001885693,0.00006169323,0.005626501],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001240168,"threshold_uncertainty_score":0.004148781,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Yongkang Gong","is_ca":false},{"name":"Haipeng Yao","is_ca":false},{"name":"Zehui Xiong","is_ca":false},{"name":"Song Guo","is_ca":false},{"name":"F. Richard Yu","is_ca":true},{"name":"Dusit Niyato","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02403014575021357,"gpt":0.2495963729410661,"spread":0.2255662271908526,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000493509,0.0007800834,0.0008454927,0.0002932962,0.0004537319,0.0008230528,0.0008369887,0.0004838818,0.001325228],"category_scores_gemma":[0.0008935291,0.0002537087,0.0004123442,0.0005749037,0.0006393535,0.0007924074,0.001019008,0.0006551286,0.0001884744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006968151,"about_ca_system_score_gemma":0.0007093945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003348989,"about_ca_topic_score_gemma":0.003372096,"domain_scores_codex":[0.9997498,0.0000837693,0.000009011324,0.00004413612,0.00005402861,0.00005923693],"domain_scores_gemma":[0.999726,0.0001535228,0.00003467166,0.00002152088,0.00004162297,0.00002265892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003623229,0.00001909821,0.0001451814,0.00003200781,0.00001169907,0.00004183568,0.00003676278,0.9799212,0.00132896,0.006918818,0.0005715398,0.01093676],"study_design_scores_gemma":[0.00000143388,0.000007220411,0.00002153642,0.000001017525,0.000001598228,0.000003584939,0.000004914304,0.9986369,0.0001027994,0.001138769,0.00007917154,0.000001122991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03954469,0.0005531482,0.9542927,0.0002263417,0.00005542999,0.00003148057,0.00003650613,0.0001506281,0.005109083],"genre_scores_gemma":[0.9679676,0.0004448677,0.02896468,0.00006322884,0.00002913731,0.00005503826,0.00003491559,0.00003379429,0.002406548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003348989,"threshold_uncertainty_score":0.006658971,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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)","authors":[{"name":"Wenjun Yang","is_ca":true},{"name":"Lin Cai","is_ca":true},{"name":"Shengjie Shu","is_ca":true},{"name":"Jianping Pan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09581041099166503,"gpt":0.3340087284376369,"spread":0.2381983174459719,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001144079,0.0004967,0.000554379,0.0005723739,0.0007479496,0.0009814585,0.001493973,0.0003287256,0.001906942],"category_scores_gemma":[0.002588447,0.0003002841,0.000214446,0.0005999188,0.0003084798,0.0005898682,0.0008215256,0.0006564285,0.0005257108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001335619,"about_ca_system_score_gemma":0.004050781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006049687,"about_ca_topic_score_gemma":0.00836732,"domain_scores_codex":[0.9994991,0.0001009546,0.00004510528,0.00007877905,0.0001799031,0.0000961216],"domain_scores_gemma":[0.9986999,0.0002996698,0.0001467456,0.0001320231,0.0005697968,0.0001517978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006219209,0.0002107347,0.005141984,0.0003276566,0.000118662,0.0004867365,0.0003044818,0.6469052,0.05950969,0.04362714,0.0111428,0.2316029],"study_design_scores_gemma":[0.00004471904,0.0000935753,0.0002876138,0.000007533065,0.00002226164,0.00007609367,0.00002743885,0.9853972,0.006874156,0.001835571,0.005316582,0.00001740962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03768417,0.0008528188,0.9545532,0.0002206495,0.0002615155,0.000306008,0.0001703344,0.002480041,0.003471322],"genre_scores_gemma":[0.7307906,0.0005020911,0.2647566,0.0001561385,0.0001402412,0.0002516899,0.0002428094,0.0002103582,0.002949468],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006049687,"threshold_uncertainty_score":0.01202893,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Chengzhi Wang","is_ca":false},{"name":"Peng Yang","is_ca":false},{"name":"Jie Lin","is_ca":false},{"name":"Wen Wu","is_ca":false},{"name":"Ning Zhang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05635500627788053,"gpt":0.32118535474641,"spread":0.2648303484685294,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004173896,0.0005293614,0.0004170706,0.0005140849,0.0002630908,0.0006739981,0.0008758178,0.0004701983,0.001069997],"category_scores_gemma":[0.001310714,0.0002038077,0.0002660598,0.0004594084,0.0002456888,0.001143086,0.0007460635,0.0006177485,0.0004825106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002526948,"about_ca_system_score_gemma":0.00033237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000766399,"about_ca_topic_score_gemma":0.001118789,"domain_scores_codex":[0.9997094,0.00004997962,0.00001659883,0.00006606546,0.0001128295,0.00004502675],"domain_scores_gemma":[0.9996166,0.0001292057,0.00005219741,0.00007263401,0.0000988387,0.00003058098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005392072,0.0002273202,0.002855702,0.000125514,0.00006379774,0.0002933098,0.0002135228,0.1426602,0.1729414,0.008418893,0.003694235,0.6679669],"study_design_scores_gemma":[0.00001768424,0.00007766802,0.0007752719,0.00000805892,0.00001456948,0.0001731708,0.0000378336,0.9524194,0.04207106,0.002449638,0.001937919,0.00001776768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02835276,0.0002652538,0.9693996,0.00005944521,0.00002208195,0.00003372864,0.0000256619,0.0005044864,0.001337015],"genre_scores_gemma":[0.476512,0.0002513659,0.5207119,0.0001078706,0.00004640423,0.00005746707,0.0001370734,0.0001142882,0.002061604],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001069997,"threshold_uncertainty_score":0.003579497,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Hao Zhou","is_ca":true},{"name":"Long Kong","is_ca":true},{"name":"Medhat Elsayed","is_ca":true},{"name":"Majid Bavand","is_ca":true},{"name":"Raimundas Gaigalas","is_ca":false},{"name":"Steve Furr","is_ca":true},{"name":"Melike Erol‐Kantarci","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03462045193845421,"gpt":0.2795320782949604,"spread":0.2449116263565062,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006077181,0.0005263867,0.0005857327,0.0002113515,0.0002642837,0.0003983746,0.0007133489,0.0004879983,0.00149467],"category_scores_gemma":[0.001634713,0.0002152554,0.0002834758,0.0001368931,0.0006126824,0.00039592,0.0006675508,0.0007411184,0.0001622698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006138272,"about_ca_system_score_gemma":0.0008465645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004218291,"about_ca_topic_score_gemma":0.003725981,"domain_scores_codex":[0.9997247,0.00008379229,0.00001055459,0.00005920946,0.00005037959,0.00007133284],"domain_scores_gemma":[0.9994075,0.000324468,0.00008122661,0.00003327514,0.0001017191,0.00005171598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005460399,0.00005086992,0.0005766517,0.00002216084,0.00002148291,0.00004876968,0.00003340795,0.976895,0.001717861,0.003829472,0.00038076,0.01636902],"study_design_scores_gemma":[0.000005163487,0.00001732122,0.00004146729,8.835386e-7,0.000001935611,0.000002963795,0.000001804572,0.9992453,0.0001033498,0.0005237737,0.0000549091,0.000001202783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1071779,0.0003196769,0.8858471,0.0002826719,0.00006249967,0.00006494532,0.00003147894,0.0005767723,0.005636909],"genre_scores_gemma":[0.9816678,0.00004167749,0.0170443,0.00006313623,0.00001298478,0.00004991073,0.00001868535,0.00001398373,0.001087514],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004218291,"threshold_uncertainty_score":0.008387446,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Yu Shen","is_ca":true},{"name":"Murat Şimşek","is_ca":true},{"name":"Burak Kantarcı","is_ca":true},{"name":"Hussein T. Mouftah","is_ca":true},{"name":"Mehran Bagheri","is_ca":true},{"name":"Petar Djukic","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04486782102638437,"gpt":0.3052362234637951,"spread":0.2603684024374108,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002818567,0.00157759,0.001214212,0.001553072,0.0006722708,0.001433781,0.001414685,0.001780327,0.001139304],"category_scores_gemma":[0.007493202,0.0002892064,0.0009011427,0.0009894765,0.0006903076,0.001988444,0.001123954,0.001581273,0.0005710682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001618128,"about_ca_system_score_gemma":0.001161568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0161063,"about_ca_topic_score_gemma":0.01603677,"domain_scores_codex":[0.9983779,0.0004085796,0.00009237078,0.0005195953,0.0003984784,0.0002030819],"domain_scores_gemma":[0.9969023,0.001727285,0.0002336523,0.0004794069,0.0005118247,0.000145545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001421461,0.001480759,0.01858815,0.0004319231,0.0002184031,0.0003611168,0.0001167336,0.7584133,0.004726286,0.002795355,0.01492684,0.1965197],"study_design_scores_gemma":[0.00002386105,0.0002237552,0.005296681,0.00003093285,0.00002702461,0.0001152289,0.00004139245,0.9882084,0.002758059,0.001904359,0.001345519,0.00002490255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8514483,0.004802537,0.1189499,0.002026006,0.000437671,0.0003905185,0.006027871,0.0035586,0.01235862],"genre_scores_gemma":[0.941354,0.0006424054,0.04295669,0.0002603413,0.00009176603,0.0001272377,0.01164461,0.00007158029,0.002851351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0161063,"threshold_uncertainty_score":0.0320251,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Hosein Zarini","is_ca":false},{"name":"Narges Gholipoor","is_ca":false},{"name":"Mohammad Robat Mili","is_ca":false},{"name":"Mehdi Rasti","is_ca":false},{"name":"Hina Tabassum","is_ca":true},{"name":"Ekram Hossain","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05031824208510208,"gpt":0.324473120299483,"spread":0.2741548782143809,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027268,0.0003885753,0.0002976961,0.0001570087,0.0001518876,0.0004102456,0.0005463749,0.0005190357,0.00095215],"category_scores_gemma":[0.0007113558,0.0001865698,0.0002122853,0.0002246247,0.0003707971,0.0007779225,0.0004258673,0.000745213,0.000385389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003297825,"about_ca_system_score_gemma":0.0003719071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007035821,"about_ca_topic_score_gemma":0.001150056,"domain_scores_codex":[0.9998778,0.00003052993,0.000005434169,0.00002869406,0.00004142305,0.00001596227],"domain_scores_gemma":[0.9998313,0.00007741312,0.00003336995,0.00002484207,0.00002552047,0.000007546311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002074119,0.0000969071,0.002431267,0.0001444953,0.00004764708,0.0001906489,0.000170913,0.6374748,0.1479966,0.01951967,0.001954222,0.1897653],"study_design_scores_gemma":[0.000002830848,0.00003289114,0.0001411561,0.000005507549,0.000004302008,0.00002252638,0.000007117856,0.981278,0.01606077,0.001536804,0.0009002964,0.000007858428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05794566,0.0005756961,0.9365473,0.0003059746,0.0000574633,0.00001789478,0.000067665,0.001158467,0.003323852],"genre_scores_gemma":[0.8686265,0.0003908034,0.127889,0.0001790367,0.00003618191,0.00005655639,0.000118852,0.00006660694,0.002636584],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00095215,"threshold_uncertainty_score":0.003185272,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Pedro Enrique Iturria-Rivera","is_ca":true},{"name":"Medhat Elsayed","is_ca":true},{"name":"Majid Bavand","is_ca":true},{"name":"Raimundas Gaigalas","is_ca":true},{"name":"Steve Furr","is_ca":true},{"name":"Melike Erol‐Kantarci","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01416045955101949,"gpt":0.2385661531507935,"spread":0.2244056935997741,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001266081,0.0005831695,0.0008609081,0.0002728868,0.0003580053,0.0006337137,0.001348333,0.0008141897,0.001465538],"category_scores_gemma":[0.002658354,0.000280242,0.0003212993,0.0002818682,0.0007519779,0.0009418193,0.001040499,0.001072336,0.0001789384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000925049,"about_ca_system_score_gemma":0.001128132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008264361,"about_ca_topic_score_gemma":0.006264497,"domain_scores_codex":[0.9995548,0.0001317598,0.00002044246,0.00008533108,0.00007388058,0.000133713],"domain_scores_gemma":[0.9987893,0.0008027068,0.0001044195,0.00005135607,0.0001648852,0.0000874197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001709403,0.0001195115,0.001180504,0.00004590732,0.00002870271,0.0000888279,0.00007781102,0.9248594,0.001714143,0.004334019,0.0008216166,0.0665585],"study_design_scores_gemma":[0.000006743104,0.00002170101,0.00005676652,0.00000136069,0.000002557193,0.000004843638,0.000004389387,0.9988657,0.0001461736,0.0008297895,0.00005839286,0.000001504431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09723919,0.0006353819,0.8985083,0.0004306236,0.00006856642,0.0000539264,0.00003821607,0.0004465004,0.002579373],"genre_scores_gemma":[0.9674305,0.0001157485,0.03082307,0.0001576613,0.00002256447,0.00003937123,0.00004139193,0.00001568548,0.001354071],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008264361,"threshold_uncertainty_score":0.01643252,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Hajar Moudoud","is_ca":true},{"name":"Soumaya Cherkaoui","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08711412784704074,"gpt":0.3200816354292165,"spread":0.2329675075821758,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002817237,0.0004987755,0.0009560502,0.0005572386,0.001179086,0.001863793,0.001455207,0.001482703,0.002458141],"category_scores_gemma":[0.004923324,0.0003798646,0.0006089322,0.00103714,0.001827351,0.003775955,0.003704052,0.001543367,0.0005130742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001272787,"about_ca_system_score_gemma":0.002648636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002310127,"about_ca_topic_score_gemma":0.002643268,"domain_scores_codex":[0.9982167,0.0006500839,0.0001123006,0.0002738103,0.000505968,0.0002410454],"domain_scores_gemma":[0.9973155,0.001182266,0.000225427,0.0007418634,0.000337813,0.0001971848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002990637,0.0001625563,0.0008070206,0.0001146366,0.00003754302,0.0003454866,0.0002802831,0.6434483,0.005435633,0.2725988,0.001901419,0.07456928],"study_design_scores_gemma":[0.00003523882,0.0000282126,0.00004307093,0.00001138293,0.00000420351,0.00002795831,0.000020719,0.8922479,0.001237145,0.104601,0.001735279,0.000007882529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02644338,0.0001857482,0.9681858,0.0005848706,0.00003622762,0.0001282187,0.00006281002,0.0004346356,0.00393821],"genre_scores_gemma":[0.8413123,0.0004053848,0.153577,0.0001283323,0.0000429615,0.0003212962,0.0001908571,0.00005882611,0.003963009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002817237,"threshold_uncertainty_score":0.01489919,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Soosan Naderi Mighan","is_ca":true},{"name":"Jelena Mišić","is_ca":true},{"name":"Vojislav B. Mišić","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02678827295741041,"gpt":0.2675001353246644,"spread":0.240711862367254,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002574315,0.0006343981,0.0006399652,0.001478667,0.0004764169,0.000681452,0.001153792,0.0006347715,0.001831529],"category_scores_gemma":[0.01137535,0.0002911551,0.0003685763,0.001322833,0.0007657639,0.002462293,0.0007615054,0.0008385821,0.000349521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002759254,"about_ca_system_score_gemma":0.000751422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004037844,"about_ca_topic_score_gemma":0.002025223,"domain_scores_codex":[0.9988518,0.0003739986,0.00003778003,0.0001669587,0.0003836351,0.0001858708],"domain_scores_gemma":[0.994245,0.004440688,0.0005493358,0.000231615,0.0004267111,0.0001066997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001934344,0.00003665959,0.002231548,0.0001174029,0.00002737764,0.000176324,0.0002037247,0.881867,0.004364487,0.09265029,0.001291608,0.0168402],"study_design_scores_gemma":[0.000003976063,0.00002799972,0.0002875175,0.00001036605,0.000006147051,0.00005856159,0.00002020953,0.9903862,0.000639365,0.007951311,0.0006009379,0.000007290467],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2334364,0.003147577,0.7512627,0.001091512,0.00009417292,0.000074004,0.0003062435,0.0004478615,0.01013954],"genre_scores_gemma":[0.9812937,0.001570759,0.0136769,0.0001163962,0.00005157992,0.00006244826,0.0001310141,0.0000820989,0.003015093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004037844,"threshold_uncertainty_score":0.02001983,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"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)","authors":[{"name":"Alice Faisal","is_ca":true},{"name":"Ibrahim Al-Nahhal","is_ca":true},{"name":"Octavia A. Dobre","is_ca":true},{"name":"Telex M. N. Ngatched","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03334916894112717,"gpt":0.2801202113228782,"spread":0.2467710423817511,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007265156,0.0006281799,0.0007678025,0.0002123104,0.0002090509,0.0006080716,0.0007004878,0.0007416727,0.001090562],"category_scores_gemma":[0.001545725,0.0002977875,0.0003250712,0.0002766148,0.0009227357,0.0005768245,0.0005952567,0.0008610582,0.0002037288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005951449,"about_ca_system_score_gemma":0.0006029403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00224449,"about_ca_topic_score_gemma":0.001882978,"domain_scores_codex":[0.9996711,0.0001188006,0.0000110752,0.0000602998,0.00008626467,0.00005242666],"domain_scores_gemma":[0.9991358,0.0005413936,0.0001189914,0.00004851691,0.0001256128,0.00002967075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004386665,0.00002464553,0.0001541714,0.00002962525,0.00001813236,0.00003765858,0.0000262715,0.9790006,0.001978378,0.004477333,0.0001829647,0.01402634],"study_design_scores_gemma":[0.000006008111,0.00001883087,0.00002234209,0.000001721239,0.000002883936,0.000007188868,0.000002903012,0.9987789,0.0002514169,0.0007909629,0.0001146368,0.000002151863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01770982,0.0002315313,0.9792423,0.0001384386,0.00002293643,0.00002084521,0.00001101716,0.00010524,0.002517965],"genre_scores_gemma":[0.9338038,0.0002184988,0.06250772,0.00009742003,0.00004006121,0.00007141493,0.00001920851,0.00002294521,0.003218885],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00224449,"threshold_uncertainty_score":0.004462838,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Dana Haj Hussein","is_ca":true},{"name":"Mohamed Ibnkahla","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04158601914213526,"gpt":0.3086542745226381,"spread":0.2670682553805028,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008856509,0.00077474,0.0003629126,0.0007916167,0.0005770727,0.001060036,0.001124433,0.001049733,0.001113727],"category_scores_gemma":[0.001853704,0.0003346986,0.0008866591,0.0008567807,0.000443154,0.001024334,0.0008380282,0.0009360081,0.000320749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008194845,"about_ca_system_score_gemma":0.000871254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007016837,"about_ca_topic_score_gemma":0.003443729,"domain_scores_codex":[0.999702,0.0001028612,0.00001971773,0.00005320634,0.00008837428,0.00003383052],"domain_scores_gemma":[0.9995013,0.0001918379,0.00007295879,0.00004960036,0.0001460229,0.00003824176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001875838,0.00005495071,0.0008046085,0.00002521117,0.00001487374,0.0001604322,0.00007839688,0.8599087,0.002331716,0.1214953,0.00150485,0.01360216],"study_design_scores_gemma":[6.813615e-7,0.000003867317,0.00003343357,0.000001853772,0.00000127861,0.00001589132,0.000004569071,0.9928524,0.0000810476,0.006482226,0.0005207251,0.00000206807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006393362,0.0001044168,0.990732,0.0001971467,0.00004104689,0.00003277485,0.00007836056,0.0002244529,0.002196492],"genre_scores_gemma":[0.6087857,0.001081035,0.3825565,0.0002264045,0.0002758066,0.000365653,0.0005534897,0.0002100692,0.005945399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007016837,"threshold_uncertainty_score":0.01395202,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Zohreh Hajiakhondi-Meybodi","is_ca":true},{"name":"Ming Hou","is_ca":true},{"name":"Arash Mohammadi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02608499948181589,"gpt":0.2615785019724933,"spread":0.2354935024906774,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009677763,0.0007287164,0.0008694871,0.0003076545,0.0003429294,0.0005374349,0.001894836,0.0008052932,0.001411939],"category_scores_gemma":[0.00294405,0.0002768663,0.0003384629,0.00026092,0.0009093042,0.0006230619,0.001074249,0.001135853,0.0002797481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005297359,"about_ca_system_score_gemma":0.0009514467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002813202,"about_ca_topic_score_gemma":0.003244608,"domain_scores_codex":[0.9995638,0.0001224441,0.00001905544,0.0001075709,0.0001053718,0.00008180279],"domain_scores_gemma":[0.9988132,0.0006593546,0.0001624198,0.00007081023,0.0001742793,0.0001200312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001748432,0.00009175567,0.001190018,0.00006813983,0.00004052952,0.0001146945,0.00008757801,0.9340475,0.003007427,0.004730512,0.001163134,0.05528392],"study_design_scores_gemma":[0.00001152292,0.0000458338,0.00006872121,0.000003003317,0.000005147558,0.00001378437,0.000005920086,0.9984539,0.0003693583,0.0008451681,0.0001732684,0.00000439204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01879604,0.0002630907,0.9782296,0.0001396396,0.00008318388,0.00005495781,0.00002560337,0.000728528,0.00167923],"genre_scores_gemma":[0.9442807,0.0001314178,0.05301073,0.0001841063,0.00005734248,0.0001256782,0.00006013362,0.00005024601,0.002099737],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002813202,"threshold_uncertainty_score":0.005593717,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Zhizhong Zhang","is_ca":false},{"name":"Peng Lin","is_ca":false},{"name":"Omair Shafiq","is_ca":true},{"name":"Yu Zhang","is_ca":false},{"name":"F. Richard Yu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04371974259148603,"gpt":0.295808043476172,"spread":0.252088300884686,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004707622,0.0007026212,0.0009443437,0.0003513579,0.0006493501,0.0009444823,0.001636855,0.0005471457,0.001176703],"category_scores_gemma":[0.001320124,0.0002736418,0.0003176926,0.0004257969,0.0006058594,0.0008217664,0.00144142,0.0006263422,0.0002078363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009872632,"about_ca_system_score_gemma":0.001027507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006351116,"about_ca_topic_score_gemma":0.005816686,"domain_scores_codex":[0.999622,0.00005220297,0.00001488899,0.0001020231,0.00008115645,0.0001277539],"domain_scores_gemma":[0.9994736,0.0001894838,0.00007587251,0.00004805066,0.0001280419,0.00008492944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006496158,0.00004671517,0.0004824243,0.00002025714,0.00001176127,0.0000506071,0.00002870227,0.9746927,0.002068143,0.002841638,0.0007327776,0.01895936],"study_design_scores_gemma":[0.000002039398,0.000006535479,0.00002760407,5.388429e-7,0.000001221735,0.000003084911,0.000003592774,0.9992126,0.0001456232,0.0005342625,0.00006157901,0.000001204548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09952769,0.0003446122,0.893088,0.0003532435,0.0000832239,0.0000800781,0.00005041329,0.0004616186,0.006010975],"genre_scores_gemma":[0.9866396,0.00006197628,0.01197189,0.00005625172,0.00001930259,0.00002791812,0.00003365504,0.00002051515,0.001168929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006351116,"threshold_uncertainty_score":0.01262832,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Ahmed M. Omara","is_ca":true},{"name":"Burak Kantarcı","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0286622598579752,"gpt":0.297633174292653,"spread":0.2689709144346777,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002121822,0.0008687087,0.0006959862,0.0003742756,0.0003491933,0.0007936486,0.0008165521,0.000926527,0.001304136],"category_scores_gemma":[0.008680999,0.0002589061,0.0005247066,0.000201589,0.001401998,0.001833323,0.00162463,0.001388607,0.0001913275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008885425,"about_ca_system_score_gemma":0.0006117646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001384101,"about_ca_topic_score_gemma":0.000752849,"domain_scores_codex":[0.998765,0.0004630913,0.00003660554,0.0002239391,0.0002720907,0.0002394434],"domain_scores_gemma":[0.9936842,0.00471815,0.0006526401,0.0004016832,0.0003774836,0.0001657218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008724072,0.00002033782,0.0008646186,0.00002196504,0.0000200115,0.00006869135,0.00002521029,0.9844333,0.001532849,0.006293508,0.0002461054,0.006386224],"study_design_scores_gemma":[0.000002377891,0.00005050512,0.0001787539,0.000003411634,0.000003317921,0.00002485649,0.00001031336,0.996465,0.0007224703,0.002410413,0.0001248015,0.000003820433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1929795,0.0004479937,0.7986402,0.0009750405,0.0001167059,0.0001035585,0.00008712594,0.000475402,0.006174547],"genre_scores_gemma":[0.991253,0.00008016523,0.007529671,0.00009368485,0.00001946676,0.00002231596,0.00002407731,0.00001375,0.0009638566],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002121822,"threshold_uncertainty_score":0.01122141,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Yufei Kang","is_ca":true},{"name":"Baochun Li","is_ca":true},{"name":"Timothy Zeyl","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05647577360395995,"gpt":0.2909231474283444,"spread":0.2344473738243845,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006134666,0.00118087,0.001564832,0.0007331057,0.000852535,0.001450757,0.002433139,0.001561839,0.00109197],"category_scores_gemma":[0.01926961,0.0004032278,0.0005989515,0.0007896391,0.001352273,0.003320411,0.002632568,0.002454563,0.0004727046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001221608,"about_ca_system_score_gemma":0.002091593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005894007,"about_ca_topic_score_gemma":0.004571192,"domain_scores_codex":[0.9980847,0.0006090892,0.0001375093,0.0004614457,0.0004482678,0.0002589076],"domain_scores_gemma":[0.993614,0.002957712,0.0004772636,0.001699952,0.001003668,0.0002474826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005300289,0.0003741866,0.003540413,0.00006518132,0.0001104943,0.0001498521,0.0001360423,0.8246927,0.002494744,0.005657053,0.003398504,0.1588509],"study_design_scores_gemma":[0.00001949019,0.00004022478,0.00009706314,0.000004102587,0.000004294827,0.00001977353,0.000008491852,0.9965572,0.0008636701,0.00222558,0.000155609,0.000004428945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09914438,0.0006320688,0.8923484,0.0005971637,0.0001503026,0.0001170109,0.0001122919,0.004927343,0.001970968],"genre_scores_gemma":[0.8715833,0.0001379072,0.1262319,0.000289294,0.00004626727,0.00008629368,0.0001972283,0.0001416693,0.001286211],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006134666,"threshold_uncertainty_score":0.03244358,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Amr M. Zaki","is_ca":true},{"name":"Sara A. Elsayed","is_ca":true},{"name":"Khalid Elgazzar","is_ca":true},{"name":"Hossam S. Hassanein","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03680475511901938,"gpt":0.3012340949858866,"spread":0.2644293398668672,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004677855,0.0004795106,0.0005162622,0.0003427323,0.0006090579,0.0007292774,0.0006904395,0.000490547,0.0005098505],"category_scores_gemma":[0.001276901,0.0002879152,0.0003279465,0.0003512499,0.0005335298,0.0007011296,0.0005102489,0.0004941803,0.00009165232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008147443,"about_ca_system_score_gemma":0.00120834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005647144,"about_ca_topic_score_gemma":0.005616637,"domain_scores_codex":[0.9996494,0.0001159684,0.0000152925,0.00006098519,0.00006741098,0.00009093164],"domain_scores_gemma":[0.9994929,0.0002685009,0.00006824768,0.00006513705,0.00006515979,0.00004005055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009267365,0.00005372698,0.0007785747,0.00002854094,0.00001788647,0.00007428995,0.00004691454,0.9661926,0.003348497,0.004477415,0.0005158437,0.02437299],"study_design_scores_gemma":[0.000003641164,0.00001620611,0.0001183631,0.000001832471,0.000003390402,0.00001124492,0.00001066439,0.9979312,0.0005555883,0.001228146,0.000116629,0.000003073401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2992618,0.0005120485,0.6942062,0.0004643461,0.0001048755,0.00007666388,0.00005279187,0.000758048,0.00456316],"genre_scores_gemma":[0.9726834,0.0000768539,0.02670703,0.0000430541,0.000009467766,0.0000221912,0.00002190453,0.00001694935,0.0004192239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005647144,"threshold_uncertainty_score":0.01122856,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Muhammad Saqib","is_ca":false},{"name":"Zakaria Ait Hmitti","is_ca":false},{"name":"Halima Elbiaze","is_ca":false},{"name":"Roch Glitho","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1327742722399738,"gpt":0.3406184952423835,"spread":0.2078442230024097,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005270816,0.0008348014,0.0008051992,0.001051326,0.0005373097,0.001862828,0.001429065,0.0006699759,0.00133817],"category_scores_gemma":[0.00226035,0.0003008173,0.0004449123,0.0009476854,0.0004545052,0.001979952,0.0007536936,0.001047619,0.001092509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001119732,"about_ca_system_score_gemma":0.001455762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004470782,"about_ca_topic_score_gemma":0.004435696,"domain_scores_codex":[0.999094,0.00009836962,0.00005105598,0.0001927729,0.0004459444,0.0001178432],"domain_scores_gemma":[0.9989838,0.0001912021,0.0001618242,0.0003136495,0.0002974441,0.00005203102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003180042,0.0002850953,0.00544402,0.0001104984,0.00005127848,0.0002103214,0.0001355139,0.2661991,0.07782078,0.01528297,0.006918184,0.6272241],"study_design_scores_gemma":[0.000004279452,0.00002703686,0.0004093265,0.000003736911,0.000006125252,0.00004205825,0.00002538851,0.9830641,0.01278417,0.001940908,0.001684804,0.000007970358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02396462,0.0001105412,0.9723766,0.0001316953,0.00003600089,0.00007353875,0.00009166395,0.002006628,0.001208586],"genre_scores_gemma":[0.4371844,0.0002208755,0.5586856,0.0001323017,0.00005698801,0.000125643,0.0004625419,0.0001505581,0.002981162],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004470782,"threshold_uncertainty_score":0.008889556,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Marah De’bas","is_ca":true},{"name":"Sara A. Elsayed","is_ca":true},{"name":"Hossam S. Hassanein","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05859603137716168,"gpt":0.2777776698856355,"spread":0.2191816385084738,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008084945,0.0005167661,0.0006733699,0.0002535619,0.0006085667,0.001180663,0.001663418,0.0005358569,0.003280606],"category_scores_gemma":[0.0009618492,0.0001886461,0.0004060916,0.0004550989,0.000646126,0.001239647,0.001490899,0.0008172456,0.0005259078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007019252,"about_ca_system_score_gemma":0.0007471762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002134016,"about_ca_topic_score_gemma":0.003010541,"domain_scores_codex":[0.999273,0.0001990728,0.00002575755,0.0001552045,0.0001388385,0.0002081343],"domain_scores_gemma":[0.9993711,0.0002137054,0.00006052156,0.0001522842,0.0001101882,0.00009222911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001041657,0.0003530746,0.001711019,0.0003099643,0.0001252749,0.0005210466,0.0004636446,0.6437226,0.06297034,0.06995146,0.0144554,0.2043744],"study_design_scores_gemma":[0.00003315717,0.0001054567,0.000308012,0.00001220778,0.00001701792,0.00009718537,0.00006153787,0.9703536,0.005655279,0.01772162,0.005613755,0.00002118142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06517464,0.0009235925,0.9245079,0.0003415766,0.0001545079,0.00009480243,0.00007585033,0.0009929001,0.007734244],"genre_scores_gemma":[0.8971528,0.0003097868,0.09786877,0.0001759358,0.00005923515,0.00008396521,0.00007614325,0.00007616475,0.00419716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003280606,"threshold_uncertainty_score":0.01097471,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Songnian Zhang","is_ca":true},{"name":"Suprio Ray","is_ca":true},{"name":"Rongxing Lu","is_ca":true},{"name":"Yunguo Guan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04884211409982258,"gpt":0.3001426850261781,"spread":0.2513005709263555,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005858425,0.000518803,0.0008825777,0.0007745432,0.001354204,0.001985805,0.001773018,0.001578954,0.002473223],"category_scores_gemma":[0.007798414,0.0003666883,0.0007326339,0.001696309,0.00233493,0.008493872,0.007501136,0.001709959,0.0007468937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00119532,"about_ca_system_score_gemma":0.001791366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001260666,"about_ca_topic_score_gemma":0.0005294051,"domain_scores_codex":[0.9938416,0.002640182,0.0004792797,0.0006335503,0.001682027,0.0007234175],"domain_scores_gemma":[0.9939132,0.00193753,0.0006992061,0.002533317,0.0006278125,0.0002888539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.005049639,0.0005172294,0.00804975,0.001061634,0.0003867432,0.002124147,0.003886166,0.1174489,0.09001067,0.4146507,0.02224066,0.3345736],"study_design_scores_gemma":[0.0006445423,0.001344281,0.003048539,0.0001181275,0.0001632298,0.002455265,0.001766301,0.62248,0.08404163,0.2365584,0.04711165,0.0002681327],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07433546,0.001093812,0.9146144,0.001496968,0.0001188356,0.0005856699,0.000471892,0.002177449,0.005105492],"genre_scores_gemma":[0.9033137,0.0004280044,0.09246266,0.0004045269,0.00008205779,0.0003178775,0.0004498461,0.00007863692,0.002462619],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005858425,"threshold_uncertainty_score":0.03098267,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Ibrahim M. Amer","is_ca":true},{"name":"Sharief Oteafy","is_ca":true},{"name":"Sara A. Elsayed","is_ca":true},{"name":"Hossam S. Hassanein","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05825631538762528,"gpt":0.3244304618272186,"spread":0.2661741464395933,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001427791,0.0007312378,0.0008315532,0.0004127668,0.0008163782,0.001115418,0.001509213,0.0006417258,0.001823817],"category_scores_gemma":[0.003813086,0.0002525729,0.0004888922,0.0006543114,0.0006022112,0.001711945,0.001536852,0.001047997,0.0003660769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006867789,"about_ca_system_score_gemma":0.00103215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001709122,"about_ca_topic_score_gemma":0.001978965,"domain_scores_codex":[0.9990933,0.0003127806,0.00005123642,0.0001695746,0.0002036859,0.0001694842],"domain_scores_gemma":[0.9981992,0.0008640728,0.0001804702,0.0003038866,0.0003153875,0.0001370134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006559541,0.0001746325,0.001530155,0.0003165192,0.00005710145,0.0004363747,0.0003605139,0.8103403,0.03540962,0.0284891,0.00512024,0.1171095],"study_design_scores_gemma":[0.00001507532,0.00008044753,0.0001676223,0.000008868884,0.00001055441,0.00008856574,0.00004796316,0.9873424,0.002909113,0.007964698,0.001352776,0.00001192184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09426942,0.001606892,0.8972642,0.0005216954,0.0001953649,0.0001206562,0.00011481,0.0007383724,0.0051685],"genre_scores_gemma":[0.9299766,0.0004062846,0.06750156,0.0001126968,0.00007876036,0.00007864455,0.0000947533,0.00007963778,0.001671022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001823817,"threshold_uncertainty_score":0.007550955,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Sifatul Mostafi","is_ca":true},{"name":"Weimin Zhao","is_ca":true},{"name":"Sittichai Sukreep","is_ca":true},{"name":"Khalid Elgazzar","is_ca":true},{"name":"Akramul Azim","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05396969457280422,"gpt":0.3215151458390331,"spread":0.2675454512662289,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005410131,0.0007296705,0.0009025129,0.0008663235,0.0003582252,0.0005944553,0.001296004,0.0008087002,0.001081737],"category_scores_gemma":[0.0008834036,0.0003757392,0.0004702099,0.000579054,0.0001861823,0.0009992317,0.001018906,0.0009287264,0.0006287927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006833636,"about_ca_system_score_gemma":0.0009898461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006881448,"about_ca_topic_score_gemma":0.007003159,"domain_scores_codex":[0.9995897,0.00002779487,0.00002689161,0.0001375932,0.0001376157,0.00008032847],"domain_scores_gemma":[0.9996277,0.00005228618,0.00005474906,0.00005117712,0.0001505477,0.00006362283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007501419,0.0008249707,0.01139405,0.0001937825,0.0001178461,0.0006166883,0.0001441746,0.07295179,0.07848873,0.001297624,0.008473095,0.8247471],"study_design_scores_gemma":[0.00002430148,0.000121381,0.00268166,0.000009041851,0.00002788573,0.0001311627,0.00002148948,0.980646,0.01485001,0.0005520604,0.0009117532,0.00002324691],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.157338,0.0005522793,0.8269091,0.0003169629,0.0002932489,0.0001343736,0.0003587203,0.01047638,0.003620871],"genre_scores_gemma":[0.863006,0.0002744385,0.1310133,0.0002870786,0.00005840932,0.000102112,0.0007237459,0.0000853933,0.004449476],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006881448,"threshold_uncertainty_score":0.01368278,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Scheduling (production processes); Bottleneck; Distributed computing; Job shop scheduling; Data center; Bandwidth (computing); Mathematical optimization; Integer programming; Computer network; Algorithm; Embedded system; Mathematics","authors":[{"name":"Binquan Guo","is_ca":false},{"name":"Zhou Zhang","is_ca":true},{"name":"Yan Ye","is_ca":true},{"name":"Hongyan Li","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04667136907575386,"gpt":0.2941921117054132,"spread":0.2475207426296593,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001018161,0.0008323784,0.0008872134,0.0004428506,0.0005789337,0.0008543159,0.0009085048,0.0005926323,0.00103461],"category_scores_gemma":[0.00143036,0.0005020718,0.0004273625,0.0006074038,0.0008069624,0.001052862,0.0008922963,0.000694697,0.0001363244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008833404,"about_ca_system_score_gemma":0.001154723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00459512,"about_ca_topic_score_gemma":0.003313024,"domain_scores_codex":[0.9994233,0.0002026811,0.0000145347,0.0001122863,0.0001068017,0.0001404851],"domain_scores_gemma":[0.9993258,0.0003876672,0.00008628407,0.00005685249,0.00007075468,0.00007269298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001773619,0.00004865132,0.0002244728,0.00003930293,0.00001375342,0.00003660157,0.0000284724,0.9784558,0.003295618,0.004043469,0.0003815113,0.01325499],"study_design_scores_gemma":[0.000005111154,0.00001295805,0.00004158977,9.681231e-7,0.000001923332,0.000003420883,0.000006119707,0.9984102,0.0004144796,0.00101428,0.00008671227,0.00000214405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1387,0.0004156536,0.8568999,0.0001967304,0.00006509841,0.00005639857,0.00005729496,0.0005210534,0.003087866],"genre_scores_gemma":[0.9333345,0.0001100528,0.06516459,0.00004375217,0.00002880892,0.00006164529,0.0000529148,0.00005375803,0.001149851],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00459512,"threshold_uncertainty_score":0.009136796,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Xingrui Yi","is_ca":false},{"name":"Yutong Liu","is_ca":false},{"name":"Linghe Kong","is_ca":false},{"name":"Guihai Chen","is_ca":false},{"name":"Xue Liu","is_ca":true},{"name":"Shahid Mumtaz","is_ca":false},{"name":"Joel J. P. C. Rodrigues","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02977274784886347,"gpt":0.2560944657299564,"spread":0.2263217178810929,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004101858,0.0007578384,0.0004300206,0.0002617952,0.0003972581,0.0005443852,0.0005704582,0.0006082052,0.003021622],"category_scores_gemma":[0.0005589803,0.0001964541,0.0002941262,0.0003580437,0.0003451346,0.001009428,0.0005962063,0.0007169871,0.001091306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005331511,"about_ca_system_score_gemma":0.0005078483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002142868,"about_ca_topic_score_gemma":0.004593177,"domain_scores_codex":[0.9997144,0.00008394723,0.00001181562,0.0000476264,0.00008714899,0.00005502995],"domain_scores_gemma":[0.9997945,0.000043664,0.00002058748,0.00002772402,0.00009463623,0.00001888369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003714836,0.0001150976,0.00334671,0.0001819063,0.0001006862,0.0003261862,0.0001778696,0.3632584,0.08778557,0.0300447,0.01173762,0.5025539],"study_design_scores_gemma":[0.00002067805,0.0001357372,0.0004705261,0.00002001395,0.00002627281,0.0001291614,0.00005462767,0.9695292,0.01414377,0.007866979,0.007584121,0.00001889593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01435998,0.0003957619,0.9781225,0.0004115695,0.0001137257,0.00002944315,0.00006875042,0.001289991,0.005208192],"genre_scores_gemma":[0.8312032,0.0007369372,0.1593923,0.000418659,0.0001282943,0.00008414486,0.0002342715,0.00009132009,0.007710901],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003021622,"threshold_uncertainty_score":0.01010835,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Petros Spachos","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05213402463928806,"gpt":0.324930110147868,"spread":0.2727960855085799,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002299884,0.00101876,0.000868472,0.0007005779,0.0004557195,0.00114143,0.001262385,0.001121471,0.002821678],"category_scores_gemma":[0.006443274,0.0003322538,0.0005563375,0.0006333633,0.0003915952,0.002022021,0.001610576,0.001484874,0.001437603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007853725,"about_ca_system_score_gemma":0.0005241758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005144104,"about_ca_topic_score_gemma":0.005052279,"domain_scores_codex":[0.998687,0.0004242919,0.00008602767,0.0003762942,0.0002439456,0.0001823823],"domain_scores_gemma":[0.9984363,0.0007658517,0.0001058313,0.0002231843,0.0004127095,0.00005616562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009687298,0.0004378137,0.003916821,0.0001595449,0.0001118562,0.000170721,0.0002784164,0.04879548,0.03015985,0.002070571,0.006287846,0.9066424],"study_design_scores_gemma":[0.0000193928,0.00009638767,0.001777397,0.00002113924,0.0000345937,0.000048849,0.00008050197,0.9832412,0.01091234,0.002525496,0.001226452,0.00001609791],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2628375,0.003497602,0.7164518,0.001459796,0.0003503449,0.000194088,0.0005929041,0.006832441,0.007783558],"genre_scores_gemma":[0.8667456,0.0006400725,0.1239569,0.0004761372,0.0000873266,0.0001780697,0.001215473,0.0002021698,0.006498218],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005144104,"threshold_uncertainty_score":0.0121631,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Ammar Abdelsamie","is_ca":true},{"name":"Ian Marsland","is_ca":true},{"name":"Ahmed Ibrahim","is_ca":true},{"name":"Halim Yanıkömeroğlu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07699485586050042,"gpt":0.2890916808832342,"spread":0.2120968250227337,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006416292,0.0004138275,0.0003925096,0.0003178341,0.0003257286,0.0006393197,0.0009022752,0.0007429984,0.00125808],"category_scores_gemma":[0.002228037,0.0002040085,0.0001896908,0.0003417521,0.0003388974,0.0009164194,0.0005753593,0.0008602709,0.0002536456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007830473,"about_ca_system_score_gemma":0.000747157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002444692,"about_ca_topic_score_gemma":0.003582639,"domain_scores_codex":[0.9996676,0.00007767856,0.00001564738,0.00005820974,0.0001342037,0.00004665941],"domain_scores_gemma":[0.9994217,0.0003257372,0.00005325192,0.00004384634,0.0001384947,0.00001706313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003941034,0.0001288329,0.001665951,0.0001447239,0.00006509742,0.0001289702,0.0001035391,0.6008472,0.03333452,0.01724622,0.001279857,0.344661],"study_design_scores_gemma":[0.000003915743,0.00003036738,0.00009817688,0.000003407483,0.000004084458,0.0000187506,0.000002457843,0.9951909,0.003754534,0.0006482979,0.0002411369,0.00000396727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05182923,0.0003843196,0.9437076,0.0002140987,0.00006653985,0.00004034953,0.00004984992,0.0005566241,0.003151279],"genre_scores_gemma":[0.776711,0.0002227264,0.2180923,0.0001410056,0.00004529961,0.00006233041,0.00008698036,0.00003166557,0.004606683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002444692,"threshold_uncertainty_score":0.005681396,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Lingshuang Liu","is_ca":true},{"name":"Cheng Huang","is_ca":true},{"name":"Dan Zhu","is_ca":true},{"name":"Dongxiao Liu","is_ca":true},{"name":"Jianbing Ni","is_ca":true},{"name":"Xuemin Shen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02364015779418623,"gpt":0.3061354620481124,"spread":0.2824953042539262,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001413539,0.0004928377,0.0005661247,0.0006703441,0.001114411,0.001441979,0.001221822,0.00063568,0.001549633],"category_scores_gemma":[0.002823926,0.0002004426,0.0004272106,0.0005361637,0.001236646,0.002278447,0.001846745,0.001193144,0.0003730998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001264446,"about_ca_system_score_gemma":0.001268868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002786649,"about_ca_topic_score_gemma":0.002230286,"domain_scores_codex":[0.9981516,0.0003400318,0.000111307,0.000336977,0.0007198076,0.0003403274],"domain_scores_gemma":[0.9985604,0.0003864685,0.0001832148,0.000407553,0.0003574948,0.0001049656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001120849,0.0003740041,0.002252968,0.0002341981,0.00009542677,0.0006109728,0.0005911118,0.3471904,0.06549934,0.326325,0.008506362,0.2471993],"study_design_scores_gemma":[0.00005384005,0.00009686829,0.0002815075,0.00000891617,0.00001537426,0.00009081251,0.00005656094,0.9684254,0.006376493,0.02010837,0.004467564,0.00001831517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03581304,0.0003397174,0.9597604,0.0002232356,0.00007538447,0.0001166009,0.0000306933,0.0007428025,0.002898174],"genre_scores_gemma":[0.9527858,0.0001697858,0.04483101,0.00007347441,0.00005676542,0.00009517212,0.0000511353,0.00002910014,0.001907752],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002786649,"threshold_uncertainty_score":0.009174287,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Mariam Elsayem","is_ca":true},{"name":"Hatem Abou-Zeid","is_ca":true},{"name":"Ali Afana","is_ca":true},{"name":"Sidney Givigi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03596270922348667,"gpt":0.2998518492864165,"spread":0.2638891400629299,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001248687,0.0005539086,0.0008204788,0.0002549848,0.0003283319,0.0005934341,0.001041321,0.0005979454,0.001778168],"category_scores_gemma":[0.003571376,0.0002292517,0.0002427491,0.0002412661,0.0007929293,0.000749998,0.0007743646,0.001165437,0.0002340532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008177926,"about_ca_system_score_gemma":0.001074366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00574766,"about_ca_topic_score_gemma":0.004318736,"domain_scores_codex":[0.9994764,0.00018626,0.00002378038,0.00009455525,0.0000866902,0.0001323247],"domain_scores_gemma":[0.9983564,0.001063733,0.0001619352,0.00006889751,0.0002251468,0.0001237361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001541249,0.0001288726,0.0007099222,0.00003153829,0.00002869562,0.00006254015,0.0000458875,0.9569854,0.001533872,0.00426217,0.0008074952,0.03524944],"study_design_scores_gemma":[0.000008903196,0.00002126739,0.0000482337,0.000001432613,0.00000213672,0.000005215209,0.000002973609,0.9986377,0.0001500228,0.001035519,0.00008450645,0.000001961604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09832557,0.0006996464,0.8954373,0.0004781441,0.0001054115,0.000101948,0.00003863057,0.0005788766,0.00423442],"genre_scores_gemma":[0.9818503,0.00007093901,0.01673214,0.00008548964,0.00001634137,0.00004843843,0.00002027987,0.00001478185,0.001161166],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00574766,"threshold_uncertainty_score":0.01142836,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Zepu Wang","is_ca":false},{"name":"Peng Sun","is_ca":false},{"name":"Yulin Hu","is_ca":false},{"name":"Azzedine Boukerche","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0273375236080369,"gpt":0.2726505909286824,"spread":0.2453130673206455,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005983912,0.0008646008,0.0006591069,0.001119089,0.000408415,0.00048298,0.0008987906,0.0005641039,0.002979558],"category_scores_gemma":[0.0009637328,0.0002618656,0.0002996475,0.0007369972,0.000278846,0.001010897,0.0006739361,0.001033081,0.001173078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006923992,"about_ca_system_score_gemma":0.001410271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01410982,"about_ca_topic_score_gemma":0.01670809,"domain_scores_codex":[0.9998529,0.00001884942,0.000006795302,0.00003350409,0.00006425413,0.00002371203],"domain_scores_gemma":[0.9997532,0.00005156358,0.00002371815,0.00002972909,0.0001136971,0.00002814863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003473844,0.0002871198,0.009444077,0.0001181702,0.00005676543,0.0001378283,0.00007923377,0.5324073,0.01163885,0.004077128,0.03123478,0.4101713],"study_design_scores_gemma":[0.000007121567,0.00002329314,0.0004065893,0.00000333332,0.000003630483,0.000007760178,0.000004317159,0.9966735,0.001047318,0.0005164855,0.001301242,0.000005491032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05978586,0.0004029071,0.9153894,0.000530438,0.0004401574,0.0001201369,0.002030558,0.01750114,0.003799484],"genre_scores_gemma":[0.673111,0.0004411898,0.3148912,0.00026766,0.0002060979,0.0002211673,0.004239807,0.0002476639,0.006374211],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01410982,"threshold_uncertainty_score":0.02805537,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Bin Xiao","is_ca":true},{"name":"Yakup Akkaya","is_ca":true},{"name":"Murat Şimşek","is_ca":true},{"name":"Burak Kantarcı","is_ca":true},{"name":"Ala Abu Alkheir","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.114175160009488,"gpt":0.3601949135115256,"spread":0.2460197535020376,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007588193,0.0007634654,0.001095142,0.002382865,0.0008003676,0.001410312,0.001313593,0.0009443266,0.001857973],"category_scores_gemma":[0.003433094,0.0003490201,0.0009687929,0.002541627,0.0003864825,0.002841176,0.001344119,0.0007244867,0.0007951641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001175058,"about_ca_system_score_gemma":0.001200421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01016043,"about_ca_topic_score_gemma":0.006896411,"domain_scores_codex":[0.9987149,0.0002303389,0.00009841711,0.0004123397,0.0004033549,0.0001405909],"domain_scores_gemma":[0.9987765,0.0004053313,0.0002100736,0.0002258421,0.0003142647,0.00006814311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004864656,0.0003275555,0.008280455,0.0002284215,0.0001500551,0.0003784774,0.0004879006,0.1677137,0.01519001,0.008323363,0.01183987,0.7865938],"study_design_scores_gemma":[0.000009744993,0.00003517553,0.001225557,0.000007937318,0.00002441846,0.00006898242,0.000109776,0.9862599,0.00528106,0.005347475,0.001615781,0.00001419514],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1599367,0.0009646874,0.8234101,0.0008045455,0.0001661099,0.0002824123,0.001579723,0.006297227,0.00655851],"genre_scores_gemma":[0.7557458,0.00039774,0.2354043,0.0002209062,0.00008797498,0.0001713248,0.003296115,0.0001134093,0.004562406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01016043,"threshold_uncertainty_score":0.02020258,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Wei Yu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03122715736480495,"gpt":0.2621237073563796,"spread":0.2308965499915747,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002577266,0.001861834,0.00154953,0.000571569,0.0004383142,0.001128587,0.002151444,0.002342568,0.003114632],"category_scores_gemma":[0.008705625,0.0005325079,0.0009730629,0.0008659864,0.001426434,0.002928346,0.002518072,0.003186022,0.0008979993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001188662,"about_ca_system_score_gemma":0.001179166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002155479,"about_ca_topic_score_gemma":0.001319037,"domain_scores_codex":[0.9989682,0.0003759462,0.00006196325,0.0002471866,0.000232355,0.0001143759],"domain_scores_gemma":[0.9976023,0.001448355,0.0001618857,0.0003591657,0.0003553398,0.00007307081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001503906,0.0001356753,0.0006899168,0.0001221253,0.00006771068,0.0001152343,0.00005492495,0.8740865,0.001783436,0.01466103,0.00276486,0.1053683],"study_design_scores_gemma":[0.000006043417,0.00002844028,0.00004671027,0.000004950391,0.000005121278,0.00001226017,0.000003613018,0.9940361,0.0006745338,0.004949429,0.0002293022,0.000003564413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01199712,0.0003684326,0.9846853,0.0003266785,0.00005346731,0.00005261971,0.00006152049,0.0007366758,0.001718102],"genre_scores_gemma":[0.7940658,0.0006313042,0.1944221,0.000618823,0.0001822814,0.0004944849,0.0006160617,0.0002702833,0.008698735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003114632,"threshold_uncertainty_score":0.01363003,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Mahmoud Abdelhadi","is_ca":true},{"name":"Sameh Sorour","is_ca":true},{"name":"Hesham ElSawy","is_ca":true},{"name":"Sara A. Elsayed","is_ca":true},{"name":"Hossam S. Hassanein","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07264076839525885,"gpt":0.2986818784623422,"spread":0.2260411100670833,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000731034,0.0006856067,0.0005989798,0.0004837229,0.0004107236,0.001061516,0.000855473,0.0006542572,0.001711058],"category_scores_gemma":[0.003199247,0.000319639,0.0005815496,0.0005680962,0.0009976398,0.001434761,0.0009507246,0.0009937244,0.000211832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001097912,"about_ca_system_score_gemma":0.001081914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006754808,"about_ca_topic_score_gemma":0.002858173,"domain_scores_codex":[0.9996556,0.00009234419,0.00001718717,0.00007306357,0.00008604289,0.00007577019],"domain_scores_gemma":[0.9988239,0.0006108619,0.0002113514,0.00008768399,0.0001737251,0.00009241857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003788708,0.00001971445,0.001455148,0.0000418147,0.00002498361,0.0001518298,0.00004840479,0.9094112,0.002302326,0.08161106,0.0007757577,0.004119833],"study_design_scores_gemma":[0.000001060476,0.000003779047,0.000130228,0.000001523148,0.000001868548,0.000009587025,0.000005907646,0.9935318,0.00009371772,0.006055663,0.0001630443,0.00000178335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0679784,0.0007918145,0.9237942,0.001024384,0.00007461304,0.00005379436,0.0001784857,0.0001440966,0.005960254],"genre_scores_gemma":[0.9725093,0.001126832,0.02310755,0.000132951,0.00008939935,0.00006677643,0.0001355283,0.00005963032,0.002772107],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006754808,"threshold_uncertainty_score":0.01343101,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Backscatter (email); Computer science; Fading; Wireless; Monte Carlo method; Electronic engineering; Real-time computing; Remote sensing; Telecommunications; Computer network; Channel (broadcasting); Engineering; Geography; Statistics","authors":[{"name":"Xu Jiang","is_ca":false},{"name":"Min Sheng","is_ca":false},{"name":"Nan Zhao","is_ca":false},{"name":"Junyu Liu","is_ca":false},{"name":"Dusit Niyato","is_ca":false},{"name":"F. Richard Yu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02594531621960327,"gpt":0.2421135798325654,"spread":0.2161682636129621,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005395933,0.0006753043,0.0005705258,0.0004408207,0.0003838366,0.0006269663,0.0007533145,0.0007913079,0.001000301],"category_scores_gemma":[0.001394758,0.0002457132,0.000377787,0.000618645,0.0006608076,0.00117742,0.0006271432,0.0005996413,0.000205161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0011354,"about_ca_system_score_gemma":0.000483963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009830348,"about_ca_topic_score_gemma":0.007390204,"domain_scores_codex":[0.9996444,0.0001104791,0.00001406938,0.00005307602,0.00008740906,0.00009045953],"domain_scores_gemma":[0.9992386,0.0003630082,0.0001626317,0.00005264813,0.0001559505,0.00002716821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002340121,0.000013019,0.001072675,0.00002996333,0.0000121256,0.0001969615,0.00008385236,0.9834293,0.001433534,0.009576185,0.0004364257,0.003692399],"study_design_scores_gemma":[8.209361e-7,0.000005710182,0.0001422587,0.000001599571,0.000002837373,0.00002176673,0.00001311645,0.9987122,0.00009524197,0.0009101662,0.0000922763,0.000001959256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3177826,0.002153046,0.6632059,0.0006489126,0.00008295402,0.00006182577,0.0004006908,0.0003540805,0.01531017],"genre_scores_gemma":[0.9908453,0.0006404064,0.006029944,0.00003508854,0.00003157965,0.00003072702,0.00007614917,0.00001665151,0.002294186],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009830348,"threshold_uncertainty_score":0.01954627,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Bassel S. Chawky","is_ca":true},{"name":"Mohamed Hefeida","is_ca":false},{"name":"Aboelmagd Noureldin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03014893014303192,"gpt":0.2945377474138365,"spread":0.2643888172708046,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00174638,0.0009044281,0.0007921483,0.0008042415,0.0004827002,0.0007941641,0.001048725,0.0007398514,0.0008129443],"category_scores_gemma":[0.008802176,0.0002828489,0.0004105958,0.0005550729,0.0004764752,0.001237762,0.0009472689,0.0004449028,0.0001645622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006228099,"about_ca_system_score_gemma":0.0004862468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001676765,"about_ca_topic_score_gemma":0.0009921172,"domain_scores_codex":[0.9979125,0.0005067676,0.0001163099,0.0002741725,0.0009204239,0.0002698289],"domain_scores_gemma":[0.9924814,0.003775599,0.000847395,0.000749713,0.001948384,0.000197615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002950262,0.000449653,0.03209862,0.001196493,0.0004443495,0.001762688,0.0009358581,0.6716293,0.1353683,0.001999984,0.0009537682,0.1502107],"study_design_scores_gemma":[0.00006575614,0.003864649,0.02584612,0.00008681061,0.0004118395,0.00102282,0.0006733586,0.8299224,0.1352437,0.001171696,0.001593831,0.00009704015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8997619,0.001106439,0.09589902,0.0001432442,0.00007520501,0.00009477438,0.00009394416,0.0005529936,0.002272476],"genre_scores_gemma":[0.9946532,0.0001063196,0.004944696,0.00001509161,0.000005543598,0.00001598021,0.00004020632,0.0000135893,0.0002054054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00174638,"threshold_uncertainty_score":0.009235859,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Yasser H. Khalil","is_ca":true},{"name":"Hussein T. Mouftah","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04719044268613398,"gpt":0.2982042658041004,"spread":0.2510138231179664,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005808519,0.001719312,0.0007317338,0.0009149213,0.0003762996,0.0006412856,0.001932142,0.0008956681,0.002230409],"category_scores_gemma":[0.001529157,0.0005133565,0.0005420783,0.0006529817,0.0003023752,0.001509861,0.001096693,0.001202237,0.001344904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008279825,"about_ca_system_score_gemma":0.001114183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01514945,"about_ca_topic_score_gemma":0.02537104,"domain_scores_codex":[0.9997063,0.00003072006,0.000009911372,0.0001267518,0.00007312263,0.00005307617],"domain_scores_gemma":[0.9997368,0.00005841838,0.00002265451,0.00004575701,0.0001081928,0.00002808314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005836469,0.0005600943,0.007107299,0.0002733193,0.0002507232,0.0002627824,0.00009202994,0.2512136,0.02896989,0.002365331,0.03621412,0.6721072],"study_design_scores_gemma":[0.00001870302,0.00007347447,0.001097597,0.00001376869,0.00002244365,0.0000463277,0.00001964641,0.9891684,0.006096964,0.001449146,0.001982335,0.00001118629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2815723,0.003922795,0.6685287,0.001329447,0.001404541,0.0003605827,0.005766221,0.02725706,0.009858376],"genre_scores_gemma":[0.8186328,0.000650764,0.1614904,0.0004602361,0.0001837947,0.0001259754,0.01051273,0.0003052457,0.007638159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01514945,"threshold_uncertainty_score":0.03012252,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Vincent Amande","is_ca":false},{"name":"Kuljeet Kaur","is_ca":true},{"name":"Sahil Garg","is_ca":false},{"name":"Mohsen Guizani","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02895436059317776,"gpt":0.2808250768288596,"spread":0.2518707162356819,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009667579,0.0005249235,0.0008150534,0.001059567,0.00166958,0.00136293,0.001359756,0.0009631512,0.002239089],"category_scores_gemma":[0.002107523,0.0002357507,0.0007238342,0.0009790214,0.001242775,0.003182148,0.003327791,0.001315545,0.001317732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001093999,"about_ca_system_score_gemma":0.001641749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008632903,"about_ca_topic_score_gemma":0.0005430505,"domain_scores_codex":[0.9983388,0.0004099159,0.0001628802,0.0001836839,0.0005027803,0.0004018238],"domain_scores_gemma":[0.9982666,0.0002445999,0.0003052738,0.000600598,0.0004101308,0.000172835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003650291,0.00047812,0.002536571,0.0006689699,0.0002197366,0.001675095,0.001149395,0.03554627,0.1997323,0.4058567,0.02014885,0.3283378],"study_design_scores_gemma":[0.0003703384,0.001415398,0.001617676,0.0001602078,0.0002122582,0.003104638,0.00041441,0.6234382,0.1901723,0.07241506,0.1062716,0.0004079865],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05780814,0.001106518,0.9278744,0.0006228606,0.0004591659,0.0004505202,0.0001692183,0.004013412,0.007495667],"genre_scores_gemma":[0.9094534,0.000361347,0.08042223,0.00028999,0.0001482885,0.0002616877,0.000216077,0.00008254261,0.008764446],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002239089,"threshold_uncertainty_score":0.007937551,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Abdalla A. Moustafa","is_ca":true},{"name":"Sara A. Elsayed","is_ca":true},{"name":"Hossam S. Hassanein","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07502582090834353,"gpt":0.2864199305602017,"spread":0.2113941096518582,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001243967,0.0005143971,0.0006774828,0.0005110783,0.001190407,0.001346814,0.001795482,0.000758627,0.003057806],"category_scores_gemma":[0.002126922,0.0002663879,0.0004697713,0.0006251817,0.0007826755,0.001993509,0.002211994,0.0008417508,0.0004261128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000990598,"about_ca_system_score_gemma":0.001580804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00356531,"about_ca_topic_score_gemma":0.004700312,"domain_scores_codex":[0.9990999,0.0002871301,0.00002484874,0.0001733699,0.0001409494,0.0002739177],"domain_scores_gemma":[0.9989055,0.000405346,0.00009587323,0.0001921614,0.0001877513,0.0002133363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005875224,0.0003422118,0.001277612,0.0002159271,0.00008983658,0.000332222,0.0003347381,0.7688464,0.01762381,0.09664442,0.009529188,0.1041761],"study_design_scores_gemma":[0.00003411489,0.00006194536,0.0002491667,0.00001168125,0.00001316907,0.00008106874,0.00009960277,0.9596629,0.002016431,0.03423901,0.003515342,0.00001566612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1160365,0.0007284204,0.8693258,0.0006827321,0.0001194384,0.0002006648,0.0001425886,0.0008478716,0.01191598],"genre_scores_gemma":[0.8660058,0.0001968775,0.1306124,0.0001787193,0.00002543627,0.0001005583,0.0001042738,0.0001001927,0.002675783],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00356531,"threshold_uncertainty_score":0.01022941,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Xinying Chen","is_ca":false},{"name":"Zheng Chang","is_ca":false},{"name":"Nan Zhao","is_ca":false},{"name":"Timo Hämäläinen","is_ca":false},{"name":"Xianbin Wang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0262809338233045,"gpt":0.2503568359805436,"spread":0.2240759021572391,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003605968,0.0006299417,0.0005701121,0.0003088276,0.0004773637,0.0004691433,0.0006995625,0.0003741994,0.0006645417],"category_scores_gemma":[0.0008024142,0.0002008517,0.0003486757,0.0004899923,0.0003931914,0.0009802463,0.001031272,0.0005436357,0.0002474295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003477113,"about_ca_system_score_gemma":0.0005488024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001140581,"about_ca_topic_score_gemma":0.001227906,"domain_scores_codex":[0.9995551,0.00009408665,0.00003123809,0.00008076282,0.0001578913,0.00008097537],"domain_scores_gemma":[0.9995846,0.0001212248,0.00008368806,0.0001093507,0.00007948781,0.00002163945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008894004,0.0001705394,0.00290903,0.0004676252,0.0001260588,0.0009246991,0.0004626812,0.5145389,0.1771184,0.06631186,0.00502228,0.2310585],"study_design_scores_gemma":[0.00002546595,0.0002453126,0.0004686334,0.0000137745,0.00002282061,0.0003309748,0.00006902257,0.9675032,0.0228883,0.005363141,0.003046639,0.00002267459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05482893,0.0007583512,0.9415267,0.0002029731,0.00008351851,0.00005290271,0.00007833561,0.000304726,0.002163601],"genre_scores_gemma":[0.9300718,0.0005373512,0.06721175,0.00008414365,0.00004223365,0.00006201997,0.0001037861,0.00001753647,0.001869279],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001140581,"threshold_uncertainty_score":0.002522886,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Ali Muhammad","is_ca":true},{"name":"Mohamed Elhattab","is_ca":true},{"name":"Mohamed Amine Arfaoui","is_ca":true},{"name":"Chadi Assi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03479680152394302,"gpt":0.2695380746530084,"spread":0.2347412731290654,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001553111,0.001392944,0.001274754,0.000543079,0.0007474124,0.001494645,0.001596805,0.001013834,0.001103478],"category_scores_gemma":[0.003370171,0.000636749,0.0006504302,0.001048636,0.001201585,0.001823624,0.00157398,0.001040476,0.0002785781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001033137,"about_ca_system_score_gemma":0.001111868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003328361,"about_ca_topic_score_gemma":0.004243738,"domain_scores_codex":[0.9990892,0.0003141054,0.00003750116,0.0001887693,0.0001684019,0.0002019638],"domain_scores_gemma":[0.998233,0.001079855,0.0002487988,0.0001376115,0.0001883445,0.0001123648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000825802,0.00003979251,0.0003890994,0.0000640846,0.00003378529,0.0001214185,0.00005547113,0.9749834,0.002281161,0.006128137,0.0006289139,0.01519211],"study_design_scores_gemma":[0.000003986496,0.0000372598,0.00009918311,0.000004184989,0.000009284549,0.0000240303,0.00002104286,0.9968353,0.0003799043,0.002373411,0.0002060886,0.000006218039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05817337,0.001074981,0.9364483,0.0003226718,0.0001159828,0.00005588364,0.00009166538,0.0002539608,0.003463231],"genre_scores_gemma":[0.9455596,0.0004301507,0.05202692,0.0001299642,0.00005657118,0.00006495494,0.00008948176,0.00004514022,0.001597173],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003328361,"threshold_uncertainty_score":0.008213758,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Bin Xiao","is_ca":true},{"name":"Murat Şimşek","is_ca":true},{"name":"Burak Kantarcı","is_ca":true},{"name":"Ala Abu Alkheir","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1309356717956981,"gpt":0.3231001213748782,"spread":0.1921644495791801,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00203978,0.00214985,0.002002113,0.005290164,0.0009018428,0.002825452,0.003144517,0.002269837,0.003106221],"category_scores_gemma":[0.005206465,0.0005843048,0.002143491,0.006865195,0.000719797,0.004766551,0.00186249,0.001948779,0.00239177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001193021,"about_ca_system_score_gemma":0.00190568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008118694,"about_ca_topic_score_gemma":0.009999016,"domain_scores_codex":[0.9982609,0.0003502415,0.000217045,0.0006130743,0.0003781204,0.0001806452],"domain_scores_gemma":[0.9968803,0.00144802,0.0003643311,0.0005296468,0.0006199059,0.0001578489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001004133,0.001060496,0.009786477,0.001403545,0.0002544481,0.00109181,0.0005765448,0.09666922,0.01707729,0.004585006,0.05937018,0.8071209],"study_design_scores_gemma":[0.00007209762,0.000188702,0.002414385,0.00007527817,0.00008449994,0.0002685249,0.0005842359,0.9634822,0.006065628,0.01466758,0.01205032,0.00004660134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07490012,0.006013657,0.8765306,0.003167084,0.0005121617,0.0008669636,0.01856947,0.01586922,0.003570752],"genre_scores_gemma":[0.3011177,0.001891177,0.6353269,0.0008421895,0.0005292224,0.0007391504,0.05372254,0.0004625061,0.005368669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008118694,"threshold_uncertainty_score":0.01614285,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}