{"meta":{"query_hash":"8c6e27d0de19","filters":{"venue":"IEEE Transactions on Network and Service Management"},"cohort_total":202,"direct_labels_cover":0,"predictions_cover":202,"exported":202,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/8c6e27d0de19","api":"https://metacan.xera.ac/api/v1/cohort?venue=IEEE+Transactions+on+Network+and+Service+Management"},"results":[{"id":"W1528573683","doi":"10.1109/tnsm.2015.2456172","title":"Managing Performance Interference in Cloud-Based Web Services","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Cloud computing; Virtual machine; Virtualization; Cloud testing; Distributed computing; Web application; Computer network; Cloud computing security; Operating system","score_opus":0.016291186141598704,"score_gpt":0.21500737156266084,"score_spread":0.19871618542106212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1528573683","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20366153,0.0001275932,0.7744822,0.0043983627,0.0014571936,0.00046733912,8.560503e-7,0.00039122353,0.015013715],"genre_scores_gemma":[0.991552,0.00011687586,0.0043998617,0.0034768383,0.00007908866,0.000048398426,8.7690967e-7,0.00001516363,0.00031087507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982725,0.00008957732,0.00030123346,0.00056087144,0.00030373127,0.000472083],"domain_scores_gemma":[0.9990934,0.0000422226,0.0000731306,0.0005917324,0.00004567475,0.00015381502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052279816,0.00025117488,0.00020626992,0.00026782494,0.00020363634,0.00020224562,0.00080822804,0.00005156877,0.000004845769],"category_scores_gemma":[1.6906216e-7,0.00024052477,0.000044668363,0.0010341777,0.000021366384,0.0000751107,0.000046172714,0.00023185166,0.00005119209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004102399,0.00010721764,0.0001369082,0.00022957071,0.00003186221,0.000024603496,0.0005371641,0.87072855,0.0000010760698,0.00046045447,0.00012563312,0.1275759],"study_design_scores_gemma":[0.0010004247,0.00013842285,0.00035884118,0.00037272248,0.000022474167,0.0000031579427,0.00035383477,0.99264663,0.000023737057,0.00033858416,0.004464196,0.00027694457],"about_ca_topic_score_codex":0.0000931564,"about_ca_topic_score_gemma":0.00033445127,"teacher_disagreement_score":0.7878905,"about_ca_system_score_codex":0.00008536449,"about_ca_system_score_gemma":0.00002137265,"threshold_uncertainty_score":0.98083156},"labels":[],"label_agreement":null},{"id":"W1569566335","doi":"10.1109/tnsm.2015.2432066","title":"XCollOpts: A Novel Improvement of Network Virtualizations in Xen for I/O-Latency Sensitive Applications on Multicores","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Atlantic Canada Opportunities Agency; Fundamental Research Funds for the Central Universities; Natural Science Foundation of Hubei Province","keywords":"Computer science; Latency (audio); Network packet; Preemption; Scheduling (production processes); Multi-core processor; Computer network; Distributed computing; Operating system","score_opus":0.020433453321016534,"score_gpt":0.2388841616265791,"score_spread":0.21845070830556257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1569566335","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005314973,0.000030783354,0.98942894,0.0013004766,0.0003686983,0.0017230619,0.000009169442,0.00010329238,0.0017206307],"genre_scores_gemma":[0.9437827,0.000076438126,0.052170146,0.0025023161,0.00016446685,0.0008289195,0.0000071360614,0.000028582812,0.0004393106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983608,0.0000409993,0.00041926734,0.00051507546,0.00027787036,0.000385979],"domain_scores_gemma":[0.998948,0.00015537678,0.00013643905,0.00051413896,0.00012720711,0.00011885983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045476042,0.00021967749,0.00024713922,0.00019016296,0.00024515242,0.000066021945,0.000372806,0.00005960467,0.0000010286078],"category_scores_gemma":[0.0000010234346,0.00021194984,0.00007317363,0.0010449381,0.000027497032,0.000034594363,0.000031927644,0.00011656154,0.000005560962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056948287,0.0004237998,0.000009466491,0.000082110135,0.00010190041,0.0000018024425,0.00065794843,0.9229608,0.000009112804,0.020628966,0.00028855947,0.054778576],"study_design_scores_gemma":[0.0023822577,0.0005270452,0.00035739836,0.00023652778,0.00009059527,0.0000014230307,0.00081002293,0.9801949,0.00009630397,0.0021041017,0.012853832,0.0003455802],"about_ca_topic_score_codex":0.000088949564,"about_ca_topic_score_gemma":0.0002333312,"teacher_disagreement_score":0.93846774,"about_ca_system_score_codex":0.00008782016,"about_ca_system_score_gemma":0.000024316196,"threshold_uncertainty_score":0.8643064},"labels":[],"label_agreement":null},{"id":"W1589728707","doi":"10.1109/tnsm.2015.2440423","title":"Greenslater: On Satisfying Green SLAs in Distributed Clouds","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; École de Technologie Supérieure","funders":"","keywords":"Cloud computing; Carbon footprint; Computer science; Service-level agreement; Service level; Environmental economics; Virtual machine; Profit (economics); Service provider; Greenhouse gas; Service (business); Business","score_opus":0.02267736028749483,"score_gpt":0.22771763346034918,"score_spread":0.20504027317285434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1589728707","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10750124,0.000083730534,0.87283874,0.007920908,0.001208489,0.00059926155,0.0000045701927,0.00038939205,0.009453676],"genre_scores_gemma":[0.99283576,0.000050826977,0.003526326,0.0028785535,0.00010979298,0.000040682822,0.0000027956337,0.000018181707,0.0005371069],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981774,0.00011859938,0.00030352923,0.0005564864,0.00037997088,0.0004640346],"domain_scores_gemma":[0.9990807,0.00006042077,0.000066717614,0.00058691896,0.000035431884,0.00016977757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046838075,0.00024802663,0.00021844875,0.00018750071,0.00021502232,0.00014237821,0.0005241738,0.00006448778,0.000004248923],"category_scores_gemma":[4.6470083e-7,0.00023224024,0.00005578859,0.000987139,0.00001915988,0.000040322946,0.000038455648,0.00024266347,0.000060183345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046954134,0.00013769414,0.00011376648,0.000057484547,0.000069679,0.00007991029,0.00071626867,0.7756759,4.6896938e-7,0.002616832,0.00077448617,0.21971059],"study_design_scores_gemma":[0.002216495,0.00035820616,0.0042659547,0.00030877595,0.00005365476,0.000010518565,0.00033020298,0.96165866,0.00001726053,0.0028572413,0.027366104,0.00055692234],"about_ca_topic_score_codex":0.00040421684,"about_ca_topic_score_gemma":0.00056245876,"teacher_disagreement_score":0.8853345,"about_ca_system_score_codex":0.000102547536,"about_ca_system_score_gemma":0.000011670495,"threshold_uncertainty_score":0.94704825},"labels":[],"label_agreement":null},{"id":"W1988395022","doi":"10.1109/tnsm.2013.043013.120304","title":"Pricing Utility-Based Virtual Networks","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Embedding; Heuristics; Network topology; Mathematical optimization; Matching (statistics); Profit (economics); Scheme (mathematics); Distributed computing; Computer network; Mathematics; Economics; Artificial intelligence; Microeconomics","score_opus":0.010680436193893576,"score_gpt":0.19861104265358578,"score_spread":0.1879306064596922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988395022","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00215464,0.00009097531,0.99168724,0.0016137502,0.0010015555,0.0005363298,7.323451e-7,0.0004262139,0.0024885638],"genre_scores_gemma":[0.9653663,0.0004182178,0.022151623,0.011410134,0.00017163916,0.0002039593,0.000003175612,0.000027436288,0.00024753914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816567,0.00008582982,0.00030311217,0.00058448524,0.0002793954,0.00058152043],"domain_scores_gemma":[0.99878234,0.00022367279,0.00007163406,0.00068281754,0.000053935793,0.00018557702],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025889653,0.00027854345,0.0002245493,0.00011339367,0.00048530495,0.00031604175,0.0005316131,0.00010215782,0.000109542474],"category_scores_gemma":[4.7409063e-7,0.00026017404,0.00008709189,0.0009481503,0.00002921556,0.00032330325,0.000016357264,0.00028019794,0.00008409609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014042464,0.00009612425,0.000044114076,0.000029315128,0.000056526194,0.0000068455474,0.00006936866,0.59888965,6.573948e-7,0.0011843856,0.0016813395,0.3979276],"study_design_scores_gemma":[0.00055633136,0.00012474804,0.0013864024,0.000072584895,0.000038656523,0.0000025951172,0.000043207943,0.986639,0.000012880448,0.0005698395,0.0102523295,0.00030142846],"about_ca_topic_score_codex":0.00010754173,"about_ca_topic_score_gemma":0.00007943919,"teacher_disagreement_score":0.9695356,"about_ca_system_score_codex":0.000032231743,"about_ca_system_score_gemma":0.000015211646,"threshold_uncertainty_score":0.99998504},"labels":[],"label_agreement":null},{"id":"W2011064078","doi":"10.1109/tnsm.2014.2360772","title":"Pushing Server Bandwidth Consumption to the Limit: Modeling and Analysis of Peer-Assisted VoD","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Peer-to-Peer Network Technologies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Bandwidth (computing); Server; Software deployment; Computer network; Peer-to-peer; Scheduling (production processes); The Internet; Schedule; Distributed computing; Bandwidth allocation; Operating system; Mathematical optimization","score_opus":0.02515554765593984,"score_gpt":0.23964420823562269,"score_spread":0.21448866057968285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011064078","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10489082,0.000043616245,0.8830253,0.0110482,0.00022691209,0.00031056086,0.000003124217,0.00018030105,0.00027116042],"genre_scores_gemma":[0.971391,0.00021393236,0.025544444,0.0026527422,0.000025343725,0.00005828176,0.0000024901997,0.000010390028,0.000101376434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984198,0.000088809065,0.00029599367,0.00048813864,0.00038659762,0.00032062348],"domain_scores_gemma":[0.99884367,0.00010989318,0.000059919625,0.0007674154,0.0001178629,0.00010126281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007571745,0.00019290383,0.0002825624,0.00038318158,0.00033326002,0.00019530882,0.00062937476,0.000069582,0.0000033151375],"category_scores_gemma":[0.0000027218612,0.00015805688,0.00007386063,0.0020378057,0.000020257738,0.00017063695,0.00005469545,0.00016258539,0.0000088500965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001707682,0.000031060365,0.00008295457,0.000034100955,0.0003448423,9.622871e-7,0.00037851394,0.83232385,0.000007148066,0.0012846156,0.00014641802,0.16534844],"study_design_scores_gemma":[0.00021587405,0.000077738594,0.006981137,0.00006465461,0.00047683835,0.0000015659089,0.000093945055,0.98927164,0.000029246648,0.00026282482,0.0023395063,0.00018503283],"about_ca_topic_score_codex":0.00010719239,"about_ca_topic_score_gemma":0.0029147232,"teacher_disagreement_score":0.8665002,"about_ca_system_score_codex":0.000031650543,"about_ca_system_score_gemma":0.0000048735396,"threshold_uncertainty_score":0.64453727},"labels":[],"label_agreement":null},{"id":"W2034295499","doi":"10.1109/tnsm.2011.110911.110116","title":"Traffic Trend Estimation for Profit Oriented Capacity Adaptation in Service Overlay Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Kalman filter; Exponential smoothing; Smoothing; Bandwidth (computing); Real-time computing; Quality of service; Computer network; Distributed computing","score_opus":0.02787122135903765,"score_gpt":0.21180318619139935,"score_spread":0.18393196483236168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034295499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015726326,0.000053241278,0.98043144,0.00090527354,0.00083803583,0.0010831469,0.000004580173,0.0002474703,0.00071049813],"genre_scores_gemma":[0.9539746,0.0001299113,0.0431973,0.0019459303,0.000077841185,0.00052786164,0.000011719705,0.000021243455,0.00011360074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827236,0.000096590025,0.00039431365,0.00056474033,0.000208978,0.00046303845],"domain_scores_gemma":[0.99919087,0.00010516588,0.00011896443,0.00038279855,0.000084097825,0.000118110336],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036693588,0.00026647229,0.00024093948,0.00017002557,0.00028527883,0.00008334318,0.0003254966,0.00011526945,0.000015675847],"category_scores_gemma":[5.767672e-7,0.00027294393,0.00006685692,0.00114002,0.000018122371,0.00041352084,0.000006462198,0.00019696367,0.000009272691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011011963,0.00006716308,0.0000065334584,0.00004841481,0.000045832046,0.0000026453506,0.0010074043,0.6405094,2.9722688e-7,0.004944721,0.00007624751,0.35318118],"study_design_scores_gemma":[0.0015507384,0.00013726854,0.0008422401,0.000099713434,0.00007536504,0.0000032799023,0.00018375392,0.9957112,0.0000051971656,0.00035035732,0.0007661578,0.0002747231],"about_ca_topic_score_codex":0.00007390314,"about_ca_topic_score_gemma":0.0033591257,"teacher_disagreement_score":0.9382483,"about_ca_system_score_codex":0.0000632831,"about_ca_system_score_gemma":0.000018635617,"threshold_uncertainty_score":0.9999723},"labels":[],"label_agreement":null},{"id":"W2054950229","doi":"10.1109/tnsm.2013.043013.120264","title":"Using Fuzzy Logic Control to Provide Intelligent Traffic Management Service for High-Speed Networks","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Network traffic control; Traffic policing; Computer network; Active queue management; Quality of service; Traffic shaping; Traffic generation model; Router; Bottleneck; Network congestion; Queueing theory; Network packet; Fuzzy logic; Network delay; Packet loss; Queue; Distributed computing; Embedded system","score_opus":0.02080185166822854,"score_gpt":0.23266621178174982,"score_spread":0.21186436011352128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054950229","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037160832,0.0001367914,0.9787236,0.010236229,0.0015407143,0.004577192,0.0000052814635,0.0004225511,0.0006415971],"genre_scores_gemma":[0.9098553,0.00028469236,0.057753712,0.030017532,0.00033440292,0.0011192478,0.000006133738,0.00005372406,0.0005752402],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99692965,0.00012342444,0.0006044264,0.0010026834,0.00037443425,0.0009654049],"domain_scores_gemma":[0.99828947,0.00016562513,0.000144121,0.00079162366,0.00023857942,0.00037059412],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041472804,0.00050332013,0.00045880993,0.00023126963,0.0006278238,0.00048218147,0.0008909787,0.0001349671,0.00004812722],"category_scores_gemma":[5.1019754e-7,0.00048143129,0.00013467485,0.0012415268,0.000023097455,0.00036480225,0.000030370826,0.0002392776,0.00014253633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008428958,0.00011778481,7.5727405e-7,0.00009648318,0.00024054715,0.000006570216,0.00009056698,0.6277,0.0000031944705,0.006903308,0.0006873693,0.3640692],"study_design_scores_gemma":[0.0020003486,0.00023322218,0.00009162222,0.00015401602,0.0002815994,0.0000062353706,0.00018336557,0.98709506,0.000006354648,0.0009136221,0.008483319,0.0005512074],"about_ca_topic_score_codex":0.00008290376,"about_ca_topic_score_gemma":0.00018609427,"teacher_disagreement_score":0.92096984,"about_ca_system_score_codex":0.00012462224,"about_ca_system_score_gemma":0.000019898178,"threshold_uncertainty_score":0.9997637},"labels":[],"label_agreement":null},{"id":"W2057867790","doi":"10.1109/tnsm.2013.122613.120358","title":"TransCom: A Virtual Disk-Based Cloud Computing Platform for Heterogeneous Services","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Cloud computing; Operating system; Server; Scalability; Virtual desktop; Software; Virtual machine; Virtualization; Distributed computing; Database","score_opus":0.011336493594032326,"score_gpt":0.2157631141719764,"score_spread":0.20442662057794408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057867790","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050170254,0.000038610793,0.94502795,0.0015261562,0.0011508594,0.00068923493,0.0000036691629,0.0004282234,0.0009650482],"genre_scores_gemma":[0.9758002,0.00001897071,0.017464977,0.0061739804,0.00028780187,0.000066090484,0.0000044871613,0.000033233562,0.00015025426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978236,0.000077742145,0.00039504585,0.0007366816,0.0003376691,0.00062924623],"domain_scores_gemma":[0.99875814,0.00024101285,0.000118791766,0.0006624623,0.000051558094,0.00016802625],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000518059,0.0003555995,0.00031296434,0.00015524839,0.000843048,0.00029558665,0.0008154325,0.000084025414,0.00000495553],"category_scores_gemma":[2.8179468e-7,0.0003350808,0.00017456214,0.00050400186,0.000027926897,0.000044579534,0.000024784858,0.00017473036,0.000016369107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050116127,0.00012290677,0.0000038333696,0.00027582824,0.00011036423,0.0000037836085,0.00035168996,0.7472855,0.000002317668,0.0031038742,0.00006615516,0.24862365],"study_design_scores_gemma":[0.0014696098,0.00040062593,0.000060271803,0.00019148963,0.00009950188,0.0000045514716,0.00010982087,0.9761983,0.00008362861,0.0005366031,0.020459088,0.000386466],"about_ca_topic_score_codex":0.000043087304,"about_ca_topic_score_gemma":0.0001465496,"teacher_disagreement_score":0.92756295,"about_ca_system_score_codex":0.00004330418,"about_ca_system_score_gemma":0.00000935916,"threshold_uncertainty_score":0.9999101},"labels":[],"label_agreement":null},{"id":"W2059577155","doi":"10.1109/tnsm.2012.061212.110032","title":"Optimal Functionality Placement for Multiplay Service Provider Architectures","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cisco Systems (Canada)","funders":"","keywords":"Computer science; Service provider; Computer network; Enhanced Data Rates for GSM Evolution; Distributed computing; Integer programming; Service (business); Key (lock); Modular design; Edge device; Set (abstract data type); Replication (statistics); Cloud computing; Computer security; Telecommunications; Operating system","score_opus":0.029491648857765382,"score_gpt":0.27900669649319426,"score_spread":0.24951504763542887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059577155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005074442,0.00009448914,0.9863184,0.00550847,0.0010262008,0.0010090175,0.000012641725,0.00016583466,0.00079051696],"genre_scores_gemma":[0.77671623,0.00007587895,0.19634075,0.024537561,0.000501066,0.0010175894,0.000016796772,0.00003402187,0.00076008786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817973,0.000107861866,0.0003073332,0.00045650185,0.0003431425,0.0006054307],"domain_scores_gemma":[0.9989724,0.00017442409,0.000082519735,0.0004966227,0.00009437545,0.0001796186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006005416,0.00025977683,0.00019629959,0.00008851284,0.00052472565,0.00016392652,0.00035536932,0.000063800806,0.000035074903],"category_scores_gemma":[6.483521e-7,0.00023835257,0.000088458284,0.00040003838,0.000016124462,0.0003224701,0.000022284483,0.00016690377,0.00003125531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031957924,0.00089645153,0.00005588177,0.0008239131,0.0005001725,0.0000030631224,0.002479188,0.86073846,0.00003418144,0.0059043276,0.0030438094,0.12520097],"study_design_scores_gemma":[0.005359608,0.0005858635,0.0047515463,0.0002256241,0.00045893135,0.000027809847,0.0016151072,0.78095156,0.0013709302,0.0012233972,0.20192327,0.001506354],"about_ca_topic_score_codex":0.00006792151,"about_ca_topic_score_gemma":0.00015032061,"teacher_disagreement_score":0.7899776,"about_ca_system_score_codex":0.00006728349,"about_ca_system_score_gemma":0.000023051754,"threshold_uncertainty_score":0.9719736},"labels":[],"label_agreement":null},{"id":"W2086975327","doi":"10.1109/tnsm.2011.120811.100033","title":"System Monitoring with Metric-Correlation Models","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Metric (unit); Heteroscedasticity; Ordinary least squares; Linear regression; Residual; Data mining; Process (computing); Linear model; Correlation; Variance (accounting); Wilcoxon signed-rank test; Rank (graph theory); Software; Machine learning; Algorithm; Statistics; Mathematics","score_opus":0.022102693191685546,"score_gpt":0.2006628953315815,"score_spread":0.17856020213989596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086975327","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010020671,0.00006041507,0.9809457,0.000049940518,0.00094089354,0.00033947852,4.5948596e-7,0.00032075352,0.007321702],"genre_scores_gemma":[0.9721884,0.0001270601,0.027382309,0.000076941215,0.000049540242,0.00008175951,2.5430558e-7,0.000009782441,0.0000839568],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989397,0.00004134071,0.0001978409,0.00034414418,0.0002380562,0.00023894594],"domain_scores_gemma":[0.9993203,0.000027597453,0.000060553426,0.0004535225,0.00006265906,0.00007531048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024526293,0.0001504443,0.00014208032,0.00012655467,0.0002961223,0.000061790044,0.00026703347,0.000053648047,0.0000026017099],"category_scores_gemma":[6.673001e-8,0.00011648458,0.000033605087,0.0008432834,0.000010451932,0.00047582365,0.000006223163,0.00011378395,0.00002427123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007483086,0.0001170548,0.0007489928,0.00048513527,0.00017148713,0.00001758266,0.0019606187,0.88014364,5.329885e-7,0.005088556,0.00002511367,0.111166425],"study_design_scores_gemma":[0.0007642706,0.00024082513,0.0050050705,0.00045090678,0.00012153003,0.000021974647,0.0008029321,0.9913864,0.00014800877,0.00043449883,0.0002461406,0.0003774046],"about_ca_topic_score_codex":0.00011397961,"about_ca_topic_score_gemma":0.000019100378,"teacher_disagreement_score":0.96216774,"about_ca_system_score_codex":0.000061036066,"about_ca_system_score_gemma":0.000009101819,"threshold_uncertainty_score":0.47501034},"labels":[],"label_agreement":null},{"id":"W2095682406","doi":"10.1109/tnsm.2009.03.090304","title":"Distributed adaptive diverse routing for voice-over-IP in service overlay networks","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Computer network; Overlay network; Voice over IP; Scalability; Overlay; Quality of service; Routing (electronic design automation); Node (physics); Distributed computing; Learning automata; Path (computing); Automaton; The Internet","score_opus":0.020859724117424518,"score_gpt":0.24558526346121773,"score_spread":0.22472553934379322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095682406","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00061179214,0.00003247574,0.9931024,0.0037753372,0.00034680837,0.0008722037,0.000014019704,0.00015752863,0.0010874902],"genre_scores_gemma":[0.9562642,0.0004779817,0.030919056,0.011839388,0.00010678272,0.0001273534,0.000026367978,0.000019252151,0.00021960541],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983189,0.000083957595,0.0003051747,0.000512069,0.00023366243,0.0005462207],"domain_scores_gemma":[0.9992038,0.00011284823,0.00008210775,0.0003616298,0.00010246525,0.00013716833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003627533,0.00022686987,0.00021395311,0.0001270765,0.00038709765,0.00019336323,0.00044009447,0.00008787696,0.000013564249],"category_scores_gemma":[7.113637e-7,0.00023291369,0.00006280668,0.0013464848,0.000010952839,0.000404744,0.000015692485,0.00022311053,0.000010375905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008257556,0.00013626553,0.000010089143,0.00002785097,0.000041586027,0.000009015856,0.0003299523,0.9351047,0.000001028212,0.0048139836,0.0004283712,0.059014536],"study_design_scores_gemma":[0.0013869557,0.00017268884,0.0017098878,0.00010384834,0.000029344325,0.000001516491,0.00020708617,0.99267507,0.00000449336,0.0007390922,0.0027142207,0.00025581074],"about_ca_topic_score_codex":0.00009371882,"about_ca_topic_score_gemma":0.00080806826,"teacher_disagreement_score":0.9621833,"about_ca_system_score_codex":0.00008609141,"about_ca_system_score_gemma":0.000016765018,"threshold_uncertainty_score":0.9497945},"labels":[],"label_agreement":null},{"id":"W2099284721","doi":"10.1109/tcomm.2011.072611.100047","title":"Inter-Domain Path Provisioning with Security Features: Architecture and Signaling Performance","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Security in Wireless Sensor Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Network for Business Sustainability","funders":"","keywords":"Computer science; Provisioning; Computer network; Distributed computing; Scalability; Authentication (law); Reservation; Signaling protocol; Quality of service; Computer security","score_opus":0.008846202419800414,"score_gpt":0.1845155972824848,"score_spread":0.17566939486268437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099284721","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23205198,0.000093839975,0.7645828,0.0002508314,0.00022428256,0.00038629925,0.0000012427304,0.00017135158,0.0022373833],"genre_scores_gemma":[0.9529959,0.0003151232,0.04551277,0.001014121,0.000058640984,0.000045913766,0.0000010147961,0.000019976122,0.000036523357],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850863,0.00008505918,0.00019729634,0.00055292126,0.00024139478,0.0004146785],"domain_scores_gemma":[0.9992359,0.000055611214,0.00007677104,0.00045856726,0.00004070387,0.00013240648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022880036,0.0002723925,0.00020075205,0.000111734196,0.00046599476,0.0001515883,0.00039901558,0.00008089582,0.000008664783],"category_scores_gemma":[1.6537972e-7,0.0002250527,0.000033950397,0.0005022338,0.000055519948,0.00024478365,0.0000244787,0.00042004287,0.000003514565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080308196,0.00059177564,0.00069546304,0.0012582308,0.0006753955,0.0002918466,0.08274118,0.28856722,0.000051503674,0.009033821,0.00023285458,0.61505765],"study_design_scores_gemma":[0.0066587003,0.0038647465,0.011385317,0.0051687113,0.0005451731,0.0007660558,0.009842337,0.93381774,0.0025079038,0.015795138,0.005736957,0.00391123],"about_ca_topic_score_codex":0.000049430884,"about_ca_topic_score_gemma":0.0002435994,"teacher_disagreement_score":0.7209439,"about_ca_system_score_codex":0.000026115897,"about_ca_system_score_gemma":0.000009482288,"threshold_uncertainty_score":0.9177383},"labels":[],"label_agreement":null},{"id":"W2111974652","doi":"10.1109/tnsm.2010.1012.i9p0338","title":"A Distributed Probabilistic Commitment Control Algorithm for Service-Oriented Systems","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Queue; Distributed computing; Probabilistic logic; Service (business); Quality of service; Algorithm; Computer network","score_opus":0.007462381133991641,"score_gpt":0.21133732195999302,"score_spread":0.20387494082600138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111974652","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026085905,0.00006849215,0.9863082,0.0041173757,0.0031895982,0.002547081,0.00017522534,0.00043027295,0.0005551757],"genre_scores_gemma":[0.8690217,0.00016260163,0.1027269,0.022807596,0.0008548625,0.003911763,0.00017914805,0.00011814079,0.0002172573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99735254,0.000105027764,0.000508196,0.0008715149,0.0004206473,0.00074208743],"domain_scores_gemma":[0.9979462,0.0002585862,0.00016798609,0.0010242133,0.0003098156,0.00029322202],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042014822,0.000455368,0.00042105923,0.00016570465,0.0007338071,0.00032280973,0.00089841615,0.00015327193,0.000010115411],"category_scores_gemma":[5.0041405e-7,0.00040783902,0.00012054667,0.0011921488,0.000029958395,0.00025916068,0.000026444557,0.00039627025,0.000026745134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041571207,0.0018798413,0.000036343463,0.0024045007,0.0017960556,0.0000606656,0.0025699001,0.5039512,0.000221047,0.07880448,0.0010780654,0.40678218],"study_design_scores_gemma":[0.0031448053,0.00027421044,0.00022733802,0.00013718363,0.00027113027,0.00002341924,0.00031173206,0.9216858,0.00006431503,0.0013173413,0.07199715,0.00054555753],"about_ca_topic_score_codex":0.00028427254,"about_ca_topic_score_gemma":0.0016268082,"teacher_disagreement_score":0.8835813,"about_ca_system_score_codex":0.000047783393,"about_ca_system_score_gemma":0.00003480463,"threshold_uncertainty_score":0.99983734},"labels":[],"label_agreement":null},{"id":"W2116055298","doi":"10.1109/tnsm.2011.050311.100028","title":"Dirichlet-Based Trust Management for Effective Collaborative Intrusion Detection Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Korea Science and Engineering Foundation; Nanyang Technological University","keywords":"Computer science; Intrusion detection system; Scalability; Robustness (evolution); Trust management (information system); Trustworthiness; Collaborative network; Network security; Computer network; Latent Dirichlet allocation; Distributed computing; Data mining; Computer security; Artificial intelligence; Topic model; Database","score_opus":0.009821254677230405,"score_gpt":0.2082034688065235,"score_spread":0.19838221412929308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116055298","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025381038,0.00009681224,0.9878949,0.00027029758,0.002000995,0.0024070223,0.000004423904,0.00034261608,0.0044448455],"genre_scores_gemma":[0.9614219,0.0008643834,0.032909065,0.002755784,0.00020461503,0.0016276645,0.000005340373,0.000039463415,0.00017178533],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978632,0.0001745842,0.00036490316,0.00078359153,0.00027861923,0.0005351314],"domain_scores_gemma":[0.9988505,0.00012762396,0.00015391524,0.00056036207,0.00015408544,0.0001534792],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047681615,0.0003629688,0.000280741,0.00021949282,0.00095022237,0.00015564654,0.0004260398,0.00015700876,0.000031991272],"category_scores_gemma":[6.642113e-7,0.00035766902,0.00012071441,0.0016495179,0.00004298997,0.00036811316,0.000023692077,0.00025868934,0.000019077099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007670186,0.00032232935,0.0000053441663,0.00019111866,0.00027471688,0.000014928752,0.0005233718,0.16695952,0.000010655456,0.0055423924,0.00036370047,0.8250249],"study_design_scores_gemma":[0.002248443,0.0010638328,0.0010079342,0.0001763555,0.0002319554,0.000004648146,0.00021301382,0.9713739,0.0018654547,0.0024559055,0.018771755,0.000586788],"about_ca_topic_score_codex":0.000033892986,"about_ca_topic_score_gemma":0.00031555604,"teacher_disagreement_score":0.9588838,"about_ca_system_score_codex":0.00011558979,"about_ca_system_score_gemma":0.000010103776,"threshold_uncertainty_score":0.9998875},"labels":[],"label_agreement":null},{"id":"W2132115996","doi":"10.1109/tnsm.2008.021103","title":"Network anomaly diagnosis via statistical analysis and evidential reasoning","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Evidential reasoning approach; Computer science; Anomaly detection; Anomaly (physics); Data mining; Artificial intelligence; Set (abstract data type); Root cause; Root (linguistics); Dempster–Shafer theory; Root cause analysis; Pattern recognition (psychology); Machine learning; Decision support system; Reliability engineering; Engineering","score_opus":0.012067733252986948,"score_gpt":0.21887092558034368,"score_spread":0.20680319232735672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132115996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02903664,0.0002768497,0.96850723,0.0006545822,0.0005872838,0.0002498765,0.0000028263032,0.0001562031,0.00052849547],"genre_scores_gemma":[0.96305394,0.004517253,0.029955668,0.0020234992,0.00023747503,0.00009910332,0.0000038632147,0.000013733748,0.00009546493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981522,0.00015295547,0.00031844052,0.0006110657,0.00031813714,0.00044718792],"domain_scores_gemma":[0.9991007,0.0001705369,0.00008448918,0.00041036966,0.000050507795,0.00018338281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030516423,0.00023040434,0.00029702648,0.0001962465,0.0010429875,0.00017139305,0.00026541553,0.00008806495,0.00008394041],"category_scores_gemma":[7.7539283e-7,0.0002359596,0.00008389594,0.0019139053,0.00005623881,0.00035402217,0.000026578136,0.00023379773,0.00001913459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019540265,0.00031249863,0.0040520565,0.00012530218,0.0017276028,0.0002135005,0.0009033688,0.59753007,0.0000029256416,0.0074301036,0.0039737234,0.38353345],"study_design_scores_gemma":[0.0007673358,0.00032585225,0.0499293,0.000105795385,0.0010446195,0.00006470706,0.000036221252,0.93631446,0.00003628374,0.0021706983,0.008593248,0.00061147934],"about_ca_topic_score_codex":0.00028463226,"about_ca_topic_score_gemma":0.001111458,"teacher_disagreement_score":0.93855155,"about_ca_system_score_codex":0.000032611384,"about_ca_system_score_gemma":0.000009330073,"threshold_uncertainty_score":0.9622153},"labels":[],"label_agreement":null},{"id":"W2132578679","doi":"10.1109/tnsm.2009.031103","title":"Queueing-Model-Based Adaptive Control of Multi-Tiered Web Applications","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Office of Naval Research; Multidisciplinary University Research Initiative; Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency","keywords":"Testbed; Computer science; Queueing theory; The Internet; Distributed computing; Scheme (mathematics); Adaptive control; Admission control; Resource allocation; Computer network; Real-time computing; Quality of service; Control (management); Operating system","score_opus":0.016810629983759044,"score_gpt":0.21129280798723757,"score_spread":0.19448217800347853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132578679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038609642,0.00018740901,0.9962047,0.0010263058,0.0001803972,0.0007772936,0.000010380426,0.00019507438,0.00103238],"genre_scores_gemma":[0.9549389,0.000288365,0.04171657,0.0023165129,0.00004542172,0.0004010578,0.0000014602011,0.000013978198,0.00027775767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865246,0.00007321746,0.000312417,0.0004247175,0.00023561923,0.0003015604],"domain_scores_gemma":[0.99903876,0.00010181585,0.00011243034,0.0005027858,0.000121446596,0.00012278235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015766252,0.00020569946,0.00025240262,0.00011718885,0.00038865217,0.000029128189,0.00041617148,0.000070212816,0.000008668551],"category_scores_gemma":[1.9171732e-7,0.00020587246,0.00009369263,0.0005868807,0.000061133585,0.0001289994,0.0000049610785,0.00015746569,0.000016707085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063626794,0.0002062833,0.0000072457424,0.000026767375,0.00010155036,0.00000479573,0.000092806375,0.85417783,0.000008633992,0.004622104,0.00012722073,0.14056115],"study_design_scores_gemma":[0.0020834876,0.00008525678,0.000063399886,0.00003777261,0.00007620713,0.0000028774468,0.000026395279,0.995029,0.000025354095,0.00012909186,0.0022475698,0.00019359903],"about_ca_topic_score_codex":0.000014304569,"about_ca_topic_score_gemma":0.000099352044,"teacher_disagreement_score":0.95455277,"about_ca_system_score_codex":0.00002923049,"about_ca_system_score_gemma":0.00006465381,"threshold_uncertainty_score":0.83952355},"labels":[],"label_agreement":null},{"id":"W2140720699","doi":"10.1109/tnsm.2008.080104","title":"Efficient fault diagnosis using incremental alarm correlation and active investigation for internet and overlay networks","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Overlay network; Overlay; Fault management; Distributed computing; Scalability; Fault (geology); Fault model; The Internet; Computer network; Real-time computing; ALARM; Engineering","score_opus":0.0186822568874934,"score_gpt":0.22349913085027587,"score_spread":0.20481687396278248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140720699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41110444,0.000080756916,0.587727,0.00016693167,0.00035543946,0.00048639512,0.0000021844319,0.000048027876,0.000028822033],"genre_scores_gemma":[0.9917854,0.0005354235,0.006845469,0.000608739,0.00005684695,0.0001321538,0.0000039479064,0.000009523517,0.00002250174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989499,0.000053291384,0.00022428078,0.00039910915,0.00015551,0.00021789683],"domain_scores_gemma":[0.99947965,0.0001196633,0.00008467708,0.00017466693,0.00005157588,0.00008979395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022622234,0.00016527867,0.00015683893,0.00008086167,0.00045895588,0.00006954822,0.00010221043,0.000076484605,0.0000023027983],"category_scores_gemma":[7.9845904e-7,0.0001534613,0.000032320706,0.00028694925,0.000054132117,0.00019922094,0.000016209116,0.00011015907,0.0000010404407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079548176,0.00008384567,0.0036405567,0.0001665579,0.000107362204,0.000002932237,0.0020211325,0.9414299,0.0000058025726,0.00030283586,0.00015131495,0.052008178],"study_design_scores_gemma":[0.0006772971,0.00011671379,0.012199058,0.000118862656,0.00005661042,0.000015313197,0.00012494465,0.98612565,0.00008385189,0.00006644783,0.00024546968,0.0001697755],"about_ca_topic_score_codex":0.00013597171,"about_ca_topic_score_gemma":0.00006441067,"teacher_disagreement_score":0.58088154,"about_ca_system_score_codex":0.00006703651,"about_ca_system_score_gemma":0.0000100916905,"threshold_uncertainty_score":0.62579703},"labels":[],"label_agreement":null},{"id":"W2160414802","doi":"10.1109/tnsm.2010.1012.0362","title":"A Hierarchical Identity Based Key Management Scheme in Tactical Mobile Ad Hoc Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Carleton University","funders":"","keywords":"Computer science; Key management; Node (physics); Computer network; Key (lock); Mobile ad hoc network; Wireless ad hoc network; Distributed computing; Scheme (mathematics); Public-key cryptography; Computer security; Wireless; Encryption; Engineering; Telecommunications","score_opus":0.0077751351545672495,"score_gpt":0.23193523546029296,"score_spread":0.22416010030572572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160414802","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011169389,0.0002293192,0.9804895,0.0012900681,0.0017251091,0.0012799221,0.0000021038013,0.00028853136,0.0035260376],"genre_scores_gemma":[0.8379822,0.0024795462,0.15185508,0.0058420836,0.00023785901,0.0012491385,0.000008083799,0.000060875616,0.00028517615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967503,0.00016396432,0.00053747516,0.0010417096,0.00058805617,0.00091851247],"domain_scores_gemma":[0.99810904,0.00017385856,0.00009605396,0.0012466991,0.00005357667,0.00032076688],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000779707,0.00041556873,0.00036341976,0.00028195282,0.00037771228,0.00039453345,0.0011316546,0.00022074301,0.00013008763],"category_scores_gemma":[6.8949385e-7,0.00043213659,0.00013128911,0.0017522405,0.00007938016,0.0006247167,0.000070822585,0.0011758701,0.00006566336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017318019,0.00084355654,0.000077891316,0.00018882233,0.00017125458,0.00026784206,0.00016154622,0.5672124,0.000010112935,0.03394548,0.000603893,0.39634404],"study_design_scores_gemma":[0.0017032912,0.0001706913,0.0020121953,0.00013012665,0.0000705917,0.000011145984,0.000052110634,0.95686394,0.000012109946,0.0017426895,0.036692273,0.0005388553],"about_ca_topic_score_codex":0.00001595625,"about_ca_topic_score_gemma":0.0016987995,"teacher_disagreement_score":0.82863444,"about_ca_system_score_codex":0.00007991934,"about_ca_system_score_gemma":0.000023304208,"threshold_uncertainty_score":0.999813},"labels":[],"label_agreement":null},{"id":"W2164817056","doi":"10.1109/tnsm.2007.021104","title":"On Leveraging Policy-Based Management for Maximizing Business Profit","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Access Control and Trust","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Scheduling (production processes); Consistency (knowledge bases); Profit (economics); Business process; Distributed computing; Static analysis; Mathematical optimization; Work in process; Programming language","score_opus":0.022326703118335935,"score_gpt":0.28324460100922744,"score_spread":0.2609178978908915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164817056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026703635,0.000036480902,0.91861486,0.011569234,0.00068176066,0.0013612277,0.0000063492757,0.00016713045,0.06489259],"genre_scores_gemma":[0.98550415,0.0002784948,0.0042382437,0.0079299975,0.0003522638,0.00020816202,0.000005629771,0.0000279218,0.0014551184],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99842024,0.000046550856,0.00022751883,0.00036505784,0.00033711834,0.000603498],"domain_scores_gemma":[0.9993519,0.00015265327,0.0000701183,0.00021110737,0.00008821266,0.00012596327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007723569,0.00019082459,0.00016822926,0.00024882826,0.0012829725,0.00014487293,0.00022449756,0.000068409325,0.00003664478],"category_scores_gemma":[0.0000012441334,0.00018927181,0.00006994901,0.00091507327,0.000042616353,0.000120291515,0.000005275418,0.00010798061,0.000015610894],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006436424,0.00020004486,0.00004322816,0.000453516,0.0002718513,0.000030470996,0.00097625103,0.20833787,0.0000015227813,0.1093602,0.0005359125,0.67914546],"study_design_scores_gemma":[0.02572816,0.00065308716,0.024742356,0.003229768,0.0023567847,0.0000037520233,0.02195086,0.08143767,0.0002966357,0.05592993,0.7792914,0.0043796003],"about_ca_topic_score_codex":0.0005992563,"about_ca_topic_score_gemma":0.001983166,"teacher_disagreement_score":0.9828338,"about_ca_system_score_codex":0.00013007525,"about_ca_system_score_gemma":0.000027262899,"threshold_uncertainty_score":0.9867717},"labels":[],"label_agreement":null},{"id":"W2212315497","doi":"10.1109/tnsm.2015.2465371","title":"Routing Algorithms for Network Function Virtualization Enabled Multicast Topology on SDN","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":89,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Multicast; Computer network; Protocol Independent Multicast; Distributed computing; Source-specific multicast; Xcast; Pragmatic General Multicast; Distance Vector Multicast Routing Protocol; Network virtualization; Virtualization; IP multicast; Firewall (physics); Multicast address; Cloud computing; Operating system","score_opus":0.03528470524017407,"score_gpt":0.25342706597566533,"score_spread":0.21814236073549126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2212315497","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005331459,0.000082136845,0.99243706,0.0015589116,0.0031003873,0.0006982605,0.000002820614,0.00032898068,0.0012583187],"genre_scores_gemma":[0.8981055,0.00040252777,0.08335185,0.014833613,0.0016237901,0.0005394399,0.000034404387,0.00007582439,0.0010330669],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981287,0.00010867451,0.0003342176,0.0006011793,0.0002561928,0.00057100673],"domain_scores_gemma":[0.9989039,0.00022449068,0.00010979227,0.00045846935,0.00012661995,0.00017675763],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055911666,0.0002632275,0.00024701402,0.00010310589,0.00053559244,0.00017111731,0.00030522773,0.00012807768,0.000010163492],"category_scores_gemma":[0.0000024046399,0.0002540136,0.000075677766,0.00076796085,0.000021441334,0.0002262494,0.000015614183,0.0001693611,0.000029772253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016961774,0.00010639591,0.000015202616,0.000029636187,0.00010039968,0.000005137784,0.00023491136,0.81749433,5.1206615e-7,0.020374045,0.0030683547,0.15840144],"study_design_scores_gemma":[0.0018001993,0.0006824595,0.00018046904,0.00009195767,0.000113286,0.000005879793,0.0001786127,0.9647985,0.000011573335,0.0046554026,0.027141524,0.00034011106],"about_ca_topic_score_codex":0.000041751184,"about_ca_topic_score_gemma":0.00012661894,"teacher_disagreement_score":0.9090852,"about_ca_system_score_codex":0.000074867385,"about_ca_system_score_gemma":0.000020683256,"threshold_uncertainty_score":0.99999124},"labels":[],"label_agreement":null},{"id":"W2221372013","doi":"10.1109/tnsm.2015.2501398","title":"Flow-Based Management For Energy Efficient Campus Networks","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Centre National de la Recherche Scientifique","keywords":"Computer science; Energy consumption; Distributed computing; Computer network; Quality of service; Routing (electronic design automation); Control reconfiguration; Efficient energy use; Greedy algorithm; Static routing; Heuristic; Software-defined networking; Routing protocol","score_opus":0.014316720368820451,"score_gpt":0.21203247766057143,"score_spread":0.19771575729175098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2221372013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016475655,0.00038778584,0.9922814,0.0013578323,0.0022847052,0.0006650353,0.0000047576573,0.0003946004,0.002459126],"genre_scores_gemma":[0.7553276,0.0009825481,0.22049615,0.019812591,0.000604014,0.0013747407,0.00003340065,0.00010392667,0.0012650462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99770284,0.00007540949,0.00035770328,0.0007563315,0.0003914047,0.0007163321],"domain_scores_gemma":[0.99856216,0.0001368479,0.00009661343,0.0007816232,0.000111094705,0.00031168677],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045441926,0.0003571771,0.00028448837,0.0001775491,0.00042606881,0.00022924728,0.00063454633,0.000110562476,0.000007585596],"category_scores_gemma":[4.300564e-7,0.00034182708,0.0001320497,0.00097194774,0.000027816648,0.00010735344,0.000025485338,0.00014406163,0.000013237384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089887406,0.00015191264,0.0000024293636,0.000052753563,0.00011590401,0.000017861252,0.00005540348,0.7908447,6.435474e-8,0.007371104,0.0046531055,0.19664492],"study_design_scores_gemma":[0.0018507367,0.00018566646,0.000039603176,0.000104986255,0.00011771318,0.0000033504261,0.000056105953,0.9478631,0.000007709043,0.0009547528,0.04844538,0.000370917],"about_ca_topic_score_codex":0.000037513462,"about_ca_topic_score_gemma":0.00011600258,"teacher_disagreement_score":0.77178526,"about_ca_system_score_codex":0.00009689919,"about_ca_system_score_gemma":0.000023297278,"threshold_uncertainty_score":0.9999034},"labels":[],"label_agreement":null},{"id":"W2343714644","doi":"10.1109/tnsm.2016.2558598","title":"Multi-Path Link Embedding for Survivability in Virtual Networks","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Network virtualization; Backup; Survivability; Distributed computing; Computer network; Redundancy (engineering); Virtualization; Fault tolerance; Cloud computing","score_opus":0.018562997648472655,"score_gpt":0.24539425614697827,"score_spread":0.2268312584985056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2343714644","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002522696,0.00008828123,0.9929418,0.0021616481,0.0012895701,0.0006593103,0.000005638915,0.00021390908,0.000117127696],"genre_scores_gemma":[0.94539326,0.00093862484,0.05097923,0.001776715,0.0002552706,0.00031681336,0.0000022871902,0.000031634776,0.00030616514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980772,0.000098118566,0.00036752896,0.00068636704,0.00017734533,0.0005934051],"domain_scores_gemma":[0.99867654,0.00048141062,0.0000726613,0.00058605993,0.00005538974,0.00012795595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064996217,0.0002571211,0.00026603363,0.00011303709,0.0002740411,0.00010978789,0.00047701036,0.00012093328,0.000011925054],"category_scores_gemma":[0.0000018679478,0.00020163979,0.00009501168,0.000650508,0.000028411123,0.00028063927,0.000020750711,0.00016093056,0.00000980503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007351601,0.0001319154,0.0001887516,0.000039490064,0.000050485272,0.000006212691,0.00013093062,0.5266,0.0000031031204,0.0017083238,0.00021757052,0.4708497],"study_design_scores_gemma":[0.0019841457,0.00017353846,0.002359558,0.0002212392,0.000029411203,0.0000020397651,0.000046869944,0.989605,0.000009854343,0.00077518955,0.0044353805,0.0003577638],"about_ca_topic_score_codex":0.000036759127,"about_ca_topic_score_gemma":0.0005890443,"teacher_disagreement_score":0.94287056,"about_ca_system_score_codex":0.00007420342,"about_ca_system_score_gemma":0.000014291243,"threshold_uncertainty_score":0.82226324},"labels":[],"label_agreement":null},{"id":"W2374516805","doi":"10.1109/tnsm.2016.2569020","title":"Orchestrating Virtualized Network Functions","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":405,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Virtual network; Network topology; Server; Computer network; Software deployment; Packet processing; Network packet; Software-defined networking; Networking hardware; Heuristic; Distributed computing; Virtualization; Network virtualization; Operating system; Cloud computing","score_opus":0.015337556774686368,"score_gpt":0.2131358494255261,"score_spread":0.19779829265083973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2374516805","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007729314,0.00013358243,0.9847529,0.005066823,0.0015382348,0.00031136684,0.0000025464299,0.00049924664,0.006922367],"genre_scores_gemma":[0.9221579,0.0024929522,0.0563676,0.011340869,0.0013361936,0.0003905753,0.0000041919225,0.00007719697,0.005832546],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980313,0.00011779949,0.00035413014,0.00062283786,0.0002708492,0.0006030849],"domain_scores_gemma":[0.9986628,0.00033733645,0.00009657654,0.0006739196,0.000058959173,0.00017040236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003816987,0.00028114754,0.00024529165,0.000089817004,0.0007136627,0.00013969514,0.00046869018,0.0000929543,0.00010382514],"category_scores_gemma":[0.0000010757414,0.00020400363,0.000083918014,0.0010338082,0.000045738438,0.00033410522,0.000018678156,0.00016010225,0.00012227597],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007321605,0.00010873421,0.00007249833,0.000048923346,0.00024248671,0.000021065633,0.00022253045,0.21075527,0.0000170449,0.03540879,0.013835821,0.7391936],"study_design_scores_gemma":[0.011686013,0.0019225948,0.0066035376,0.0027840275,0.0007934322,0.00010855974,0.0006128433,0.31494886,0.000120412675,0.05817034,0.5982305,0.004018874],"about_ca_topic_score_codex":0.000033912413,"about_ca_topic_score_gemma":0.00016074648,"teacher_disagreement_score":0.9283853,"about_ca_system_score_codex":0.00004415103,"about_ca_system_score_gemma":0.000016216603,"threshold_uncertainty_score":0.8319027},"labels":[],"label_agreement":null},{"id":"W2433280216","doi":"10.1109/tnsm.2016.2580590","title":"Optimization of SDN Flow Operations in Multi-Failure Restoration Scenarios","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Mitacs","keywords":"Dijkstra's algorithm; Computer science; Path (computing); Minimum-cost flow problem; Flow network; Shortest path problem; Mathematical optimization; Software-defined networking; Integer programming; Flow (mathematics); Maximum flow problem; Integer (computer science); Software; Distributed computing; Algorithm; Computer network; Graph; Theoretical computer science; Mathematics","score_opus":0.01596123671252649,"score_gpt":0.2203505701790603,"score_spread":0.2043893334665338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2433280216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001407128,0.000040619037,0.99476546,0.0029222243,0.0003327726,0.00034192862,0.0000035205885,0.00008768557,0.00009865827],"genre_scores_gemma":[0.6549192,0.00085918006,0.34291556,0.000889888,0.000050630977,0.00010698567,0.0000043822943,0.000016631113,0.00023752615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894804,0.00007127945,0.0002819313,0.0003210731,0.00016540487,0.00021228021],"domain_scores_gemma":[0.99940217,0.000059756643,0.000046797566,0.00036437582,0.00007139296,0.000055534205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019484985,0.00013792638,0.00014355485,0.00015260106,0.00016296499,0.00006152841,0.00023585853,0.000069434784,0.00002282903],"category_scores_gemma":[0.0000010320633,0.00011013799,0.00003432112,0.0007408209,0.00001717251,0.00038091294,0.000008561509,0.00008018052,0.000009160155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001531717,0.00010098345,0.00003965413,0.000022297158,0.000020558993,0.0000025671302,0.0002043132,0.93930167,0.000011846105,0.0007662171,0.00016129832,0.059353262],"study_design_scores_gemma":[0.0009728858,0.00006493394,0.0005746147,0.00017257535,0.000018576931,0.0000015398064,0.000032946704,0.9971255,0.000043650896,0.00008160845,0.0007610873,0.00015006909],"about_ca_topic_score_codex":0.000054211825,"about_ca_topic_score_gemma":0.0019024182,"teacher_disagreement_score":0.6535121,"about_ca_system_score_codex":0.00004315063,"about_ca_system_score_gemma":0.000017539987,"threshold_uncertainty_score":0.44912967},"labels":[],"label_agreement":null},{"id":"W2439498752","doi":"10.1109/tnsm.2016.2581484","title":"A Reliable Embedding Framework for Elastic Virtualized Services in the Cloud","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Qatar National Research Fund","keywords":"Computer science; Cloud computing; Embedding; Distributed computing; Virtualization; Elasticity (physics); Computer network; Operating system; Artificial intelligence","score_opus":0.013790745504451035,"score_gpt":0.24796509824194699,"score_spread":0.23417435273749596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2439498752","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024683373,0.00014415431,0.9903375,0.0048090704,0.0011030559,0.000609801,0.0000036601189,0.00014964014,0.0003747909],"genre_scores_gemma":[0.89009434,0.001627885,0.093002476,0.013801302,0.00042604626,0.0007152488,0.0000018453181,0.000038788454,0.0002920654],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844295,0.000077105724,0.0002825267,0.0004783505,0.00023664224,0.00048240338],"domain_scores_gemma":[0.99837893,0.0008576947,0.0000752653,0.00058495114,0.00003655315,0.000066584944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005103208,0.00021380636,0.00020239707,0.000097127675,0.00036897644,0.00018980133,0.00072928844,0.0000932286,0.000015087189],"category_scores_gemma":[0.0000012627281,0.00013299391,0.00007499177,0.00078181486,0.000018352779,0.00023113313,0.000014161275,0.00015153905,0.000023742636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004231005,0.00043365455,0.00010254546,0.000539574,0.0002735038,0.000035174435,0.003367891,0.3711107,0.000008677815,0.2245035,0.0022826218,0.39691904],"study_design_scores_gemma":[0.00566771,0.0007370323,0.001273131,0.003002705,0.00028661767,0.000023118842,0.0012792381,0.66820705,0.000051915376,0.19133933,0.12684907,0.0012830532],"about_ca_topic_score_codex":0.000031478434,"about_ca_topic_score_gemma":0.0001744291,"teacher_disagreement_score":0.897335,"about_ca_system_score_codex":0.00003283551,"about_ca_system_score_gemma":0.000009469003,"threshold_uncertainty_score":0.5423334},"labels":[],"label_agreement":null},{"id":"W2525648680","doi":"10.1109/tnsm.2016.2574239","title":"Dedicated Protection for Survivable Virtual Network Embedding","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); Cisco Systems (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Backup; Computer science; Provisioning; Drone; Network virtualization; Computer network; Distributed computing; Virtualization; Virtual network; Embedding; Heuristic; Service provider; Service (business); Operating system; Cloud computing","score_opus":0.01893925904148043,"score_gpt":0.2270963733186233,"score_spread":0.2081571142771429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2525648680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011862564,0.00006560774,0.9929135,0.0025954596,0.0014526035,0.00085339515,0.0000041051517,0.00044124317,0.00048782994],"genre_scores_gemma":[0.9428207,0.0011517418,0.04818287,0.0038453203,0.00089613727,0.0011010838,0.000004528428,0.000068250934,0.001929337],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818754,0.00007493641,0.00029322086,0.00059025694,0.00023627668,0.00061777275],"domain_scores_gemma":[0.99899966,0.00021383225,0.000094860836,0.00047574448,0.000077541576,0.00013834985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048538658,0.00024435506,0.00021917383,0.00009182961,0.00064059254,0.00014326972,0.0004024077,0.0001072157,0.000024558352],"category_scores_gemma":[0.000001234097,0.00018875841,0.00008609213,0.00079362147,0.000023754694,0.00031569123,0.000015757149,0.00012236043,0.000032770964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013695844,0.00009279908,0.000015971424,0.000063662046,0.0001750547,0.000004644701,0.00007290488,0.37068605,0.000018216653,0.00726772,0.0038409561,0.61762506],"study_design_scores_gemma":[0.0037446194,0.0009108281,0.00056926697,0.0007022754,0.00019218161,0.000015946658,0.00007618096,0.8628344,0.00022709684,0.010239361,0.11945981,0.0010280347],"about_ca_topic_score_codex":0.00003271131,"about_ca_topic_score_gemma":0.00011028161,"teacher_disagreement_score":0.94473064,"about_ca_system_score_codex":0.00005705231,"about_ca_system_score_gemma":0.000016567357,"threshold_uncertainty_score":0.76973444},"labels":[],"label_agreement":null},{"id":"W2531066886","doi":"10.1109/tnsm.2016.2616283","title":"Surviving Multiple Failures in Multicast Virtual Networks With Virtual Machines Migration","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Qatar Foundation","keywords":"Computer science; Multicast; Computer network; Node (physics); Quality of service; Distributed computing","score_opus":0.008116528444978417,"score_gpt":0.19517057707913424,"score_spread":0.1870540486341558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2531066886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038828857,0.00004829496,0.95740503,0.0024632858,0.0005356393,0.00037106514,0.0000031687393,0.00021598554,0.0001286669],"genre_scores_gemma":[0.9895333,0.0005223033,0.008510636,0.0009655919,0.00012883016,0.00010497816,0.0000024885273,0.00002629948,0.0002055468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981707,0.00012300053,0.00031087908,0.0006071067,0.00027939098,0.0005089192],"domain_scores_gemma":[0.99888176,0.00039535912,0.000079106765,0.00047168627,0.000050163155,0.0001219092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003004385,0.00029832532,0.0002423682,0.0001657669,0.00028316598,0.00016616801,0.00038839894,0.00009318088,0.000016312915],"category_scores_gemma":[0.0000014621571,0.00020659064,0.0000502884,0.00080512115,0.000039009654,0.00047704703,0.000019520641,0.00018462013,0.000014841504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001203344,0.00011718258,0.0018279743,0.000013271976,0.00006409106,0.000018018376,0.0002247306,0.65169454,0.000006882751,0.0013201033,0.00015242594,0.3444405],"study_design_scores_gemma":[0.0022619832,0.00043747085,0.011913355,0.00036332084,0.000040580642,0.000009831102,0.00012253491,0.9821678,0.000021312584,0.00011194597,0.0020692702,0.00048058835],"about_ca_topic_score_codex":0.00032668654,"about_ca_topic_score_gemma":0.01597037,"teacher_disagreement_score":0.95070446,"about_ca_system_score_codex":0.000048605136,"about_ca_system_score_gemma":0.000012008202,"threshold_uncertainty_score":0.89118415},"labels":[],"label_agreement":null},{"id":"W2564347979","doi":"10.1109/tnsm.2016.2642838","title":"Subscriber-Driven Interference Detection for Cloud-Based Web Services","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Cloud computing; Computer science; Interference (communication); Virtual machine; Web application; Web service; Computer network; Response time; Distributed computing; Operating system; World Wide Web; Channel (broadcasting)","score_opus":0.012393388315710518,"score_gpt":0.2122716075252048,"score_spread":0.19987821920949428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564347979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031877626,0.000028333976,0.9613045,0.0036844735,0.0013057581,0.00056657294,0.0000048024463,0.00036363376,0.000864287],"genre_scores_gemma":[0.989839,0.00006654134,0.0068368204,0.0023836852,0.00017006553,0.00016734783,7.506096e-7,0.00002025246,0.0005155456],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983706,0.00006982692,0.0002712067,0.0006244236,0.00022044725,0.00044348973],"domain_scores_gemma":[0.99898636,0.00014813464,0.000094532224,0.00058219244,0.000074595766,0.000114152215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025778875,0.00025133943,0.00018949916,0.00016064607,0.00044159498,0.00016079521,0.00064318196,0.00006786247,0.000009373833],"category_scores_gemma":[3.1777043e-7,0.00018934498,0.00010344064,0.00047742095,0.000024895096,0.00005708398,0.000025113493,0.00009543618,0.000033857785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015287587,0.0001994943,0.00002168092,0.0004478634,0.00020388878,0.000008303095,0.00025802088,0.23071814,0.0003317903,0.0019673833,0.00030801265,0.7653825],"study_design_scores_gemma":[0.0017399756,0.00034541328,0.00018207697,0.0004374765,0.00009508075,0.0000029967189,0.00009245867,0.9681942,0.0009853173,0.00071348704,0.026803276,0.00040827476],"about_ca_topic_score_codex":0.000032024058,"about_ca_topic_score_gemma":0.00045148595,"teacher_disagreement_score":0.9579614,"about_ca_system_score_codex":0.00006472466,"about_ca_system_score_gemma":0.000011256667,"threshold_uncertainty_score":0.77212644},"labels":[],"label_agreement":null},{"id":"W2592041560","doi":"10.1109/tnsm.2017.2679191","title":"Practical Network Coding for the Update Problem in Cloud Storage Systems","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Advanced Data Storage Technologies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Cloud computing; Linear network coding; Cloud storage; Distributed computing; Coding (social sciences); Computer network; Operating system","score_opus":0.03417553641555506,"score_gpt":0.2830107472452313,"score_spread":0.24883521082967622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592041560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000905159,0.00015762266,0.98610026,0.010117361,0.0016259087,0.0011674374,0.000006796213,0.00022555135,0.00050854124],"genre_scores_gemma":[0.7686317,0.0026585406,0.22549212,0.0016178739,0.00034442876,0.00097537047,0.000003343398,0.00003410803,0.00024252656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985323,0.00005245259,0.00026034086,0.0004751879,0.00019316177,0.00048653642],"domain_scores_gemma":[0.99811006,0.00026530804,0.00017706209,0.001359015,0.000039221675,0.00004933262],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00067246816,0.00019495246,0.00020328253,0.000057762267,0.0013225737,0.0005739469,0.001169048,0.00007767559,0.0000018020328],"category_scores_gemma":[0.000003157105,0.00015348881,0.00004100322,0.00027564025,0.00006284419,0.0006479556,0.00006506773,0.00028683714,0.000010962968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047767917,0.00005317845,0.000009309721,0.00012198902,0.000082820465,0.000032978467,0.000075861455,0.8247632,9.4426025e-7,0.124931596,0.0038810505,0.04599932],"study_design_scores_gemma":[0.00081317825,0.000087426386,0.00019923135,0.00020833428,0.000070949995,0.000013737914,0.00025589237,0.9151733,0.000009945671,0.0063380837,0.07651278,0.00031715294],"about_ca_topic_score_codex":0.000052982406,"about_ca_topic_score_gemma":0.000338659,"teacher_disagreement_score":0.76854116,"about_ca_system_score_codex":0.00006385653,"about_ca_system_score_gemma":0.000014174551,"threshold_uncertainty_score":0.9999776},"labels":[],"label_agreement":null},{"id":"W2614519474","doi":"10.1109/tnsm.2017.2704427","title":"An Efficient Survivable Design With Bandwidth Guarantees for Multi-Tenant Cloud Networks","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Backup; Cloud computing; Provisioning; Computer network; Bandwidth (computing); Distributed computing; Bandwidth allocation; Redundancy (engineering); Operating system","score_opus":0.028929509744287027,"score_gpt":0.25082635747113496,"score_spread":0.22189684772684792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2614519474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00678982,0.000060415412,0.98930615,0.0010342908,0.001003024,0.0011867513,0.0000021632532,0.00025652413,0.00036083176],"genre_scores_gemma":[0.8737365,0.000120448625,0.12390656,0.0010503609,0.00023860166,0.00020608559,0.0000017534553,0.00004053537,0.00069917337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978005,0.00011355545,0.00028030056,0.0008156866,0.00029841467,0.00069153873],"domain_scores_gemma":[0.99797875,0.00009797825,0.00017804756,0.0014803269,0.00008959943,0.00017529498],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0008424543,0.00034427855,0.00030223266,0.00010149544,0.0023022455,0.0008048796,0.0012885534,0.00007517965,0.000003288515],"category_scores_gemma":[6.240647e-7,0.00024065484,0.00008425733,0.0002444753,0.00005972382,0.00007074363,0.000034429766,0.00017611528,0.0000062326626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016890767,0.00031149248,0.000014033151,0.00007302172,0.0001574595,0.00001882282,0.00020488548,0.91424245,0.0000010490836,0.0010791685,0.00016835888,0.08356033],"study_design_scores_gemma":[0.001776899,0.0005548079,0.000670769,0.00017125503,0.00010895584,0.0000055779738,0.00012336354,0.9932584,0.000029975401,0.00007607154,0.0028337792,0.00039013795],"about_ca_topic_score_codex":0.000113201655,"about_ca_topic_score_gemma":0.00022440065,"teacher_disagreement_score":0.86694664,"about_ca_system_score_codex":0.000039888946,"about_ca_system_score_gemma":0.0000150573815,"threshold_uncertainty_score":0.9989966},"labels":[],"label_agreement":null},{"id":"W2619310525","doi":"10.1109/tnsm.2017.2706085","title":"Mobile-Edge Computing Versus Centralized Cloud Computing Over a Converged FiWi Access Network","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":118,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Cloud computing; Computer science; Computer network; Mobile edge computing; Backhaul (telecommunications); Access network; Edge computing; Distributed computing; Server; Base station; Operating system","score_opus":0.030424587864527605,"score_gpt":0.2874673404486047,"score_spread":0.2570427525840771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2619310525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034343947,0.00015663478,0.91578335,0.0005430806,0.04001367,0.00079927297,8.0146486e-7,0.00046000216,0.00789924],"genre_scores_gemma":[0.9805202,0.00033331627,0.011972854,0.0025282542,0.004304214,0.000025530124,0.000006202748,0.000051990533,0.00025742204],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968847,0.0001590562,0.00052444875,0.0008944933,0.0004066106,0.0011307037],"domain_scores_gemma":[0.9978249,0.00021003556,0.0003611523,0.0012727145,0.00008258551,0.00024860806],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00067085476,0.0004495903,0.0004618471,0.000103504324,0.0033122345,0.001614008,0.0020344388,0.0001322878,0.000015038241],"category_scores_gemma":[0.0000016704103,0.00046902738,0.00016966088,0.00051823544,0.000071702016,0.0006075268,0.0002206603,0.0004431075,0.00004123041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043205218,0.00030917415,0.0006721255,0.0004171911,0.0008305738,0.00013142302,0.0019369775,0.5254431,0.000004648753,0.002503087,0.03089861,0.43642104],"study_design_scores_gemma":[0.0041362643,0.0001796376,0.004544364,0.00040384952,0.00014884451,0.000006894552,0.00006635452,0.9281643,0.000036991605,0.00039235302,0.061169606,0.00075051095],"about_ca_topic_score_codex":0.0001493983,"about_ca_topic_score_gemma":0.00005541349,"teacher_disagreement_score":0.9461763,"about_ca_system_score_codex":0.00009306068,"about_ca_system_score_gemma":0.00003102279,"threshold_uncertainty_score":0.9997761},"labels":[],"label_agreement":null},{"id":"W2731118119","doi":"10.1109/tnsm.2017.2723090","title":"A Reliability-Aware Network Service Chain Provisioning With Delay Guarantees in NFV-Enabled Enterprise Datacenter Networks","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":185,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Computer network; Virtual network; Distributed computing; Virtualization; Network service; Provisioning; Software-defined networking; Reliability (semiconductor); Integer programming; Network virtualization; Cloud computing; Operating system; Algorithm","score_opus":0.009713939271523593,"score_gpt":0.21952395814978953,"score_spread":0.20981001887826595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2731118119","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014156748,0.00020934542,0.9796152,0.0024708237,0.0010759814,0.001178216,0.0000059698173,0.0003087267,0.000978997],"genre_scores_gemma":[0.9771547,0.0011397939,0.015676096,0.0050730654,0.0003099878,0.00031778606,0.000014449241,0.00006490479,0.00024927352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963768,0.00019271887,0.00058351323,0.0012601799,0.0004572368,0.001129566],"domain_scores_gemma":[0.9968694,0.00018313574,0.00029253835,0.002297037,0.00013226604,0.00022564073],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00087210804,0.0005551984,0.0005269425,0.00014689904,0.0015626865,0.0009271558,0.0017524062,0.00017632968,0.000021245223],"category_scores_gemma":[0.0000017709671,0.0004721393,0.00009499012,0.00095289055,0.00006461447,0.0010196378,0.00013381348,0.0006254752,0.000018543136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003638026,0.00022848131,0.001924381,0.00016352831,0.0001322416,0.00015667861,0.00052827003,0.9552655,4.519243e-7,0.0004582957,0.00069745124,0.040080912],"study_design_scores_gemma":[0.0024237328,0.00024204334,0.010055368,0.0013020348,0.00010268734,0.000027894743,0.00017428875,0.9796853,0.0000032667924,0.000451115,0.0048539783,0.00067827],"about_ca_topic_score_codex":0.00053691946,"about_ca_topic_score_gemma":0.0063154176,"teacher_disagreement_score":0.9639391,"about_ca_system_score_codex":0.00010278677,"about_ca_system_score_gemma":0.00003791037,"threshold_uncertainty_score":0.999773},"labels":[],"label_agreement":null},{"id":"W2745255455","doi":"10.1109/tnsm.2017.2738026","title":"Simultaneous Cost and QoS Optimization for Cloud Resource Allocation","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Cloud computing; Computer science; Quality of service; Software deployment; Workload; Resource allocation; Distributed computing; Service provider; Computer network; Service (business); Operating system","score_opus":0.014070793320044747,"score_gpt":0.23425720706312986,"score_spread":0.22018641374308512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2745255455","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030202297,0.000045300323,0.98717576,0.0057668006,0.00069255626,0.00085988734,0.0000020214661,0.00014758419,0.0022898582],"genre_scores_gemma":[0.94374424,0.0002922793,0.05082588,0.0029484504,0.00040638528,0.00017170516,0.0000044597614,0.000029038183,0.0015775604],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880534,0.00003991551,0.00019690234,0.00048444606,0.00017513995,0.00029822424],"domain_scores_gemma":[0.99888146,0.00013035462,0.00012637912,0.000710402,0.000053966443,0.0000974636],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003197747,0.00017862715,0.00014808618,0.0000730994,0.0015283222,0.0005252403,0.0005119088,0.000056415956,0.0000029742234],"category_scores_gemma":[0.0000022969618,0.00017538006,0.000041322328,0.00012994972,0.00003471545,0.00005465197,0.000036309837,0.000095012125,0.000003976274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002626709,0.0000412818,0.0000021530643,0.0000758393,0.00004986128,0.000003908087,0.00017137127,0.7500831,5.834513e-7,0.0015910765,0.0003803987,0.24757415],"study_design_scores_gemma":[0.00071764446,0.000088578825,0.00006168269,0.00007828205,0.000061798,0.0000035141143,0.000095481504,0.95768213,0.00001286446,0.0001827918,0.040820766,0.00019448985],"about_ca_topic_score_codex":0.000031226526,"about_ca_topic_score_gemma":0.00005251797,"teacher_disagreement_score":0.940724,"about_ca_system_score_codex":0.00003128243,"about_ca_system_score_gemma":0.0000055905125,"threshold_uncertainty_score":0.99977154},"labels":[],"label_agreement":null},{"id":"W2760151663","doi":"10.1109/tnsm.2017.2757266","title":"On the Interplay Between Network Function Mapping and Scheduling in VNF-Based Networks: A Column Generation Approach","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Computer science; Distributed computing; Virtual network; Chaining; Scheduling (production processes); Server; Cloud computing; Virtualization; Cache; Quality of service; Computer network; Operating system; Mathematical optimization","score_opus":0.029472142123807374,"score_gpt":0.23811215266533056,"score_spread":0.2086400105415232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2760151663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028524,0.00015673875,0.96685374,0.0019838924,0.00090480875,0.00065427186,0.0000012500446,0.00011717574,0.00080412935],"genre_scores_gemma":[0.9806365,0.00023594395,0.014472939,0.0037890174,0.0005924634,0.00019314954,0.0000074387194,0.000023817134,0.000048726677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980691,0.00016681323,0.00034386804,0.0006583739,0.00025053133,0.00051131845],"domain_scores_gemma":[0.9985488,0.0002748269,0.00017863503,0.00085695623,0.00003911621,0.00010168182],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00090597617,0.00028906422,0.00026831916,0.000113548995,0.0016592862,0.00083280593,0.0005766086,0.00012991588,0.0000063793505],"category_scores_gemma":[0.0000018077437,0.00024473746,0.00006398771,0.0004992662,0.000051999716,0.00030421585,0.000034880504,0.0004251714,0.000005236344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042553598,0.000053184696,0.00057663483,0.000034214183,0.00007892746,0.0000038176922,0.00010687877,0.91730344,7.483637e-7,0.004479029,0.0004647549,0.07685581],"study_design_scores_gemma":[0.00074103964,0.00011735758,0.008264836,0.000247937,0.00005507239,0.0000011004084,0.000052113905,0.98804396,0.0000026737714,0.0010265448,0.0011760554,0.00027129837],"about_ca_topic_score_codex":0.0000829916,"about_ca_topic_score_gemma":0.00038513402,"teacher_disagreement_score":0.9523808,"about_ca_system_score_codex":0.000053339965,"about_ca_system_score_gemma":0.000014509062,"threshold_uncertainty_score":0.9996404},"labels":[],"label_agreement":null},{"id":"W2775484223","doi":"10.1109/tnsm.2017.2774246","title":"Guest Editors’ Introduction: Special Issue on Advances in Management of Softwarized Networks","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Universiteit Gent","keywords":"Computer science; Cloud computing; Virtualization; Network management; Network Functions Virtualization; Service provider; Software-defined networking; Network virtualization; Service (business); Telecommunications; Network service; Computer network; Operating system","score_opus":0.008491614352372737,"score_gpt":0.23258823426569117,"score_spread":0.22409661991331842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775484223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030959494,0.00031714965,0.93442154,0.005780388,0.04412407,0.0008621435,0.000003586688,0.00020153377,0.0139799705],"genre_scores_gemma":[0.50464404,0.08214432,0.10191185,0.005961841,0.2969824,0.0011084541,0.00003671577,0.00025163376,0.0069587245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99791056,0.00006592484,0.00043148923,0.000721868,0.0003860847,0.00048408163],"domain_scores_gemma":[0.9982984,0.00007864915,0.00022590168,0.0012385427,0.00005411983,0.000104366736],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003895303,0.00030508998,0.00035440046,0.00015818946,0.0006092661,0.00022811374,0.0009452328,0.0001055244,0.00006884585],"category_scores_gemma":[8.566957e-7,0.0002987459,0.00008676869,0.0004946577,0.000067414454,0.0005461832,0.000045657627,0.00029316658,0.00003465565],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023205251,0.00031108124,0.00006412671,0.00021520715,0.00013657962,0.000050844337,0.00012226806,0.50953543,1.8210953e-7,0.005062503,0.021249793,0.4630199],"study_design_scores_gemma":[0.003473836,0.00047508444,0.0046950695,0.000784173,0.00015581788,0.0000068114505,0.00015131479,0.07705723,0.000037832368,0.0021001715,0.91025376,0.0008088764],"about_ca_topic_score_codex":0.000034189936,"about_ca_topic_score_gemma":0.00016565774,"teacher_disagreement_score":0.889004,"about_ca_system_score_codex":0.00005335325,"about_ca_system_score_gemma":0.000007376594,"threshold_uncertainty_score":0.9999465},"labels":[],"label_agreement":null},{"id":"W2805672896","doi":"10.1109/tnsm.2018.2842195","title":"Event Detection in Wireless Body Area Networks Using Kalman Filter and Power Divergence","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Constant false alarm rate; Real-time computing; Kalman filter; False alarm; Metric (unit); Data mining; Event (particle physics); Change detection; ALARM; Software deployment; False positive rate; Divergence (linguistics); Artificial intelligence","score_opus":0.02597501679253093,"score_gpt":0.27448755209742814,"score_spread":0.24851253530489723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805672896","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7904332,0.0000646307,0.20782804,0.00031716278,0.00067304366,0.00038035106,8.840286e-7,0.00006573914,0.00023688377],"genre_scores_gemma":[0.99809515,0.000631634,0.00030919036,0.00074799295,0.00011805153,0.00003204525,0.0000011434586,0.000014346852,0.00005043214],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902254,0.000042215706,0.00021801978,0.00029784106,0.000116288575,0.00030308432],"domain_scores_gemma":[0.99962515,0.000024162753,0.000043362958,0.00017430489,0.000042313975,0.00009069825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017586554,0.00014751745,0.0001713697,0.00014543328,0.00028001925,0.000011980877,0.00004754914,0.00013582161,0.000030748102],"category_scores_gemma":[3.8127234e-7,0.00014363084,0.000025267262,0.00040863547,0.00004711736,0.000064280975,0.000006729956,0.00026993797,0.0000040692466],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020943573,0.00078451604,0.05123838,0.0009581412,0.0005744027,0.00023328085,0.0020220059,0.053456903,0.00086761726,0.00022743693,0.00013314161,0.8874098],"study_design_scores_gemma":[0.0038684288,0.0022604198,0.18409295,0.0024131008,0.000520867,0.00010856346,0.0014340504,0.7987235,0.0023443825,0.00021906124,0.0031709813,0.00084370066],"about_ca_topic_score_codex":0.00018227307,"about_ca_topic_score_gemma":0.0007586867,"teacher_disagreement_score":0.8865661,"about_ca_system_score_codex":0.00006460873,"about_ca_system_score_gemma":0.000007213992,"threshold_uncertainty_score":0.5857095},"labels":[],"label_agreement":null},{"id":"W2894036243","doi":"10.1109/tnsm.2019.2912526","title":"Smart Roaming: How Operator Cooperation Can Increase Spectrum Usage Efficiency at Practically No Cost","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Roaming; Computer science; Operator (biology); Snapshot (computer storage); Base station; Computer network; Cellular network; Personalization; Telecommunications link; Hotspot (geology); Telecommunications; Distributed computing; World Wide Web; Database","score_opus":0.006023510330360445,"score_gpt":0.1911303525689301,"score_spread":0.18510684223856966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894036243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03382187,0.00006496758,0.9529865,0.0008816776,0.0013488255,0.0015688093,0.000016870725,0.00032890958,0.008981566],"genre_scores_gemma":[0.99328303,0.00047057105,0.0025961748,0.000683406,0.00009064542,0.00013972067,0.000027402244,0.000058596008,0.0026504244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988813,0.00005275733,0.00021985844,0.00033386782,0.00016585812,0.0003463768],"domain_scores_gemma":[0.9993867,0.00005065899,0.000040815914,0.00033450138,0.000059578764,0.00012771675],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001635373,0.00024160986,0.00019901259,0.00008920775,0.00024437037,0.00012297988,0.0001103213,0.00007828535,0.0001351826],"category_scores_gemma":[0.0000012159633,0.00025017263,0.00003356767,0.00040854697,0.0000120746845,0.00025166845,0.0000055811747,0.00016981979,0.00019495047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004693923,0.00004842851,0.000027928207,0.00017004281,0.000067009496,0.000007319828,0.00011430707,0.99685174,0.00034751755,0.00009295883,0.00034833408,0.0018774484],"study_design_scores_gemma":[0.0010514202,0.00011888805,0.00010387406,0.00012213926,0.00009225395,0.000010884317,0.00014211537,0.9724241,0.00066640845,0.0000045249776,0.024846284,0.00041713312],"about_ca_topic_score_codex":0.000032988803,"about_ca_topic_score_gemma":0.0008056834,"teacher_disagreement_score":0.9594612,"about_ca_system_score_codex":0.00021025827,"about_ca_system_score_gemma":0.000011046666,"threshold_uncertainty_score":0.99999505},"labels":[],"label_agreement":null},{"id":"W2896030746","doi":"10.1109/tnsm.2018.2876697","title":"NFV-Based Architecture for the Interworking Between WebRTC and IMS","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"WebRTC; Computer science; Quality of service; Testbed; Cloud computing; Virtual network; Computer network; Distributed computing; Provisioning; Service (business); Virtualization; Resource allocation; Operating system","score_opus":0.01850929494574218,"score_gpt":0.2334218174354356,"score_spread":0.21491252248969342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896030746","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015428351,0.00018325252,0.9903648,0.006181486,0.00071660517,0.0004944702,0.0000041182357,0.00014934882,0.00036309563],"genre_scores_gemma":[0.96213925,0.00018708354,0.029784007,0.007021383,0.00060121337,0.0001393268,0.0000020672942,0.000021732432,0.000103964594],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988759,0.000041253214,0.00017771941,0.00040656916,0.00014791725,0.00035060543],"domain_scores_gemma":[0.9989691,0.00041987898,0.000052851763,0.00043637576,0.000041189356,0.000080621256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002831022,0.00018785456,0.00015463604,0.00007246121,0.00071073417,0.00021418936,0.0004254933,0.000058453974,0.0000079882275],"category_scores_gemma":[4.6712438e-7,0.00013625846,0.000060137812,0.0004478945,0.000058548292,0.000075588025,0.000016734102,0.00016551898,0.0000056677122],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060993047,0.000032176657,0.000104946244,0.00007644893,0.000173875,0.0000020755963,0.0005100093,0.05514721,0.0000010015003,0.0012901896,0.0012732476,0.9413278],"study_design_scores_gemma":[0.0021396491,0.00072457496,0.0038584545,0.0004048008,0.00038162115,0.000009124409,0.00019103718,0.77652735,0.00012368547,0.007291109,0.20765598,0.0006926427],"about_ca_topic_score_codex":0.000026398322,"about_ca_topic_score_gemma":0.00029388405,"teacher_disagreement_score":0.9605964,"about_ca_system_score_codex":0.0000147877545,"about_ca_system_score_gemma":0.000008758511,"threshold_uncertainty_score":0.5556459},"labels":[],"label_agreement":null},{"id":"W2908800330","doi":"10.1109/tnsm.2019.2893503","title":"User Preference Aware Task Coordination and Proactive Bandwidth Allocation in a FiWi-Based Human–Agent–Robot Teamwork Ecosystem","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Teamwork; Computer science; Task (project management); Bandwidth (computing); Robot; Human–robot interaction; Bandwidth allocation; Preference; Human–computer interaction; Collaborative software; Knowledge management; Distributed computing; Computer network; Artificial intelligence; Engineering; Systems engineering","score_opus":0.01614531632090299,"score_gpt":0.21751322470815712,"score_spread":0.20136790838725413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908800330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1514118,0.000032750584,0.8439807,0.0008014984,0.001654456,0.0010540383,7.771826e-7,0.00013051335,0.0009334519],"genre_scores_gemma":[0.9962757,0.000029762767,0.0026614731,0.00051216787,0.00011381623,0.00009789364,0.00000601348,0.00001412466,0.0002890085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851865,0.00011642335,0.00026970042,0.00055460504,0.00021847793,0.00032212012],"domain_scores_gemma":[0.9993396,0.00007111195,0.00009404234,0.00035855363,0.00006663396,0.00007005011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033117746,0.00021065887,0.00019963106,0.00019079621,0.00028802562,0.00018944462,0.00027871167,0.00007703144,0.0000045962224],"category_scores_gemma":[3.2623538e-7,0.00021086393,0.00003318832,0.0006609229,0.000009881722,0.00032335435,0.000016977392,0.00018793318,0.000022249047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016608086,0.0006920838,0.00334319,0.0013997972,0.0002845251,0.00002025732,0.003810773,0.7551738,0.00016044677,0.0021483845,0.0011783721,0.23162231],"study_design_scores_gemma":[0.0019045079,0.00024304757,0.021087214,0.00060993846,0.000059644655,0.0000033431193,0.00020062753,0.97052926,0.00021829095,0.0003649477,0.0042768237,0.0005023368],"about_ca_topic_score_codex":0.00007168408,"about_ca_topic_score_gemma":0.00027544002,"teacher_disagreement_score":0.84486395,"about_ca_system_score_codex":0.000099919605,"about_ca_system_score_gemma":0.000021695843,"threshold_uncertainty_score":0.8598782},"labels":[],"label_agreement":null},{"id":"W2912715275","doi":"10.1109/tnsm.2019.2894955","title":"Optimized Provisioning of Edge Computing Resources With Heterogeneous Workload in IoT Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Server; Distributed computing; Provisioning; Edge computing; Dimensioning; Computer network; Mobile edge computing; Enhanced Data Rates for GSM Evolution; Workload; Operating system","score_opus":0.006657079028651821,"score_gpt":0.19645471429568676,"score_spread":0.18979763526703494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912715275","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18634641,0.00018502146,0.8100466,0.0002114617,0.001321853,0.00046343845,5.2246122e-8,0.00009733129,0.0013278063],"genre_scores_gemma":[0.951905,0.000094395225,0.04698774,0.00069651613,0.00018907725,0.000011593861,5.526539e-7,0.000020862715,0.00009425058],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983456,0.00009330685,0.00036399617,0.0004974395,0.00023608044,0.00046356494],"domain_scores_gemma":[0.9991718,0.00013511033,0.00013556187,0.00043883955,0.000045057917,0.00007366534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003810279,0.00022493937,0.00031672078,0.00017876181,0.00020342255,0.00012360762,0.0004530042,0.000068145804,0.0000033885347],"category_scores_gemma":[1.9890375e-7,0.00019958676,0.0000547153,0.00097332365,0.00002135115,0.000101974074,0.000031673924,0.0002491957,0.0000068290333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008618347,0.00006542715,0.0001829503,0.00008511148,0.00005426379,0.000014181614,0.00065806386,0.8741767,0.0000019114775,0.000033273882,0.000024723957,0.12461723],"study_design_scores_gemma":[0.0011919992,0.00018210767,0.0007521518,0.0007579125,0.000023837558,0.000009302805,0.00007415181,0.99594706,0.000035442798,0.000031875075,0.0007491553,0.00024499287],"about_ca_topic_score_codex":0.00005291015,"about_ca_topic_score_gemma":0.000022858243,"teacher_disagreement_score":0.7655586,"about_ca_system_score_codex":0.00003356505,"about_ca_system_score_gemma":0.000012010095,"threshold_uncertainty_score":0.8138912},"labels":[],"label_agreement":null},{"id":"W2918288824","doi":"10.1109/tnsm.2019.2901879","title":"A Novel Approach for Profile Optimization in DOCSIS 3.1 Networks Exploiting Traffic Information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Optical Network Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; Exfo Electro-Optical Engineering (Canada); Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Throughput; Cable modem; Real-time computing; Coaxial; Interface (matter); Computer network; Noise (video); Telecommunications; Wireless; Artificial intelligence","score_opus":0.008198299725712215,"score_gpt":0.1815302460456472,"score_spread":0.17333194631993498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2918288824","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01093126,0.000038348746,0.9836121,0.00008495279,0.0003070926,0.0013443716,0.000005817172,0.00048570693,0.0031903454],"genre_scores_gemma":[0.7861384,0.00050390436,0.2122585,0.00025415243,0.00004856735,0.0006826692,0.000046690904,0.00003543673,0.00003167797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990336,0.000008920564,0.00029715884,0.00018985546,0.000108545944,0.00036196117],"domain_scores_gemma":[0.9996487,0.000053545395,0.000033707398,0.00019906083,0.000027530356,0.000037466612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015891327,0.00018721128,0.00018411316,0.00016638944,0.000083479696,0.000075623444,0.00012709387,0.00013158881,0.00001463688],"category_scores_gemma":[4.7209102e-7,0.00019761069,0.00004103436,0.0006444689,0.0000107229935,0.0003591415,0.000003995612,0.00019511014,0.000007579099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003162211,0.000040848452,0.0000045530105,0.00031191582,0.00004327519,1.5648683e-7,0.000076367665,0.9301905,0.0000012430218,0.00031030853,0.00007573223,0.06891348],"study_design_scores_gemma":[0.0008229072,0.00004761152,0.00004810212,0.00007971329,0.00003227203,0.0000010580194,0.00052599143,0.9977463,0.0000074489503,0.000010504662,0.00046717093,0.00021092493],"about_ca_topic_score_codex":0.0000032421333,"about_ca_topic_score_gemma":0.000015821875,"teacher_disagreement_score":0.77520716,"about_ca_system_score_codex":0.000064765285,"about_ca_system_score_gemma":0.0000027389433,"threshold_uncertainty_score":0.805833},"labels":[],"label_agreement":null},{"id":"W2948949321","doi":"10.1109/tnsm.2019.2946949","title":"Probabilistic Virtual Link Embedding Under Demand Uncertainty","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Testbed; Probabilistic logic; Embedding; Mathematical optimization; Bandwidth (computing); Distributed computing; TRACE (psycholinguistics); Optimization problem; Network congestion; Algorithm; Computer network; Network packet; Mathematics; Artificial intelligence","score_opus":0.010936395632554517,"score_gpt":0.22231750595276234,"score_spread":0.21138111032020782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948949321","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014381709,0.00008885378,0.97922444,0.0019408519,0.0013511342,0.000554245,0.0000018511948,0.00028519088,0.0021717462],"genre_scores_gemma":[0.9877971,0.00035980664,0.0062875357,0.004355192,0.000165356,0.00007228157,0.00000311378,0.000024211053,0.0009354002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983455,0.00007074103,0.00025881646,0.00059302826,0.00027587896,0.00045601776],"domain_scores_gemma":[0.9989787,0.00018272753,0.00006396268,0.00059831777,0.00004925726,0.00012705372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028111527,0.00024423836,0.00022608378,0.0000934794,0.0003004749,0.00020673768,0.00044028086,0.00008949051,0.000060701175],"category_scores_gemma":[3.9905257e-7,0.00022294668,0.00007318743,0.0006653623,0.00002085661,0.00022071513,0.000020697127,0.00023008567,0.00013520928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002628916,0.00005145893,0.00001642297,0.0000622192,0.00008192004,0.0000055867745,0.00015010346,0.9175036,0.0000013727303,0.01269452,0.00028176475,0.06912476],"study_design_scores_gemma":[0.0008814735,0.00022384535,0.0004780623,0.00016358319,0.000070697584,0.000007702048,0.00014020542,0.98539037,0.0000063855405,0.005032842,0.007215887,0.00038896193],"about_ca_topic_score_codex":0.00002782952,"about_ca_topic_score_gemma":0.00009835322,"teacher_disagreement_score":0.9734154,"about_ca_system_score_codex":0.00005702664,"about_ca_system_score_gemma":0.000018340084,"threshold_uncertainty_score":0.9091502},"labels":[],"label_agreement":null},{"id":"W2952699079","doi":"10.1109/tnsm.2019.2922904","title":"Human-Agent-Robot Task Coordination in FiWi-Based Tactile Internet Infrastructures Using Context- and Self-Awareness","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robot; Context (archaeology); The Internet; Task (project management); Automation; Human–robot interaction; Human–computer interaction; Computer network; Distributed computing; Artificial intelligence; World Wide Web; Engineering","score_opus":0.013010406507997072,"score_gpt":0.23983061775629794,"score_spread":0.22682021124830087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952699079","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3935231,0.000037089714,0.6033231,0.00026007145,0.0021278085,0.0003299971,3.841988e-7,0.0000984205,0.00030001922],"genre_scores_gemma":[0.9936541,0.000010280173,0.0050504133,0.0010505915,0.000114773546,0.000013645644,0.0000025512074,0.0000131407505,0.0000905205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987852,0.00007551451,0.0002429187,0.00043035278,0.00016617226,0.00029981913],"domain_scores_gemma":[0.999499,0.000049525275,0.000081747734,0.00026768725,0.000039753115,0.0000622364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019630643,0.00019649164,0.00019525722,0.00020139427,0.00021194747,0.0002083016,0.00026413266,0.000067308116,0.000009126744],"category_scores_gemma":[1.9672643e-7,0.0002020567,0.00003354877,0.00045878382,0.000013092409,0.0002584443,0.000021451662,0.00016979271,0.0000071811814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058323512,0.00032961485,0.0038946415,0.000629368,0.00018451182,0.00003212084,0.0028290518,0.870321,0.00027648435,0.00087336305,0.0012839612,0.119287595],"study_design_scores_gemma":[0.0011700867,0.00009179562,0.010600776,0.00019033697,0.00003513905,0.000004455424,0.000098813885,0.98409843,0.00018835302,0.00026864442,0.0029697195,0.000283416],"about_ca_topic_score_codex":0.00037317877,"about_ca_topic_score_gemma":0.00020773592,"teacher_disagreement_score":0.600131,"about_ca_system_score_codex":0.000077473276,"about_ca_system_score_gemma":0.000017710094,"threshold_uncertainty_score":0.82396334},"labels":[],"label_agreement":null},{"id":"W2959716986","doi":"10.1109/tnsm.2019.2927886","title":"A Hybrid Deep Learning-Based Model for Anomaly Detection in Cloud Datacenter Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":284,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Anomaly detection; Data mining; Benchmark (surveying); Cloud computing; Convolutional neural network; Artificial intelligence; Anomaly (physics); Data modeling; False positive paradox; Data set; Machine learning","score_opus":0.009220484267888218,"score_gpt":0.20577934321853847,"score_spread":0.19655885895065026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2959716986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015822683,0.00006358676,0.9811542,0.00035178347,0.0012952923,0.0008763333,0.000001905525,0.00016773587,0.00026650372],"genre_scores_gemma":[0.9934947,0.00029385838,0.0036483565,0.0020144964,0.00011226149,0.00017690181,0.000006970175,0.00002219538,0.00023027367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983503,0.00009285039,0.00030202835,0.0006103203,0.00019107612,0.0004533878],"domain_scores_gemma":[0.99924487,0.0000845612,0.00008989934,0.00044564044,0.0000493045,0.00008570487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039396575,0.00022765638,0.00021300917,0.00019130783,0.0003012879,0.0001595025,0.00034531052,0.0000891406,0.000016779732],"category_scores_gemma":[4.399207e-7,0.00024116336,0.000087287786,0.0005388402,0.000012656853,0.00036158186,0.000014421442,0.0003398039,0.000020911079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018193726,0.00008889368,0.000013133212,0.000068143476,0.000027416816,0.0000024265237,0.000074992706,0.90172625,0.0000057969723,0.00025477918,0.00005136114,0.09750486],"study_design_scores_gemma":[0.0012923275,0.00024251278,0.00008713005,0.00007496734,0.00002780131,0.0000031294303,0.000017385488,0.99360085,0.00010704342,0.0005017902,0.0037901702,0.00025492412],"about_ca_topic_score_codex":0.000035194305,"about_ca_topic_score_gemma":0.0012425391,"teacher_disagreement_score":0.977672,"about_ca_system_score_codex":0.00007364857,"about_ca_system_score_gemma":0.00001022661,"threshold_uncertainty_score":0.9834357},"labels":[],"label_agreement":null},{"id":"W2962590879","doi":"10.1109/tnsm.2020.2972405","title":"BotChase: Graph-Based Bot Detection Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Royal Bank of Canada","keywords":"Computer science; Robustness (evolution); Leverage (statistics); Graph; Machine learning; Network topology; Artificial intelligence; Overhead (engineering); Data mining; Distributed computing; Theoretical computer science; Computer network","score_opus":0.0200293515757753,"score_gpt":0.2149806893712793,"score_spread":0.194951337795504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962590879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014801972,0.0001161928,0.98149633,0.0020535865,0.0006118972,0.00027823026,0.0000013228373,0.00037175408,0.00026868423],"genre_scores_gemma":[0.9839304,0.00033781392,0.0094437655,0.0060562156,0.00015887617,0.000026097858,0.0000017315332,0.000019495732,0.000025586032],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985576,0.00012931818,0.00024045858,0.0004896789,0.00026341216,0.00031948203],"domain_scores_gemma":[0.99942136,0.00004105643,0.000085565145,0.00024409595,0.000042116466,0.00016580545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019464656,0.0002115891,0.00017118247,0.00012249763,0.0007603658,0.00017005201,0.00026133278,0.00007801223,0.000036861686],"category_scores_gemma":[6.021703e-7,0.00022185959,0.000084083644,0.0012652142,0.000018848934,0.00027292932,0.000011825912,0.00037846074,0.000022432132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009202276,0.000055523204,0.0000072970743,0.000084360065,0.000054050528,0.000010555577,0.00020550267,0.88215333,0.00024956092,0.00023066066,0.000022868124,0.116834246],"study_design_scores_gemma":[0.00057818455,0.00026262717,0.00003119747,0.000045537778,0.00005275167,0.0000053576314,0.000034318342,0.9866353,0.0016865273,0.00019930425,0.010236431,0.00023244649],"about_ca_topic_score_codex":0.00010950972,"about_ca_topic_score_gemma":0.00025428104,"teacher_disagreement_score":0.9720526,"about_ca_system_score_codex":0.000035561978,"about_ca_system_score_gemma":0.000009933082,"threshold_uncertainty_score":0.90471715},"labels":[],"label_agreement":null},{"id":"W2962725067","doi":"10.1109/tnsm.2019.2929425","title":"DDoS Detection System: Using a Set of Classification Algorithms Controlled by Fuzzy Logic System in Apache Spark","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Denial-of-service attack; Algorithm; Naive Bayes classifier; Statistical classification; Intrusion detection system; Fuzzy logic; Decision tree; Data mining; Machine learning; Artificial intelligence; The Internet","score_opus":0.020264766247133123,"score_gpt":0.22617635280354284,"score_spread":0.20591158655640973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962725067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055375066,0.00014456853,0.9397769,0.0001361455,0.0013847967,0.0013262798,0.0000045448082,0.00017086102,0.0016808613],"genre_scores_gemma":[0.9972028,0.00013086743,0.0022636785,0.00016148007,0.000066692024,0.00010446224,0.0000026457346,0.000013404534,0.000053959608],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810463,0.00024203966,0.000534361,0.0004948624,0.0003180039,0.00030609357],"domain_scores_gemma":[0.9990752,0.00007170765,0.00024829837,0.00045966767,0.00007656543,0.000068592424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006570045,0.0002163903,0.00040226962,0.00024117055,0.00020215505,0.00009240062,0.00028260768,0.00013928708,0.0000054746456],"category_scores_gemma":[3.6701638e-7,0.00020674523,0.00008666305,0.0010816649,0.00001650137,0.00028293312,0.000010543315,0.00021515839,0.00002115456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009334319,0.00030549438,0.000036641115,0.002356453,0.00036538072,0.00001411355,0.0008902013,0.8080573,0.0030262952,0.0105943,0.00008227602,0.17333813],"study_design_scores_gemma":[0.0023110316,0.0001662645,0.00009966307,0.0003449207,0.00007290502,0.000014959353,0.00081673515,0.99490565,0.0005341495,0.000081810715,0.0004514369,0.00020044798],"about_ca_topic_score_codex":0.00022578491,"about_ca_topic_score_gemma":0.00016251692,"teacher_disagreement_score":0.9418277,"about_ca_system_score_codex":0.0002183234,"about_ca_system_score_gemma":0.000012639654,"threshold_uncertainty_score":0.8430826},"labels":[],"label_agreement":null},{"id":"W2969803484","doi":"10.1109/tnsm.2019.2937020","title":"Delay-Constrained Teleoperation Task Scheduling and Assignment for Human+Machine Hybrid Activities Over FiWi Enhanced Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Time Synchronization Technologies","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Teleoperation; Computer science; Scheduling (production processes); Robot; Task (project management); Telerobotics; Network packet; Distributed computing; Real-time computing; Human–robot interaction; The Internet; Artificial intelligence; Human–computer interaction; Computer network; Engineering; Mobile robot; Operating system; Systems engineering; Operations management","score_opus":0.0061205186590739405,"score_gpt":0.21198164854708623,"score_spread":0.20586112988801228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969803484","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03437988,0.00017021384,0.96235603,0.00054789317,0.00049179734,0.0011100867,0.000006050555,0.00035343145,0.00058459374],"genre_scores_gemma":[0.97147036,0.00049513445,0.026441094,0.0009172534,0.00007560012,0.0002142336,0.000013036414,0.000024521094,0.0003487544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998387,0.000051991658,0.00030875736,0.00062264065,0.00020723179,0.00042240616],"domain_scores_gemma":[0.9991778,0.000118403994,0.00011616659,0.00046976624,0.000047353107,0.000070468406],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026174917,0.00028478404,0.00026195816,0.00013950068,0.0005286808,0.00028233128,0.00032460495,0.00009208252,0.000037443373],"category_scores_gemma":[5.553139e-7,0.0002840067,0.000054732143,0.00038209194,0.000044207976,0.00045105297,0.000028489501,0.00020297126,0.000006093063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032964057,0.0000710047,0.000022537653,0.000105605635,0.00015493791,0.0000023600214,0.00011238938,0.82168776,0.0002720239,0.0064412868,0.0000959542,0.1710012],"study_design_scores_gemma":[0.0013149432,0.0002591309,0.00013281577,0.00011322491,0.000062833504,0.0000055301903,0.0001257708,0.9942599,0.00122243,0.0011625363,0.0009574482,0.00038344992],"about_ca_topic_score_codex":0.000015172306,"about_ca_topic_score_gemma":0.00007487962,"teacher_disagreement_score":0.9370905,"about_ca_system_score_codex":0.00007200967,"about_ca_system_score_gemma":0.000014902977,"threshold_uncertainty_score":0.9999612},"labels":[],"label_agreement":null},{"id":"W2976566089","doi":"10.1109/tnsm.2019.2944170","title":"ESSO: An Energy Smart Service Function Chain Orchestrator","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Carbon footprint; Computer science; Renewable energy; Energy consumption; Server; Computer network; Context (archaeology); Environmental economics; Telecommunications; Greenhouse gas; Electrical engineering; Engineering","score_opus":0.010737815779017156,"score_gpt":0.19670863952162024,"score_spread":0.18597082374260307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976566089","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015629994,0.00010109494,0.97650087,0.0018216842,0.0019143147,0.00033303822,0.000002873733,0.00045563275,0.0032404743],"genre_scores_gemma":[0.97017163,0.0004681454,0.00684216,0.021076923,0.00026168278,0.00012920689,0.000013411489,0.00004368668,0.0009931631],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980815,0.00010794891,0.000275484,0.00071682635,0.0003178458,0.00050037215],"domain_scores_gemma":[0.99871427,0.000073905896,0.00008052573,0.00084898464,0.00008364676,0.00019865476],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002791328,0.0002959548,0.0002355268,0.00012777124,0.00032248147,0.00023826132,0.0005349631,0.00012377561,0.000078138844],"category_scores_gemma":[1.392845e-7,0.00028976504,0.00006459556,0.0011727756,0.000011279513,0.0005046767,0.000018120116,0.00020326005,0.00012259293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022874837,0.0004855276,0.00021459139,0.0002631881,0.00033054815,0.00002420033,0.00053076673,0.5253717,0.000038972208,0.03873881,0.0013810692,0.4323919],"study_design_scores_gemma":[0.0021714265,0.00087459787,0.0043419013,0.00021232261,0.00018694885,0.000018632787,0.00032139884,0.8730757,0.000100017955,0.0045707994,0.11303184,0.0010943642],"about_ca_topic_score_codex":0.00026646513,"about_ca_topic_score_gemma":0.0010524348,"teacher_disagreement_score":0.96965873,"about_ca_system_score_codex":0.000041568743,"about_ca_system_score_gemma":0.000022980044,"threshold_uncertainty_score":0.9999555},"labels":[],"label_agreement":null},{"id":"W2981309377","doi":"10.1109/tnsm.2019.2948137","title":"Placement and Chaining for Run-Time IoT Service Deployment in Edge-Cloud","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cloud computing; Chaining; Distributed computing; Network topology; Virtual network; Computer network; Heuristic; Integer programming; Software deployment; Provisioning; Enhanced Data Rates for GSM Evolution; Algorithm","score_opus":0.011595992669695781,"score_gpt":0.21275041145406698,"score_spread":0.2011544187843712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981309377","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07084823,0.00033328816,0.9197161,0.0043025888,0.0013044329,0.001894395,0.0000062042823,0.000272655,0.001322058],"genre_scores_gemma":[0.915297,0.0013566383,0.055078227,0.024868742,0.0004286635,0.0008700872,0.00001400399,0.00010545818,0.001981221],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982076,0.000051634655,0.00032011632,0.00065685384,0.00023021472,0.0005335331],"domain_scores_gemma":[0.9991002,0.00018579088,0.000075451884,0.0004663223,0.000046813624,0.00012541412],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042519282,0.00027450352,0.00028512356,0.00014564373,0.00023109176,0.00015463364,0.000351946,0.00008342947,0.000030831845],"category_scores_gemma":[3.2149228e-7,0.00027323602,0.00004928727,0.00068151567,0.0000102717795,0.00013999318,0.00003162396,0.0001643687,0.000050703617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022233541,0.00026282144,0.00022930089,0.00049489446,0.00020206075,0.000011431645,0.0016324176,0.85907763,0.00001754286,0.0025624787,0.0012593428,0.13402773],"study_design_scores_gemma":[0.003161224,0.00036936312,0.0010752499,0.00037111668,0.00007405838,0.0000069947228,0.00029817477,0.9727845,0.000037034904,0.000664822,0.020606404,0.00055105746],"about_ca_topic_score_codex":0.000057938,"about_ca_topic_score_gemma":0.00027246692,"teacher_disagreement_score":0.8646379,"about_ca_system_score_codex":0.000062252024,"about_ca_system_score_gemma":0.000016029402,"threshold_uncertainty_score":0.999972},"labels":[],"label_agreement":null},{"id":"W2982393339","doi":"10.1109/tnsm.2019.2949753","title":"CoDeC: A Cost-Effective and Delay-Aware SFC Deployment","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Codec; Integer programming; Resource allocation; Virtual network; Software deployment; Distributed computing; Computer network; Algorithm; Computer hardware","score_opus":0.009117302484810364,"score_gpt":0.21571968750951778,"score_spread":0.20660238502470743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982393339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013624498,0.0002717949,0.9804446,0.0011784722,0.00090671965,0.001926046,0.0000044649732,0.00027184922,0.001371567],"genre_scores_gemma":[0.98560107,0.0014477671,0.0058036433,0.0059721475,0.00008262293,0.0005886928,0.0000030235806,0.000030904932,0.00047010355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984464,0.00007384609,0.00020663228,0.0006093366,0.00024215263,0.00042162108],"domain_scores_gemma":[0.999078,0.00016241356,0.000059109356,0.0005007551,0.00004698314,0.0001527597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021534381,0.00025529022,0.0002382427,0.00009774175,0.0002772185,0.00018268787,0.00030331273,0.00007832594,0.000027899701],"category_scores_gemma":[1.8264363e-7,0.0002366367,0.000056398112,0.0005341574,0.000019900848,0.00023056896,0.000026363366,0.00019972293,0.00007309619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011095895,0.00019187451,0.00038688557,0.00019102127,0.00030561734,0.000039969724,0.0005344382,0.20500943,0.0000032049131,0.0038752232,0.0013315179,0.78801984],"study_design_scores_gemma":[0.0038676332,0.00082114426,0.0067955162,0.00043243408,0.00024775486,0.000057912624,0.00027853175,0.8930416,0.00007851046,0.0019706942,0.09132771,0.0010805661],"about_ca_topic_score_codex":0.000055462006,"about_ca_topic_score_gemma":0.0001904563,"teacher_disagreement_score":0.97464097,"about_ca_system_score_codex":0.00004890756,"about_ca_system_score_gemma":0.000009740262,"threshold_uncertainty_score":0.96497643},"labels":[],"label_agreement":null},{"id":"W2987473969","doi":"10.1109/tnsm.2019.2952462","title":"An Extended Framework of Privacy-Preserving Computation With Flexible Access Control","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"National Postdoctoral Program for Innovative Talents; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project; China Postdoctoral Science Foundation; Academy of Finland; National Natural Science Foundation of China","keywords":"Computer science; Encryption; Access control; Cloud computing; Information privacy; Computation; Upload; Division (mathematics); Cryptography; Multiplication (music); Distributed computing; Security analysis; Computer network; Computer security; Algorithm","score_opus":0.011460808517467386,"score_gpt":0.26026823834325036,"score_spread":0.24880742982578297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2987473969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038146757,0.000035636815,0.9595893,0.00044405536,0.00026509922,0.0005124774,0.000006528844,0.0001343826,0.00086574093],"genre_scores_gemma":[0.9402124,0.00009571432,0.05859657,0.001023438,0.00002501703,0.000027325077,0.000004700005,0.000009724117,0.0000050807357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876004,0.00007835083,0.00021083013,0.0004257917,0.0002760168,0.00024895786],"domain_scores_gemma":[0.9988796,0.00009840567,0.00010033688,0.0007575213,0.00007417713,0.00008995598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002110545,0.00016151872,0.00020185925,0.00013708876,0.00016857214,0.00021244936,0.0008546744,0.000057181733,0.00003158964],"category_scores_gemma":[2.3599017e-7,0.00014395406,0.000041726584,0.0009012336,0.000020670985,0.00085071364,0.000020539928,0.00016346472,0.00000617421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037326833,0.00055271044,0.0008194309,0.00045585303,0.00028246033,0.0000071994345,0.00094889884,0.8734154,0.00003003536,0.042903487,0.00010490001,0.08010636],"study_design_scores_gemma":[0.0023843537,0.0007397295,0.023252271,0.0004150242,0.00013798247,0.0000052567125,0.0002708263,0.94220555,0.00026959128,0.028809866,0.0010110493,0.00049848267],"about_ca_topic_score_codex":0.00006771048,"about_ca_topic_score_gemma":0.000061528255,"teacher_disagreement_score":0.9020657,"about_ca_system_score_codex":0.000010864285,"about_ca_system_score_gemma":0.000012473336,"threshold_uncertainty_score":0.58702767},"labels":[],"label_agreement":null},{"id":"W2991499984","doi":"10.1109/tnsm.2019.2954340","title":"Introducing an Unsupervised Automated Solution for Root Cause Diagnosis in Mobile Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Exfo Electro-Optical Engineering (Canada)","funders":"","keywords":"Troubleshooting; Computer science; Cellular network; Inefficiency; Data mining; Network monitoring; Root cause analysis; Process (computing); Mobile phone; Root (linguistics); Root cause; Real-time computing; Computer network; Reliability engineering; Engineering; Telecommunications; Operating system","score_opus":0.009956108810982673,"score_gpt":0.2374777786698255,"score_spread":0.22752166985884284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991499984","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21170886,0.00011511955,0.78454256,0.00035363107,0.0012809343,0.0014875405,0.0000018945143,0.00045816353,0.0000512949],"genre_scores_gemma":[0.99071914,0.00038774952,0.007313713,0.00047081517,0.00008625191,0.0009539867,0.0000075446833,0.00001742989,0.000043388434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983843,0.00009060084,0.00032745817,0.0006043083,0.00016427718,0.0004290599],"domain_scores_gemma":[0.9990257,0.000106559455,0.000058851085,0.000662221,0.000060925107,0.0000857116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054713816,0.00019608208,0.00023419553,0.00013689052,0.0002296541,0.00011770711,0.00034287295,0.000103723214,0.000013473178],"category_scores_gemma":[4.1735854e-7,0.00018324223,0.000057278798,0.00074252184,0.000011639123,0.00048211895,0.000011264469,0.00014539562,0.000018082002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043794564,0.00017424514,0.0018825661,0.00028363316,0.000045200584,0.0000017797089,0.00036177045,0.9212185,0.0000032340465,0.00007157289,0.00015201916,0.0757617],"study_design_scores_gemma":[0.00092404184,0.00026104104,0.0061484114,0.00017156881,0.00003035235,0.0000017680895,0.00007335239,0.9910689,0.000024871608,0.000054424123,0.0010178057,0.00022348168],"about_ca_topic_score_codex":0.00013019134,"about_ca_topic_score_gemma":0.0006356624,"teacher_disagreement_score":0.77901024,"about_ca_system_score_codex":0.000091535236,"about_ca_system_score_gemma":0.000014948438,"threshold_uncertainty_score":0.7472401},"labels":[],"label_agreement":null},{"id":"W2996751449","doi":"10.1109/tnsm.2019.2953216","title":"Guest Editorial: Special Issue on Latest Developments for the Management of Softwarized Networks","year":2019,"lang":"en","type":"editorial","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Cloud computing; Virtualization; Computer science; Network management; Network Functions Virtualization; Service provider; Software-defined networking; Service (business); Telecommunications; Service virtualization; Network virtualization; Function (biology); Network service; Computer security; Data virtualization; Computer network; Business; Operating system","score_opus":0.009050854463659188,"score_gpt":0.22886489644469246,"score_spread":0.21981404198103327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996751449","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.7045664e-7,0.00012832963,0.36881363,0.00035358142,0.62781876,0.0015780182,0.000034970584,0.00014092444,0.0011310969],"genre_scores_gemma":[0.0000810525,0.00765231,0.0087526115,0.0006746877,0.97923046,0.0006271019,0.000091252914,0.000105558436,0.0027849902],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99569184,0.00010231916,0.00083426177,0.0012115446,0.0012786548,0.0008813569],"domain_scores_gemma":[0.9957815,0.001922121,0.00045074115,0.0014057916,0.00029397986,0.00014587429],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000923803,0.00077415997,0.0007900685,0.00019065122,0.00068580464,0.00036011296,0.001890329,0.0006923395,0.000025866046],"category_scores_gemma":[0.0000030513977,0.00063023565,0.00027377447,0.00095079106,0.00005352664,0.00017547482,0.00008370588,0.00089104014,0.00007049965],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032883408,0.0001639544,5.9907524e-7,0.0005152547,0.0010424923,0.000007289025,0.00008325176,0.1528543,2.0663343e-8,0.0003068595,0.7870423,0.05765485],"study_design_scores_gemma":[0.0022290195,0.00030962192,0.000018696452,0.0008401206,0.0005616292,4.9439376e-7,0.00003337198,0.014126325,0.0000019889133,0.00016336844,0.9810982,0.0006171661],"about_ca_topic_score_codex":0.000045779605,"about_ca_topic_score_gemma":0.000087229666,"teacher_disagreement_score":0.36006102,"about_ca_system_score_codex":0.00015145759,"about_ca_system_score_gemma":0.00007638391,"threshold_uncertainty_score":0.9996149},"labels":[],"label_agreement":null},{"id":"W2996997956","doi":"10.1109/tnsm.2019.2961613","title":"PRIMA: Subscriber-Driven Interference Mitigation for Cloud Services","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; York University","funders":"","keywords":"Computer science; Cloud computing; Workload; Computer network; Resource allocation; Interference (communication); Resource (disambiguation); Distributed computing; Operating system","score_opus":0.009390540530137258,"score_gpt":0.21232011810372925,"score_spread":0.20292957757359198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996997956","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.107889496,0.000049520448,0.8826018,0.0023126446,0.0017002536,0.0009950189,0.0000029416447,0.00027056737,0.0041777617],"genre_scores_gemma":[0.9801213,0.00007529876,0.0147629855,0.003386703,0.0001562809,0.00011097286,0.00000429998,0.000020643029,0.0013615182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984374,0.00005283515,0.00027006314,0.0006101426,0.00022966936,0.00039985566],"domain_scores_gemma":[0.99907005,0.00008523682,0.00009647303,0.00058777747,0.00006873613,0.00009170208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023745082,0.00023171854,0.00020669244,0.00011732919,0.00030154217,0.00022475862,0.00066356466,0.000062284766,0.000012836943],"category_scores_gemma":[1.339117e-7,0.00021960355,0.0000891045,0.0004601044,0.000014486689,0.00006902137,0.00003530164,0.00013912312,0.00007721025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008582056,0.00022035344,0.00010795851,0.0011084231,0.00029821065,0.000005905465,0.00142934,0.78318685,0.000034846817,0.015678724,0.0006773451,0.1971662],"study_design_scores_gemma":[0.0011213502,0.00027117017,0.00066541915,0.00032856842,0.00008338923,0.000003707679,0.00035515407,0.97352827,0.000121086254,0.0014053208,0.021721924,0.00039465423],"about_ca_topic_score_codex":0.000038072747,"about_ca_topic_score_gemma":0.00009236481,"teacher_disagreement_score":0.8722318,"about_ca_system_score_codex":0.000046090532,"about_ca_system_score_gemma":0.000008263553,"threshold_uncertainty_score":0.8955173},"labels":[],"label_agreement":null},{"id":"W3000429356","doi":"10.1109/tnsm.2020.2967721","title":"Analyzing Data Granularity Levels for Insider Threat Detection Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":173,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Insider threat; Insider; Computer science; Granularity; Computer security; Machine learning; Artificial intelligence; Set (abstract data type)","score_opus":0.07647239340849372,"score_gpt":0.2690621962632918,"score_spread":0.1925898028547981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000429356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0099713085,0.00015312625,0.9870645,0.0015326589,0.0005254814,0.00043800412,0.000010421037,0.00022632461,0.00007820046],"genre_scores_gemma":[0.96826607,0.00046155122,0.028599078,0.002355729,0.0002408713,0.000024554414,0.000009574431,0.00002112832,0.000021430327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984735,0.000105223735,0.00026564693,0.00065852696,0.00018732324,0.00030973204],"domain_scores_gemma":[0.9991858,0.000073359544,0.000091255635,0.00046776698,0.000056627392,0.0001251682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003604637,0.0002007253,0.00020228319,0.000089920526,0.0009305955,0.00021553757,0.00047552158,0.000079860394,0.000014221243],"category_scores_gemma":[0.0000019443735,0.0002074867,0.00006179218,0.00084808207,0.000015491201,0.0006239984,0.000046576148,0.00031372692,0.0000061568144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014710634,0.000073518684,0.000038080052,0.0002383725,0.00021137825,0.000008552338,0.00052810734,0.63432777,0.00036501195,0.00055950304,0.000046265908,0.36345634],"study_design_scores_gemma":[0.0005457303,0.00014066912,0.000088936424,0.00004091424,0.00011089424,0.000006306644,0.000039167477,0.9894972,0.0006054146,0.0005579616,0.0081456145,0.0002211799],"about_ca_topic_score_codex":0.00015448406,"about_ca_topic_score_gemma":0.0007560777,"teacher_disagreement_score":0.9584654,"about_ca_system_score_codex":0.000034805424,"about_ca_system_score_gemma":0.00000998397,"threshold_uncertainty_score":0.8461062},"labels":[],"label_agreement":null},{"id":"W3001474836","doi":"10.1109/tnsm.2020.2969172","title":"FCTrees: A Front-Coded Family of Compressed Tree-Based FIB Structures for NDN Routers","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Forwarding plane; Routing table; Routing (electronic design automation); Routing protocol","score_opus":0.024015985158536316,"score_gpt":0.21327631186938098,"score_spread":0.18926032671084467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3001474836","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010252259,0.00010223498,0.98556244,0.00270633,0.00036840155,0.00045205228,0.00001992686,0.00013228632,0.00040406818],"genre_scores_gemma":[0.97675365,0.00006578467,0.01523977,0.0077508073,0.00005773182,0.000060689894,0.0000058538985,0.000013326265,0.000052405823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889094,0.00004793646,0.00024026276,0.00038429763,0.00019932442,0.00023724276],"domain_scores_gemma":[0.9993557,0.000094758696,0.000076857745,0.000321033,0.00004971564,0.000101944956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000083181134,0.00017913591,0.00023122343,0.000067162284,0.00017278113,0.0000835637,0.00043557756,0.000044815766,0.000005051047],"category_scores_gemma":[3.7343355e-7,0.00017048589,0.000120121884,0.00024375529,0.000017855471,0.00011037182,0.0000076156102,0.000099548306,0.0000028722945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041591382,0.00010680781,0.000008910329,0.00035162873,0.000302771,0.0000069911457,0.0007485873,0.901096,0.0004993707,0.0010784917,0.0018764287,0.093508124],"study_design_scores_gemma":[0.0018021798,0.00026622874,0.0003293944,0.000073309755,0.00012199971,3.2048192e-7,0.00020323806,0.99404985,0.00038251816,0.000248562,0.0023099782,0.00021240766],"about_ca_topic_score_codex":0.00011049833,"about_ca_topic_score_gemma":0.00011051432,"teacher_disagreement_score":0.97032267,"about_ca_system_score_codex":0.000015896352,"about_ca_system_score_gemma":0.000014836678,"threshold_uncertainty_score":0.6952213},"labels":[],"label_agreement":null},{"id":"W3034842625","doi":"10.1109/tnsm.2020.3001691","title":"Robust Planning and Operation of Multi-Cell Homogeneous and Heterogeneous Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Scheduling (production processes); Heterogeneous network; Transmitter power output; Base station; Network topology; Homogeneous; Mathematical optimization; Distributed computing; Reuse; Linear programming; Cellular network; Topology (electrical circuits); Computer network; Algorithm; Wireless network; Transmitter; Wireless; Mathematics","score_opus":0.020095512277516252,"score_gpt":0.1979506959107513,"score_spread":0.17785518363323505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034842625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009210795,0.0019381476,0.9880221,0.00005496328,0.00014307235,0.0003403517,0.000004410762,0.00010993613,0.00017623357],"genre_scores_gemma":[0.98657286,0.0021388961,0.010902398,0.000253194,0.000049270406,0.000027340573,0.000005113121,0.000031364598,0.000019585048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993597,0.000019801488,0.00020014748,0.00020753185,0.000057794954,0.00015502032],"domain_scores_gemma":[0.9997517,0.00001973031,0.00003065969,0.00009602739,0.000017862869,0.00008403899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000044051343,0.00015270365,0.00016239232,0.000038550475,0.000111142734,0.000032603977,0.000043255295,0.000058527366,0.000004390689],"category_scores_gemma":[1.3923407e-7,0.00016678288,0.000015998328,0.00014992987,0.000012754208,0.00007927092,0.0000038188446,0.00009156977,9.4075654e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021569444,0.00001424968,0.000018258797,0.00030610515,0.00005346627,0.000007035306,0.00038722574,0.9889176,0.00006664259,0.000003111155,0.000017234592,0.010187489],"study_design_scores_gemma":[0.00056261924,0.000059206075,0.000026578886,0.00005973139,0.00006376843,0.000006849687,0.00016413305,0.99848074,0.00025560305,0.0000015006501,0.00017218986,0.00014707608],"about_ca_topic_score_codex":0.0000067812407,"about_ca_topic_score_gemma":0.0000336131,"teacher_disagreement_score":0.97736204,"about_ca_system_score_codex":0.000012213646,"about_ca_system_score_gemma":0.0000014333102,"threshold_uncertainty_score":0.6801208},"labels":[],"label_agreement":null},{"id":"W3041616951","doi":"10.1109/tnsm.2020.3008005","title":"Multi-Domain Network Slicing With Latency Equalization","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Latency (audio); Network delay; Distributed computing; Network architecture; Network virtualization; Slicing; Network simulation; Virtualization; Telecommunications; Cloud computing","score_opus":0.021411474349279983,"score_gpt":0.21375437127439303,"score_spread":0.19234289692511305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041616951","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015450008,0.00014541535,0.99239206,0.00391726,0.00038173242,0.0004140311,0.0000013674727,0.00041258364,0.00079052895],"genre_scores_gemma":[0.7333695,0.0010236184,0.23796509,0.026815357,0.00047330774,0.000117825264,0.000008462408,0.00005972966,0.00016705717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998329,0.00008566999,0.00026957443,0.00057053467,0.00026810242,0.0004771076],"domain_scores_gemma":[0.9992756,0.000068892215,0.00008631511,0.0003278119,0.000049810948,0.00019154791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019392848,0.00025589412,0.00022607678,0.000050063223,0.00047286996,0.00018157845,0.0003654933,0.00006886855,0.000019519648],"category_scores_gemma":[3.3269666e-7,0.00022488444,0.0000495784,0.0013886162,0.00001807127,0.00025791032,0.000016758107,0.00019831146,0.00003315673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063909094,0.00006485262,0.00012604317,0.00008695367,0.000118375516,0.000030707277,0.0007910688,0.9524423,0.0000033572644,0.0032447975,0.0009149195,0.042112704],"study_design_scores_gemma":[0.0015644307,0.00032912038,0.0010319588,0.00020621717,0.00009470188,0.0000075130915,0.00016360387,0.98296845,0.000011720853,0.00063730706,0.0124711115,0.000513862],"about_ca_topic_score_codex":0.000031826705,"about_ca_topic_score_gemma":0.00016931767,"teacher_disagreement_score":0.75442696,"about_ca_system_score_codex":0.00002427817,"about_ca_system_score_gemma":0.000013502093,"threshold_uncertainty_score":0.91705215},"labels":[],"label_agreement":null},{"id":"W3047861058","doi":"10.1109/tnsm.2020.3014870","title":"Bringing Intelligence to Software Defined Networks: Mitigating DDoS Attacks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Denial-of-service attack; Domain Name System; Computer network; Computer security; Application layer DDoS attack; Server; The Internet; Scalability; Botnet; Software-defined networking; Trinoo; Operating system","score_opus":0.01856969091733624,"score_gpt":0.22462592537661605,"score_spread":0.2060562344592798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047861058","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030009553,0.000095223804,0.9892781,0.0053152437,0.00077700155,0.00043677035,0.0000012117507,0.00046681668,0.0006287006],"genre_scores_gemma":[0.8818928,0.0005542926,0.08851663,0.028470188,0.00037494532,0.00009227594,0.0000018246002,0.000030879557,0.00006615065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981679,0.00007382807,0.00034156174,0.00065452326,0.00027273793,0.0004894645],"domain_scores_gemma":[0.9990544,0.000108508684,0.000094356976,0.0003728819,0.000059774386,0.00031013388],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021698736,0.00025117147,0.00021438162,0.00009218406,0.00058887195,0.00027179805,0.00054923986,0.000075445525,0.000042522493],"category_scores_gemma":[0.0000028441666,0.0002711936,0.00007651407,0.0015606004,0.000016291748,0.0002942728,0.00004434305,0.00031926844,0.0000859917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025452886,0.000021872855,0.0000068954396,0.000053937623,0.00003758141,0.000010983771,0.00050098327,0.6595328,0.0000036723047,0.0010136382,0.00039993497,0.33839223],"study_design_scores_gemma":[0.00015798709,0.0002070992,0.00008846898,0.00019348874,0.00003585357,0.000007024545,0.00013528141,0.9897388,0.0002639151,0.000533166,0.008277782,0.00036112452],"about_ca_topic_score_codex":0.000029729727,"about_ca_topic_score_gemma":0.000104416715,"teacher_disagreement_score":0.9007614,"about_ca_system_score_codex":0.00003830219,"about_ca_system_score_gemma":0.000009656806,"threshold_uncertainty_score":0.999974},"labels":[],"label_agreement":null},{"id":"W3048313003","doi":"10.1109/tnsm.2020.3014929","title":"Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":274,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Intrusion detection system; Computer science; Oversampling; Artificial intelligence; Machine learning; Constant false alarm rate; Feature selection; Sample size determination; Network security; Data mining; Feature (linguistics); Dependency (UML); Mathematics; Statistics; Computer security","score_opus":0.029539816150324756,"score_gpt":0.24994243535635285,"score_spread":0.2204026192060281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048313003","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00078314036,0.00019580021,0.99305046,0.0028688267,0.0014355321,0.0009165007,0.000003985237,0.00054575386,0.00019998074],"genre_scores_gemma":[0.42879573,0.0030423563,0.5548112,0.01171468,0.0009179281,0.0003429289,0.000010213834,0.00006684497,0.00029815346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978928,0.00017158246,0.00039535481,0.00073734607,0.00026543986,0.00053746556],"domain_scores_gemma":[0.9989798,0.00020288765,0.00015996212,0.00035316765,0.00007526625,0.00022888627],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004063375,0.00031775908,0.00031009174,0.00008869976,0.0011801993,0.00024815078,0.00042464948,0.00018203669,0.000056296387],"category_scores_gemma":[0.000004503285,0.0003228312,0.00014229499,0.001150628,0.000022648057,0.0003366814,0.000034423592,0.0006065512,0.000031161424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033947194,0.000064222375,0.0000025889594,0.00012127313,0.00009042952,0.000005043875,0.0004989277,0.8154268,0.000035817728,0.0017422631,0.000085986285,0.18158717],"study_design_scores_gemma":[0.001274928,0.00041633868,0.000031633273,0.000093064125,0.00006650282,0.0000029341993,0.000062042265,0.95165205,0.00027597573,0.0008469165,0.044945266,0.00033232872],"about_ca_topic_score_codex":0.000033770557,"about_ca_topic_score_gemma":0.0001548895,"teacher_disagreement_score":0.4382393,"about_ca_system_score_codex":0.00004691692,"about_ca_system_score_gemma":0.000009906377,"threshold_uncertainty_score":0.9999224},"labels":[],"label_agreement":null},{"id":"W3092297419","doi":"10.1109/tnsm.2020.3029108","title":"Ensuring Reliability and Low Cost When Using a Parallel VNF Processing Approach to Embed Delay-Constrained Slices","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Concordia University","funders":"Zayed University; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Reliability (semiconductor); Constraint (computer-aided design); Distributed computing; Virtual network; Embedding; Routing (electronic design automation); Key (lock); Tabu search; Computer network; Algorithm","score_opus":0.028601312233643317,"score_gpt":0.22898966534974088,"score_spread":0.20038835311609757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092297419","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02319304,0.000114015595,0.97255594,0.0022156262,0.00015123884,0.0007716689,0.000002951493,0.0002629519,0.0007325905],"genre_scores_gemma":[0.78001577,0.00012006957,0.21368892,0.005929186,0.00010965814,0.00009296516,0.0000018027736,0.000021734637,0.00001990616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981145,0.000074961485,0.00031662418,0.0007915654,0.00024893557,0.00045339172],"domain_scores_gemma":[0.9991512,0.000068693524,0.000078040284,0.000343986,0.00006018188,0.00029794],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028144565,0.0002730479,0.00027986124,0.00007292473,0.00049505814,0.00035915404,0.000368018,0.0000761709,0.0000045981515],"category_scores_gemma":[0.0000012977997,0.0002647363,0.0000510872,0.0007331923,0.00003417488,0.00032791457,0.000034911216,0.00021593769,0.0000043182195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006663792,0.00009255843,0.000062412546,0.00035452176,0.000053638665,0.000009162354,0.0021036917,0.8485404,0.000006038286,0.00023291615,0.00009350924,0.14838448],"study_design_scores_gemma":[0.0006666989,0.000064887936,0.00036002946,0.00014451332,0.000067365945,0.000010870458,0.00023991297,0.996544,0.000009244031,0.00026894294,0.0012988455,0.00032469432],"about_ca_topic_score_codex":0.00005311001,"about_ca_topic_score_gemma":0.00003554634,"teacher_disagreement_score":0.75886697,"about_ca_system_score_codex":0.00003512578,"about_ca_system_score_gemma":0.000023761702,"threshold_uncertainty_score":0.9999805},"labels":[],"label_agreement":null},{"id":"W3097713188","doi":"10.1109/tnsm.2020.3035442","title":"Machine Learning-Based Radio Coverage Prediction in Urban Environments","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Transmitter; Computer science; Radio propagation; Radio frequency power transmission; Artificial neural network; Predictive modelling; Artificial intelligence; Machine learning; Feature (linguistics); Transmitter power output; Data mining; Telecommunications","score_opus":0.011439391315237364,"score_gpt":0.17369087050533372,"score_spread":0.16225147919009636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097713188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011552143,0.0001512541,0.9859749,0.00034842908,0.00018302094,0.0002740333,0.0000094722545,0.00016421435,0.0013425144],"genre_scores_gemma":[0.9969951,0.0010031072,0.00045396664,0.0013023061,0.00005026179,0.000042121577,0.000020723637,0.000027380163,0.00010504866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992724,0.000034474055,0.00018930057,0.00019842373,0.00012598299,0.00017939517],"domain_scores_gemma":[0.99978656,0.000013509956,0.000017397633,0.00009326728,0.0000035362978,0.00008574766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007890446,0.00014272903,0.00011707525,0.00006636146,0.00008961583,0.000025451296,0.00005732274,0.000045037163,0.000104264225],"category_scores_gemma":[1.8239757e-7,0.00015807014,0.000031321862,0.00020762555,0.0000057471934,0.00006260794,0.00000158839,0.00022160345,0.000034885863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000354246,0.000027338858,0.00007673757,0.000107048945,0.000040781288,0.000005062381,0.00021597095,0.98668224,0.0001389704,0.0000027947187,0.00009617164,0.012571475],"study_design_scores_gemma":[0.0008523195,0.000064561355,0.00020572961,0.000035412795,0.00003713709,3.8557158e-7,0.000027290262,0.9853513,0.0004677754,0.000009036508,0.012817833,0.00013121977],"about_ca_topic_score_codex":0.000009855388,"about_ca_topic_score_gemma":0.00002888359,"teacher_disagreement_score":0.98552096,"about_ca_system_score_codex":0.0000458286,"about_ca_system_score_gemma":0.0000020060504,"threshold_uncertainty_score":0.64459133},"labels":[],"label_agreement":null},{"id":"W3111540539","doi":"10.1109/tnsm.2020.3045174","title":"Reliability-Oriented and Resource-Efficient Service Function Chain Construction and Backup","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Backup; Computer science; Computer network; Network service; Virtual network; Reliability (semiconductor); Distributed computing; Network virtualization; Reliability engineering; Virtualization; Cloud computing; Operating system","score_opus":0.009819927536014971,"score_gpt":0.1869780516179303,"score_spread":0.17715812408191534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111540539","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039812494,0.00021870085,0.94918966,0.0088215545,0.0005171364,0.0004973285,0.0000043668724,0.00031203727,0.00062673574],"genre_scores_gemma":[0.93198687,0.0010440927,0.039517384,0.026886359,0.00031489262,0.00011175759,0.000010330974,0.000043350163,0.00008493861],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982635,0.000105081825,0.00028892045,0.00075230154,0.00025124484,0.00033899106],"domain_scores_gemma":[0.99913365,0.00010776323,0.00008040716,0.00036086357,0.00007485372,0.00024248305],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027052886,0.0002520613,0.00022379577,0.000081936676,0.00050096074,0.00016451826,0.00017339028,0.000094152856,0.000013814809],"category_scores_gemma":[0.0000013123214,0.00024533644,0.000036569563,0.0010793803,0.000049391307,0.00017136666,0.000034760873,0.00024113792,0.000011588519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004384585,0.00017175946,0.00020293119,0.00058395317,0.00018835157,0.00001945291,0.0023373133,0.5597981,0.000018787678,0.010813439,0.0011358106,0.42429164],"study_design_scores_gemma":[0.0015905362,0.0003623745,0.0027536165,0.00013032775,0.0001601135,0.000027942044,0.000771965,0.9282128,0.000015409936,0.0005756405,0.06493982,0.00045945143],"about_ca_topic_score_codex":0.00004287785,"about_ca_topic_score_gemma":0.00004991165,"teacher_disagreement_score":0.90967226,"about_ca_system_score_codex":0.000026380669,"about_ca_system_score_gemma":0.00000993571,"threshold_uncertainty_score":0.9999999},"labels":[],"label_agreement":null},{"id":"W3119037122","doi":"10.1109/tnsm.2021.3049718","title":"Delay-Sensitive Multi-Source Multicast Resource Optimization in NFV-Enabled Networks: A Column Generation Approach","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Computer science; Multicast; Computer network; Unicast; Distributed computing; Bandwidth (computing)","score_opus":0.0193014765577517,"score_gpt":0.21362184230269501,"score_spread":0.19432036574494332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119037122","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013093408,0.00021567386,0.99584955,0.0006046999,0.00040960943,0.00055971305,0.0000023849661,0.00021563556,0.00083338434],"genre_scores_gemma":[0.5507801,0.0014410806,0.43928942,0.006729196,0.00035444947,0.0002697786,0.00008025316,0.00006777991,0.0009879506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976046,0.00029053606,0.00041745102,0.0008561438,0.0002816869,0.0005495973],"domain_scores_gemma":[0.9989125,0.0001273151,0.00010046116,0.00058546633,0.00012959028,0.0001446814],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038580855,0.00030030982,0.00030039006,0.00015315271,0.00046224918,0.0003322093,0.00027941813,0.00015349642,0.000011651031],"category_scores_gemma":[0.0000019230417,0.0003296165,0.000077899764,0.0017545861,0.00002669858,0.00028780973,0.00003145208,0.00032540652,0.000006929243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036275425,0.00030193076,0.00001892109,0.000035630987,0.00007841844,0.00006116549,0.0004721366,0.95906574,0.0000032121147,0.00027249,0.00037002066,0.039284077],"study_design_scores_gemma":[0.0014489151,0.000046323257,0.00020507538,0.00008544502,0.00005921701,0.000028707123,0.000301828,0.9953433,0.000022925758,0.0000110201345,0.0021109653,0.00033623897],"about_ca_topic_score_codex":0.0000782789,"about_ca_topic_score_gemma":0.0006075948,"teacher_disagreement_score":0.55656016,"about_ca_system_score_codex":0.000096052485,"about_ca_system_score_gemma":0.000026129728,"threshold_uncertainty_score":0.9999156},"labels":[],"label_agreement":null},{"id":"W3122635809","doi":"10.1109/tnsm.2021.3054528","title":"Mitigating TCP Protocol Misuse With Programmable Data Planes","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Stateful firewall; Stream Control Transmission Protocol; Computer network; Protocol (science); Extended finite-state machine; Stateless protocol; Distributed computing; Host (biology); Transmission Control Protocol; Finite-state machine; Forwarding plane; Algorithm","score_opus":0.025498769226067627,"score_gpt":0.25371266810350823,"score_spread":0.2282138988774406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122635809","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012700175,0.000030933305,0.9812081,0.002093041,0.00035961106,0.01113714,0.00000543412,0.000328206,0.0035675443],"genre_scores_gemma":[0.23761117,0.0013324583,0.6350748,0.023634251,0.00155933,0.09467466,0.0001546982,0.00020465218,0.005753978],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984384,0.00008800835,0.0002163836,0.00062999607,0.00028335088,0.00034386167],"domain_scores_gemma":[0.9987507,0.00004500944,0.00007010842,0.00095556444,0.00007349838,0.000105150786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022367688,0.00018139569,0.0001509837,0.000051846415,0.00051792746,0.0003596444,0.00052496133,0.000054116725,0.000045879096],"category_scores_gemma":[3.859273e-7,0.00016015643,0.000024209088,0.0008727786,0.00002225054,0.0004742132,0.000039331517,0.00020915555,0.000020116307],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022864273,0.00057080475,0.000032041557,0.0008310149,0.0003352764,0.00033247063,0.000569979,0.13551426,0.000027291035,0.0032337715,0.0025962768,0.85572815],"study_design_scores_gemma":[0.002193977,0.00039930036,0.000120316065,0.0005837883,0.00010668565,0.00016425319,0.00029608197,0.60890216,0.0011044109,0.000909977,0.3846023,0.0006167278],"about_ca_topic_score_codex":0.000038111484,"about_ca_topic_score_gemma":0.0008767359,"teacher_disagreement_score":0.8551114,"about_ca_system_score_codex":0.000017930322,"about_ca_system_score_gemma":0.000030070667,"threshold_uncertainty_score":0.65309894},"labels":[],"label_agreement":null},{"id":"W3124203340","doi":"10.1109/tnsm.2021.3054356","title":"Uncovering Lateral Movement Using Authentication Logs","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Royal Bank of Canada","keywords":"Computer science; Leverage (statistics); Artificial intelligence; Machine learning; Network security; Overhead (engineering); Feature extraction; Data mining; Computer security","score_opus":0.014780143259899334,"score_gpt":0.22305100604280884,"score_spread":0.20827086278290952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124203340","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063062474,0.00007253379,0.9332034,0.0010244304,0.0012332803,0.00016679155,0.0000010037531,0.0001200368,0.0011160475],"genre_scores_gemma":[0.97321224,0.00077790825,0.01996206,0.005410253,0.0001418126,0.000029563402,0.0000026103162,0.000014806789,0.00044874736],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885374,0.000064708925,0.00021163534,0.00038941368,0.00021505423,0.00026544268],"domain_scores_gemma":[0.99941254,0.000022078275,0.000052434672,0.00038035866,0.00005886101,0.000073704156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015961332,0.00014127158,0.00011855248,0.000071000584,0.00039625936,0.00019806511,0.00018363894,0.000051716983,0.00005162991],"category_scores_gemma":[2.009933e-7,0.00015135658,0.000050866794,0.00064154743,0.000010515512,0.00029629827,0.000019223437,0.00013487843,0.000020970967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030589297,0.00020639978,0.000015536161,0.00013026412,0.00016321061,0.000043319484,0.00071720727,0.8245247,0.000567696,0.009028738,0.00015729132,0.16441506],"study_design_scores_gemma":[0.00061826484,0.00007583055,0.000554549,0.00015759098,0.00007954831,0.000018165369,0.00009679851,0.9768252,0.0030135247,0.008668944,0.009551794,0.0003398211],"about_ca_topic_score_codex":0.00004548196,"about_ca_topic_score_gemma":0.00015394417,"teacher_disagreement_score":0.9132413,"about_ca_system_score_codex":0.00006376688,"about_ca_system_score_gemma":0.000014734624,"threshold_uncertainty_score":0.61721426},"labels":[],"label_agreement":null},{"id":"W3127358348","doi":"10.1109/tnsm.2021.3057761","title":"Optimal Security Risk Management Mechanism for the 5G Cloudified Infrastructure","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cloud computing; Provisioning; Denial-of-service attack; Computer security; Service (business); Enhanced Data Rates for GSM Evolution; Computer network; The Internet; World Wide Web; Artificial intelligence; Operating system","score_opus":0.008263186432202583,"score_gpt":0.20921795711630925,"score_spread":0.20095477068410666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127358348","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001212992,0.00028657203,0.99148107,0.0026353935,0.0018251396,0.0007356274,0.00001568689,0.00020505639,0.0016024634],"genre_scores_gemma":[0.75909775,0.016491238,0.20541073,0.01622214,0.00060377805,0.00088157697,0.000016228314,0.00007269239,0.0012038447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822927,0.000085597436,0.00027113204,0.0006322808,0.000292632,0.0004890689],"domain_scores_gemma":[0.99865115,0.00021592836,0.00009358637,0.00082789623,0.000106408086,0.00010503449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003059376,0.00027446137,0.00021189575,0.000057317106,0.0009408525,0.00028877083,0.00060579524,0.00009120835,0.00003087165],"category_scores_gemma":[7.573105e-7,0.0002201869,0.00013926503,0.0007489728,0.00002177203,0.00013696212,0.000035359695,0.00028234016,0.000011543304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070479095,0.00010897471,0.0000058774876,0.00015960241,0.00064724276,0.000037987313,0.00050484814,0.6678178,9.1082103e-7,0.12779905,0.004602699,0.19824459],"study_design_scores_gemma":[0.0021266153,0.00015355094,0.0005622351,0.00011499901,0.0006469854,0.000024335686,0.00076912297,0.8549011,0.00015692817,0.07015685,0.06976758,0.0006197171],"about_ca_topic_score_codex":0.00002132922,"about_ca_topic_score_gemma":0.00013804914,"teacher_disagreement_score":0.78607035,"about_ca_system_score_codex":0.00003928015,"about_ca_system_score_gemma":0.000014550764,"threshold_uncertainty_score":0.8978961},"labels":[],"label_agreement":null},{"id":"W3132288171","doi":"10.1109/tnsm.2021.3059696","title":"DND: Driver Node Detection for Control Message Diffusion in Smart Transportations","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Controllability; Computer science; Node (physics); Distributed computing; Metric (unit); Computer network; Engineering","score_opus":0.005355994250945168,"score_gpt":0.18685530370524883,"score_spread":0.18149930945430368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132288171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07430935,0.00017087904,0.922736,0.00033337754,0.0006830005,0.0006588595,0.000023085047,0.00019127502,0.0008941565],"genre_scores_gemma":[0.99597704,0.0009948991,0.001639346,0.00073051336,0.0000732405,0.00033773205,0.000027062415,0.00004741065,0.00017276959],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988601,0.000039629656,0.00028047035,0.00029894972,0.00014436508,0.00037646948],"domain_scores_gemma":[0.9995487,0.00007086131,0.000023806708,0.00022996812,0.000041136587,0.00008554975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013732523,0.00020957427,0.00022198033,0.000105467436,0.00020231155,0.000043754942,0.000076493845,0.00010377382,0.000048226695],"category_scores_gemma":[2.7621942e-7,0.00023933251,0.00009135817,0.000477299,0.0000108617805,0.000108121494,0.0000010736987,0.00020493325,0.000008899554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049995513,0.00007420983,0.00003609961,0.00018560597,0.00013824928,0.000026605094,0.00016278267,0.97388214,0.00031551474,0.000058959853,0.00008690763,0.024982952],"study_design_scores_gemma":[0.00219882,0.000030995037,0.0035787388,0.0001163898,0.00021538978,0.000004565417,0.00016820943,0.9846826,0.00037074482,0.00014697261,0.008216746,0.00026982167],"about_ca_topic_score_codex":0.00002966576,"about_ca_topic_score_gemma":0.0082605975,"teacher_disagreement_score":0.9216677,"about_ca_system_score_codex":0.00007302246,"about_ca_system_score_gemma":0.0000071092704,"threshold_uncertainty_score":0.9759697},"labels":[],"label_agreement":null},{"id":"W3133621865","doi":"10.1109/tnsm.2021.3058871","title":"Guest Editors Introduction: Special Issue on Advanced Management of Softwarized Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Cloud computing; Virtualization; Network Functions Virtualization; Network management; Service provider; Software-defined networking; Network virtualization; Service (business); Computer network; Telecommunications; Operating system","score_opus":0.00675532864623182,"score_gpt":0.20945524338814375,"score_spread":0.20269991474191193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133621865","genre_codex":"methods","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025409952,0.00020826161,0.9498848,0.0050463784,0.037043028,0.00051531824,0.0000039965344,0.00028448174,0.006759668],"genre_scores_gemma":[0.15775271,0.040955868,0.37020442,0.022899447,0.38633564,0.0011920347,0.00016090844,0.0004057459,0.0200932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975561,0.00010324576,0.0004735896,0.0008699807,0.00047371167,0.00052337546],"domain_scores_gemma":[0.99849284,0.00011210467,0.00014229171,0.00097628776,0.00012923544,0.00014726493],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026360262,0.00034501648,0.00038078715,0.00012372468,0.00037863659,0.00013551871,0.0004936246,0.00011642431,0.00017638071],"category_scores_gemma":[7.1640466e-7,0.0003532714,0.0001327498,0.0014843686,0.000039955965,0.00023526035,0.00004034995,0.0003067956,0.00004872564],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015452877,0.00032386906,0.0000048209386,0.00015789896,0.00032581663,0.00008389935,0.000096463424,0.60262144,0.0000012100363,0.004189375,0.054266207,0.3377745],"study_design_scores_gemma":[0.002760325,0.00039546634,0.0004917349,0.00043745188,0.00028423878,0.000018932076,0.0002697496,0.054692052,0.00024333288,0.0009307056,0.93871963,0.0007563716],"about_ca_topic_score_codex":0.000007586198,"about_ca_topic_score_gemma":0.00003433538,"teacher_disagreement_score":0.8844534,"about_ca_system_score_codex":0.000061065395,"about_ca_system_score_gemma":0.00001568325,"threshold_uncertainty_score":0.99989194},"labels":[],"label_agreement":null},{"id":"W3146793828","doi":"10.1109/tnsm.2021.3071025","title":"Optimizing All-to-All Data Transmission in WANs","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Peer-to-Peer Network Technologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Multicast; Distributed computing; Data transmission; Throughput; Transmission (telecommunications); Overlay network; Latency (audio); Wireless; The Internet","score_opus":0.041715607435522745,"score_gpt":0.27082771030052577,"score_spread":0.22911210286500303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3146793828","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014472023,0.00020948154,0.96213305,0.03323243,0.00041251964,0.00041791456,0.0000069202583,0.00038184205,0.0017586445],"genre_scores_gemma":[0.18983223,0.0023806137,0.778986,0.027802562,0.00008357025,0.0001481589,0.000026279049,0.000045466273,0.000695138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976805,0.00007660427,0.00031991513,0.0009854694,0.00033798954,0.0005995142],"domain_scores_gemma":[0.99808055,0.000069396585,0.000033379783,0.0015829939,0.00004414065,0.00018955629],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038067636,0.0002516454,0.00024929346,0.00024785454,0.00016945186,0.00021849472,0.0016250076,0.0000968335,0.00001735396],"category_scores_gemma":[7.9381323e-7,0.00026226137,0.000039199353,0.0018770227,0.0000117232175,0.0003418707,0.0001256874,0.00029020867,0.000046443918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002205111,0.00012754298,0.0000034438035,0.000053657623,0.00008658313,0.00014589672,0.0006945741,0.55718184,0.00006989143,0.0006356836,0.0033357607,0.43764308],"study_design_scores_gemma":[0.0009760015,0.00015183887,0.00033867758,0.0004526537,0.00011272577,0.000022338703,0.00048416175,0.59495646,0.00069241674,0.0010014047,0.4000723,0.00073904323],"about_ca_topic_score_codex":0.000054844364,"about_ca_topic_score_gemma":0.001008155,"teacher_disagreement_score":0.43690404,"about_ca_system_score_codex":0.00006604598,"about_ca_system_score_gemma":0.000024757881,"threshold_uncertainty_score":0.99998295},"labels":[],"label_agreement":null},{"id":"W3152283462","doi":"10.1109/tnsm.2021.3071087","title":"A New Mutual Authentication and Key Agreement Protocol for Mobile Client—Server Environment","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Advanced Authentication Protocols Security","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Protocol (science); Mutual authentication; Authentication protocol; Key (lock); Key-agreement protocol; Authentication (law); Computer security; Public-key cryptography; Key distribution; Encryption","score_opus":0.017974030373918638,"score_gpt":0.27080958700583624,"score_spread":0.2528355566319176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152283462","genre_codex":"methods","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019917621,0.00001148939,0.9013058,0.00104784,0.0001013119,0.0970435,0.000003408135,0.0000684281,0.00021905471],"genre_scores_gemma":[0.02629742,0.00008018149,0.21033007,0.0017861204,0.000085857864,0.7588112,0.00000824725,0.000028629007,0.0025723155],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99841315,0.000061348066,0.00031332925,0.00063632586,0.00027167107,0.0003041699],"domain_scores_gemma":[0.99903053,0.00005313158,0.00010224113,0.0006065429,0.000050794217,0.00015675694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019775353,0.00019969925,0.00015546086,0.000053892276,0.00031277628,0.00015753285,0.00025503678,0.000052091404,0.00011036038],"category_scores_gemma":[4.7744817e-7,0.00020391552,0.000054618682,0.00023145265,0.000020891972,0.0002677125,0.000032383625,0.00009746858,0.00004331095],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002930937,0.0020343284,0.000049774564,0.0013946602,0.00038334986,0.00001708914,0.004853741,0.07752746,0.00019668892,0.15087353,0.0008836651,0.7614926],"study_design_scores_gemma":[0.012777911,0.0011713017,0.002012626,0.00045429173,0.00028471625,0.000025458792,0.0006988828,0.38225412,0.0054901023,0.084098876,0.50930834,0.0014233787],"about_ca_topic_score_codex":0.0000051211305,"about_ca_topic_score_gemma":0.000034846576,"teacher_disagreement_score":0.76006925,"about_ca_system_score_codex":0.00006172297,"about_ca_system_score_gemma":0.000028825767,"threshold_uncertainty_score":0.8315433},"labels":[],"label_agreement":null},{"id":"W3153493802","doi":"10.1109/tnsm.2021.3071928","title":"Anomaly Detection for Insider Threats Using Unsupervised Ensembles","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":129,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Insider threat; Anomaly detection; Computer science; Robustness (evolution); Insider; Unsupervised learning; Machine learning; Artificial intelligence; Data mining","score_opus":0.030151253745282488,"score_gpt":0.24449942541858202,"score_spread":0.21434817167329953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153493802","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040195,0.0002625022,0.95647997,0.00069020595,0.0012456345,0.00036635782,0.000002226401,0.00015859232,0.0005995396],"genre_scores_gemma":[0.96280307,0.0012500115,0.032361027,0.0030181005,0.00021435764,0.00008969993,0.0000024660783,0.00002249681,0.00023878046],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865514,0.00008047791,0.00023996053,0.0005130532,0.00018457122,0.0003268255],"domain_scores_gemma":[0.99924576,0.00007745182,0.000055757635,0.00040599742,0.00012729166,0.00008772164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018554051,0.00018911807,0.00017718002,0.000105232706,0.0006996929,0.00020830514,0.0001769616,0.00009350334,0.000021465881],"category_scores_gemma":[5.264027e-7,0.00020020804,0.0000931548,0.0008093281,0.000014027616,0.0003399629,0.000012728941,0.0001361765,0.000007867893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000115427625,0.0001934652,0.0000058875275,0.00021529626,0.00021340088,0.000028383596,0.00046359262,0.27493757,0.001257423,0.0029267173,0.0001070835,0.71953577],"study_design_scores_gemma":[0.001124425,0.00018096816,0.000202122,0.00012281176,0.00013497165,0.000051228348,0.00017275555,0.96864897,0.01190087,0.005691515,0.0113752335,0.00039412786],"about_ca_topic_score_codex":0.00004146078,"about_ca_topic_score_gemma":0.0009688612,"teacher_disagreement_score":0.92411894,"about_ca_system_score_codex":0.00005149417,"about_ca_system_score_gemma":0.000021632104,"threshold_uncertainty_score":0.8164247},"labels":[],"label_agreement":null},{"id":"W3156382242","doi":"10.1109/tnsm.2021.3073414","title":"Understanding Selfish Mining in Imperfect Bitcoin and Ethereum Networks With Extended Forks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Blockchain; Computer science; Imperfect; Revenue; Metric (unit); Markov chain; Block (permutation group theory); Cryptocurrency; Computer security; Data mining; Machine learning; Business","score_opus":0.021205526319592157,"score_gpt":0.216323868797464,"score_spread":0.19511834247787183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156382242","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026517693,0.00023782374,0.96871203,0.002846823,0.00010514087,0.00025156155,7.1241936e-7,0.00014683745,0.0011813643],"genre_scores_gemma":[0.9772625,0.0011229907,0.020126525,0.0013201273,0.000017861257,0.00008868976,0.0000012097419,0.000012693461,0.00004739808],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988077,0.00006240866,0.00018039173,0.0005014554,0.00011575876,0.00033227957],"domain_scores_gemma":[0.9993521,0.00009317198,0.000044837223,0.00041737282,0.000026489404,0.00006605786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002568994,0.0001694549,0.00017975832,0.00011707061,0.0003371014,0.000121281795,0.00020683552,0.00011971234,0.0000064240817],"category_scores_gemma":[2.4412802e-7,0.00016181103,0.000023658677,0.0010983872,0.00003963,0.00011783326,0.000020412019,0.00027954194,8.309272e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013089014,0.0006275092,0.0008822488,0.00028794547,0.0005153029,0.00036626763,0.0029949392,0.53561604,0.000019920839,0.14551467,0.000497828,0.31254643],"study_design_scores_gemma":[0.0013531828,0.00014603896,0.0016671679,0.00021016471,0.00006665076,0.00007762134,0.0018791497,0.98656595,0.0000647099,0.006451176,0.0011102267,0.00040795206],"about_ca_topic_score_codex":0.000023107646,"about_ca_topic_score_gemma":0.0019379823,"teacher_disagreement_score":0.9507448,"about_ca_system_score_codex":0.000054333363,"about_ca_system_score_gemma":0.000015400341,"threshold_uncertainty_score":0.6598462},"labels":[],"label_agreement":null},{"id":"W3159727427","doi":"10.1109/tnsm.2021.3128160","title":"Cloud Computing as a Platform for Monetizing Data Services: A Two-Sided Game Business Model","year":2021,"lang":"en","type":"preprint","venue":"IEEE Transactions on Network and Service Management","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cégep de l'Outaouais; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cloud computing; Big data; Computer science; Service provider; Business model; Variety (cybernetics); Service (business); Data science; Business; Marketing; Artificial intelligence; Data mining","score_opus":0.036116669314128665,"score_gpt":0.27052802850715546,"score_spread":0.2344113591930268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159727427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014183446,0.00039769005,0.9792361,0.0027027114,0.0008903346,0.0015510673,0.000059101767,0.0005728062,0.00040673505],"genre_scores_gemma":[0.75887847,0.0017401929,0.23387378,0.0043546376,0.00024013648,0.0005376583,0.00021983926,0.0000618033,0.00009349719],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99673605,0.000043302793,0.00057849474,0.001716845,0.00031382302,0.00061148114],"domain_scores_gemma":[0.99604577,0.00014609506,0.00027435162,0.003151465,0.00025440103,0.00012794769],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000560416,0.00051050854,0.00054795505,0.0002121972,0.00065513613,0.0005661554,0.002804127,0.00036878465,0.000003942691],"category_scores_gemma":[9.65183e-7,0.0005628444,0.00010815053,0.000966884,0.000041596115,0.0002608899,0.0006191437,0.00073463435,0.0000063723123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034883957,0.0002060909,7.6182675e-7,0.0011276901,0.0003233767,0.000013784658,0.0009261362,0.90649235,0.0000040719724,0.011586669,0.000095155425,0.079189],"study_design_scores_gemma":[0.0007738021,0.000027384614,0.000012128225,0.0006706294,0.00022684528,0.0000155833,0.00035438954,0.9784719,0.000030494228,0.01746736,0.0014293022,0.00052023004],"about_ca_topic_score_codex":0.00035359335,"about_ca_topic_score_gemma":0.0013461659,"teacher_disagreement_score":0.74536234,"about_ca_system_score_codex":0.00007362784,"about_ca_system_score_gemma":0.00009915415,"threshold_uncertainty_score":0.9996823},"labels":[],"label_agreement":null},{"id":"W3164836019","doi":"10.1109/tnsm.2021.3083073","title":"Data-Driven Energy Conservation in Cellular Networks: A Systems Approach","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Academy of Finland","keywords":"Computer science; Energy consumption; Provisioning; Key (lock); Overhead (engineering); Context (archaeology); Energy (signal processing); Efficient energy use; Cellular network; Energy conservation; Base station; Distributed computing; Real-time computing; Computer network; Computer security","score_opus":0.016930186562502048,"score_gpt":0.19867422239972352,"score_spread":0.18174403583722146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164836019","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003591202,0.0009892609,0.9941142,0.00006818875,0.0007765608,0.0002767722,0.000015896701,0.00018965747,0.0032103376],"genre_scores_gemma":[0.9809774,0.004555069,0.012682556,0.0004939422,0.00020107465,0.00023997591,0.00036944816,0.00007537986,0.00040513038],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988515,0.000074735945,0.0003231158,0.00036293277,0.000118542885,0.0002691742],"domain_scores_gemma":[0.9992952,0.000035703033,0.000037395046,0.0005329363,0.000041136904,0.000057633326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013378802,0.00018659991,0.00021652032,0.00008904539,0.00009524713,0.00007131001,0.00015691096,0.000089076115,0.0000070176397],"category_scores_gemma":[2.5661993e-7,0.00021601339,0.000020767882,0.00072570064,0.000008945601,0.00021748035,0.000007466668,0.0001342693,0.0000034423847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009156308,0.000044748806,0.0000084306785,0.00027486656,0.00009091454,0.00002037068,0.000049674414,0.99489826,0.0000128563,0.0005444089,0.000470403,0.0035759243],"study_design_scores_gemma":[0.00043009722,0.000007807644,0.000017854014,0.00013637113,0.000053442698,0.000006095462,0.0003387088,0.99128634,0.00001663988,0.000017530248,0.00749071,0.00019841625],"about_ca_topic_score_codex":0.00008180704,"about_ca_topic_score_gemma":0.00072855473,"teacher_disagreement_score":0.98143166,"about_ca_system_score_codex":0.00007195366,"about_ca_system_score_gemma":0.000007757662,"threshold_uncertainty_score":0.880877},"labels":[],"label_agreement":null},{"id":"W3172196691","doi":"10.1109/tnsm.2021.3086721","title":"Deep Reinforcement Learning-Based Content Migration for Edge Content Delivery Networks With Vehicular Nodes","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Concordia University","funders":"CHIST-ERA; Fonds Québécois de la Recherche sur la Nature et les Technologies; Concordia University; Agence Nationale de la Recherche","keywords":"Computer science; Server; Reinforcement learning; Upload; Cache; Computer network; Enhanced Data Rates for GSM Evolution; Quality of experience; Content delivery network; Content delivery; Process (computing); Edge device; Cloud computing; Quality of service; Operating system; Artificial intelligence","score_opus":0.023044868425251778,"score_gpt":0.19902911930316913,"score_spread":0.17598425087791736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172196691","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013383121,0.00041105633,0.98266256,0.0021047334,0.0005017589,0.0005956362,9.391684e-7,0.00016825143,0.00017194978],"genre_scores_gemma":[0.9875302,0.00066973217,0.005313241,0.0053511956,0.00008116673,0.00028909132,0.000030333538,0.000022560012,0.0007125043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983089,0.000098153905,0.0003101981,0.00056400197,0.000300389,0.0004183693],"domain_scores_gemma":[0.9989135,0.000119730954,0.00010617364,0.00043699399,0.00029723073,0.00012633727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024230909,0.000269779,0.0002513778,0.000087077424,0.00057709374,0.0002678667,0.00024953924,0.000073495736,0.000008570488],"category_scores_gemma":[8.902691e-7,0.00024269376,0.0001367211,0.00039100347,0.000023312385,0.00020806692,0.000011591317,0.00021518276,0.000005606122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019278165,0.000117347176,0.000041969677,0.00008953512,0.0002337441,0.000034157452,0.000117547745,0.97767997,0.000052129704,0.0005618324,0.0001005555,0.02077846],"study_design_scores_gemma":[0.0018322737,0.0003400359,0.00022693223,0.00019441011,0.0001772735,0.000007726108,0.00037274495,0.9935326,0.00027443576,0.000011809932,0.0027182433,0.00031154268],"about_ca_topic_score_codex":0.00012854475,"about_ca_topic_score_gemma":0.0009265744,"teacher_disagreement_score":0.97734934,"about_ca_system_score_codex":0.00007718336,"about_ca_system_score_gemma":0.00003297837,"threshold_uncertainty_score":0.9896764},"labels":[],"label_agreement":null},{"id":"W3183602784","doi":"10.1109/tnsm.2021.3100308","title":"Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Anomaly detection; Computer network; The Internet; Content delivery network; Perspective (graphical); Data mining; Cache; Content delivery; Denial-of-service attack; Distributed computing; Server; Artificial intelligence; World Wide Web","score_opus":0.02844334208369939,"score_gpt":0.23823668524556782,"score_spread":0.20979334316186843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183602784","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029563915,0.0006950887,0.9656495,0.00063214847,0.0022810888,0.000519293,0.0000033302506,0.00031325748,0.0003424154],"genre_scores_gemma":[0.89866906,0.0029872488,0.094558775,0.0033369784,0.00026581634,0.00007187934,0.0000026053272,0.000033313143,0.0000742923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99732465,0.00033002946,0.00044384247,0.00095593865,0.00035107884,0.0005944747],"domain_scores_gemma":[0.9984122,0.00014658815,0.00013325491,0.00072761596,0.0003682745,0.00021209224],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034707275,0.00038212672,0.00037789185,0.00016778123,0.0009822557,0.00036346982,0.00038921944,0.00025466812,0.00006150858],"category_scores_gemma":[0.0000026374948,0.0004128448,0.00019052948,0.0016476145,0.000046182515,0.0005462725,0.000043948607,0.0006446304,0.000014794314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002866957,0.0004312765,0.000007451575,0.00006569299,0.00039096226,0.00009825836,0.0009199327,0.9499985,0.0001970901,0.002331534,0.000029743807,0.045242872],"study_design_scores_gemma":[0.0013737292,0.00013953872,0.00019402985,0.00018943565,0.00015100244,0.000045211178,0.00084023125,0.99376327,0.0010208547,0.001248478,0.00057902635,0.0004551831],"about_ca_topic_score_codex":0.0003377821,"about_ca_topic_score_gemma":0.0008990465,"teacher_disagreement_score":0.8710907,"about_ca_system_score_codex":0.0002327526,"about_ca_system_score_gemma":0.000037580092,"threshold_uncertainty_score":0.99983233},"labels":[],"label_agreement":null},{"id":"W3184338320","doi":"10.1109/tnsm.2021.3098784","title":"TSAGen: Synthetic Time Series Generation for KPI Anomaly Detection","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Anomaly detection; Computer science; Data mining; Performance indicator; Time series; Anomaly (physics); Series (stratigraphy); Overhead (engineering); Machine learning","score_opus":0.011575568544009708,"score_gpt":0.211022926873319,"score_spread":0.1994473583293093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184338320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018910377,0.00005216284,0.993576,0.0021392913,0.000284923,0.00044778432,0.000005108464,0.0002970765,0.0013065998],"genre_scores_gemma":[0.8612567,0.0007475961,0.12908454,0.002549716,0.00025274395,0.0011095252,0.000011283621,0.0000320965,0.0049557616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989943,0.0000411753,0.00019619429,0.0004433414,0.00011711806,0.00020787785],"domain_scores_gemma":[0.99933285,0.00002882634,0.00005339685,0.00042615552,0.000097630706,0.000061149796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001399714,0.00014273875,0.00012188993,0.0000699951,0.00059359905,0.00017660913,0.00016523154,0.000063484455,0.000030779407],"category_scores_gemma":[3.8854418e-7,0.00015353705,0.0000712473,0.0005419856,0.000013762504,0.00024089633,0.00000795907,0.00007761056,0.000031194617],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004739063,0.00026775186,0.0000014908283,0.00016021283,0.00020392495,0.000010839724,0.00022485659,0.058824692,0.010513801,0.01428999,0.0012830404,0.914172],"study_design_scores_gemma":[0.0005740271,0.00033188876,0.00012198921,0.000050954834,0.0001647883,0.00006350829,0.00010107267,0.7952775,0.09896606,0.0031210277,0.10068023,0.0005469791],"about_ca_topic_score_codex":0.00000898138,"about_ca_topic_score_gemma":0.00013893034,"teacher_disagreement_score":0.913625,"about_ca_system_score_codex":0.000036818128,"about_ca_system_score_gemma":0.0000138311125,"threshold_uncertainty_score":0.6261059},"labels":[],"label_agreement":null},{"id":"W3190306355","doi":"10.1109/tnsm.2021.3103509","title":"Efficient Inter-Cloud Authentication and Micropayment Protocol for IoT Edge Computing","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Notation; Computer science; Mathematics; Discrete mathematics; Algebra over a field; Pure mathematics; Arithmetic","score_opus":0.017430615025691737,"score_gpt":0.2640005757143076,"score_spread":0.24656996068861584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190306355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002295936,0.000017977018,0.9801445,0.0013655662,0.00036337343,0.015513273,0.00000781774,0.00008034496,0.00021123739],"genre_scores_gemma":[0.5589322,0.000080512866,0.36888966,0.0066697365,0.00044857516,0.06476701,0.00002691954,0.000052971212,0.00013241002],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988794,0.000053485106,0.00022174299,0.00046954985,0.0001247211,0.00025111507],"domain_scores_gemma":[0.9993489,0.000066166154,0.000058478672,0.00037600836,0.000068597874,0.00008189249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023992927,0.00014803407,0.00012843272,0.00006773505,0.00039378856,0.00020598511,0.00020012973,0.000037244343,0.000008339148],"category_scores_gemma":[3.838829e-7,0.00014541198,0.000053796553,0.00042011958,0.000021870512,0.00004277296,0.000027985723,0.000093627204,0.0000035977941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026488645,0.0020826773,0.000049453032,0.0025696373,0.00044796572,0.000030816915,0.0049119852,0.09337215,0.00032997163,0.13020106,0.002648068,0.7630913],"study_design_scores_gemma":[0.0023493448,0.00018724725,0.0006060216,0.00031666335,0.00007983332,0.000016765218,0.00035627862,0.93325615,0.00088186923,0.0028078894,0.058732025,0.00040990117],"about_ca_topic_score_codex":0.000007842772,"about_ca_topic_score_gemma":0.000057868823,"teacher_disagreement_score":0.839884,"about_ca_system_score_codex":0.000020818874,"about_ca_system_score_gemma":0.000014684332,"threshold_uncertainty_score":0.5929729},"labels":[],"label_agreement":null},{"id":"W3195303402","doi":"10.1109/tnsm.2021.3106577","title":"A Machine Learning Framework for Handling Delayed/Lost Packets in Tactile Internet Remote Robotic Surgery","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Zayed University","keywords":"Computer science; The Internet; Network packet; Artificial intelligence; Robot; Human–computer interaction; Computer vision; Computer network; Multimedia; Machine learning; Operating system","score_opus":0.02286304986216143,"score_gpt":0.24069417500710857,"score_spread":0.21783112514494715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195303402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068159313,0.00033049166,0.9858013,0.0013762626,0.004849097,0.00023290532,3.5001258e-7,0.00011096435,0.000482711],"genre_scores_gemma":[0.84901154,0.0011098941,0.14491417,0.0032255922,0.00079587207,0.000033450262,0.000014489812,0.00005107271,0.0008439371],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983607,0.0001125991,0.00033560293,0.00052996847,0.00016996624,0.00049119233],"domain_scores_gemma":[0.99888206,0.000559059,0.00008457534,0.00032283532,0.00006105353,0.00009042025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043880937,0.00021136805,0.00028743374,0.00016490533,0.0002788363,0.00024314996,0.00024467803,0.00008954249,0.000009020091],"category_scores_gemma":[0.0000048541733,0.00022784797,0.00010582545,0.00086020434,0.000010093399,0.00017754699,0.000025923451,0.0003909291,0.000017959072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087365705,0.00015569656,0.000186787,0.00033651126,0.0001976974,0.00015147123,0.0012293997,0.5234101,0.000005628495,0.0011889436,0.0006949591,0.47235548],"study_design_scores_gemma":[0.00034063114,0.000054168082,0.0002329919,0.0005435605,0.000045955723,0.000016825015,0.000070818,0.9875897,0.00010382233,0.0029523566,0.0077681243,0.00028102408],"about_ca_topic_score_codex":0.000093276096,"about_ca_topic_score_gemma":0.00023697414,"teacher_disagreement_score":0.8421956,"about_ca_system_score_codex":0.000056828576,"about_ca_system_score_gemma":0.000026995313,"threshold_uncertainty_score":0.929137},"labels":[],"label_agreement":null},{"id":"W3203092761","doi":"10.1109/tnsm.2022.3202200","title":"Planning 5G Networks for Rural Fixed Wireless Access","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Telecommunications link; Computer science; Computer network; Broadband; MIMO; Precoding; Limit (mathematics); Wireless network; Bandwidth (computing); Base station; Fixed point; Wireless; Topology (electrical circuits); Telecommunications; Mathematics; Channel (broadcasting); Electrical engineering; Engineering","score_opus":0.013716570309732076,"score_gpt":0.23438430392184176,"score_spread":0.22066773361210967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203092761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024186503,0.0001772068,0.99321026,0.000101489655,0.0014768435,0.00076552934,0.000014992335,0.00033201644,0.0015029873],"genre_scores_gemma":[0.99602914,0.00024422686,0.0017444324,0.00048866775,0.00011765062,0.0009896827,0.00003287355,0.00006121063,0.00029211305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913424,0.000027932634,0.00021653868,0.00019320239,0.00011147975,0.0003166005],"domain_scores_gemma":[0.9996417,0.000053090254,0.00003821705,0.0001901026,0.000020526628,0.000056337176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012208085,0.00017531442,0.00016552748,0.00008757254,0.00043635932,0.00006510387,0.00018694856,0.00003777632,0.00003074095],"category_scores_gemma":[8.2567524e-8,0.00020647803,0.00004519473,0.00041925712,0.000006607251,0.00015964555,0.0000060829093,0.00017722546,0.0000014219567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005749359,0.000019202576,0.000008327646,0.00014818512,0.00010242693,0.000003072314,0.00014459889,0.97823906,0.000006016724,0.00011559841,0.001142684,0.020013362],"study_design_scores_gemma":[0.0006384048,0.000043491,0.000040303756,0.000054825163,0.00006653832,0.0000035566723,0.00056815357,0.9928637,0.000016078538,0.00006870933,0.0054048444,0.00023137862],"about_ca_topic_score_codex":0.000010268709,"about_ca_topic_score_gemma":0.000033064876,"teacher_disagreement_score":0.9936105,"about_ca_system_score_codex":0.000088308356,"about_ca_system_score_gemma":0.0000029092055,"threshold_uncertainty_score":0.841993},"labels":[],"label_agreement":null},{"id":"W4206672858","doi":"10.1109/tnsm.2022.3141942","title":"ML-Based IDPS Enhancement With Complementary Features for Home IoT Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Software deployment; Intrusion detection system; Leverage (statistics); Hacker; Computer security; Internet of Things; Upgrade; Intrusion prevention system; Software; Artificial intelligence; Software engineering","score_opus":0.00976315132315055,"score_gpt":0.20977433044076074,"score_spread":0.20001117911761018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206672858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030259483,0.00016285252,0.9903732,0.0035791954,0.0011361279,0.0011143739,0.0000110063775,0.00018068778,0.00041659237],"genre_scores_gemma":[0.94047093,0.000270407,0.03445416,0.022420444,0.00024361277,0.0016285185,0.000038312028,0.000041207117,0.00043238353],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980844,0.00011985442,0.00026831945,0.0006100044,0.00041610957,0.0005013012],"domain_scores_gemma":[0.99913335,0.00009954461,0.00010613737,0.0004991644,0.000051051036,0.00011076213],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00037550335,0.00026165292,0.000218785,0.00014921182,0.0016378437,0.0001467482,0.0005228893,0.000038875936,0.00016360873],"category_scores_gemma":[6.740869e-8,0.0002523647,0.00008275184,0.000863192,0.00002504391,0.000120588455,0.000028736138,0.00030970026,0.0000033222088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003966971,0.00023999166,0.0000029788418,0.00006853107,0.000141368,0.000007630172,0.00017930866,0.90088284,0.0000051866073,0.0020926853,0.0044636647,0.091519095],"study_design_scores_gemma":[0.0023259996,0.0016123598,0.00016828436,0.0000622834,0.00011918962,0.000012158852,0.00023669141,0.8758184,0.00014247368,0.00072635355,0.118288375,0.0004874888],"about_ca_topic_score_codex":0.00006726427,"about_ca_topic_score_gemma":0.00048999256,"teacher_disagreement_score":0.955919,"about_ca_system_score_codex":0.00011155513,"about_ca_system_score_gemma":0.000020039764,"threshold_uncertainty_score":0.99999285},"labels":[],"label_agreement":null},{"id":"W4210445337","doi":"10.1109/tnsm.2022.3142254","title":"An Online Entropy-Based DDoS Flooding Attack Detection System With Dynamic Threshold","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Denial-of-service attack; Application layer DDoS attack; Entropy (arrow of time); Network packet; Computer security; Computer network; Server; Intrusion detection system; Flooding (psychology); The Internet; Real-time computing","score_opus":0.014184575589094155,"score_gpt":0.21816365611299934,"score_spread":0.20397908052390518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210445337","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.106383406,0.000039207007,0.89099085,0.00043901431,0.0010364102,0.00049066474,0.000008231793,0.0004306966,0.0001815277],"genre_scores_gemma":[0.9932737,0.000059084963,0.004992601,0.0013208201,0.00008438161,0.00018866971,0.000009845289,0.000024099842,0.000046812966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813735,0.0001642149,0.0002585382,0.000605564,0.00045662824,0.00037769676],"domain_scores_gemma":[0.99909973,0.000029636634,0.00010478635,0.00059584656,0.0000470924,0.00012291635],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00029651192,0.00023579673,0.00018851516,0.00021114365,0.0014569588,0.00018413644,0.00046409835,0.00004857204,0.00003215594],"category_scores_gemma":[6.694426e-8,0.00023130086,0.00005865687,0.0011815511,0.000017785245,0.0003202781,0.000016898624,0.0003853273,0.000007876308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017148316,0.0002333468,0.000004465836,0.000097550146,0.00006120292,0.000026199808,0.00015198263,0.9475436,0.00007260154,0.00048502817,0.000020532878,0.051131982],"study_design_scores_gemma":[0.00085535465,0.00079894,0.00013444407,0.00006729888,0.00006667504,0.00003245342,0.00044266548,0.99472296,0.00017277017,0.000037048227,0.0023956299,0.00027375657],"about_ca_topic_score_codex":0.000056071684,"about_ca_topic_score_gemma":0.001144595,"teacher_disagreement_score":0.8868903,"about_ca_system_score_codex":0.00021307953,"about_ca_system_score_gemma":0.000018544168,"threshold_uncertainty_score":0.999843},"labels":[],"label_agreement":null},{"id":"W4213070403","doi":"10.1109/tnsm.2022.3151083","title":"Quantitative Comparison of Two Chain-Selection Protocols Under Selfish Mining Attack","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Beijing Municipal Natural Science Foundation; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Protocol (science); Throughput; Selection (genetic algorithm); Blockchain; Profitability index; Markov chain; Chain (unit); Distributed computing; Computer security; Artificial intelligence; Machine learning; Operating system","score_opus":0.04205772958807542,"score_gpt":0.318812487946594,"score_spread":0.2767547583585186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213070403","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028009586,0.00003302718,0.9659845,0.0017833271,0.00012977401,0.0030814118,0.0000037994125,0.00019228364,0.0007822725],"genre_scores_gemma":[0.9545096,0.000020045314,0.038541753,0.0007915224,0.000013274856,0.0060269176,0.0000019760014,0.000010781386,0.00008409009],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873906,0.000113399496,0.00029004487,0.00038671657,0.00023679188,0.00023400465],"domain_scores_gemma":[0.99932134,0.00007319072,0.00014273702,0.00036459492,0.00005734297,0.000040817366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003283075,0.00013798384,0.00020157929,0.0001601249,0.0007450774,0.00003778558,0.0004537369,0.000040456776,0.0000280426],"category_scores_gemma":[1.8655382e-7,0.00015327675,0.000045115205,0.0012684917,0.000033786557,0.0000835327,0.00003084474,0.0002604442,0.0000033844483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058670197,0.00064894976,0.00014767096,0.000087462766,0.00015724261,0.0000015711828,0.0015758283,0.83011526,0.000036532394,0.11666954,0.00079803617,0.049703233],"study_design_scores_gemma":[0.00089689076,0.0006003219,0.0003909217,0.00003604185,0.000047774545,0.0000063951334,0.0018607402,0.9818179,0.00040857782,0.0038584874,0.009820827,0.00025513174],"about_ca_topic_score_codex":0.000028952554,"about_ca_topic_score_gemma":0.00022321453,"teacher_disagreement_score":0.9274428,"about_ca_system_score_codex":0.000053556978,"about_ca_system_score_gemma":0.000016425753,"threshold_uncertainty_score":0.62504447},"labels":[],"label_agreement":null},{"id":"W4225502174","doi":"10.1109/tnsm.2022.3159479","title":"Toward Adaptive Joint Node and Link Mapping Algorithms for Embedding Virtual Networks: A Conciliation Strategy","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Network virtualization; Virtual network; Scalability; Distributed computing; Node (physics); Heuristic; Computer network; Embedding; Virtualization; Algorithm; Cloud computing; Artificial intelligence","score_opus":0.0487566144604629,"score_gpt":0.24396421253349465,"score_spread":0.19520759807303176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225502174","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00097349583,0.00035521074,0.9946399,0.0018214296,0.0010086783,0.00076660485,0.000014149007,0.00021567981,0.00020487144],"genre_scores_gemma":[0.9359363,0.00093647797,0.055956077,0.005684196,0.00039621335,0.00082431204,0.000016947193,0.00003974341,0.00020975829],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816525,0.00010317091,0.00033898957,0.0006326324,0.00028331793,0.00047664362],"domain_scores_gemma":[0.99918634,0.00020209857,0.00012217631,0.00030297675,0.000064995256,0.00012140166],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00050860783,0.00025479755,0.00026254283,0.00013466981,0.001013247,0.00021540192,0.0002920892,0.000063800304,0.000015193975],"category_scores_gemma":[6.8920326e-7,0.00027392778,0.00008109598,0.00067443156,0.00002243086,0.0002413166,0.00004565071,0.00029717275,0.0000017021886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044777964,0.00003591697,0.00000238568,0.000031661064,0.00010185555,0.000007904597,0.0005901865,0.7460688,0.0000010116966,0.003467626,0.0003186208,0.24932927],"study_design_scores_gemma":[0.0009900213,0.00040684125,0.00014934433,0.000055607507,0.000060486258,0.0000118165935,0.0012157955,0.9907939,0.0000034243412,0.0011170746,0.0048897783,0.0003059519],"about_ca_topic_score_codex":0.000046225523,"about_ca_topic_score_gemma":0.000035658824,"teacher_disagreement_score":0.9386838,"about_ca_system_score_codex":0.00009743512,"about_ca_system_score_gemma":0.000023646558,"threshold_uncertainty_score":0.9999713},"labels":[],"label_agreement":null},{"id":"W4285104669","doi":"10.1109/tnsm.2022.3181063","title":"A Learned Bloom Filter-Assisted Scheme for Packet Classification in Software-Defined Networking","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Packet Processing and Optimization","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Bloom filter; Network packet; Tuple space; Hash function; Header; Packet forwarding; Hash table; Computer network; Tuple","score_opus":0.03887829327121172,"score_gpt":0.2502355229666417,"score_spread":0.21135722969542997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285104669","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021698528,0.00014923951,0.99282,0.0025961967,0.00087278296,0.00061093475,0.0000044159906,0.0002682625,0.00050826103],"genre_scores_gemma":[0.7417713,0.0006582608,0.24960238,0.0049541225,0.00024364672,0.0016528951,0.00006260675,0.00006355357,0.0009912831],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817944,0.00014407461,0.00034656766,0.0006103861,0.00027499683,0.0004445256],"domain_scores_gemma":[0.9991606,0.00014570741,0.00014082796,0.00042545117,0.00005330198,0.00007414345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005779295,0.00020947041,0.00020704577,0.00020056838,0.0008977091,0.00020260859,0.00043189173,0.00005715104,0.000016965932],"category_scores_gemma":[0.0000010260926,0.00023664077,0.000064493455,0.0015353652,0.000015896272,0.00022102689,0.000024976378,0.00025760423,0.0000036372423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012540996,0.00023070067,0.00007291477,0.00009953679,0.000054431053,0.000005847121,0.0003388902,0.63948166,0.000006948978,0.0011874597,0.0009616285,0.35743454],"study_design_scores_gemma":[0.0013475127,0.000158895,0.00061251863,0.0000817839,0.000041925457,0.000006407261,0.00017203209,0.978461,0.000008544928,0.0016862715,0.017115587,0.0003075101],"about_ca_topic_score_codex":0.000019453957,"about_ca_topic_score_gemma":0.00013865187,"teacher_disagreement_score":0.74321765,"about_ca_system_score_codex":0.00011220617,"about_ca_system_score_gemma":0.00003168904,"threshold_uncertainty_score":0.96499306},"labels":[],"label_agreement":null},{"id":"W4285123959","doi":"10.1109/tnsm.2022.3176365","title":"Introduction and Evaluation of Attachability for Mobile IoT Routing Protocols With Markov Chain Analysis","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computer network; Routing protocol; Network packet; Distributed computing; Link-state routing protocol; Dynamic Source Routing; Routing table; Markov chain; Source routing; Wireless Routing Protocol; Static routing; Routing (electronic design automation)","score_opus":0.01652014758513882,"score_gpt":0.26995615275737556,"score_spread":0.25343600517223674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285123959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031142037,0.000028800328,0.9555252,0.00077384774,0.00013721915,0.012218317,0.0000065509357,0.000055150413,0.000112863025],"genre_scores_gemma":[0.94937426,0.000013180005,0.015946934,0.00019253408,0.00009269996,0.034302525,0.000007160124,0.0000116501615,0.000059048318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981578,0.0002782402,0.00028745402,0.000566532,0.00048030136,0.00022969057],"domain_scores_gemma":[0.9990211,0.00009013542,0.0001564613,0.00053829816,0.00014380057,0.000050201543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021880402,0.00014615034,0.00023247066,0.00015138637,0.0004832979,0.000059073485,0.00022755312,0.000027287477,0.0000476268],"category_scores_gemma":[6.1311533e-7,0.00014190521,0.000060322833,0.0014977824,0.000023670844,0.00011066598,0.000021887805,0.00013117284,2.4499369e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010658928,0.00012394988,0.0000914373,0.000090065645,0.00028893273,2.751591e-7,0.00030271395,0.82052284,0.000003707454,0.00045592533,0.000059042355,0.1779545],"study_design_scores_gemma":[0.00088083086,0.00047889393,0.0013005309,0.000016436265,0.0005962237,0.0000021536766,0.0002941846,0.99422663,0.000030663003,0.00023732905,0.0017875751,0.00014853207],"about_ca_topic_score_codex":0.00003410605,"about_ca_topic_score_gemma":0.00023251737,"teacher_disagreement_score":0.9395783,"about_ca_system_score_codex":0.000102117345,"about_ca_system_score_gemma":0.00002195159,"threshold_uncertainty_score":0.57867265},"labels":[],"label_agreement":null},{"id":"W4285218962","doi":"10.1109/tnsm.2022.3181169","title":"Dual-Hop Mixed FSO-VLC Underwater Wireless Communication Link","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Optical Wireless Communication Technologies","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Fundação para a Ciência e a Tecnologia; King Saud University; Russian Foundation for Basic Research; Tomsk Polytechnic University","keywords":"Computer science; Cumulative distribution function; Nakagami distribution; Wireless; Fading; Channel (broadcasting); Underwater; Bit error rate; Optical wireless; Wireless network; Probability density function; Electronic engineering; Computer network; Telecommunications; Real-time computing; Mathematics; Engineering; Statistics","score_opus":0.01300480241905245,"score_gpt":0.20360970983077487,"score_spread":0.19060490741172242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285218962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21157546,0.0028581182,0.69777656,0.035041228,0.002446018,0.0024162212,0.000064077714,0.0068766424,0.040945657],"genre_scores_gemma":[0.9904258,0.0051394245,0.0030646767,0.00062050874,0.00002272821,0.00041726034,0.000019641673,0.000042704432,0.00024724903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897474,0.00009759248,0.00025665722,0.00019885915,0.00019671243,0.00027541703],"domain_scores_gemma":[0.998953,0.00009121319,0.000032331944,0.0008485848,0.00002469974,0.000050179555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019864892,0.00018238113,0.0001677131,0.00012523204,0.00063400745,0.00006034095,0.00043732257,0.00006536759,0.0001094369],"category_scores_gemma":[1.5436525e-7,0.00020256333,0.000046555117,0.0005458649,0.00004214466,0.00009044718,0.000045407745,0.0004990816,0.000038958548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020271504,0.00007812,0.0000038922253,0.000089276255,0.0001656492,0.0000033756617,0.00022656414,0.816051,0.00004581404,0.0026297963,0.0010059063,0.17968032],"study_design_scores_gemma":[0.0016441228,0.00018485203,0.0005053822,0.0001255622,0.00021229997,0.000015118402,0.0049045384,0.7781949,0.0018589303,0.0040675374,0.20725079,0.001035958],"about_ca_topic_score_codex":0.000019208423,"about_ca_topic_score_gemma":0.00014246667,"teacher_disagreement_score":0.7788503,"about_ca_system_score_codex":0.00011163154,"about_ca_system_score_gemma":0.000003819022,"threshold_uncertainty_score":0.8260293},"labels":[],"label_agreement":null},{"id":"W4293371094","doi":"10.1109/tnsm.2022.3201953","title":"A Vehicular Task Offloading Method With Eliminating Redundant Tasks in 5G HetNets","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Base station; Task (project management); Heterogeneous network; Computer network; Distributed computing; Database transaction; Wireless; Wireless network; Database; Operating system","score_opus":0.010732091002936794,"score_gpt":0.22778063500280768,"score_spread":0.2170485439998709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293371094","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018844388,0.00009145541,0.9748488,0.0015792528,0.0018983156,0.00038307795,4.3202957e-7,0.00014111307,0.0022131365],"genre_scores_gemma":[0.89339346,0.00006398603,0.10269396,0.0031437252,0.0002555487,0.00020192511,0.0000029123514,0.000032844793,0.00021165151],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981265,0.00021499241,0.0002699643,0.0005502762,0.0003575593,0.00048070523],"domain_scores_gemma":[0.9993381,0.00008845294,0.00008409179,0.0003840757,0.000027640006,0.00007762076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084110006,0.00020395243,0.00021533894,0.00022589976,0.00085350254,0.00014317564,0.00046833116,0.000029825293,0.000006345555],"category_scores_gemma":[2.6563026e-7,0.00020211605,0.000046752397,0.0013410131,0.000011439982,0.00017148042,0.000039079394,0.00038722777,0.0000044278136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033272416,0.00010132519,0.00001708111,0.000070463364,0.00006313486,0.00012274896,0.0017134126,0.7310349,0.000028186489,0.00042571043,0.00020659757,0.2661832],"study_design_scores_gemma":[0.0008409449,0.0002298128,0.0005517174,0.00012750359,0.000049836504,0.00005421036,0.00048280324,0.97860324,0.0000915106,0.00035245207,0.018256463,0.00035950146],"about_ca_topic_score_codex":0.00015381827,"about_ca_topic_score_gemma":0.0000619586,"teacher_disagreement_score":0.87454903,"about_ca_system_score_codex":0.00011304049,"about_ca_system_score_gemma":0.000021266957,"threshold_uncertainty_score":0.82420534},"labels":[],"label_agreement":null},{"id":"W4293704439","doi":"10.1109/tnsm.2022.3202801","title":"Retracted: A Hybrid Multistage DNN-Based Collaborative IDPS for High-Risk Smart Factory Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":21,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Exploit; Intrusion detection system; Industrial control system; Latency (audio); Artificial intelligence; Machine learning; Deep neural networks; Low latency (capital markets); Schema (genetic algorithms); Artificial neural network; Deep learning; The Internet; Classifier (UML); Data mining; Computer security; Computer network; Control (management)","score_opus":0.00797254497115034,"score_gpt":0.20340948517289956,"score_spread":0.1954369402017492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293704439","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010774675,0.00014728382,0.98335683,0.0008922025,0.002809471,0.0012730417,0.000117237185,0.00031014043,0.00031912362],"genre_scores_gemma":[0.98427,0.00046861838,0.009928811,0.003898947,0.00018992912,0.0008802998,0.000039055478,0.00003420256,0.00029015864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99761426,0.00032694603,0.00038090916,0.00073170266,0.0004242337,0.0005219456],"domain_scores_gemma":[0.9986162,0.00031144999,0.00021300332,0.0005896597,0.00012481867,0.000144858],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0006410049,0.00030516862,0.0002806019,0.00019559577,0.002101219,0.00020378019,0.0005370251,0.00008259892,0.000115789226],"category_scores_gemma":[0.0000012195405,0.00033379835,0.00009996466,0.0012152536,0.00003358078,0.0002670455,0.00002930505,0.00067286735,0.000007838457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002956991,0.00022795443,0.000008609477,0.000050412717,0.00013553031,0.000017809645,0.00019933537,0.86993605,0.0000037131604,0.0011762771,0.0036002877,0.12434832],"study_design_scores_gemma":[0.0016180401,0.00069190736,0.00033749134,0.000029743913,0.00010627121,0.0000042746988,0.0001705972,0.9215419,0.00011828571,0.0006704391,0.07429652,0.00041450115],"about_ca_topic_score_codex":0.00017458781,"about_ca_topic_score_gemma":0.00055628916,"teacher_disagreement_score":0.9734953,"about_ca_system_score_codex":0.00016989211,"about_ca_system_score_gemma":0.000044072058,"threshold_uncertainty_score":0.9999114},"labels":[],"label_agreement":null},{"id":"W4295832472","doi":"10.1109/tnsm.2022.3205415","title":"Resource Allocation in an Open RAN System Using Network Slicing","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; C-RAN; Radio access network; Baseband; Computer network; Quality of service; Resource allocation; Distributed computing; Integer programming; Wireless network; Wireless; Algorithm; Base station; Telecommunications","score_opus":0.028092866025729815,"score_gpt":0.24683471554368475,"score_spread":0.21874184951795494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295832472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022623807,0.00013072602,0.97291595,0.00074014044,0.00085772265,0.0009116457,0.0000020717323,0.0002478645,0.0015700868],"genre_scores_gemma":[0.97897977,0.000052026113,0.017050436,0.0033826844,0.00016258097,0.0002406126,0.0000073553424,0.000031292144,0.00009326583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978641,0.0003463471,0.00035792615,0.0006251029,0.0003167688,0.00048973144],"domain_scores_gemma":[0.99898756,0.00008926312,0.000108383174,0.0006791571,0.000026867596,0.00010875536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009643139,0.00020867508,0.00024717662,0.00014351665,0.0011625924,0.00036475217,0.001118368,0.000045765555,0.000015607322],"category_scores_gemma":[1.8719226e-7,0.00023208695,0.000035846086,0.0017088364,0.0000104354285,0.00041245265,0.00010125449,0.00029487413,0.000003665334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006293484,0.00008766496,0.00006164805,0.000049129365,0.000033038366,0.00002367592,0.00057815015,0.95682806,0.000002168096,0.004579509,0.00026230206,0.03743173],"study_design_scores_gemma":[0.0008327004,0.00015903659,0.00044447696,0.00015135878,0.000042998734,0.000017660359,0.0010683471,0.9895121,0.0000023583211,0.0002691093,0.007204601,0.00029523773],"about_ca_topic_score_codex":0.00037887998,"about_ca_topic_score_gemma":0.00063543697,"teacher_disagreement_score":0.9563559,"about_ca_system_score_codex":0.00020625,"about_ca_system_score_gemma":0.000023071581,"threshold_uncertainty_score":0.9464231},"labels":[],"label_agreement":null},{"id":"W4312252175","doi":"10.1109/tnsm.2022.3209317","title":"REVAL: Recommend Which Variables to Log With Pretrained Model and Graph Neural Network","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Snippet; ENCODE; Graph; Data mining; Source code; Information retrieval; Theoretical computer science; Programming language","score_opus":0.012468343258661743,"score_gpt":0.21287121948915924,"score_spread":0.2004028762304975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312252175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025950817,0.00011970817,0.9651742,0.0056246016,0.00076778996,0.0009041122,0.0000075323583,0.00025169994,0.0011995343],"genre_scores_gemma":[0.9589954,0.0003155312,0.033459768,0.006411465,0.00008054242,0.0004259393,0.0000035248213,0.000023223689,0.00028462528],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828225,0.00012973018,0.0002519326,0.0005946606,0.00030638708,0.0004350561],"domain_scores_gemma":[0.99910367,0.00007028179,0.000060515296,0.0005624393,0.00005771553,0.00014536422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063561765,0.00021920304,0.00022428442,0.00010112492,0.0009584243,0.00012383243,0.00040351306,0.000040278184,0.000017757102],"category_scores_gemma":[3.188946e-7,0.00018865708,0.00003344776,0.0014651793,0.000016124068,0.00021211612,0.000046097186,0.00025560983,0.0000024254564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010584418,0.00006435237,0.00010626456,0.000119337026,0.00007566584,0.0000042981646,0.0005210221,0.9715678,6.6601956e-7,0.0005837261,0.0014936836,0.025357328],"study_design_scores_gemma":[0.0007000822,0.00046297346,0.00082693534,0.00007353011,0.00007488556,0.000024030403,0.00016181322,0.99050033,0.0000019108766,0.0014720267,0.0053622737,0.0003392208],"about_ca_topic_score_codex":0.0000509935,"about_ca_topic_score_gemma":0.0002827567,"teacher_disagreement_score":0.93304455,"about_ca_system_score_codex":0.000046918536,"about_ca_system_score_gemma":0.000020829024,"threshold_uncertainty_score":0.76932126},"labels":[],"label_agreement":null},{"id":"W4312295420","doi":"10.1109/tnsm.2022.3217723","title":"Reinforcement Learning-Based Optimization Framework for Application Component Migration in NFV Cloud-Fog Environments","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Concordia University","keywords":"Computer science; Cloud computing; Virtual network; Reinforcement learning; Distributed computing; Markov decision process; Component (thermodynamics); Computer network; Markov process; Artificial intelligence","score_opus":0.010058180463990273,"score_gpt":0.2130066221827625,"score_spread":0.20294844171877224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312295420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014028591,0.000024494417,0.99389154,0.0016561343,0.0018618606,0.0009450193,3.8594655e-7,0.00008598635,0.00013171652],"genre_scores_gemma":[0.9006184,0.00012577404,0.094567314,0.0028665499,0.0003264573,0.001241036,0.00006765543,0.000027802043,0.00015906675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862075,0.000097029726,0.00029562437,0.00041454917,0.00028046028,0.00029157088],"domain_scores_gemma":[0.99942684,0.00009179738,0.00012097107,0.00029452966,0.00001431193,0.000051530325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038644826,0.00015957135,0.00013392958,0.00015138189,0.0007357808,0.0000780787,0.00029285665,0.000043165284,0.0000084778585],"category_scores_gemma":[4.4648965e-7,0.00018807067,0.000049059745,0.0005406633,0.000009018349,0.00011130691,0.000020992004,0.00022892891,0.00000553448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005504305,0.00012883353,0.000024872592,0.000041840067,0.000021150963,0.0000010430227,0.0003344126,0.9743805,0.0000050256485,0.00084055413,0.00014343439,0.024023293],"study_design_scores_gemma":[0.0006361244,0.00016378505,0.00013980981,0.000029546853,0.00002175832,5.989473e-7,0.00006526068,0.9735363,0.00004009156,0.0004673324,0.02471731,0.00018209069],"about_ca_topic_score_codex":0.000041731393,"about_ca_topic_score_gemma":0.000014953572,"teacher_disagreement_score":0.89932424,"about_ca_system_score_codex":0.00017869468,"about_ca_system_score_gemma":0.000011155953,"threshold_uncertainty_score":0.7669299},"labels":[],"label_agreement":null},{"id":"W4312410281","doi":"10.1109/tnsm.2022.3221670","title":"Deep Reinforcement Learning-Based Joint User Association and CU–DU Placement in O-RAN","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"C-RAN; Computer science; Radio access network; Cloud computing; Reinforcement learning; Markov decision process; Software deployment; Computer network; Ran; Cellular network; Distributed computing; Optimization problem; Base station; Markov process; Operating system; Artificial intelligence; Algorithm","score_opus":0.0093893842766279,"score_gpt":0.19442715243266293,"score_spread":0.18503776815603504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312410281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065224827,0.000107743275,0.9876815,0.003604059,0.0005732205,0.00053044554,7.355769e-7,0.00015176917,0.0008280653],"genre_scores_gemma":[0.98973393,0.00050735736,0.0023526868,0.0063515455,0.000052248848,0.00036434707,0.000008409461,0.000018999755,0.0006104935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820465,0.000173364,0.00031602482,0.00044909786,0.00044904527,0.00040781664],"domain_scores_gemma":[0.99935216,0.00012705845,0.00012416067,0.0002771191,0.000031307045,0.00008818087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006921388,0.0001981275,0.00019728606,0.00018346476,0.000659839,0.00013907619,0.00023280468,0.000045909015,0.000098605444],"category_scores_gemma":[0.0000010726122,0.00021451981,0.000049945476,0.00073080737,0.0000086835225,0.00014132506,0.000037459988,0.00034521526,0.0000075360354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049454575,0.000106674495,0.0005582901,0.000038755566,0.000057424357,0.00001253992,0.000458767,0.9836881,7.9670394e-7,0.00046432085,0.0007418733,0.013823009],"study_design_scores_gemma":[0.0015754777,0.00027990426,0.0023734707,0.000036214737,0.00004139023,0.0000021300598,0.0002728678,0.973532,0.0000066710954,0.00011064425,0.021514324,0.00025486352],"about_ca_topic_score_codex":0.00013104231,"about_ca_topic_score_gemma":0.00036188855,"teacher_disagreement_score":0.9853288,"about_ca_system_score_codex":0.00029267362,"about_ca_system_score_gemma":0.00001734882,"threshold_uncertainty_score":0.87478644},"labels":[],"label_agreement":null},{"id":"W4312995559","doi":"10.1109/tnsm.2022.3210827","title":"FLoadNet: Load Balancing in Fog Networks With Cooperative Multiagent Using Actor–Critic Method","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Reinforcement learning; Distributed computing; Load balancing (electrical power); Cloud computing; Edge computing; Edge device; Context (archaeology); Enhanced Data Rates for GSM Evolution; Workload; Server; Shared resource; Computer network; Artificial intelligence","score_opus":0.01611502068863893,"score_gpt":0.24853917710541473,"score_spread":0.2324241564167758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312995559","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020937538,0.00007875724,0.99534523,0.0004151442,0.0006946694,0.0005968301,0.0000012420447,0.000107216205,0.00066717574],"genre_scores_gemma":[0.8772879,0.0001361685,0.11911666,0.002943091,0.00006175107,0.00015835503,0.00000301716,0.000032548567,0.0002604952],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978231,0.000300217,0.00031521975,0.0005442762,0.00049722294,0.0005199445],"domain_scores_gemma":[0.99916315,0.00013469078,0.00008693846,0.00045507864,0.00006230483,0.00009782056],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00061316264,0.0002568033,0.0002489097,0.0001531028,0.00072953996,0.0001711774,0.00046765784,0.000040255396,0.00005191974],"category_scores_gemma":[6.1238967e-7,0.0002542831,0.000042197174,0.0012687506,0.00001845248,0.00025068872,0.000047283716,0.00049899094,0.0000036215374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050637263,0.00006810198,0.00004949464,0.00004220543,0.00009522126,0.000060189723,0.0010029649,0.98781776,0.000007665615,0.0003312514,0.000033839802,0.010440651],"study_design_scores_gemma":[0.0009327535,0.00021143915,0.00022997067,0.000085643325,0.00005406337,0.00001763395,0.0005543383,0.9961434,0.000018636292,0.000014699285,0.0014451101,0.0002923109],"about_ca_topic_score_codex":0.00019305479,"about_ca_topic_score_gemma":0.0002660656,"teacher_disagreement_score":0.8762286,"about_ca_system_score_codex":0.00035978205,"about_ca_system_score_gemma":0.00005082438,"threshold_uncertainty_score":0.99999094},"labels":[],"label_agreement":null},{"id":"W4318823673","doi":"10.1109/tnsm.2022.3227775","title":"Guest Editorial: Special Issue on Machine Learning and Artificial Intelligence for Managing Networks, Systems, and Services—Part I","year":2022,"lang":"en","type":"editorial","venue":"IEEE Transactions on Network and Service Management","topic":"Scientific Measurement and Uncertainty Evaluation","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Western University; University of Saskatchewan; Ontario Tech University; Dalhousie University","funders":"","keywords":"Computer science; Big data; Artificial intelligence; Cloud computing; Data science; Telecommunications; Data mining","score_opus":0.06811678727619286,"score_gpt":0.3363979240685288,"score_spread":0.26828113679233595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318823673","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000021815442,0.0005492941,0.03267132,0.00063758914,0.9622414,0.0016981126,0.00012945612,0.00009009343,0.0019608866],"genre_scores_gemma":[0.00047944536,0.0033159105,0.0001318574,0.00020866162,0.9910317,0.00035208595,0.0002693543,0.000065230786,0.004145731],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99247634,0.0006200883,0.0011946498,0.0016691075,0.0034085612,0.00063126866],"domain_scores_gemma":[0.99571127,0.0024023189,0.0005764822,0.0005788675,0.00050669356,0.00022439638],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00881338,0.00058173726,0.00072452886,0.0005474958,0.0020290094,0.0017066323,0.0006275269,0.0003807611,0.00037915466],"category_scores_gemma":[0.000055267046,0.0005348547,0.00011993105,0.0010249967,0.0000758465,0.00023946854,0.000054275875,0.0009937652,0.000035979057],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005863958,0.00006696789,0.0000024040314,0.00026091587,0.00013038494,0.000003841734,0.0002753081,0.25213116,2.8263602e-7,0.00016244338,0.66988534,0.076494575],"study_design_scores_gemma":[0.0003545023,0.0002934642,0.0000014740775,0.00023851018,0.00029922143,7.560201e-7,0.0015424555,0.18341577,0.0000010963369,0.0010000899,0.8124448,0.00040788623],"about_ca_topic_score_codex":0.0002052578,"about_ca_topic_score_gemma":0.0014920841,"teacher_disagreement_score":0.14255945,"about_ca_system_score_codex":0.00015469524,"about_ca_system_score_gemma":0.00004850344,"threshold_uncertainty_score":0.9997103},"labels":[],"label_agreement":null},{"id":"W4319068962","doi":"10.1109/tnsm.2023.3242205","title":"Opportunistic UAV Deployment for Intelligent On-Demand IoV Service Management","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Quality of service; Automotive industry; Cloud computing; Software deployment; Service (business); The Internet; Computer network; Distributed computing; Operating system","score_opus":0.022345603154775864,"score_gpt":0.22952514871004356,"score_spread":0.20717954555526769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319068962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002549934,0.000036475856,0.9818411,0.0014084751,0.0007243558,0.0018031392,0.000043737695,0.00079344935,0.010799356],"genre_scores_gemma":[0.9291475,0.03180215,0.019690584,0.007677643,0.00043232265,0.0064775213,0.0004203167,0.00035184404,0.0040001185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986767,0.000016913136,0.00031534344,0.00037621506,0.00020604134,0.00040878344],"domain_scores_gemma":[0.9993224,0.000060818467,0.000035234694,0.00040508938,0.000047044134,0.00012938822],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020078695,0.00026914311,0.00018600735,0.00021227785,0.00033120706,0.00008060405,0.00018410552,0.00006841657,0.00005398447],"category_scores_gemma":[1.408383e-7,0.00028224563,0.00006394502,0.00090740755,0.000010012233,0.000061218554,0.000007545135,0.00010870195,0.0001773567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003879378,0.000077336306,0.0000017549532,0.00056595716,0.00026284845,0.000005726579,0.00012429526,0.8998553,0.0000043472637,0.0020566038,0.0038186412,0.09318837],"study_design_scores_gemma":[0.0007955362,0.000096560216,0.00024560557,0.0001612668,0.00032652996,0.0000015338805,0.0005155131,0.8704583,0.00009249093,0.00060057966,0.12628226,0.00042384074],"about_ca_topic_score_codex":0.000010652801,"about_ca_topic_score_gemma":0.00011974444,"teacher_disagreement_score":0.9621505,"about_ca_system_score_codex":0.00008289469,"about_ca_system_score_gemma":0.0000041365615,"threshold_uncertainty_score":0.999963},"labels":[],"label_agreement":null},{"id":"W4319342119","doi":"10.1109/tnsm.2023.3243435","title":"Look-Ahead VNF-FG Embedding Framework for Latency-Sensitive Network Services","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); Concordia University","funders":"","keywords":"Computer science; Scalability; Embedding; Heuristics; Key (lock); Distributed computing; Heuristic; Latency (audio); Artificial intelligence; Computer security","score_opus":0.018016072625380516,"score_gpt":0.258097356027288,"score_spread":0.24008128340190749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319342119","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028933955,0.00018589184,0.98847914,0.0029182995,0.002650193,0.0008916027,0.00001269615,0.001065664,0.00090312905],"genre_scores_gemma":[0.6730056,0.005268741,0.29465812,0.022203965,0.0022591213,0.00093989103,0.000056573917,0.00019181242,0.0014162126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997133,0.000089513145,0.00042489974,0.0009210403,0.0003685831,0.0010629551],"domain_scores_gemma":[0.9981302,0.00064200937,0.00013594616,0.00076559663,0.00011445899,0.00021180316],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005696728,0.00042037162,0.00040058722,0.00018625439,0.0010381651,0.00034672237,0.0006694847,0.00020672398,0.000016391174],"category_scores_gemma":[0.0000011112058,0.00041679628,0.00017222809,0.0022332575,0.000031997493,0.0003371448,0.00004472976,0.0003456136,0.00013452482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009099218,0.000069748494,0.00004016724,0.0002943668,0.0002900191,0.000041842133,0.0008056443,0.86469615,0.0000013319332,0.024626194,0.0029783973,0.106065124],"study_design_scores_gemma":[0.0008680731,0.0002251724,0.0010516122,0.00077106134,0.00018399426,0.000009518075,0.00048845966,0.9359291,0.000024910745,0.041815273,0.017905103,0.0007276954],"about_ca_topic_score_codex":0.000049224273,"about_ca_topic_score_gemma":0.00022196204,"teacher_disagreement_score":0.693821,"about_ca_system_score_codex":0.000047577396,"about_ca_system_score_gemma":0.00001866223,"threshold_uncertainty_score":0.9998284},"labels":[],"label_agreement":null},{"id":"W4319993380","doi":"10.1109/tnsm.2023.3239522","title":"LogEncoder: Log-Based Contrastive Representation Learning for Anomaly Detection","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Anomaly detection; Benchmark (surveying); Event (particle physics); Artificial intelligence; Data mining; Class (philosophy); Encoding (memory); Anomaly (physics); Representation (politics); Sequence (biology); Machine learning; Pattern recognition (psychology)","score_opus":0.017937002940569196,"score_gpt":0.24845196631645983,"score_spread":0.23051496337589064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319993380","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023505202,0.000015352369,0.9736557,0.00069110875,0.0008320937,0.00064979994,0.0000016059803,0.00045408632,0.00019504086],"genre_scores_gemma":[0.99608076,0.00010507213,0.0027667743,0.0004695863,0.00006760524,0.00031847882,0.000005443227,0.000010639676,0.00017564805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988808,0.000072482355,0.00020560686,0.00040401006,0.00016342568,0.00027366096],"domain_scores_gemma":[0.99936026,0.00018508061,0.00006539535,0.00025517851,0.000080167854,0.000053923734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037144666,0.00012940864,0.00013763116,0.00013510435,0.00051044085,0.000082491184,0.0001641301,0.000061621264,0.0000035403577],"category_scores_gemma":[0.0000015277392,0.00012229587,0.000068523485,0.00089390663,0.000015713895,0.00018473796,0.000004659386,0.0001070189,0.000033175653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000674048,0.000031617798,0.00018600453,0.00013974326,0.000054044795,0.0000027469068,0.0001982143,0.82312685,0.000033472686,0.00007123768,0.000083764084,0.1760049],"study_design_scores_gemma":[0.0007693252,0.00020060115,0.0048288624,0.00004880957,0.00004108415,0.00000159952,0.00018262997,0.9904405,0.00092136266,0.00032977146,0.0020736868,0.00016177454],"about_ca_topic_score_codex":0.00004765227,"about_ca_topic_score_gemma":0.00013035123,"teacher_disagreement_score":0.97257555,"about_ca_system_score_codex":0.000041074163,"about_ca_system_score_gemma":0.000013273161,"threshold_uncertainty_score":0.4987081},"labels":[],"label_agreement":null},{"id":"W4321194411","doi":"10.1109/tnsm.2023.3246420","title":"Two-Level Closed Loops for RAN Slice Resources Management Serving Flying and Ground-Based Cars","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Provisioning; Computer science; Quality of service; Scheduling (production processes); Distributed computing; Computer network; Resource (disambiguation); Resource management (computing)","score_opus":0.03126605707441282,"score_gpt":0.24542615823738434,"score_spread":0.21416010116297152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321194411","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018823901,0.00017145755,0.97503746,0.002306669,0.0008372932,0.0010704264,0.000011721423,0.00066325703,0.0010778024],"genre_scores_gemma":[0.86108,0.0013928912,0.12354787,0.0112721175,0.0003322133,0.00090951874,0.000026184996,0.00010549458,0.0013337034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977117,0.00008342109,0.00035244366,0.000809593,0.0003497538,0.0006930934],"domain_scores_gemma":[0.9987017,0.00034972673,0.00009437947,0.00063107564,0.000058256246,0.00016488414],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006229525,0.0003408225,0.0002923508,0.00029409694,0.0010897528,0.0003969372,0.0005621009,0.000079202626,0.0000062551085],"category_scores_gemma":[6.5412695e-7,0.00034696813,0.00010279904,0.0014890144,0.000029471483,0.00024119826,0.000044995537,0.00017470242,0.000016491009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013523879,0.00010131523,0.00005357758,0.00076159975,0.00035155355,0.000052839685,0.0012017381,0.5346492,0.0000064074047,0.004428003,0.0011392843,0.45711926],"study_design_scores_gemma":[0.0036941061,0.00016621414,0.004126782,0.00041329322,0.0002621912,0.0000058867454,0.0010440595,0.96804774,0.000020751622,0.0020970667,0.01948465,0.0006372472],"about_ca_topic_score_codex":0.00012903177,"about_ca_topic_score_gemma":0.0004917282,"teacher_disagreement_score":0.8514896,"about_ca_system_score_codex":0.000047652677,"about_ca_system_score_gemma":0.000009528312,"threshold_uncertainty_score":0.99989825},"labels":[],"label_agreement":null},{"id":"W4327661826","doi":"10.1109/tnsm.2023.3258192","title":"Dynamic Joint VNF Forwarding Graph Composition and Embedding: A Deep Reinforcement Learning Framework","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Ericsson (Canada)","funders":"","keywords":"Computer science; Virtual network; Scalability; Distributed computing; Reinforcement learning; Embedding; Network virtualization; Heuristics; Computer network; Virtualization; Artificial intelligence; Cloud computing","score_opus":0.01264321239575909,"score_gpt":0.23582155007674077,"score_spread":0.2231783376809817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327661826","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075260927,0.00013944328,0.9887884,0.0013791685,0.000655371,0.00038168527,5.1582157e-7,0.0006261518,0.0005031424],"genre_scores_gemma":[0.9712487,0.002912796,0.023961343,0.0015140899,0.000055137498,0.00011520211,0.000009475668,0.000027346281,0.00015593095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982607,0.00006912159,0.0003082815,0.00054153136,0.00030058628,0.0005198082],"domain_scores_gemma":[0.9992172,0.00014825648,0.0000913829,0.00036096206,0.000039059414,0.0001431205],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036413805,0.00025620573,0.000241412,0.00027399848,0.0008540527,0.00028672983,0.00023714258,0.00009909858,0.000012301591],"category_scores_gemma":[8.0398723e-7,0.00025984383,0.000084755135,0.0013555128,0.000025117079,0.00024867602,0.000037283357,0.000352601,0.000032436314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020076079,0.00002303437,0.000018384719,0.0001021707,0.00011402094,0.000018974084,0.0006212672,0.92307127,0.000006543371,0.003370558,0.00011821578,0.07251548],"study_design_scores_gemma":[0.0004274462,0.00012346108,0.0006486543,0.00028857533,0.00007262502,0.000009650956,0.00026266754,0.99307877,0.000010396481,0.003785564,0.0009961566,0.0002960173],"about_ca_topic_score_codex":0.00001929428,"about_ca_topic_score_gemma":0.000027938584,"teacher_disagreement_score":0.96482706,"about_ca_system_score_codex":0.0000495992,"about_ca_system_score_gemma":0.0000059663175,"threshold_uncertainty_score":0.9999854},"labels":[],"label_agreement":null},{"id":"W4327785466","doi":"10.1109/tnsm.2023.3258692","title":"Accelerating Reinforcement Learning via Predictive Policy Transfer in 6G RAN Slicing","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Computer science; Slicing; Ran; Transfer of learning; Transfer (computing); Predictive power; Artificial intelligence; Computer network; World Wide Web; Parallel computing","score_opus":0.011969820286294628,"score_gpt":0.21158377832037964,"score_spread":0.19961395803408502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327785466","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08466588,0.00005015096,0.89995635,0.00029853708,0.0005768891,0.0004961042,0.0000014313858,0.0012199559,0.012734699],"genre_scores_gemma":[0.9967376,0.0015631989,0.0004385245,0.0003677609,0.00023315981,0.00020379016,0.000012506577,0.000073938594,0.0003695168],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983922,0.00006282148,0.0003846527,0.00031749657,0.00022797748,0.00061484624],"domain_scores_gemma":[0.9995253,0.00012634609,0.00002084284,0.00020532969,0.00002161118,0.00010058501],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030767923,0.0002809545,0.00024048678,0.00036929545,0.00030133312,0.00007391391,0.00015027403,0.00010761153,0.000022710605],"category_scores_gemma":[9.210947e-7,0.00031689435,0.000053832482,0.0016456317,0.000017097835,0.0001653779,0.0000058257147,0.00050254125,0.0000240444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027428643,0.000013429678,0.000044228804,0.00020124855,0.00011338435,0.000018139799,0.0010019402,0.9511074,0.00005576089,0.00017298537,0.000056058907,0.047188],"study_design_scores_gemma":[0.0007140519,0.00006094294,0.0007390616,0.00036127036,0.000041919157,0.000002199679,0.00040453416,0.99624544,0.00014729459,0.000048602258,0.0009505943,0.00028410516],"about_ca_topic_score_codex":0.00018379842,"about_ca_topic_score_gemma":0.00062419777,"teacher_disagreement_score":0.9120717,"about_ca_system_score_codex":0.00013831336,"about_ca_system_score_gemma":0.00000833852,"threshold_uncertainty_score":0.9999283},"labels":[],"label_agreement":null},{"id":"W4361983774","doi":"10.1109/tnsm.2023.3263831","title":"Retracted: Communication-Efficient Personalized Federated Meta-Learning in Edge Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":34,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"King Saud University","keywords":"Computer science; Overhead (engineering); Differential privacy; Edge device; Distributed computing; Upload; Personalization; Edge computing; Enhanced Data Rates for GSM Evolution; Autoencoder; Information privacy; Machine learning; Computer network; Artificial intelligence; Deep learning; Data mining; Cloud computing; Computer security","score_opus":0.037570142289888156,"score_gpt":0.26217562833415514,"score_spread":0.224605486044267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361983774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038360595,0.00043269785,0.95844096,0.032609094,0.00042998267,0.0005407154,0.000003967542,0.0018130299,0.0018934809],"genre_scores_gemma":[0.9517029,0.005979097,0.03897641,0.002311831,0.000034842255,0.00040045922,0.000038852526,0.00004137593,0.0005142575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791676,0.00027007394,0.00035476586,0.0005972204,0.0003244913,0.0005366667],"domain_scores_gemma":[0.99676794,0.0002982346,0.00010044621,0.0027010133,0.000059293376,0.0000730833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010655447,0.00024917765,0.00031011304,0.00032590982,0.0006393652,0.00028423607,0.0050930637,0.00016196551,0.000035557372],"category_scores_gemma":[0.00003359752,0.00024247158,0.0000834379,0.0032925394,0.000054231936,0.00023005687,0.0011359042,0.0008012876,0.00005211088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002795655,0.00012265332,0.00001774107,0.00005293924,0.0004403481,0.000035504727,0.00020955282,0.93701994,0.0000034635589,0.0011978464,0.014726616,0.04614541],"study_design_scores_gemma":[0.00048237998,0.00003226935,0.0005546858,0.0000698012,0.00011128089,0.0000030893966,0.00023201482,0.98982257,0.000013244549,0.0017478493,0.0066888705,0.00024194537],"about_ca_topic_score_codex":0.00010158875,"about_ca_topic_score_gemma":0.0002733075,"teacher_disagreement_score":0.9478668,"about_ca_system_score_codex":0.000078436424,"about_ca_system_score_gemma":0.000015315436,"threshold_uncertainty_score":0.9887704},"labels":[],"label_agreement":null},{"id":"W4361984681","doi":"10.1109/tnsm.2023.3264005","title":"VNF and CNF Placement in 5G: Recent Advances and Future Trends","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Zayed University","keywords":"Computer science; Virtual network; Scalability; Distributed computing; Virtualization; Cloud computing; Computer network; Flexibility (engineering); Operating system","score_opus":0.012367001563816994,"score_gpt":0.23387872267643628,"score_spread":0.2215117211126193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361984681","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1253215,0.011967176,0.7072279,0.076321386,0.037510615,0.0017652388,0.0000043942273,0.0016128945,0.03826893],"genre_scores_gemma":[0.4831865,0.4505961,0.032858513,0.01810554,0.007827816,0.00053758424,0.000035421726,0.00016115018,0.006691372],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99882483,0.00004490879,0.00019061651,0.000426284,0.00016340682,0.00034994382],"domain_scores_gemma":[0.9996014,0.000043723256,0.000036787227,0.00021579547,0.00001672682,0.00008551633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026713152,0.00016831796,0.00015305419,0.00023496387,0.0002872634,0.00013010747,0.00016741702,0.000045187568,0.000005429673],"category_scores_gemma":[1.1804876e-7,0.00015963701,0.000019063753,0.0012047334,0.000016046519,0.00020671698,0.000020893198,0.0001397441,0.000007225071],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022660946,0.000035731875,0.00004883172,0.0001080725,0.000027839806,0.000018783807,0.0010311395,0.016378812,0.0000011225517,0.0002580462,0.0016150875,0.98045385],"study_design_scores_gemma":[0.0013358993,0.0001732231,0.00888463,0.00014113491,0.00003870697,0.000009573865,0.0008176685,0.21276031,0.000018437102,0.0007454973,0.7746522,0.0004227559],"about_ca_topic_score_codex":0.000015103806,"about_ca_topic_score_gemma":0.00012108621,"teacher_disagreement_score":0.98003113,"about_ca_system_score_codex":0.000025457977,"about_ca_system_score_gemma":0.000005363012,"threshold_uncertainty_score":0.65098083},"labels":[],"label_agreement":null},{"id":"W4375928904","doi":"10.1109/tnsm.2023.3273991","title":"FLPK-BiSeNet: Federated Learning Based on Priori Knowledge and Bilateral Segmentation Network for Image Edge Extraction","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Pyramid (geometry); Artificial intelligence; Pooling; Segmentation; Context (archaeology); Image segmentation; Enhanced Data Rates for GSM Evolution; Fuse (electrical); Path (computing); Feature extraction; A priori and a posteriori; Pattern recognition (psychology); Data mining; Machine learning; Computer vision","score_opus":0.0111368626421753,"score_gpt":0.24693094575201557,"score_spread":0.23579408310984026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375928904","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1220237,0.00015099722,0.86884767,0.0004346596,0.0026909644,0.0013535974,0.000016095359,0.0022348685,0.002247422],"genre_scores_gemma":[0.99410796,0.0011994068,0.0029705311,0.0002176414,0.00035689515,0.00027383002,0.00010056505,0.000087023356,0.0006861618],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988645,0.000050258604,0.00024316354,0.0003275159,0.00012465603,0.0003899131],"domain_scores_gemma":[0.99959564,0.00012141021,0.00004848953,0.000115547075,0.000038580703,0.00008032862],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025485142,0.00025317914,0.00017840751,0.00018858645,0.0006575129,0.00015822583,0.000049184764,0.000114957315,0.000018586565],"category_scores_gemma":[5.589177e-7,0.0002625643,0.000052310104,0.0006294565,0.000012610154,0.0001896732,0.00000231532,0.00023765191,0.00003741884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010355343,0.00004057556,0.000016579756,0.00033336607,0.000087787055,0.000005534679,0.00010913215,0.9131819,0.00076496066,0.00001305996,0.0026213056,0.08272225],"study_design_scores_gemma":[0.0009323996,0.00014083143,0.0017410985,0.00018939206,0.00011826686,0.0000030697618,0.00012674404,0.9834197,0.0006960117,0.000044005134,0.012308547,0.00027994206],"about_ca_topic_score_codex":0.000009424082,"about_ca_topic_score_gemma":0.000070224625,"teacher_disagreement_score":0.87208426,"about_ca_system_score_codex":0.00008019629,"about_ca_system_score_gemma":0.0000067568103,"threshold_uncertainty_score":0.99998266},"labels":[],"label_agreement":null},{"id":"W4377707878","doi":"10.1109/tnsm.2023.3278937","title":"CorrFL: Correlation-Based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT Environment","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Unavailability; Computer science; Intersection (aeronautics); Benchmark (surveying); Artificial intelligence; Machine learning; Architecture; Artificial neural network; Feature (linguistics); Feature vector; Field (mathematics); Function (biology); Distributed computing; Data mining; Engineering; Geography; Mathematics","score_opus":0.02540857739997295,"score_gpt":0.24166090771348916,"score_spread":0.2162523303135162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377707878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49153438,0.000038741648,0.5025,0.0019945872,0.0011810092,0.0017782402,0.000034745088,0.00027858434,0.00065975386],"genre_scores_gemma":[0.99611306,0.00004438686,0.0022557597,0.0008513928,0.00013367715,0.00026678582,0.000016550448,0.000027678254,0.00029068234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984001,0.00009644829,0.0002956126,0.0004786163,0.0002324862,0.0004967458],"domain_scores_gemma":[0.99930227,0.00024929582,0.00006826101,0.00027556156,0.000004022759,0.000100589554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042388076,0.0002088931,0.00018269257,0.000044474524,0.00037177288,0.000031097756,0.0001430863,0.00007733994,0.00014599561],"category_scores_gemma":[0.0000010687226,0.00021086077,0.00007518999,0.00047716222,0.00006513272,0.000031481326,0.000011832359,0.00019162215,0.00007593963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011950827,0.000061059814,0.0026146497,0.000055444594,0.000019745876,0.000006650058,0.00021680731,0.9357106,0.0000038082878,0.0000061920437,0.0002975355,0.06088801],"study_design_scores_gemma":[0.0008023529,0.00016417625,0.009772164,0.000060959217,0.00005222958,0.0000014431215,0.00009984697,0.978578,0.00001873306,0.0003831926,0.009798321,0.00026861913],"about_ca_topic_score_codex":0.00014290522,"about_ca_topic_score_gemma":0.00057590014,"teacher_disagreement_score":0.5045787,"about_ca_system_score_codex":0.0001445094,"about_ca_system_score_gemma":0.0000038106853,"threshold_uncertainty_score":0.85986525},"labels":[],"label_agreement":null},{"id":"W4379931265","doi":"10.1109/tnsm.2023.3284206","title":"User-Centric Slice Allocation Scheme in 5G Networks and Beyond","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Computer science; Radio access network; C-RAN; Quality of service; Distributed computing; Computer network; User equipment; Resource allocation; Cloud computing; Flexibility (engineering); Ran; Slicing; Software deployment; Heuristic; Base station; Operating system","score_opus":0.010617136527503228,"score_gpt":0.2157930752356075,"score_spread":0.20517593870810427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379931265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01732845,0.00040618723,0.9755697,0.003447935,0.00089979713,0.0005291148,0.0000014563208,0.0004593882,0.0013579434],"genre_scores_gemma":[0.97666645,0.009181691,0.007683733,0.0055410042,0.00017429462,0.00018025431,0.000011001049,0.00003537886,0.0005261794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983224,0.000070696435,0.0002849906,0.0005695392,0.00023012777,0.0005221943],"domain_scores_gemma":[0.99919343,0.00015053211,0.000059369562,0.00043366913,0.000035994934,0.00012700564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039301126,0.00022413867,0.0002006122,0.0002696782,0.00029403338,0.0001900796,0.00033987523,0.00010205034,0.000008802068],"category_scores_gemma":[6.3353235e-7,0.00023077146,0.000038607766,0.0027263025,0.000020692172,0.00029305636,0.000023038046,0.00023867731,0.000034507535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031815336,0.00009651409,0.00035755153,0.000081186314,0.00006866999,0.000034474622,0.0002908774,0.7475702,0.0000019938263,0.006457878,0.0021397613,0.24286908],"study_design_scores_gemma":[0.000936357,0.00007070758,0.012026355,0.000093842245,0.0000337317,0.0000059636504,0.00012476718,0.9753352,0.0000055445907,0.00077286764,0.010268733,0.00032593517],"about_ca_topic_score_codex":0.00008614237,"about_ca_topic_score_gemma":0.00043436151,"teacher_disagreement_score":0.967886,"about_ca_system_score_codex":0.00003846175,"about_ca_system_score_gemma":0.000010721092,"threshold_uncertainty_score":0.9410587},"labels":[],"label_agreement":null},{"id":"W4381415999","doi":"10.1109/tnsm.2023.3287757","title":"Cost-Efficient Cluster Migration of VNFs for Service Function Chain Embedding","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); Concordia University","funders":"","keywords":"Computer science; Scalability; Virtual network; Network Functions Virtualization; Distributed computing; Latency (audio); Embedding; Network virtualization; Computer network; Software deployment; Virtualization; Operating system; Cloud computing","score_opus":0.029523655122839144,"score_gpt":0.2584166368381917,"score_spread":0.22889298171535258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381415999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010897524,0.00004196119,0.98281,0.0034216675,0.0012248937,0.0010450787,0.000008069658,0.00031859046,0.00023217851],"genre_scores_gemma":[0.97508794,0.00050989527,0.014274344,0.008458605,0.00026776828,0.0008226558,0.000034570967,0.000045665663,0.0004985679],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856585,0.000051196937,0.00031180168,0.00043908478,0.00025160445,0.0003804352],"domain_scores_gemma":[0.9991044,0.00018940854,0.000103327315,0.00040270275,0.00012105257,0.00007910607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044359796,0.00019160516,0.000193587,0.00020118964,0.00036594225,0.000092150185,0.00027342944,0.00007902209,0.0000068264612],"category_scores_gemma":[6.53573e-7,0.00018752334,0.000081082435,0.0016313539,0.000010576402,0.00014238086,0.000016285176,0.00010242343,0.000026762835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000857504,0.00006683248,0.000007939656,0.00019405752,0.00007194461,0.000001103893,0.00047699062,0.9215085,0.0000104608225,0.0016364624,0.0016203187,0.07431964],"study_design_scores_gemma":[0.0008023043,0.00012686456,0.0006033855,0.00011298191,0.00007763929,0.0000013711798,0.00022402655,0.9863963,0.00004705677,0.0006156522,0.010800744,0.00019168774],"about_ca_topic_score_codex":0.00005889019,"about_ca_topic_score_gemma":0.00034757957,"teacher_disagreement_score":0.9685357,"about_ca_system_score_codex":0.000034858287,"about_ca_system_score_gemma":0.000010497188,"threshold_uncertainty_score":0.764698},"labels":[],"label_agreement":null},{"id":"W4382561979","doi":"10.1109/tnsm.2023.3282130","title":"Guest Editorial: Special Section on the Latest Developments in Federated Learning for the Management of Networked Systems and Resources","year":2023,"lang":"en","type":"editorial","venue":"IEEE Transactions on Network and Service Management","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Concordia University","funders":"","keywords":"Computer science; Special section; Context (archaeology); Learning Management; Data science; Section (typography); Service (business); Knowledge management; World Wide Web","score_opus":0.02021955424056407,"score_gpt":0.2444029818308377,"score_spread":0.22418342759027363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382561979","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006345796,0.00011448099,0.05057667,0.0027771401,0.94396335,0.0016416745,0.000022195643,0.00032326105,0.00051777746],"genre_scores_gemma":[0.0022956908,0.010623233,0.002154445,0.00008955913,0.98278254,0.001246909,0.000062084146,0.00009251272,0.00065302424],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99714875,0.00020744512,0.00055220374,0.00079525786,0.000785346,0.00051099254],"domain_scores_gemma":[0.99562436,0.0021852881,0.00030420665,0.0017173664,0.00012591676,0.000042877724],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017455375,0.00039663172,0.00038025933,0.00022097612,0.00092210446,0.0005527458,0.004513307,0.00041829483,0.0000011625572],"category_scores_gemma":[0.00007760506,0.0002876796,0.000056415298,0.0012553907,0.000064051346,0.00014416824,0.0010891289,0.0009958474,0.000005732985],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011966647,0.000042231644,0.0000015304774,0.0004704879,0.0004257536,0.0000075146386,0.00011018292,0.08812734,1.4762921e-7,0.00010300008,0.9035448,0.0070473934],"study_design_scores_gemma":[0.0007101322,0.00013505228,0.000051633,0.001209413,0.00012886438,4.1404397e-7,0.00040597157,0.08258221,0.0000022428057,0.00043395362,0.9140461,0.00029402925],"about_ca_topic_score_codex":0.00019934823,"about_ca_topic_score_gemma":0.0005559183,"teacher_disagreement_score":0.04842222,"about_ca_system_score_codex":0.00014832409,"about_ca_system_score_gemma":0.000025798246,"threshold_uncertainty_score":0.99995756},"labels":[],"label_agreement":null},{"id":"W4382561988","doi":"10.1109/tnsm.2023.3280230","title":"Guest Editorial: Special Section on Machine Learning and Artificial Intelligence for Managing Networks, Systems, and Services—Part II","year":2023,"lang":"en","type":"editorial","venue":"IEEE Transactions on Network and Service Management","topic":"Scientific Measurement and Uncertainty Evaluation","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Western University; University of Saskatchewan; Ontario Tech University; Dalhousie University","funders":"Lawrence Berkeley National Laboratory","keywords":"Computer science; Special section; Big data; Artificial intelligence; Marketing and artificial intelligence; Section (typography); Data science; Telecommunications; Intelligent decision support system; Engineering; Operating system","score_opus":0.08531320931578722,"score_gpt":0.34035254902201284,"score_spread":0.2550393397062256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382561988","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009238124,0.00022569935,0.050645467,0.0004848357,0.9462611,0.0014178571,0.00008421241,0.0001574045,0.00063104805],"genre_scores_gemma":[0.0009865251,0.0030966818,0.00008838247,0.000089729256,0.9916213,0.00026101083,0.0002283653,0.00007363135,0.0035543423],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9933521,0.00043613277,0.0011608123,0.0015869207,0.0028279424,0.0006360674],"domain_scores_gemma":[0.9955489,0.0025391018,0.00054578745,0.000489717,0.0006608065,0.00021570454],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.008413273,0.00056325004,0.0006658106,0.000630028,0.0022185498,0.0015515293,0.00044240875,0.00054719136,0.000042398377],"category_scores_gemma":[0.00008252535,0.000508912,0.00011902371,0.0012520939,0.000071810726,0.00026693247,0.000044698467,0.0008619308,0.000037804944],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005576857,0.000057168094,0.0000021692235,0.0002912379,0.00014583018,0.0000027411988,0.00028150744,0.26051354,7.7239406e-7,0.00017772552,0.6947082,0.04326138],"study_design_scores_gemma":[0.00034058272,0.00031943497,0.0000032238188,0.00045363992,0.00028702605,5.187388e-7,0.0008777839,0.23629273,0.0000017712554,0.0011539783,0.75989026,0.00037906153],"about_ca_topic_score_codex":0.00023565673,"about_ca_topic_score_gemma":0.0037173412,"teacher_disagreement_score":0.065182,"about_ca_system_score_codex":0.00014101947,"about_ca_system_score_gemma":0.00004215913,"threshold_uncertainty_score":0.99973625},"labels":[],"label_agreement":null},{"id":"W4383220203","doi":"10.1109/tnsm.2023.3292392","title":"Leveraging Graph Neural Networks for SLA Violation Prediction in Cloud Computing","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"European Commission; Hellenic Academic Libraries Link","keywords":"Computer science; Cloud computing; Graph; Artificial neural network; Distributed computing; Theoretical computer science; Computer network; Artificial intelligence; Operating system","score_opus":0.018773134493756487,"score_gpt":0.22259422694904152,"score_spread":0.20382109245528504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383220203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020000882,0.000039970313,0.9774917,0.0006228741,0.0009767091,0.0003496133,0.0000016560718,0.0002489922,0.000267618],"genre_scores_gemma":[0.9947068,0.0001386201,0.0042808317,0.00052034436,0.00019285436,0.000042961547,0.000012377749,0.00001416562,0.000091036505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986978,0.000049361763,0.00030845808,0.00040560547,0.00014851466,0.00039021397],"domain_scores_gemma":[0.99950844,0.0000971669,0.00007645227,0.00022735569,0.000036450223,0.000054109147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004766567,0.00015261701,0.00016581816,0.00023789595,0.00044263044,0.00014150131,0.00020847165,0.00005105346,0.000003771811],"category_scores_gemma":[4.0498523e-7,0.0001584614,0.00007925476,0.0017391897,0.000009534081,0.00023181291,0.000013612095,0.00014391838,0.000003741528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014774876,0.000015696713,0.000076151795,0.000033253065,0.000035973157,0.0000028732593,0.0002870246,0.81419986,0.0000015416724,0.00046606312,0.00014148014,0.18472533],"study_design_scores_gemma":[0.00042193965,0.000052366973,0.0031017165,0.00006666835,0.00003492196,0.0000017777552,0.00020343378,0.9950337,0.0000032153202,0.0005170331,0.00042460967,0.00013862907],"about_ca_topic_score_codex":0.000044756296,"about_ca_topic_score_gemma":0.00013319316,"teacher_disagreement_score":0.97470593,"about_ca_system_score_codex":0.000030574374,"about_ca_system_score_gemma":0.000003475035,"threshold_uncertainty_score":0.6461868},"labels":[],"label_agreement":null},{"id":"W4383503654","doi":"10.1109/tnsm.2023.3292986","title":"Proactive VNF Scaling and Placement in 5G O-RAN Using ML","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Orchestration; Radio access network; Reinforcement learning; C-RAN; Computer network; Ran; Distributed computing; Resource allocation; Latency (audio); Flexibility (engineering); Software deployment; Base station; Artificial intelligence; Operating system; Telecommunications","score_opus":0.02614641893240011,"score_gpt":0.24359960014391116,"score_spread":0.21745318121151105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383503654","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09387633,0.00012613091,0.9024562,0.001082128,0.0006274057,0.00061450293,0.0000023040518,0.0002974075,0.0009175799],"genre_scores_gemma":[0.98597956,0.0011893875,0.010959724,0.0014804408,0.00009121542,0.00010693243,0.0000025258116,0.00002525771,0.00016498286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985594,0.00006222201,0.00022973857,0.0005001435,0.00021673446,0.00043172998],"domain_scores_gemma":[0.99943614,0.00010767977,0.000045910314,0.00029367578,0.000022835029,0.00009373506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035029187,0.00019296588,0.00018334876,0.0002222304,0.00032094953,0.00015363064,0.00021263231,0.000059941234,0.000006752138],"category_scores_gemma":[3.907196e-7,0.00019181606,0.000033318294,0.0013844501,0.000019231331,0.00021827631,0.000019369765,0.00017599412,0.0000121286275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046616453,0.000075283344,0.00010895371,0.00009096238,0.000091487855,0.000036380494,0.001109469,0.8468578,0.0000067094884,0.0008688181,0.00016606528,0.15054147],"study_design_scores_gemma":[0.0009965203,0.00007527632,0.003610164,0.00020053635,0.00005064981,0.000006656759,0.0005549675,0.99167514,0.000041416766,0.0009191097,0.0015542026,0.00031538965],"about_ca_topic_score_codex":0.00012877774,"about_ca_topic_score_gemma":0.00024681585,"teacher_disagreement_score":0.8921032,"about_ca_system_score_codex":0.000053963344,"about_ca_system_score_gemma":0.000011798145,"threshold_uncertainty_score":0.7822032},"labels":[],"label_agreement":null},{"id":"W4383503677","doi":"10.1109/tnsm.2023.3293027","title":"Robust and Reliable SFC Placement in Resource-Constrained Multi-Tenant MEC-Enabled Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Distributed computing; Quality of service; Resilience (materials science); Orchestration; Slicing; Multitenancy; Mobile edge computing; Heuristic; Computer network; Server; Software","score_opus":0.02259068628376834,"score_gpt":0.21402917290891865,"score_spread":0.1914384866251503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383503677","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003024847,0.0002536194,0.9903028,0.0031427448,0.00070222217,0.0008278349,0.000003327403,0.00056069373,0.0011818954],"genre_scores_gemma":[0.88424927,0.014962766,0.07735315,0.014331509,0.00047337156,0.00109357,0.00004730511,0.00017738882,0.00731166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99750787,0.000097988246,0.00044330308,0.000810209,0.0002883892,0.0008522606],"domain_scores_gemma":[0.9989133,0.00018710355,0.000085885804,0.0005780152,0.000036912454,0.00019880637],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00077835505,0.00033941778,0.0003393573,0.00030043305,0.00046543364,0.0002425775,0.00041982517,0.00013539387,0.000021641166],"category_scores_gemma":[0.0000010523412,0.00028707337,0.00006231909,0.002056481,0.000041053503,0.00022113799,0.000049843045,0.0003439304,0.00002576514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007579017,0.00010386254,0.000081175844,0.00007649085,0.00007750783,0.00007725092,0.00027059676,0.95170534,0.0000010567647,0.00068134407,0.003087371,0.043762233],"study_design_scores_gemma":[0.0020438274,0.00013699112,0.0014156605,0.00024375274,0.00004819279,0.000009130655,0.00032497407,0.97876745,0.0000051848474,0.00021155576,0.016412092,0.0003812054],"about_ca_topic_score_codex":0.000093134186,"about_ca_topic_score_gemma":0.00061374356,"teacher_disagreement_score":0.9129497,"about_ca_system_score_codex":0.000060643946,"about_ca_system_score_gemma":0.000018168452,"threshold_uncertainty_score":0.99995816},"labels":[],"label_agreement":null},{"id":"W4383903547","doi":"10.1109/tnsm.2023.3293806","title":"ML Models for Detecting QoE Degradation in Low-Latency Applications: A Cloud-Gaming Case Study","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Anomaly detection; Robustness (evolution); Cloud computing; Data mining; Latency (audio); Machine learning; Metrics; Artificial intelligence; Real-time computing; Computer network","score_opus":0.02832418174741278,"score_gpt":0.2668916230136526,"score_spread":0.23856744126623983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383903547","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034671597,0.000016908341,0.9617305,0.0003460532,0.00012573405,0.0023629915,0.0000029251057,0.0005391664,0.00020411993],"genre_scores_gemma":[0.9738206,0.0001744036,0.020671882,0.00026350166,0.000054650118,0.004836847,0.0000024965802,0.000019270063,0.00015631528],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986727,0.000042704658,0.00033420915,0.000517452,0.00013846502,0.0002944174],"domain_scores_gemma":[0.9992437,0.00010023266,0.00008006458,0.0004585822,0.000057496694,0.000059945178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042986393,0.00016011513,0.00014261626,0.0002837015,0.00063402735,0.00012140847,0.00026539652,0.00005402178,0.0000016097634],"category_scores_gemma":[3.34981e-7,0.0001728903,0.000049092345,0.0019309935,0.0000090272015,0.0002730499,0.000015250005,0.00013446473,0.000009931647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014836301,0.00029718212,0.000015393529,0.0001319057,0.00004922351,0.000046145637,0.0009526524,0.5025407,0.000011771143,0.0038096688,0.000041922787,0.4920886],"study_design_scores_gemma":[0.0005211525,0.000116049116,0.00006613121,0.000045750356,0.000038304373,0.000033102562,0.0022199033,0.99084026,0.0000802545,0.0052550384,0.0005667237,0.00021733175],"about_ca_topic_score_codex":0.00021840868,"about_ca_topic_score_gemma":0.0009270251,"teacher_disagreement_score":0.94105864,"about_ca_system_score_codex":0.000056192184,"about_ca_system_score_gemma":0.000010580301,"threshold_uncertainty_score":0.7050262},"labels":[],"label_agreement":null},{"id":"W4385246573","doi":"10.1109/tnsm.2023.3298533","title":"Unknown, Atypical and Polymorphic Network Intrusion Detection: A Systematic Survey","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Intrusion detection system; Computer science; Artificial intelligence","score_opus":0.01785447269983144,"score_gpt":0.21881637959366343,"score_spread":0.20096190689383198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385246573","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045649495,0.00036899213,0.9477086,0.0012490164,0.0028103883,0.0010270636,0.0000026446749,0.0007565433,0.00042725352],"genre_scores_gemma":[0.99459255,0.002170101,0.0009344998,0.0015004417,0.00025985762,0.00015796651,0.0000038070937,0.000024469673,0.0003563236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976416,0.00043154342,0.0004247516,0.0006225193,0.00035135602,0.0005282297],"domain_scores_gemma":[0.9988057,0.00030006302,0.00010711613,0.00053761026,0.00006904517,0.00018047975],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011745329,0.00027540608,0.00035377376,0.00020615438,0.00094591134,0.00027236046,0.00033787003,0.00014208967,0.000018130213],"category_scores_gemma":[0.0000024908422,0.00025648728,0.00006608514,0.0025822695,0.0000377589,0.00028230937,0.00004785572,0.0003068549,0.00011009697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008577166,0.00034222635,0.00014223807,0.009209306,0.0008377044,0.00014940188,0.0014785656,0.5792727,0.000043814965,0.014657086,0.0040481645,0.3889611],"study_design_scores_gemma":[0.00084584084,0.00033303624,0.008591857,0.0014722127,0.00013212148,0.000059959653,0.000076431905,0.98213786,0.000049302744,0.0027829974,0.002965375,0.0005530264],"about_ca_topic_score_codex":0.00008475795,"about_ca_topic_score_gemma":0.0017523867,"teacher_disagreement_score":0.948943,"about_ca_system_score_codex":0.000041957825,"about_ca_system_score_gemma":0.000010625751,"threshold_uncertainty_score":0.99998873},"labels":[],"label_agreement":null},{"id":"W4385819637","doi":"10.1109/tnsm.2023.3304894","title":"DR-PIFO: A Dynamic Ranking Packet Scheduler Using a Push-In-First-Out Queue","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Network packet; Forwarding plane; Scheduling (production processes); Distributed computing; Software-defined networking; Packet processing; Queue; Network scheduler; Computer network; Processing delay; Transmission delay","score_opus":0.023966883163184877,"score_gpt":0.2527335447875491,"score_spread":0.22876666162436424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385819637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035826117,0.00015496765,0.95814914,0.0026926317,0.0015027698,0.00049805286,0.0000030656167,0.00055339816,0.00061984535],"genre_scores_gemma":[0.9804894,0.0015337712,0.01472847,0.0025249203,0.000121706136,0.00013589059,0.0000054742045,0.000050311002,0.00041006174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786353,0.000082465456,0.0003504714,0.0006711865,0.00032607582,0.00070624496],"domain_scores_gemma":[0.99902,0.00014417124,0.00007508546,0.0006023162,0.000038345097,0.00012011077],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047101837,0.00029253465,0.00028160313,0.0003506953,0.00048316355,0.00024891735,0.0005047746,0.00010762572,0.000020332347],"category_scores_gemma":[7.0264247e-7,0.00029763847,0.00009315229,0.0022252016,0.000023726494,0.0003056247,0.000033347133,0.0002897418,0.00009473705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000404468,0.000095017844,0.00011437381,0.00014841897,0.000112825204,0.00010469647,0.00081931986,0.93674093,0.0000075754006,0.0013077741,0.00039875187,0.06010984],"study_design_scores_gemma":[0.0009471232,0.00004639906,0.0012772962,0.00034309545,0.000052902018,0.000008375153,0.00014282552,0.9913624,0.0000089929135,0.0017560684,0.0036863433,0.00036818348],"about_ca_topic_score_codex":0.00013028574,"about_ca_topic_score_gemma":0.0021725318,"teacher_disagreement_score":0.9446633,"about_ca_system_score_codex":0.00010009664,"about_ca_system_score_gemma":0.00002166593,"threshold_uncertainty_score":0.99994755},"labels":[],"label_agreement":null},{"id":"W4386025630","doi":"10.1109/tnsm.2023.3307013","title":"Cost-Efficient and Trust-Aware Virtual Network Embedding for Dense Industrial IoT Systems Using Multiagent Systems","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Embedding; Internet of Things; Distributed computing; Computer network; Embedded system; Artificial intelligence","score_opus":0.07637608821862601,"score_gpt":0.282548393209856,"score_spread":0.20617230499122996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386025630","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042899523,0.00038524412,0.9471992,0.0002483923,0.006361776,0.002359011,0.000023108621,0.0004898751,0.000033819124],"genre_scores_gemma":[0.99253553,0.00046406264,0.0038640848,0.00064513076,0.0012769275,0.00077284576,0.00001342848,0.000072829505,0.00035518312],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99723977,0.00015661816,0.00052667875,0.0008035476,0.00039561244,0.0008777881],"domain_scores_gemma":[0.9985748,0.000405457,0.00016396571,0.00051644037,0.000093822426,0.00024550306],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008523741,0.0003832081,0.0004298867,0.00021237157,0.0010733841,0.00054880575,0.00038464274,0.00019854614,0.000002692007],"category_scores_gemma":[0.0000017891125,0.00037381612,0.0000992882,0.0014045113,0.0000326917,0.000093212104,0.00004390228,0.00026264385,0.000011716722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007747189,0.000050377676,0.00003368058,0.00014924232,0.00017592411,0.000025668804,0.00028485863,0.9678564,0.0000011939226,0.0011206771,0.0016265122,0.028597984],"study_design_scores_gemma":[0.0015041241,0.00014875185,0.00006365801,0.00044423802,0.00015264843,0.000018514735,0.00069599366,0.98855245,0.0000023408118,0.000025759735,0.007999013,0.00039247924],"about_ca_topic_score_codex":0.00013655038,"about_ca_topic_score_gemma":0.000039845643,"teacher_disagreement_score":0.949636,"about_ca_system_score_codex":0.00011556464,"about_ca_system_score_gemma":0.000032233216,"threshold_uncertainty_score":0.9998714},"labels":[],"label_agreement":null},{"id":"W4386025681","doi":"10.1109/tnsm.2023.3306971","title":"Comprehensive Performance and Robustness Analysis of Expander-Based Data Centers","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Concordia University","keywords":"Computer science; Robustness (evolution)","score_opus":0.03663001884870668,"score_gpt":0.2482290247677811,"score_spread":0.21159900591907443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386025681","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38406223,0.00003761206,0.61401016,0.0009954707,0.0002388293,0.00019650515,0.0000068995887,0.00016382178,0.0002884804],"genre_scores_gemma":[0.99579287,0.00035789347,0.0029637455,0.0006929506,0.00001924205,0.000014395448,0.000015492846,0.000009454647,0.00013397852],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986107,0.00005282885,0.00024139084,0.0005399161,0.00026883296,0.00028637127],"domain_scores_gemma":[0.9987769,0.00010155806,0.0000787949,0.0009270597,0.000041197763,0.000074490774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025378418,0.00016756491,0.0002571267,0.0004321359,0.0002801827,0.000081795995,0.00066398486,0.000033394528,0.000004105811],"category_scores_gemma":[1.802098e-7,0.00015706677,0.000052571424,0.0026606661,0.000036291487,0.000046357756,0.000083042745,0.00009439564,0.0000035464197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017002061,0.000052117473,0.00014418869,0.0002055669,0.00058977574,0.0000065960785,0.0001306683,0.91386086,0.0000018130434,0.00005739018,0.00025406622,0.084679954],"study_design_scores_gemma":[0.000438677,0.000052816584,0.011249477,0.00007436242,0.0004096712,5.6129517e-7,0.00021948056,0.9854175,0.00000988472,0.000006426243,0.0019661977,0.00015498059],"about_ca_topic_score_codex":0.00004421933,"about_ca_topic_score_gemma":0.000054025037,"teacher_disagreement_score":0.6117306,"about_ca_system_score_codex":0.000013593799,"about_ca_system_score_gemma":0.000006466502,"threshold_uncertainty_score":0.6404997},"labels":[],"label_agreement":null},{"id":"W4386025741","doi":"10.1109/tnsm.2023.3306179","title":"Optimizing Age of Information in RIS-Empowered Uplink Cooperative NOMA Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Age of Information Optimization","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Telecommunications link; Noma; Computer science; Base station; Convex optimization; Optimization problem; Transmitter power output; Reduction (mathematics); Power (physics); Channel (broadcasting); Mathematical optimization; Power control; Regular polygon; Computer network; Mathematics; Algorithm; Transmitter; Physics","score_opus":0.010089181112721468,"score_gpt":0.2159558038780796,"score_spread":0.20586662276535814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386025741","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017694464,0.000011729027,0.9922965,0.00058409706,0.0003690454,0.0004395317,0.0000017771272,0.0001720811,0.004355755],"genre_scores_gemma":[0.9246247,0.0011729926,0.07184195,0.0019514741,0.000032423886,0.00012668075,0.00006355709,0.000014563707,0.00017165259],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883884,0.00004575795,0.00045994323,0.00016817584,0.0001952233,0.0002920565],"domain_scores_gemma":[0.9993717,0.000060249873,0.00013130628,0.0002888448,0.000086339576,0.000061537605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003941721,0.00014779181,0.0001675786,0.0003598794,0.00017346068,0.00013792356,0.00029955333,0.00007111067,0.000013238317],"category_scores_gemma":[0.0000010145608,0.00015271641,0.00003481658,0.0018971651,0.000017868555,0.0012765324,0.000017936094,0.00014508203,0.00003786377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002156563,0.00002103898,0.0000053045273,0.00004994335,0.000026849139,0.0000049370988,0.0020340218,0.9626625,8.5935864e-7,0.0014661625,0.00015856879,0.033548214],"study_design_scores_gemma":[0.0006235672,0.00005355913,0.0005920817,0.00008738376,0.0000100239995,0.000001086188,0.00039788024,0.99713427,0.000025266112,0.000070744914,0.00085877883,0.00014535352],"about_ca_topic_score_codex":0.000026626652,"about_ca_topic_score_gemma":0.000057846974,"teacher_disagreement_score":0.92285526,"about_ca_system_score_codex":0.000049737915,"about_ca_system_score_gemma":0.000015038872,"threshold_uncertainty_score":0.62275946},"labels":[],"label_agreement":null},{"id":"W4386232099","doi":"10.1109/tnsm.2023.3308065","title":"Performance Modeling and Joint Resource Allocation Algorithms for Online Virtual Network Embedding","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Network virtualization; Resource allocation; Blocking (statistics); Distributed computing; Benchmark (surveying); Virtualization; Key (lock); Node (physics); Virtual network; Resource management (computing); Process (computing); Network topology; Algorithm; Computer network; Cloud computing","score_opus":0.03510086193262814,"score_gpt":0.2512799185335226,"score_spread":0.21617905660089448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386232099","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0259599,0.000114673,0.9705161,0.0017168125,0.0006257398,0.00049669796,0.000005002405,0.00044236978,0.00012270082],"genre_scores_gemma":[0.85648996,0.0052205035,0.13140875,0.004736715,0.0009650319,0.0003644051,0.00005808675,0.00007557226,0.00068096543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983053,0.000040806604,0.00032944893,0.0005598263,0.00022862165,0.0005359695],"domain_scores_gemma":[0.9992566,0.00012464526,0.00006904657,0.0003742217,0.000057347035,0.00011812742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005278469,0.00023211134,0.00022545413,0.00013701363,0.0007200966,0.0001673967,0.0002472064,0.00008223392,0.0000023110374],"category_scores_gemma":[7.8279993e-7,0.00023190289,0.00005885701,0.0009758785,0.000016899407,0.00024964986,0.000029291172,0.00017421941,0.000008021478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023430957,0.000024711337,0.0000043993036,0.000063814725,0.000046111756,0.0000018387972,0.00017092514,0.73841923,9.102189e-7,0.0005409277,0.0006303231,0.26007336],"study_design_scores_gemma":[0.00056342693,0.00015708842,0.00019886247,0.00018731997,0.000052342923,0.0000042815577,0.00014964478,0.9929567,0.0000037152558,0.00047721915,0.004995054,0.00025433456],"about_ca_topic_score_codex":0.000015291518,"about_ca_topic_score_gemma":0.000047503137,"teacher_disagreement_score":0.83910733,"about_ca_system_score_codex":0.00003150022,"about_ca_system_score_gemma":0.000011295046,"threshold_uncertainty_score":0.9456725},"labels":[],"label_agreement":null},{"id":"W4386362960","doi":"10.1109/tnsm.2023.3310790","title":"Age of Information Optimization in RIS-Assisted Wireless Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Age of Information Optimization","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Optimization problem; Wireless; Scheduling (production processes); Wireless network; Network packet; Distributed computing; Base station; Mathematical optimization; Computer network; Algorithm","score_opus":0.00966022925799449,"score_gpt":0.2061082348590664,"score_spread":0.19644800560107192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386362960","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012094073,0.000005215449,0.9941183,0.00047568194,0.00034386833,0.00037015878,0.0000016815129,0.00020671058,0.003269027],"genre_scores_gemma":[0.9223331,0.0015085094,0.074139066,0.0015937813,0.000030580344,0.00013090599,0.00011094118,0.000015932319,0.00013713712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988611,0.00005120168,0.00044467105,0.00014951116,0.00023306697,0.000260455],"domain_scores_gemma":[0.9993836,0.000046531994,0.00014768749,0.00029177096,0.00007542503,0.000054983193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003747285,0.00012979758,0.00014618944,0.00041499076,0.00014042741,0.00011114269,0.00028106646,0.000074552954,0.000011361884],"category_scores_gemma":[6.91467e-7,0.00013779997,0.00003185068,0.0023834154,0.000014685039,0.0011829938,0.000013401484,0.000110809255,0.00001978817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015942407,0.000024039835,0.000010702918,0.00006940924,0.000018068255,0.0000029036564,0.0005146224,0.9109457,3.8056984e-7,0.0012794641,0.00011191094,0.08700687],"study_design_scores_gemma":[0.00053760625,0.000031373907,0.0020358723,0.00008143486,0.000011012419,0.0000012598815,0.00018343594,0.9965363,0.00000914264,0.000060935196,0.0003856631,0.00012594841],"about_ca_topic_score_codex":0.000030742245,"about_ca_topic_score_gemma":0.00006527635,"teacher_disagreement_score":0.92112374,"about_ca_system_score_codex":0.000049391692,"about_ca_system_score_gemma":0.000012096775,"threshold_uncertainty_score":0.561932},"labels":[],"label_agreement":null},{"id":"W4387331371","doi":"10.1109/tnsm.2023.3318406","title":"EV Charging Infrastructure Discovery to Contextualize Its Deployment Security","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software Reliability and Analysis Research","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; Concordia University","funders":"Concordia University","keywords":"Software deployment; Computer science; Computer security; Computer network; Telecommunications","score_opus":0.01603467153867251,"score_gpt":0.26332560933928556,"score_spread":0.24729093780061306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387331371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042981815,0.000070519396,0.94818556,0.006733737,0.0004634923,0.0005468664,0.000009695099,0.0004316778,0.00057663437],"genre_scores_gemma":[0.99350107,0.0012354256,0.0012359796,0.003082947,0.000079064,0.00014483358,0.0000039737206,0.000016116986,0.0007006084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813735,0.00010344749,0.00023332343,0.000566924,0.000470757,0.00048819568],"domain_scores_gemma":[0.99906623,0.00013521696,0.000032516517,0.00051777833,0.000065315966,0.00018292916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004397156,0.00018827831,0.00020646476,0.00024099695,0.00041058377,0.0003406351,0.0005426616,0.00005420305,0.000029097158],"category_scores_gemma":[0.0000020308314,0.00017283423,0.000087286346,0.0021743318,0.00001471548,0.0003450315,0.000034695342,0.0002076295,0.00016829393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000731736,0.00016684656,0.00015327195,0.00041519964,0.00042344638,0.00006575316,0.0027645214,0.7419848,0.000056513076,0.007882629,0.004331757,0.24168213],"study_design_scores_gemma":[0.0014893005,0.00030123425,0.007094462,0.00041015155,0.00015426622,0.00000847762,0.0012603408,0.9156516,0.0008581986,0.0077384464,0.063999586,0.0010338873],"about_ca_topic_score_codex":0.000046865167,"about_ca_topic_score_gemma":0.00015126927,"teacher_disagreement_score":0.95051926,"about_ca_system_score_codex":0.00006684233,"about_ca_system_score_gemma":0.000018676046,"threshold_uncertainty_score":0.70479757},"labels":[],"label_agreement":null},{"id":"W4387448769","doi":"10.1109/tnsm.2023.3315095","title":"Guest Editors’ Introduction: Special Section on Robust and Reliable Networks of the Future","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fundação para a Ciência e a Tecnologia; University of California, Davis; University of Waterloo; National Science Foundation","keywords":"Computer science; Cloud computing; Virtualization; Software-defined networking; Special section; Distributed computing; Convergence (economics); Network virtualization; The Internet; Computer network; Internet of Things; Telecommunications; Computer security; World Wide Web; Operating system","score_opus":0.0080217975727155,"score_gpt":0.18842827150963393,"score_spread":0.18040647393691842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387448769","genre_codex":"methods","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005481514,0.00025442347,0.73306894,0.030024625,0.22650252,0.00109212,0.000006595918,0.00068411813,0.0028851314],"genre_scores_gemma":[0.28417012,0.016491214,0.007114957,0.007847717,0.67892075,0.00033632317,0.000026730972,0.00014398781,0.004948227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986491,0.00006150303,0.00023145138,0.00046201335,0.00028175712,0.00031417253],"domain_scores_gemma":[0.9991874,0.00006603098,0.00008637178,0.00053750124,0.00005365316,0.000069014175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028774363,0.00019204151,0.00017425888,0.00010537603,0.0005549045,0.000109041204,0.00031013283,0.00011172942,0.00001688037],"category_scores_gemma":[4.722349e-7,0.00015159823,0.00006236758,0.001677326,0.00003514399,0.00016521299,0.000025652931,0.00030527855,0.0000104290175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040221024,0.000046074787,0.000032595293,0.000041754683,0.00005152595,0.000002757075,0.00010807806,0.7909954,6.8076946e-7,0.001460721,0.18096901,0.026251148],"study_design_scores_gemma":[0.00088436477,0.00029547574,0.0067387647,0.00017091472,0.00011352602,0.000013836345,0.0002397469,0.29008883,0.00003178734,0.0006256657,0.7003962,0.00040089566],"about_ca_topic_score_codex":0.00002110121,"about_ca_topic_score_gemma":0.0001582327,"teacher_disagreement_score":0.725954,"about_ca_system_score_codex":0.000031042877,"about_ca_system_score_gemma":0.000009063722,"threshold_uncertainty_score":0.61819965},"labels":[],"label_agreement":null},{"id":"W4387986615","doi":"10.1109/tnsm.2023.3328016","title":"Smart Dynamic Pricing and Cooperative Resource Management for Mobility-Aware and Multi-Tier Slice-Enabled 5G and Beyond Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Cellular network; Computer network; Resource allocation; Optimization problem; Reinforcement learning; Task (project management); Resource management (computing); Shared resource; Distributed computing","score_opus":0.012150300286742143,"score_gpt":0.23187995122385063,"score_spread":0.2197296509371085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387986615","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012088234,0.000620455,0.982863,0.0015365137,0.0004386288,0.0016628435,0.000008682975,0.00034551378,0.00043611365],"genre_scores_gemma":[0.94130975,0.012069165,0.035543542,0.006604308,0.00013671952,0.0011198146,0.00003851093,0.00010142615,0.0030767682],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99777615,0.00009490852,0.0003438957,0.0009573882,0.00020097925,0.0006266536],"domain_scores_gemma":[0.99888736,0.00030158405,0.00008034331,0.00047233453,0.000062467,0.00019590517],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005810102,0.00036645101,0.0003480993,0.00020204845,0.0009630993,0.0003643092,0.00025326898,0.000116711344,0.0000035147757],"category_scores_gemma":[8.517715e-7,0.0003492133,0.000049155242,0.0010121695,0.00006925804,0.00026516768,0.00006919449,0.00019461787,0.000002808546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001973779,0.00018962754,0.00019939212,0.00085188774,0.0005577042,0.00004565771,0.0016856551,0.56194746,0.000002426201,0.002787961,0.0019717037,0.4295631],"study_design_scores_gemma":[0.0018241344,0.0001908346,0.0038557127,0.00016849888,0.00016423796,0.000008549021,0.00094362046,0.9834318,0.0000023256173,0.00039513348,0.008607734,0.00040737746],"about_ca_topic_score_codex":0.000030321167,"about_ca_topic_score_gemma":0.00031954952,"teacher_disagreement_score":0.94731945,"about_ca_system_score_codex":0.00004160618,"about_ca_system_score_gemma":0.000007154376,"threshold_uncertainty_score":0.999896},"labels":[],"label_agreement":null},{"id":"W4388676663","doi":"10.1109/tnsm.2023.3332509","title":"Community Detection-Empowered Self-Adaptive Network Slicing in Multi-Tier Edge-Cloud System","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Load balancing (electrical power); Distributed computing; Quality of service; Provisioning; Cloud computing; Computer network","score_opus":0.02561609308584151,"score_gpt":0.23088842338332424,"score_spread":0.20527233029748274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388676663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01399199,0.00011245694,0.97915864,0.00036864803,0.0029897157,0.00073247193,0.0000031092827,0.0014941992,0.0011487479],"genre_scores_gemma":[0.98473537,0.0004328618,0.013044776,0.0010713501,0.00023032879,0.00023060522,0.0000037678467,0.000041640487,0.00020932537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975125,0.00043333083,0.00045027927,0.0005371345,0.00029604972,0.0007707011],"domain_scores_gemma":[0.99855375,0.0002942271,0.00011217154,0.0008198095,0.00006722412,0.00015281618],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010109611,0.00034166657,0.00035708124,0.00025531708,0.0011434248,0.00018524694,0.0006640274,0.00014755294,0.000004642313],"category_scores_gemma":[7.5706055e-7,0.00035095497,0.0000992018,0.0032189696,0.000022979997,0.0002546425,0.000053283395,0.000667975,0.000092114846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006544621,0.00019850077,0.000105141,0.00021623877,0.0002141428,0.00005291767,0.002039114,0.9350265,0.0000016835887,0.0010708411,0.00097450375,0.06003492],"study_design_scores_gemma":[0.0012549936,0.0001563742,0.005310713,0.00040330982,0.00007041694,0.000010102314,0.0013404367,0.98743564,0.000013690693,0.00029112265,0.0032641995,0.0004490221],"about_ca_topic_score_codex":0.0005762185,"about_ca_topic_score_gemma":0.0032748156,"teacher_disagreement_score":0.97074336,"about_ca_system_score_codex":0.00018051745,"about_ca_system_score_gemma":0.000019127145,"threshold_uncertainty_score":0.99989426},"labels":[],"label_agreement":null},{"id":"W4389352441","doi":"10.1109/tnsm.2023.3339302","title":"ALAP: Availability- and Latency-Aware Protection for O-RAN: A Deep <i>Q</i>-Learning Approach","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Computer network; Backup; C-RAN; Latency (audio); Quality of service; Distributed computing; Cellular network; Radio access network; Core network; Telecommunications; Base station; Operating system","score_opus":0.019808116363631206,"score_gpt":0.21661907004405673,"score_spread":0.19681095368042553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389352441","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00869337,0.000040907853,0.9863445,0.0010578273,0.001752871,0.00088985264,2.9422128e-7,0.0004913436,0.0007289999],"genre_scores_gemma":[0.94961053,0.0007414471,0.044312257,0.0015768037,0.0011382,0.0009862579,0.000015592668,0.0000693607,0.0015495688],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998612,0.000071080314,0.0002089975,0.0005421845,0.00016260466,0.00040316317],"domain_scores_gemma":[0.99946666,0.00007963467,0.000052569958,0.0002654085,0.000052927535,0.0000827992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050428323,0.00018116299,0.00016639424,0.00013655574,0.00079120946,0.00019257564,0.00021619453,0.000069967464,0.000001312892],"category_scores_gemma":[8.5656177e-7,0.00017934368,0.000054847133,0.0009009867,0.000018431696,0.0001900059,0.000021039703,0.0001971745,0.000017094544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062352825,0.00009669226,0.00003466021,0.00068553304,0.00011618313,0.0000054156626,0.0020259437,0.26298207,0.000013394626,0.00031009922,0.0005567436,0.7331109],"study_design_scores_gemma":[0.0004983428,0.00014246395,0.00042205144,0.000051376803,0.000039008963,0.0000046782293,0.00016999256,0.9838651,0.000017132897,0.00078159757,0.013793488,0.00021478455],"about_ca_topic_score_codex":0.000031352614,"about_ca_topic_score_gemma":0.000014382851,"teacher_disagreement_score":0.9420323,"about_ca_system_score_codex":0.000025852654,"about_ca_system_score_gemma":0.000007060896,"threshold_uncertainty_score":0.7313423},"labels":[],"label_agreement":null},{"id":"W4389543765","doi":"10.1109/tnsm.2023.3341296","title":"A Hybrid Optimized Intelligent Resource-Constrained Service Scheduling for Unified IoT Applications in Smart Cities","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Distributed computing; Quality of service; Scheduling (production processes); Edge computing; Computer network; Context (archaeology); Resource allocation; Edge device; Internet of Things; Cloud computing; Embedded system","score_opus":0.025004118607033242,"score_gpt":0.24208309949171897,"score_spread":0.21707898088468572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389543765","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014645072,0.000041321913,0.9776809,0.00375769,0.001238764,0.0011153518,0.000002011784,0.00043307696,0.0010858128],"genre_scores_gemma":[0.6533496,0.00091930514,0.32429972,0.014480571,0.0018735989,0.003215015,0.00012492137,0.00016644571,0.001570866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982777,0.00006261296,0.00039055978,0.00054060225,0.00018710904,0.0005414102],"domain_scores_gemma":[0.9990001,0.00029185766,0.00007479619,0.00045443515,0.000074006835,0.00010482],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005533728,0.00023719884,0.000248831,0.00034958508,0.00053498335,0.000228047,0.00053024583,0.000056762437,0.0000032278128],"category_scores_gemma":[9.1240383e-7,0.00025011855,0.00008132924,0.0016107009,0.000021754217,0.000121722245,0.000031444415,0.00019151089,0.000031807293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006654688,0.0000916918,0.000008927914,0.00030626648,0.00010501705,0.000008659409,0.001329345,0.903891,0.000007733855,0.0020577523,0.000531593,0.091595456],"study_design_scores_gemma":[0.00092097564,0.000045643006,0.0001104945,0.00013996901,0.000038979662,0.000003306347,0.00085864187,0.9679684,0.000114958886,0.0019617078,0.027529111,0.00030780025],"about_ca_topic_score_codex":0.000051185074,"about_ca_topic_score_gemma":0.00006712523,"teacher_disagreement_score":0.65338117,"about_ca_system_score_codex":0.000054784312,"about_ca_system_score_gemma":0.000027942047,"threshold_uncertainty_score":0.9999951},"labels":[],"label_agreement":null},{"id":"W4390097354","doi":"10.1109/tnsm.2023.3346202","title":"LMPT: A Novel Authenticated Data Structure to Eliminate Storage Bottlenecks for High Performance Blockchains","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Advanced Data Storage Technologies","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Database transaction; Throughput; Latency (audio); Data structure; Parallel computing; Transaction processing; Computer network; Distributed computing; Operating system; Database","score_opus":0.02984141835768819,"score_gpt":0.25417483543368186,"score_spread":0.22433341707599366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390097354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032075133,0.000017004415,0.9609888,0.003978422,0.0007520946,0.00081377797,0.00033823063,0.0010010332,0.000035494304],"genre_scores_gemma":[0.874174,0.00024810666,0.1234659,0.0013838287,0.000053854914,0.00017093396,0.00009021264,0.000030992545,0.00038216996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981136,0.000014945322,0.00023868534,0.00084122753,0.00025195978,0.0005395963],"domain_scores_gemma":[0.99787825,0.00007845419,0.00006675476,0.0018238763,0.00005740607,0.000095243224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023017799,0.00025265972,0.00020851537,0.00026548744,0.00046180826,0.00012715138,0.0017983651,0.000085095,0.0000059281315],"category_scores_gemma":[0.0000023107687,0.0002450109,0.000023860755,0.0016637556,0.000028953003,0.00039309773,0.00015805733,0.00018103118,0.00003144748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007295497,0.00009429868,0.0000021388803,0.00027109927,0.00015829942,0.000016617638,0.00041339942,0.68441343,0.00026936777,0.0065236785,0.0026565697,0.30510813],"study_design_scores_gemma":[0.0008173766,0.00021265434,0.00062233076,0.0001047394,0.00008553211,0.0000054361367,0.00018557553,0.9797212,0.0006685502,0.0015126661,0.015620816,0.00044314933],"about_ca_topic_score_codex":0.000015283615,"about_ca_topic_score_gemma":0.00011680646,"teacher_disagreement_score":0.8420989,"about_ca_system_score_codex":0.000048434376,"about_ca_system_score_gemma":0.00001304607,"threshold_uncertainty_score":0.9991254},"labels":[],"label_agreement":null},{"id":"W4390691512","doi":"10.1109/tnsm.2024.3352014","title":"Multi-Agent Deep Reinforcement Learning for Packet Routing in Tactical Mobile Sensor Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Security in Wireless Sensor Networks","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Computer network; Network packet; Wireless sensor network; Reinforcement learning; Source routing; Routing protocol; Intrusion detection system; Packet forwarding; Jamming; Distributed computing; Link-state routing protocol; Computer security; Artificial intelligence","score_opus":0.016666668081750595,"score_gpt":0.25422665743417794,"score_spread":0.23755998935242734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390691512","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002592955,0.00037429717,0.99261487,0.000687016,0.0017278482,0.0012287464,7.2653444e-7,0.00041268455,0.0003608557],"genre_scores_gemma":[0.97058535,0.0014459483,0.02568231,0.0011742979,0.00023895409,0.00051284616,0.000007793051,0.00004595262,0.00030657786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974551,0.00013172283,0.0005044461,0.00080259057,0.0002965703,0.0008095667],"domain_scores_gemma":[0.9988559,0.0004222012,0.00006628318,0.0004461487,0.000051484367,0.00015800643],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00058823713,0.00034179393,0.00029974536,0.00020884257,0.00039655928,0.00045620144,0.00039190336,0.00015516834,0.000021346536],"category_scores_gemma":[0.0000015130772,0.00034977365,0.00013116543,0.0009776161,0.000027549966,0.00029640805,0.000028282418,0.0006157476,0.00002620411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003707538,0.000085573374,0.000011944447,0.00017349665,0.000117203075,0.00005710037,0.0009269548,0.905856,0.0000031983375,0.0018363753,0.00009619571,0.09079889],"study_design_scores_gemma":[0.00071394286,0.00016314631,0.000069257185,0.00030348022,0.000068373956,0.000011582835,0.00045600536,0.99146384,0.000020126898,0.00006842428,0.006300168,0.00036164097],"about_ca_topic_score_codex":0.000049582904,"about_ca_topic_score_gemma":0.00026933447,"teacher_disagreement_score":0.96799237,"about_ca_system_score_codex":0.00016563093,"about_ca_system_score_gemma":0.000015671407,"threshold_uncertainty_score":0.99989545},"labels":[],"label_agreement":null},{"id":"W4390956195","doi":"10.1109/tnsm.2024.3355310","title":"Labeling Cloud Metrics Data for Fault Detection in Cloud Using Active Learning With Test Suite","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); Concordia University","funders":"","keywords":"Cloud computing; Suite; Test suite; Computer science; Test (biology); Machine learning; Operating system; Test case; Geology; Geography","score_opus":0.025388512425434436,"score_gpt":0.2706910840739789,"score_spread":0.24530257164854447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390956195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007506926,0.00016695517,0.99008393,0.00047147326,0.0010042309,0.00036182665,0.000009116307,0.00022950287,0.00016603437],"genre_scores_gemma":[0.9478394,0.00037689027,0.050685156,0.00035814918,0.00037380258,0.000049568425,0.000012845104,0.00003745815,0.00026673963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858814,0.00007182995,0.00019164315,0.000614995,0.00020721025,0.00032616043],"domain_scores_gemma":[0.9991188,0.00034203057,0.000048589594,0.00039032474,0.00004134564,0.000058904938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005439707,0.0001814805,0.00015795288,0.0002838163,0.0003923143,0.00030733683,0.00038467752,0.000053760923,0.000002617992],"category_scores_gemma":[0.0000035644566,0.0001640696,0.00002884056,0.0018242048,0.000011414301,0.00037396906,0.000024109893,0.00042732293,0.0000042865627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002352839,0.000038188915,0.00000959036,0.00015054767,0.00006206802,0.000013947261,0.00026914713,0.6398065,0.000017980037,0.00010708493,0.000011010011,0.35949036],"study_design_scores_gemma":[0.00042213165,0.00016440265,0.000035519522,0.00022806606,0.00008259523,0.000010491163,0.00021755545,0.9888373,0.000086314714,0.00010374058,0.009610398,0.00020145574],"about_ca_topic_score_codex":0.00020667764,"about_ca_topic_score_gemma":0.00049214694,"teacher_disagreement_score":0.9403325,"about_ca_system_score_codex":0.00006727757,"about_ca_system_score_gemma":0.00001976006,"threshold_uncertainty_score":0.6690564},"labels":[],"label_agreement":null},{"id":"W4391096978","doi":"10.1109/tnsm.2024.3356973","title":"Operating Multi-User Massive MIMO Networks: Trade-Off Between Performance and Runtime","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Cisco Systems","keywords":"Computer science; MIMO; Precoding; Leverage (statistics); Transmitter power output; Online algorithm; Multi-user; Algorithm; Multi-user MIMO; Dirty paper coding; Channel (broadcasting); Computer network; Machine learning","score_opus":0.009023270818513377,"score_gpt":0.21070635357242487,"score_spread":0.2016830827539115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391096978","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008479566,0.0014144684,0.98723316,0.0002021008,0.00076304853,0.000530545,0.000008690979,0.0005103862,0.0008580599],"genre_scores_gemma":[0.9831483,0.0053549777,0.010288407,0.00018146269,0.0002539145,0.00011977797,0.000012171154,0.00007880146,0.0005621953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989513,0.000026516587,0.00027657818,0.00032271116,0.000099921075,0.00032297187],"domain_scores_gemma":[0.9996453,0.000053965738,0.000019561528,0.0001795318,0.000013953466,0.00008768012],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012783309,0.00025394475,0.00020765667,0.00010470517,0.00025237445,0.00015002595,0.00008494408,0.00009042025,0.000016051008],"category_scores_gemma":[1.5224008e-7,0.00025611196,0.000033897828,0.00043083658,0.000017284003,0.00030852595,0.0000043816917,0.00024728107,0.000014889863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003010354,0.000008552757,0.000045451685,0.00046869073,0.00017057847,0.0000066738335,0.0002614609,0.9204334,0.00000685308,0.000038731607,0.00020028066,0.07835632],"study_design_scores_gemma":[0.0003445715,0.000033746088,0.0005532164,0.00044553092,0.00013897213,0.0000039358465,0.00017743578,0.99061817,0.00003771547,0.0000042488055,0.0073677516,0.00027473448],"about_ca_topic_score_codex":0.00000575749,"about_ca_topic_score_gemma":0.000031462823,"teacher_disagreement_score":0.97694474,"about_ca_system_score_codex":0.00005390731,"about_ca_system_score_gemma":0.0000035883336,"threshold_uncertainty_score":0.9999891},"labels":[],"label_agreement":null},{"id":"W4391341607","doi":"10.1109/tnsm.2024.3360082","title":"KimeraPAD: A Novel Low-Overhead Real-Time Defense Against Website Fingerprinting Attacks Based on Deep Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Network packet; Reinforcement learning; Overhead (engineering); Computer security; Anonymity; Classifier (UML); Router; Computer network; Randomness; Adversarial system; Real-time computing; Artificial intelligence; Operating system","score_opus":0.008452949856790175,"score_gpt":0.21644155170280951,"score_spread":0.20798860184601933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391341607","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076616453,0.00004941596,0.9758063,0.00069919584,0.0005706121,0.00028848322,8.4070143e-7,0.00046181053,0.014461687],"genre_scores_gemma":[0.98899794,0.00026606987,0.006265709,0.0026901038,0.00013024562,0.000044202683,0.00000926673,0.00003553107,0.0015609076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976263,0.00008381168,0.0004641676,0.00079044304,0.00047159704,0.0005636503],"domain_scores_gemma":[0.99915713,0.00019446139,0.000095510564,0.00035956863,0.000060625604,0.00013270408],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00058264413,0.0003546748,0.00029172798,0.00037336475,0.0005080819,0.0005649595,0.00040525832,0.00009039907,0.00006929924],"category_scores_gemma":[0.0000010802228,0.00033948378,0.0002119782,0.0010915552,0.000019768784,0.0002266711,0.000021367847,0.00045353547,0.00020349014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003183708,0.000080850805,0.0000014042666,0.00016220442,0.00022691311,0.000041127994,0.00059224834,0.94660383,0.000040935636,0.0070355884,0.0001587544,0.0450243],"study_design_scores_gemma":[0.0004038967,0.00013006087,0.000023132174,0.000717914,0.00011223285,0.0000026707758,0.00007455041,0.99390996,0.00003626163,0.000004548912,0.0042372015,0.00034755323],"about_ca_topic_score_codex":0.000024884337,"about_ca_topic_score_gemma":0.000075238095,"teacher_disagreement_score":0.9813363,"about_ca_system_score_codex":0.00010896942,"about_ca_system_score_gemma":0.00002397301,"threshold_uncertainty_score":0.9999057},"labels":[],"label_agreement":null},{"id":"W4393034384","doi":"10.1109/tnsm.2024.3378972","title":"AUTOMA: Automated Generation of Attack Hypotheses and Their Variants for Threat Hunting Using Knowledge Discovery","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Ericsson (Canada)","funders":"","keywords":"Computer science; Computer security; Data science","score_opus":0.05084952304846543,"score_gpt":0.27543383526163073,"score_spread":0.2245843122131653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393034384","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10696109,0.0006436431,0.8901274,0.00019721867,0.001113984,0.00036609164,0.000006959036,0.00031556847,0.00026803874],"genre_scores_gemma":[0.9903392,0.0006848911,0.008453729,0.0001738567,0.00018745956,0.000037953778,0.0000021503374,0.000016326894,0.00010440344],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990301,0.00005566759,0.00023668043,0.00037974818,0.00008748054,0.00021033139],"domain_scores_gemma":[0.9995358,0.000120354074,0.000049156013,0.00020974487,0.000044549575,0.00004038523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002699548,0.00016401672,0.00016712953,0.00013788961,0.00035968114,0.0003208264,0.0001374557,0.00006474686,0.0000036380975],"category_scores_gemma":[4.8115385e-7,0.00014072857,0.00005667844,0.0005572088,0.000021802185,0.0005913483,0.000014375032,0.00008795102,0.0000018388035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000619878,0.00021409904,0.0000058684145,0.0013124743,0.00045013573,0.0000059455415,0.0025328705,0.46662825,0.002329225,0.010611471,0.0007186905,0.51512897],"study_design_scores_gemma":[0.0002203794,0.00008923338,0.0000859234,0.000261046,0.000060652408,0.000008219736,0.000057029203,0.9959194,0.0010792355,0.0005361262,0.0015378746,0.00014488389],"about_ca_topic_score_codex":0.00003206311,"about_ca_topic_score_gemma":0.00018410537,"teacher_disagreement_score":0.88337815,"about_ca_system_score_codex":0.000029857865,"about_ca_system_score_gemma":0.000017201739,"threshold_uncertainty_score":0.5738745},"labels":[],"label_agreement":null},{"id":"W4393285839","doi":"10.1109/tnsm.2024.3382301","title":"Safety-Aware Age of Information (S-AoI) for Collision Risk Minimization in Cell-Free mMIMO Platooning Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Age of Information Optimization","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Qatar National Research Fund","keywords":"Computer science; Metric (unit); Overhead (engineering); Collision; Minification; Real-time computing; Reduction (mathematics); Performance metric; Simulation; Embedded system; Engineering","score_opus":0.005780169401622869,"score_gpt":0.19855004953389788,"score_spread":0.192769880132275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393285839","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036580075,0.00005895715,0.99523336,0.00036054078,0.0006823333,0.0008346848,0.00001910322,0.00014818585,0.0022970221],"genre_scores_gemma":[0.7763497,0.004905061,0.21663935,0.0012150878,0.00010025649,0.00029456112,0.00021903255,0.00003717033,0.00023972843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866796,0.0000498909,0.00058108533,0.00022216674,0.00024189359,0.00023698836],"domain_scores_gemma":[0.9991606,0.00016210835,0.0001604219,0.0003734607,0.0000916463,0.000051751224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044998425,0.00017152741,0.00017025023,0.00036815967,0.00023033614,0.00024397383,0.00035090285,0.00010182462,0.000006698569],"category_scores_gemma":[0.0000019259833,0.00017578821,0.000057797206,0.0012144871,0.000013234962,0.0015634722,0.000017623734,0.00015524772,0.0000062189933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060899343,0.000028250357,0.0000040220866,0.00028889763,0.000025631192,0.0000016846699,0.0009967281,0.91154087,2.9005176e-7,0.0022499613,0.0004983352,0.084304444],"study_design_scores_gemma":[0.0009913555,0.000087006054,0.00013870775,0.00026526515,0.000039961884,0.0000011165799,0.0002525228,0.9923306,0.00003800971,0.00038347108,0.005305613,0.00016634003],"about_ca_topic_score_codex":0.000028506585,"about_ca_topic_score_gemma":0.000102022816,"teacher_disagreement_score":0.778594,"about_ca_system_score_codex":0.000079658435,"about_ca_system_score_gemma":0.000022492304,"threshold_uncertainty_score":0.7168435},"labels":[],"label_agreement":null},{"id":"W4393906066","doi":"10.1109/tnsm.2024.3384942","title":"DAIDNet: A Lightweight Domain-Aware Architecture for Automated Detection of Network Penetrations","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Network packet; Intrusion detection system; Overhead (engineering); Domain (mathematical analysis); Data mining; Artificial intelligence; Similarity (geometry); Artificial neural network; Machine learning; Architecture; Network security; Computer network; Operating system","score_opus":0.008282136332024032,"score_gpt":0.2222825059494186,"score_spread":0.21400036961739455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393906066","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018354845,0.00042472105,0.99131215,0.001583167,0.0022423172,0.0008180747,0.000011649492,0.0009017858,0.0008706333],"genre_scores_gemma":[0.9693793,0.00083652337,0.02754557,0.0010997498,0.00052485435,0.00035366413,0.000009166474,0.00003827161,0.00021292397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983227,0.00009008751,0.000383514,0.0005546414,0.00024913662,0.0003999227],"domain_scores_gemma":[0.9991628,0.00015514737,0.000077243414,0.00042140097,0.00007908908,0.00010432606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032329193,0.00024904832,0.00022641347,0.00021983458,0.0005732491,0.00022526592,0.00031880726,0.00013422566,0.000019775483],"category_scores_gemma":[3.7974849e-7,0.00023296817,0.00014081899,0.0015238838,0.000029469802,0.00027305045,0.000012744291,0.00024639303,0.000012712382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013354458,0.00012565196,0.0000010192612,0.00069315993,0.00038037685,0.000012165738,0.0009774837,0.72666603,0.00026357875,0.014432544,0.003151252,0.25316316],"study_design_scores_gemma":[0.00043160838,0.00037316274,0.000076833014,0.00033337055,0.00011848793,0.000020726522,0.0000415186,0.91813076,0.00073881965,0.010101847,0.06933439,0.0002984642],"about_ca_topic_score_codex":0.000024866613,"about_ca_topic_score_gemma":0.00059416227,"teacher_disagreement_score":0.9675438,"about_ca_system_score_codex":0.000046532306,"about_ca_system_score_gemma":0.000025831718,"threshold_uncertainty_score":0.9500166},"labels":[],"label_agreement":null},{"id":"W4393927997","doi":"10.1109/tnsm.2025.3635028","title":"Proactive Service Assurance in 5G and B5G Networks: A Closed-Loop Algorithm for End-to-End Network Slices","year":2025,"lang":"en","type":"preprint","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Mitacs","keywords":"End-to-end principle; Computer science; Slicing; Service (business); Algorithm; Computer network; Distributed computing; Business","score_opus":0.015146915307265784,"score_gpt":0.24421745973230288,"score_spread":0.2290705444250371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393927997","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006576983,0.001865624,0.98382527,0.0044350205,0.0034239062,0.0041039535,0.00009356452,0.00041264287,0.0011823126],"genre_scores_gemma":[0.12511618,0.029070677,0.7672403,0.0572416,0.0038814452,0.014048862,0.00023004829,0.00034628247,0.0028246162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99473464,0.00027002135,0.0008955398,0.0022272994,0.0005019778,0.0013705106],"domain_scores_gemma":[0.99687284,0.0008687369,0.0003182191,0.0013466605,0.0002543802,0.00033915663],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010287039,0.0009841805,0.0010554724,0.0004207323,0.0006811571,0.0006765265,0.0014401887,0.00054187735,0.000012707426],"category_scores_gemma":[0.0000021355197,0.0010379056,0.00020540264,0.0025762164,0.000046761754,0.0003498512,0.00021870685,0.0011391678,0.0000068316554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000105401254,0.00012957059,0.00002324905,0.00048690243,0.00038280844,0.00001450434,0.0003653675,0.5797443,9.586453e-8,0.0005514747,0.0012094579,0.4169869],"study_design_scores_gemma":[0.0016318767,0.00019665915,0.0021109711,0.0019248764,0.0003658711,0.000006823883,0.00013281341,0.972926,0.0000059133463,0.0033288714,0.0162402,0.0011291279],"about_ca_topic_score_codex":0.000784172,"about_ca_topic_score_gemma":0.006478027,"teacher_disagreement_score":0.4158578,"about_ca_system_score_codex":0.0001750255,"about_ca_system_score_gemma":0.00013202883,"threshold_uncertainty_score":0.99920714},"labels":[],"label_agreement":null},{"id":"W4394698768","doi":"10.1109/tnsm.2024.3387275","title":"VNF Placement and Dynamic NUMA Node Selection Through Core Consolidation at the Edge and Cloud","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cloud computing; Computer network; Enhanced Data Rates for GSM Evolution; Distributed computing; Selection (genetic algorithm); Consolidation (business); Node (physics); Operating system; Telecommunications","score_opus":0.015297385706428614,"score_gpt":0.2417130690816116,"score_spread":0.226415683375183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394698768","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025444843,0.0015717279,0.96565485,0.0040663974,0.0016569177,0.0005001119,0.0000057907705,0.0002627115,0.000836659],"genre_scores_gemma":[0.97993165,0.00871039,0.0056060967,0.0041377754,0.00023450171,0.00015159785,0.000010959682,0.00003054026,0.0011864975],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879605,0.000052145373,0.00019321672,0.0004985645,0.00018267959,0.00027737598],"domain_scores_gemma":[0.99946123,0.00015548697,0.00003846087,0.0002573509,0.000026972557,0.000060479248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022442568,0.00019936374,0.00013449456,0.000056175966,0.0006644998,0.00033057798,0.00014895202,0.000063878324,0.000024734722],"category_scores_gemma":[2.810285e-7,0.00015597172,0.000032671564,0.0005386223,0.000045611538,0.00022572988,0.000027316122,0.0001805158,0.000018395507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023027053,0.00018497425,0.00015503704,0.0009033328,0.00077647495,0.000041495572,0.005654521,0.45638302,0.00004981115,0.027150145,0.020655436,0.48781547],"study_design_scores_gemma":[0.00065646897,0.00015874968,0.00074402627,0.00022964392,0.00018210281,0.00006304371,0.00024585283,0.9404829,0.00003659105,0.0022718667,0.05459844,0.0003302871],"about_ca_topic_score_codex":0.000043102038,"about_ca_topic_score_gemma":0.0005026218,"teacher_disagreement_score":0.96004874,"about_ca_system_score_codex":0.00007098748,"about_ca_system_score_gemma":0.000011624535,"threshold_uncertainty_score":0.63603425},"labels":[],"label_agreement":null},{"id":"W4394843841","doi":"10.1109/tnsm.2024.3389048","title":"Stochastic Resource Optimization for Metaverse Data Marketplace by Leveraging Quantum Neural Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Metaverse; Reservation; Resource allocation; Data science; Virtual reality; Artificial intelligence; Computer network","score_opus":0.024900317488063484,"score_gpt":0.2393456832169986,"score_spread":0.2144453657289351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394843841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000120188444,0.00049535325,0.99094236,0.0018924343,0.0054921983,0.00047439482,0.000005366277,0.00033499417,0.00024271861],"genre_scores_gemma":[0.7677013,0.001191322,0.20564343,0.015086481,0.0061941925,0.0003995472,0.0006284547,0.00030806594,0.0028471844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998288,0.00007849351,0.0002556564,0.00073454453,0.00019611654,0.0004471929],"domain_scores_gemma":[0.99890435,0.00026423333,0.000045612982,0.00066022074,0.00003015464,0.00009544532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005911596,0.00022669895,0.00017295503,0.00011698229,0.00050997926,0.00053363293,0.0007136254,0.00005904175,0.0000066819916],"category_scores_gemma":[8.154386e-7,0.0002269365,0.000055158856,0.00069454004,0.00001530588,0.0004553694,0.000049199803,0.00020813351,0.000005120657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029769917,0.000024253728,1.6922264e-7,0.00010995595,0.00010477449,0.0000059543536,0.0001463429,0.87193334,3.4894265e-7,0.00014884617,0.03221676,0.09527946],"study_design_scores_gemma":[0.00028674616,0.00004303015,0.0000018871675,0.00011575885,0.00012863372,0.0000067382275,0.000050802595,0.9577899,0.0000011342551,0.000084288986,0.04126117,0.00022993384],"about_ca_topic_score_codex":0.000028515533,"about_ca_topic_score_gemma":0.0000089493515,"teacher_disagreement_score":0.78529894,"about_ca_system_score_codex":0.000040404146,"about_ca_system_score_gemma":0.000011500155,"threshold_uncertainty_score":0.9254202},"labels":[],"label_agreement":null},{"id":"W4394966847","doi":"10.1109/tnsm.2024.3391664","title":"Energy and Delay Aware General Task Dependent Offloading in UAV-Aided Smart Farms","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Task (project management); Computer science; Energy (signal processing); Human–computer interaction; Embedded system; Real-time computing; Engineering; Mathematics; Systems engineering","score_opus":0.010084724067843056,"score_gpt":0.21323765877415907,"score_spread":0.203152934706316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394966847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024687734,0.0005507045,0.9665638,0.0010886592,0.005345596,0.00014997271,5.1645014e-7,0.00020110946,0.0014118626],"genre_scores_gemma":[0.99131745,0.0009327241,0.0045036036,0.0019526909,0.0005662867,0.00004401535,0.0000025639022,0.000022753335,0.00065791025],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854094,0.00006055047,0.00025311843,0.0005460483,0.00019047988,0.00040888906],"domain_scores_gemma":[0.99953914,0.000059307127,0.000025241707,0.00026012177,0.000019011315,0.00009715141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026128575,0.00020695588,0.00016984531,0.0002277364,0.00025499606,0.00036244444,0.00026449552,0.00006313051,0.000003448301],"category_scores_gemma":[1.1134233e-7,0.00020017709,0.000045700195,0.00068034243,0.000013464253,0.00024574593,0.000029970608,0.00017044766,0.000009084825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024930134,0.00008909255,0.00008318074,0.00030513026,0.00019528539,0.00036031211,0.0014399366,0.12166219,0.000045898487,0.0034984255,0.0020326811,0.8702629],"study_design_scores_gemma":[0.00034988907,0.000052795338,0.00024751938,0.00022516787,0.000040315506,0.00003255995,0.00005626272,0.97165245,0.00007601551,0.0009504718,0.026030134,0.00028640847],"about_ca_topic_score_codex":0.00028468206,"about_ca_topic_score_gemma":0.000577995,"teacher_disagreement_score":0.96662974,"about_ca_system_score_codex":0.000060379694,"about_ca_system_score_gemma":0.000015254519,"threshold_uncertainty_score":0.8162985},"labels":[],"label_agreement":null},{"id":"W4396982215","doi":"10.1109/tnsm.2024.3402074","title":"A Machine Learning-Based Toolbox for P4 Programmable Data-Planes","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Toolbox; Computer architecture; Artificial intelligence; Programming language","score_opus":0.02303931375915281,"score_gpt":0.24487777337396321,"score_spread":0.2218384596148104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396982215","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019934682,0.0006399539,0.9933362,0.0025860616,0.001303243,0.00061822636,0.000018527215,0.0005793464,0.00071912195],"genre_scores_gemma":[0.9077291,0.003275616,0.076125644,0.0072035533,0.0009180646,0.0009308795,0.00025132098,0.00009964521,0.0034661884],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986129,0.00005701015,0.00020140981,0.0005999365,0.00019421379,0.00033456992],"domain_scores_gemma":[0.99920803,0.00012242168,0.000032412816,0.000523576,0.00003386868,0.00007966803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004028329,0.00018162736,0.00014393557,0.00012469493,0.0004736051,0.00048996153,0.0004940201,0.00006371754,0.00003361781],"category_scores_gemma":[4.7547985e-7,0.00016665163,0.00005234156,0.0006960936,0.000015984271,0.0003671613,0.000013707859,0.00023170128,0.000036605812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008436797,0.0001017333,0.0000015484335,0.00048823457,0.00013031167,0.000020923157,0.000120911216,0.40675852,0.0000033425652,0.0034649533,0.0015848941,0.5872403],"study_design_scores_gemma":[0.00023150223,0.00015488293,0.000003958573,0.000076792276,0.00004927468,0.0000052585324,0.0000088015295,0.63299704,0.000028979483,0.00043564505,0.36588266,0.0001252167],"about_ca_topic_score_codex":0.00007860267,"about_ca_topic_score_gemma":0.0005199463,"teacher_disagreement_score":0.9172105,"about_ca_system_score_codex":0.000023610848,"about_ca_system_score_gemma":0.000019081293,"threshold_uncertainty_score":0.67958564},"labels":[],"label_agreement":null},{"id":"W4398765190","doi":"10.1109/tnsm.2024.3405004","title":"CRS-FL: Conditional Random Sampling for Communication-Efficient and Privacy-Preserving Federated Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Federated learning; Sampling (signal processing); Artificial intelligence; Telecommunications","score_opus":0.0312840824549279,"score_gpt":0.27881774955399774,"score_spread":0.24753366709906985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398765190","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032691031,0.0009510427,0.9740567,0.019173263,0.0003742673,0.00068475015,0.00001894606,0.0009386772,0.00053325057],"genre_scores_gemma":[0.7972486,0.0020232466,0.19926333,0.0008309507,0.000052328996,0.00036649764,0.000049138587,0.00003452392,0.0001313478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983374,0.0000997905,0.0003083149,0.0006304625,0.00024081915,0.00038320327],"domain_scores_gemma":[0.9972282,0.00075363676,0.000061375285,0.0018045253,0.00007672309,0.00007556179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000677661,0.00022601266,0.00020514836,0.00019606808,0.0011062123,0.00082551275,0.0035339023,0.00009478148,0.000016700063],"category_scores_gemma":[0.00006447524,0.00022614542,0.000056597342,0.00066577183,0.000057784855,0.00035115387,0.0013969613,0.00038369172,0.0000094720535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017538872,0.0002144863,0.000019254085,0.0013780192,0.000752032,0.000021110109,0.0007967842,0.70588356,0.00010023478,0.024352891,0.022861013,0.2434452],"study_design_scores_gemma":[0.00077916466,0.00004921755,0.0000800616,0.00033504784,0.00005792394,0.0000094965035,0.00015044112,0.9454149,0.00007063588,0.034767255,0.018047588,0.00023829311],"about_ca_topic_score_codex":0.000027506972,"about_ca_topic_score_gemma":0.000038112736,"teacher_disagreement_score":0.7939795,"about_ca_system_score_codex":0.000060695907,"about_ca_system_score_gemma":0.000022265063,"threshold_uncertainty_score":0.92219424},"labels":[],"label_agreement":null},{"id":"W4399039891","doi":"10.1109/tnsm.2024.3405901","title":"A Novel Framework for Optical Layer Device Board Failure Localization in Optical Transport Network","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Advanced Photonic Communication Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Layer (electronics); Optical Transport Network; Transport layer; Materials science; Computer science; Optoelectronics; Computer network; Optical performance monitoring; Nanotechnology; Wavelength-division multiplexing","score_opus":0.019733160808507524,"score_gpt":0.25693548742912486,"score_spread":0.23720232662061735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399039891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009432896,0.00049367186,0.9941083,0.0008985843,0.00078197295,0.00087618217,0.000011189473,0.0003177129,0.0015690739],"genre_scores_gemma":[0.9075172,0.0007507877,0.09017303,0.0005987011,0.00018752484,0.00061226275,0.000017527997,0.000080535,0.00006243444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861944,0.000017427015,0.00041705015,0.00034414415,0.00018083915,0.0004211034],"domain_scores_gemma":[0.99919593,0.00025426585,0.00001803997,0.00039571206,0.000032960852,0.00010308508],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002482113,0.00024915417,0.00025689654,0.000103763414,0.00013309151,0.00007070515,0.00019260244,0.00017280743,0.000033822493],"category_scores_gemma":[5.9783645e-7,0.00026543255,0.00007996337,0.00087259227,0.000021543077,0.00013880875,0.000003800724,0.0003889645,0.000020764306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004216464,0.000045817123,0.0000065013214,0.0005560033,0.00013418008,0.000006073219,0.0002756662,0.9676388,0.000014872206,0.015926465,0.00016940186,0.015184092],"study_design_scores_gemma":[0.00046901123,0.000036243837,0.00009700523,0.00085255003,0.00012865255,0.000006196169,0.0002686644,0.90163714,0.00004768322,0.0014533221,0.0946814,0.0003221572],"about_ca_topic_score_codex":0.0000127675785,"about_ca_topic_score_gemma":0.0011648705,"teacher_disagreement_score":0.9065739,"about_ca_system_score_codex":0.000111159155,"about_ca_system_score_gemma":0.000012111991,"threshold_uncertainty_score":0.9999798},"labels":[],"label_agreement":null},{"id":"W4399619764","doi":"10.1109/tnsm.2024.3414267","title":"A Graph Learning-Based Approach for Lateral Movement Detection","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Movement (music); Computer science; Artificial intelligence; Graph; Computer vision; Theoretical computer science; Physics; Acoustics","score_opus":0.013196508519219476,"score_gpt":0.2179085273981057,"score_spread":0.20471201887888624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399619764","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036711065,0.000105121966,0.9955008,0.00070038566,0.0009442509,0.00070497073,0.0000030290244,0.0003937694,0.001280581],"genre_scores_gemma":[0.9827121,0.000096618016,0.013942691,0.0017148199,0.00014142206,0.00073733553,0.0000068527065,0.000021154987,0.0006270161],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988445,0.00006696175,0.00019172467,0.00044924,0.00018792649,0.00025966665],"domain_scores_gemma":[0.99959636,0.000059181526,0.00002936149,0.00020117754,0.000041810978,0.00007210275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031213867,0.00016339561,0.00012874533,0.00018835523,0.0002933041,0.00031156908,0.00016281156,0.00005784885,0.000005510511],"category_scores_gemma":[1.4902031e-7,0.00014793569,0.000094103896,0.00066020107,0.000008648034,0.00015223169,0.000003405157,0.00015132473,0.000016164851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035260244,0.00009423233,0.0000020819286,0.00067261,0.00018070887,0.00000621038,0.00029038882,0.69966775,0.000028026076,0.0008216778,0.00017808056,0.29802296],"study_design_scores_gemma":[0.0004910653,0.0001813467,0.00003376671,0.00011148228,0.000063508836,0.0000033733686,0.000059994076,0.9740717,0.0003317325,0.00065983,0.023790285,0.00020194046],"about_ca_topic_score_codex":0.000017651757,"about_ca_topic_score_gemma":0.00005424875,"teacher_disagreement_score":0.982345,"about_ca_system_score_codex":0.00003617876,"about_ca_system_score_gemma":0.000010052493,"threshold_uncertainty_score":0.6032642},"labels":[],"label_agreement":null},{"id":"W4399666315","doi":"10.1109/tnsm.2024.3414305","title":"ENIDS: A Deep Learning-Based Ensemble Framework for Network Intrusion Detection Systems","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia; Western University","funders":"","keywords":"Computer science; Intrusion detection system; Ensemble learning; Deep learning; Artificial intelligence; Machine learning","score_opus":0.011056134332368472,"score_gpt":0.22722897910646322,"score_spread":0.21617284477409476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399666315","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00077198166,0.0013291509,0.9884005,0.00085002143,0.0063768374,0.000966773,0.0000016030486,0.0008748986,0.00042818513],"genre_scores_gemma":[0.9712246,0.0012914246,0.024092851,0.0014299465,0.0010500445,0.00059153093,0.0000050264007,0.00005167748,0.00026291283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977141,0.00018183903,0.00039638754,0.0007791461,0.0003331668,0.0005953457],"domain_scores_gemma":[0.99880064,0.0004261348,0.000088055895,0.00045191825,0.00008663093,0.00014659546],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006558339,0.00032111956,0.0002666143,0.00022030709,0.0010537888,0.00071341806,0.0003455196,0.00023265791,0.000020268619],"category_scores_gemma":[0.0000020021594,0.0003162322,0.0001529266,0.0015333564,0.000023688517,0.0003435971,0.00001572512,0.0005604135,0.000053072166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008821275,0.000040259347,6.0919683e-7,0.00033242826,0.00008521737,0.0000081661965,0.0001641257,0.67346436,0.000009817843,0.011239799,0.00021285684,0.31435412],"study_design_scores_gemma":[0.0003144947,0.00041331074,0.000014094641,0.0005593349,0.000099435005,0.0000111451045,0.00006487998,0.9084921,0.00016369196,0.007669503,0.08187692,0.0003210816],"about_ca_topic_score_codex":0.000044037224,"about_ca_topic_score_gemma":0.00025475753,"teacher_disagreement_score":0.9704526,"about_ca_system_score_codex":0.0001095126,"about_ca_system_score_gemma":0.000020153986,"threshold_uncertainty_score":0.99992895},"labels":[],"label_agreement":null},{"id":"W4399768589","doi":"10.1109/tnsm.2024.3416031","title":"A Modular, End-to-End Next-Generation Network Testbed: Toward a Fully Automated Network Management Platform","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"End-to-end principle; Testbed; Modular design; Computer science; End user; Network management; Computer network; Embedded system; Operating system","score_opus":0.0291041480607154,"score_gpt":0.24252505881875264,"score_spread":0.21342091075803724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399768589","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00751165,0.0007468325,0.9761887,0.0028562685,0.0057105063,0.0016814219,0.0000068773224,0.0024898066,0.002807933],"genre_scores_gemma":[0.93904734,0.0015486816,0.051887035,0.004692449,0.0012631329,0.0006941936,0.000026113932,0.00006493915,0.00077610393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99638504,0.00010571255,0.00067373854,0.0011757173,0.00064678513,0.0010130345],"domain_scores_gemma":[0.99844986,0.0000986695,0.00008343453,0.0009993408,0.00008594177,0.0002827221],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009886547,0.000507905,0.0004059651,0.00024354864,0.0007670758,0.00094292685,0.0007327609,0.00016919909,0.00005953755],"category_scores_gemma":[5.2683555e-7,0.00045724446,0.00016928604,0.0027358453,0.000032083142,0.0007824313,0.000065363834,0.00033893826,0.00027523443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036819278,0.00007956563,0.000014421705,0.0005559581,0.00036558593,0.000074376294,0.00045678424,0.774178,0.0000060171915,0.0032223926,0.01012802,0.21088201],"study_design_scores_gemma":[0.00044044518,0.00020928499,0.00076204026,0.00056709023,0.00018320787,0.000023406821,0.00009516981,0.9440089,0.000014949755,0.0009538628,0.052196108,0.00054554327],"about_ca_topic_score_codex":0.00007228397,"about_ca_topic_score_gemma":0.00022951144,"teacher_disagreement_score":0.9315357,"about_ca_system_score_codex":0.000201048,"about_ca_system_score_gemma":0.000042706302,"threshold_uncertainty_score":0.9997879},"labels":[],"label_agreement":null},{"id":"W4400020740","doi":"10.1109/tnsm.2024.3419051","title":"Preemptive Prediction-Based Placement of Time-Critical SFCs With VNF Sharing at the Edge","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Artificial intelligence","score_opus":0.00792640342129182,"score_gpt":0.20299816659373723,"score_spread":0.1950717631724454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400020740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053276615,0.00041883954,0.98296237,0.0004270552,0.00031661786,0.0005866618,0.000039957642,0.0006184977,0.009302335],"genre_scores_gemma":[0.9972009,0.00037178746,0.0011655149,0.00021773638,0.000069542344,0.00023996618,0.000007692051,0.00004056647,0.0006863177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992317,0.000018167268,0.00016752347,0.00021295655,0.00017096777,0.00019871829],"domain_scores_gemma":[0.9995926,0.00010602989,0.000009600586,0.00022307993,0.00002249332,0.000046180237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001485861,0.0001562104,0.00011940447,0.00007318763,0.00014580262,0.000048512447,0.00010561105,0.000046686255,0.00014295327],"category_scores_gemma":[1.417244e-7,0.00011438195,0.000041043037,0.00029498455,0.000038043025,0.000063520245,0.0000039659776,0.00015331805,0.00002149338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008010465,0.000050712104,0.000014602451,0.0008204807,0.00031868237,0.000011421923,0.00029942248,0.98284763,0.00012960439,0.0002923294,0.002729107,0.012405877],"study_design_scores_gemma":[0.00031112702,0.00025382236,0.00009309768,0.00069962506,0.00035514118,0.0000050569224,0.00012441112,0.98728275,0.0036965518,0.00013212203,0.006832425,0.00021387573],"about_ca_topic_score_codex":0.000009235079,"about_ca_topic_score_gemma":0.000055252476,"teacher_disagreement_score":0.9918732,"about_ca_system_score_codex":0.00006889085,"about_ca_system_score_gemma":0.0000061265764,"threshold_uncertainty_score":0.4664361},"labels":[],"label_agreement":null},{"id":"W4400489440","doi":"10.1109/tnsm.2024.3423762","title":"Authentication of Smart Grid by Integrating QKD and Blockchain in SCADA Systems","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Blockchain; SCADA; Smart grid; Internet of Things; Computer science; Authentication (law); Grid; Computer security; Computer network; Engineering; Electrical engineering; Geology","score_opus":0.004897634748142978,"score_gpt":0.1902326991166655,"score_spread":0.1853350643685225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400489440","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77241623,0.007526781,0.21243519,0.0005961417,0.003569401,0.0007688616,0.00003141734,0.0003153232,0.0023406364],"genre_scores_gemma":[0.99806,0.001596417,0.000116097865,0.00004151104,0.000046132434,0.000040377556,0.0000021264045,0.000012030544,0.00008531199],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939626,0.000022422582,0.00019017063,0.00015580501,0.00009432078,0.00014104029],"domain_scores_gemma":[0.99980074,0.00004754601,0.000011129235,0.00009684497,0.000008918905,0.000034805114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016056627,0.00010268262,0.00011186024,0.00009780941,0.00005518317,0.00004680591,0.000055252993,0.000044852826,0.000004847704],"category_scores_gemma":[1.837907e-7,0.00009596967,0.000017321161,0.00035147584,0.000016912914,0.000045501907,0.0000018777267,0.00013513441,0.000002761939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019597112,0.000055103934,0.000055094883,0.0028552013,0.00014000233,0.000009815826,0.0022181594,0.9649667,0.00038565695,0.0013537112,0.0011318073,0.026809113],"study_design_scores_gemma":[0.00015351237,0.000031888892,0.00016786843,0.00070691115,0.00004499887,0.000003817119,0.0008776589,0.9930726,0.00023706313,0.000053553922,0.004525744,0.00012435888],"about_ca_topic_score_codex":0.00012265565,"about_ca_topic_score_gemma":0.000402357,"teacher_disagreement_score":0.22564374,"about_ca_system_score_codex":0.000022733155,"about_ca_system_score_gemma":0.0000026943685,"threshold_uncertainty_score":0.39135295},"labels":[],"label_agreement":null},{"id":"W4400680251","doi":"10.1109/tnsm.2024.3416861","title":"Guest Editorial: Special section on Networks, Systems, and Services Operations and Management Through Intelligence","year":2024,"lang":"en","type":"editorial","venue":"IEEE Transactions on Network and Service Management","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; Western University; Dalhousie University","funders":"","keywords":"Special section; Section (typography); Computer science; Engineering management; Telecommunications; Engineering; Operating system; Engineering physics","score_opus":0.02197589345926555,"score_gpt":0.2607808064686497,"score_spread":0.23880491300938417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400680251","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003881695,0.001100321,0.024234453,0.0005288138,0.9626817,0.0014435073,0.000096416115,0.00031652703,0.009559428],"genre_scores_gemma":[0.0009971068,0.03158624,0.00013212077,0.00093848584,0.9641843,0.00034802457,0.00039853065,0.00013063797,0.0012845435],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99610215,0.000046814563,0.00075169397,0.0014798489,0.000964037,0.00065547036],"domain_scores_gemma":[0.9986307,0.00016290767,0.00020970806,0.0006527458,0.00028386165,0.000060083385],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00055358175,0.00089353975,0.0006417718,0.00045840399,0.0010254472,0.0027890862,0.00049244775,0.0007084562,0.000054841126],"category_scores_gemma":[0.0000024323542,0.0008407578,0.00009107188,0.0011524006,0.00010802819,0.0011385678,0.00011423999,0.0010791696,0.00017899895],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022027128,0.00014350627,0.000001391706,0.007227855,0.00048216537,0.00003919806,0.00008248846,0.07787908,2.0563057e-7,0.0050273943,0.8977198,0.011176652],"study_design_scores_gemma":[0.00031000542,0.000061073086,0.000009298098,0.0024790824,0.001111689,0.0000032611774,0.0004897378,0.023780491,9.625019e-7,0.0003925967,0.9705753,0.00078655896],"about_ca_topic_score_codex":0.0016049569,"about_ca_topic_score_gemma":0.0033886784,"teacher_disagreement_score":0.07285545,"about_ca_system_score_codex":0.000102908096,"about_ca_system_score_gemma":0.000018153756,"threshold_uncertainty_score":0.9994043},"labels":[],"label_agreement":null},{"id":"W4401070593","doi":"10.1109/tnsm.2024.3434955","title":"Optimized FlexEthernet for Inter-Domain Traffic Restoration","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Computer network; Domain (mathematical analysis)","score_opus":0.01642176701726595,"score_gpt":0.23981779184458898,"score_spread":0.22339602482732304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401070593","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010055839,0.00039526017,0.9914808,0.00356634,0.0017609587,0.0005812142,0.000005126714,0.0005333182,0.00067136117],"genre_scores_gemma":[0.63120586,0.0019175749,0.35593843,0.006628557,0.0007231466,0.0010227623,0.000024777588,0.0000962333,0.0024426798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876285,0.00005434859,0.00023363251,0.0004864956,0.00015417689,0.0003084768],"domain_scores_gemma":[0.99934274,0.00016481978,0.000031088417,0.00034963473,0.00003229535,0.000079411286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033797984,0.00019227494,0.0001638407,0.00012902178,0.00024345228,0.00036995075,0.00030562063,0.00007184138,0.000017689654],"category_scores_gemma":[2.6815889e-7,0.00017680707,0.00009338819,0.0006345235,0.000015133778,0.0002392814,0.0000070599417,0.00014369415,0.000029823766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072057555,0.000052005205,2.4396726e-7,0.00014076754,0.000115549476,0.000010568827,0.0004643445,0.65217936,0.0000015806042,0.007471167,0.0057909205,0.33370143],"study_design_scores_gemma":[0.000689497,0.00017394837,0.000011985304,0.00019417313,0.00006740901,0.000005604095,0.000079070465,0.8999234,0.000010641124,0.003087073,0.095511906,0.00024532794],"about_ca_topic_score_codex":0.000009283086,"about_ca_topic_score_gemma":0.00007228228,"teacher_disagreement_score":0.6355424,"about_ca_system_score_codex":0.000045957117,"about_ca_system_score_gemma":0.0000156585,"threshold_uncertainty_score":0.7209983},"labels":[],"label_agreement":null},{"id":"W4401211238","doi":"10.1109/tnsm.2024.3437165","title":"A Survey on Replica Transfer Optimization Schemes in Geographically Distributed Data Centers","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Replica; Transfer (computing); Distributed database; Distributed computing; Data center; Computer network; Parallel computing","score_opus":0.027917972642492548,"score_gpt":0.25037009155462886,"score_spread":0.22245211891213632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401211238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00096211646,0.00014080964,0.99543214,0.0015940701,0.0007420068,0.000366858,0.00018358575,0.0002866855,0.00029172943],"genre_scores_gemma":[0.99240863,0.00059987593,0.005572039,0.0008561989,0.000047502333,0.000034863646,0.00041479233,0.000017945953,0.00004812489],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808794,0.00017459123,0.00032462334,0.0008100191,0.00026163264,0.00034121715],"domain_scores_gemma":[0.998811,0.000142794,0.00002023515,0.0009024253,0.000032851007,0.0000906894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006742779,0.0002089498,0.00018912315,0.00018537426,0.00016169669,0.00040407214,0.00078953616,0.00007203834,0.0000084177],"category_scores_gemma":[8.119997e-7,0.00019715841,0.00004607308,0.0017234586,0.000017089546,0.00023132366,0.000020275285,0.00023137659,0.000013254574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046648605,0.00014800653,0.000055937377,0.00014035538,0.000105216575,0.000036986778,0.00008780288,0.96979845,5.8831563e-7,0.0014834157,0.0009943587,0.027102217],"study_design_scores_gemma":[0.00036718653,0.00007202272,0.0017045431,0.00037848036,0.000024828443,0.0000026733667,0.000012480813,0.9864435,0.0000019122979,0.0000570526,0.010724154,0.00021112662],"about_ca_topic_score_codex":0.00018831198,"about_ca_topic_score_gemma":0.00043919813,"teacher_disagreement_score":0.99144655,"about_ca_system_score_codex":0.00003333127,"about_ca_system_score_gemma":0.000017880637,"threshold_uncertainty_score":0.8039887},"labels":[],"label_agreement":null},{"id":"W4401211274","doi":"10.1109/tnsm.2024.3436887","title":"Coexistence of Hybrid VLC-RF and Wi-Fi for Indoor Wireless Communication Systems: An Intelligent Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Thousand Young Talents Program of China; National Natural Science Foundation of China; Natural Science Foundation of Xiamen City","keywords":"Computer science; Visible light communication; Wireless; Computer network; Telecommunications; Electrical engineering; Light-emitting diode","score_opus":0.02268869220296484,"score_gpt":0.24024817285904182,"score_spread":0.21755948065607697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401211274","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07224264,0.0024788445,0.92065424,0.0001465558,0.00099662,0.0010215839,0.00004198866,0.00032090553,0.0020966232],"genre_scores_gemma":[0.9926383,0.003929177,0.0028340842,0.000047292542,0.00005689209,0.00031751208,0.00001850191,0.000033397082,0.0001248318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920285,0.00003367817,0.0002638349,0.0002192107,0.00011288802,0.00016755138],"domain_scores_gemma":[0.9994996,0.00005819541,0.000026657546,0.0003145634,0.000036786696,0.000064150685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021493578,0.00015142899,0.00017369208,0.000110638466,0.00012590694,0.00008641447,0.00014233975,0.000038351278,0.0000037778295],"category_scores_gemma":[1.5444613e-7,0.00015653142,0.00003426259,0.00025207552,0.000035968118,0.00014219573,0.000003972325,0.00013258168,0.0000025682828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035748897,0.00007093425,0.0000020629936,0.002934747,0.00028172464,0.0000014180221,0.0007196778,0.949799,0.000054687065,0.0029443938,0.00046776762,0.04268786],"study_design_scores_gemma":[0.00022714621,0.00006631808,0.000020753685,0.0003855775,0.00015753292,0.000017769997,0.0017040346,0.9853439,0.00044588206,0.00016011743,0.0112777995,0.0001931816],"about_ca_topic_score_codex":0.000035580688,"about_ca_topic_score_gemma":0.000025154575,"teacher_disagreement_score":0.9203957,"about_ca_system_score_codex":0.000045910456,"about_ca_system_score_gemma":0.0000046945015,"threshold_uncertainty_score":0.6383166},"labels":[],"label_agreement":null},{"id":"W4401211277","doi":"10.1109/tnsm.2024.3437217","title":"Generalizable 5G RAN/MEC Slicing and Admission Control for Reliable Network Operation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Rogers Communications (Canada); University of Waterloo","funders":"","keywords":"Computer science; Ran; Slicing; Computer network; Admission control; Radio access network; Quality of service; World Wide Web; Base station","score_opus":0.007678012404077505,"score_gpt":0.21153311780919276,"score_spread":0.20385510540511526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401211277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005250157,0.0017349179,0.9936953,0.00027841475,0.0012757295,0.00093328353,0.000008066974,0.00039057704,0.0011586861],"genre_scores_gemma":[0.9545997,0.006169574,0.03490821,0.0010714817,0.00070293556,0.00063993066,0.000033963544,0.00013414871,0.0017400774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907976,0.000020201225,0.00024382719,0.0002806987,0.000082290695,0.0002932041],"domain_scores_gemma":[0.999661,0.000058787722,0.000017606822,0.00015327772,0.000028482888,0.000080798054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019917318,0.000190492,0.00018218788,0.00007244561,0.00029807008,0.00014251859,0.00005089566,0.0000770801,0.000020047433],"category_scores_gemma":[3.0973317e-7,0.00018905259,0.000035236262,0.00028018025,0.000006386596,0.00022647777,0.0000013935371,0.00010302738,0.000006717308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038361737,0.000007747869,0.0000019953077,0.00065418734,0.00011133309,0.000002265841,0.000115585484,0.98394483,0.00008438484,0.0005536551,0.0022962743,0.012189395],"study_design_scores_gemma":[0.0006974088,0.000047138426,0.00000477284,0.00035712714,0.00014170828,0.0000035428411,0.000067412126,0.9703013,0.0000921188,0.00022736662,0.027866116,0.00019403528],"about_ca_topic_score_codex":0.000021163925,"about_ca_topic_score_gemma":0.000095845404,"teacher_disagreement_score":0.9587871,"about_ca_system_score_codex":0.000057154662,"about_ca_system_score_gemma":0.0000057842603,"threshold_uncertainty_score":0.7709341},"labels":[],"label_agreement":null},{"id":"W4401211278","doi":"10.1109/tnsm.2024.3436674","title":"UAV-Employed Intelligent Approach to Identify Injured Soldier on Blockchain-Integrated Internet of Battlefield Things","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Blockchain; Battlefield; Computer science; Internet of Things; The Internet; Computer security; Embedded system; Computer network; World Wide Web","score_opus":0.02693479638974784,"score_gpt":0.2869319036430954,"score_spread":0.25999710725334757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401211278","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072399885,0.00010875256,0.9845405,0.0014863177,0.0015787064,0.00041860066,0.0000037253446,0.0002677165,0.004355653],"genre_scores_gemma":[0.9569174,0.00017933302,0.038804162,0.0030738688,0.0000701351,0.000083370214,0.0000030124863,0.000027785769,0.0008409247],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981479,0.00015044857,0.00037839785,0.00065259816,0.00032685933,0.0003438029],"domain_scores_gemma":[0.9990434,0.00018131822,0.000049058523,0.0005407113,0.00006729546,0.000118204945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075604476,0.00026014406,0.00027887028,0.000305709,0.00010919549,0.0002430239,0.00057286245,0.00008885044,0.000021007798],"category_scores_gemma":[0.0000016315076,0.0002248626,0.00011518878,0.001176175,0.000020678122,0.00012617483,0.000024825207,0.00033065258,0.00004097079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001644032,0.00043271872,0.000011980795,0.0006305119,0.0006927722,0.00003422567,0.0047307983,0.21899721,0.00008678636,0.017929317,0.0041356655,0.75215364],"study_design_scores_gemma":[0.00080042955,0.001005366,0.00093784847,0.0017827216,0.00024694335,0.00002041617,0.000665947,0.9380199,0.0073003266,0.004568684,0.043530528,0.0011209317],"about_ca_topic_score_codex":0.00014167788,"about_ca_topic_score_gemma":0.000051441435,"teacher_disagreement_score":0.9496774,"about_ca_system_score_codex":0.000045056408,"about_ca_system_score_gemma":0.00001678447,"threshold_uncertainty_score":0.91696304},"labels":[],"label_agreement":null},{"id":"W4401328662","doi":"10.1109/tnsm.2024.3438438","title":"5G Service Function Chain Provisioning: A Deep Reinforcement Learning-Based Framework","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal; Concordia University; École de Technologie Supérieure","funders":"Mitacs","keywords":"Computer science; Reinforcement learning; Provisioning; Computer network; Function (biology); Chain (unit); Distributed computing; Artificial intelligence","score_opus":0.009964577736315216,"score_gpt":0.20665660305126587,"score_spread":0.19669202531495067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401328662","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001035383,0.00018469099,0.96486926,0.00096656324,0.0016152448,0.0005432598,0.0000026234177,0.0012587978,0.02952415],"genre_scores_gemma":[0.99618846,0.00026668882,0.000762878,0.0017584203,0.00015357959,0.00026711044,0.00002380541,0.00007066732,0.00050839526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986143,0.000027042952,0.00034980732,0.00030527564,0.00031448092,0.00038908442],"domain_scores_gemma":[0.9994649,0.000092995884,0.000023420329,0.0002499512,0.000044355882,0.00012432014],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018503539,0.00029402765,0.00016554382,0.00021001122,0.00025555948,0.00031462585,0.00013639501,0.00016784071,0.00022577691],"category_scores_gemma":[4.317953e-7,0.00030354556,0.00007237384,0.001066308,0.000013709184,0.0003256213,0.0000023767102,0.0006056381,0.00022871538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004106137,0.000025126337,0.0000017519471,0.0011495884,0.00018618965,0.000007697052,0.0003428055,0.9359397,0.0000029997097,0.0011544516,0.00033549755,0.060813103],"study_design_scores_gemma":[0.00030464705,0.00011087416,0.000028798773,0.0007432928,0.00013349879,0.0000026708683,0.0004635177,0.9314515,0.00007956724,0.000335386,0.06602926,0.00031700186],"about_ca_topic_score_codex":0.000010778344,"about_ca_topic_score_gemma":0.000045405126,"teacher_disagreement_score":0.99515307,"about_ca_system_score_codex":0.00010603971,"about_ca_system_score_gemma":0.00001376207,"threshold_uncertainty_score":0.99994165},"labels":[],"label_agreement":null},{"id":"W4401387452","doi":"10.1109/tnsm.2024.3438621","title":"SATI: Sidechain-Based Access Control &amp; Trust Mechanism for IoT Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Access control; Computer network; Mechanism (biology); Control (management); Internet of Things; Computer security; Distributed computing; Artificial intelligence","score_opus":0.015041433367642134,"score_gpt":0.2500456639144614,"score_spread":0.2350042305468193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401387452","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006044692,0.00019037169,0.9797238,0.016583906,0.0006977903,0.001108911,0.000011531951,0.0007642349,0.0003149611],"genre_scores_gemma":[0.95224935,0.00023645369,0.035479944,0.010483901,0.000119566714,0.0012024397,0.000005537379,0.000029110228,0.00019371926],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844456,0.000044054643,0.00027586723,0.0006507107,0.0001614168,0.00042337744],"domain_scores_gemma":[0.998881,0.00022178821,0.000049099388,0.00067837053,0.000073815085,0.00009591747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035767944,0.00024153816,0.00021562948,0.00019724036,0.00050880655,0.0003574679,0.000749585,0.00016880732,0.000017553382],"category_scores_gemma":[5.137076e-7,0.00023297395,0.00010784894,0.0009348593,0.000032058128,0.000100734775,0.000012430887,0.0002685511,0.000015245298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003411836,0.0001172377,0.000002258548,0.00019325025,0.0001999291,0.0000067306787,0.00007010638,0.400464,0.000006561163,0.45285317,0.0013782923,0.14467436],"study_design_scores_gemma":[0.00060370803,0.000062744584,0.000017515753,0.00006857482,0.00010343444,0.0000029507403,0.000014808308,0.9352823,0.000070193986,0.043802377,0.019733904,0.0002374828],"about_ca_topic_score_codex":0.000030570354,"about_ca_topic_score_gemma":0.00039240782,"teacher_disagreement_score":0.95164484,"about_ca_system_score_codex":0.000046592773,"about_ca_system_score_gemma":0.000029625166,"threshold_uncertainty_score":0.95004016},"labels":[],"label_agreement":null},{"id":"W4401507831","doi":"10.1109/tnsm.2024.3442688","title":"TARA: Tenant-Aware Resource Allocation in Multi-Tenant Data Centers","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Multitenancy; Resource allocation; Computer network; Resource management (computing); Distributed computing; Computer security; Operating system; Software; Software as a service","score_opus":0.03062626894540357,"score_gpt":0.25225895813153787,"score_spread":0.2216326891861343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401507831","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021021836,0.0002805696,0.9818774,0.012997795,0.0010208115,0.00051897846,0.0000068546324,0.00043001684,0.0007653892],"genre_scores_gemma":[0.98203194,0.0007164128,0.010474153,0.0036010316,0.00015256311,0.00007851575,0.000024701436,0.00004386952,0.0028768226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997814,0.00010596611,0.00034128656,0.0009468254,0.00030914397,0.00048274905],"domain_scores_gemma":[0.9986399,0.000073967545,0.00004348512,0.0011196573,0.000020093987,0.0001029259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059582246,0.0002497447,0.00018681074,0.00027383142,0.00026386668,0.0003384753,0.0011068956,0.000061982086,0.000005705631],"category_scores_gemma":[4.3927756e-7,0.00017219128,0.000052064734,0.0011242043,0.000023383776,0.000100463665,0.00010728689,0.0002779749,0.00004950842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025249738,0.00019320114,0.000012669228,0.0003169531,0.00014050186,0.00013600975,0.00065185555,0.55383563,0.0000020630807,0.0006173425,0.0022639341,0.44180462],"study_design_scores_gemma":[0.00042705407,0.000059722217,0.00037092564,0.00048563533,0.000047444395,0.000007418526,0.0002931608,0.89456725,0.00000614588,0.000054461583,0.1034459,0.0002348524],"about_ca_topic_score_codex":0.0001642103,"about_ca_topic_score_gemma":0.000646748,"teacher_disagreement_score":0.97992975,"about_ca_system_score_codex":0.000082034785,"about_ca_system_score_gemma":0.00001578431,"threshold_uncertainty_score":0.7021757},"labels":[],"label_agreement":null},{"id":"W4401726558","doi":"10.1109/tnsm.2024.3447532","title":"Real-Time Adaptive Anomaly Detection in Industrial IoT Environments","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Anomaly detection; Internet of Things; Real-time computing; Embedded system; Data mining","score_opus":0.014855758813272366,"score_gpt":0.2152373455103004,"score_spread":0.20038158669702805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401726558","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010619025,0.000028312825,0.98418736,0.0005869712,0.00030199802,0.0004807698,0.000003086702,0.00033891137,0.0034535488],"genre_scores_gemma":[0.99055165,0.0006893038,0.0070577455,0.00026500158,0.00009634508,0.00030252003,0.0000010990153,0.00001743316,0.0010188876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888515,0.000050457093,0.00021661594,0.00046147747,0.00015591197,0.00023038244],"domain_scores_gemma":[0.99956113,0.00003959023,0.000031436575,0.00029706227,0.000006868118,0.000063888845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018179893,0.0001549879,0.000118416545,0.00020071174,0.00019034282,0.00012690543,0.00021554879,0.00009336276,0.00003252604],"category_scores_gemma":[9.148087e-8,0.00015940196,0.00004853289,0.00091645407,0.000017108847,0.00016689781,0.000010757732,0.00021766416,0.00010225416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048884634,0.00015389088,0.000004501554,0.00003203166,0.00009958378,0.000028574008,0.00022317779,0.040875908,0.00070840336,0.0039369515,0.00028695422,0.9536011],"study_design_scores_gemma":[0.0006541843,0.0004057311,0.00089014415,0.00018182334,0.00008177225,0.000016210963,0.00010249623,0.94971037,0.00410232,0.002461644,0.04088255,0.00051077135],"about_ca_topic_score_codex":0.0001517243,"about_ca_topic_score_gemma":0.00012408065,"teacher_disagreement_score":0.97993267,"about_ca_system_score_codex":0.00010720611,"about_ca_system_score_gemma":0.000010251829,"threshold_uncertainty_score":0.65002227},"labels":[],"label_agreement":null},{"id":"W4401749422","doi":"10.1109/tnsm.2024.3448312","title":"IoTDL<sup>2</sup>AIDS: Toward IoT-Based System Architecture Supporting Distributed LSTM Learning for Adaptive IDS on UAS","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Internet of Things; Architecture; Distributed computing; Computer network; Computer architecture; Embedded system","score_opus":0.01696263295054499,"score_gpt":0.24646261700509312,"score_spread":0.22949998405454813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401749422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014504174,0.000059073463,0.99329215,0.0013770637,0.0003891567,0.00088043057,0.00011506981,0.0016737994,0.0007628443],"genre_scores_gemma":[0.8987416,0.000027408332,0.09968168,0.0007278937,0.00014005468,0.00041673574,0.000079738566,0.00004711356,0.00013777944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99768466,0.0001366331,0.00039762323,0.00086191186,0.00034340526,0.0005757912],"domain_scores_gemma":[0.99880534,0.00033085365,0.00009455952,0.0005755102,0.000060901715,0.00013281908],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000584204,0.00036066803,0.000304355,0.00027381757,0.00045533717,0.00044538477,0.0006357969,0.000111882924,0.0000077219065],"category_scores_gemma":[0.0000022819083,0.00033537738,0.00014664116,0.0009373446,0.000027643397,0.00014748704,0.000024720403,0.0004489044,0.000022640024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009683618,0.00006620561,0.0000029934386,0.0007714284,0.00020112442,0.00005662081,0.00060189713,0.7408222,0.000004638423,0.006520661,0.0011423656,0.24971302],"study_design_scores_gemma":[0.00039617857,0.0007474419,0.00001617821,0.0011771326,0.00014834749,0.00000961327,0.00064987614,0.97323245,0.00025680667,0.00035519857,0.022635687,0.00037509695],"about_ca_topic_score_codex":0.000044194,"about_ca_topic_score_gemma":0.000020345038,"teacher_disagreement_score":0.8972912,"about_ca_system_score_codex":0.00016330434,"about_ca_system_score_gemma":0.000039026465,"threshold_uncertainty_score":0.9999098},"labels":[],"label_agreement":null},{"id":"W4402263009","doi":"10.1109/tnsm.2024.3454758","title":"Multi-Agent DRL-Based Two-Timescale Resource Allocation for Network Slicing in V2X Communications","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"National Natural Science Foundation of China; Basic and Applied Basic Research Foundation of Guangdong Province; Shanghai Science and Technology Development Foundation","keywords":"Computer science; Slicing; Computer network; Resource management (computing); Resource allocation; Distributed computing; World Wide Web","score_opus":0.027964229502700513,"score_gpt":0.2680237836998674,"score_spread":0.2400595541971669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402263009","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00033828558,0.0012471393,0.988876,0.006946965,0.0006959014,0.0009112642,0.000004827592,0.0004154469,0.0005641603],"genre_scores_gemma":[0.72248286,0.0009145561,0.2674894,0.0073302374,0.0002706779,0.0009395102,0.00003783676,0.00006266093,0.00047225165],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819833,0.00013042663,0.00039228165,0.00058273406,0.00019712023,0.00049910956],"domain_scores_gemma":[0.99833447,0.00045744685,0.00005745634,0.0009988536,0.000046697827,0.00010509433],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000651261,0.0002458217,0.00021754141,0.00018385971,0.00050273305,0.0002974941,0.00072949176,0.00008147203,0.0000071238824],"category_scores_gemma":[8.126385e-7,0.00025069522,0.00010204839,0.0014168943,0.000030962085,0.00019811327,0.000028803102,0.0002735834,0.000020657535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022296703,0.00013213251,0.00002558409,0.00014022981,0.00008226091,0.000005099696,0.00031746982,0.88252354,0.0000033634121,0.0037530223,0.0018731933,0.1111218],"study_design_scores_gemma":[0.000811482,0.000064567204,0.0002847987,0.00042926206,0.000070018796,0.0000018245179,0.00006174348,0.9410103,0.000010472099,0.00054942624,0.056453325,0.00025280053],"about_ca_topic_score_codex":0.00010545055,"about_ca_topic_score_gemma":0.001313417,"teacher_disagreement_score":0.7221446,"about_ca_system_score_codex":0.00010321471,"about_ca_system_score_gemma":0.000031315594,"threshold_uncertainty_score":0.9999945},"labels":[],"label_agreement":null},{"id":"W4402401309","doi":"10.1109/tnsm.2024.3457579","title":"Time-Distributed Feature Learning for Internet of Things Network Traffic Classification","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"NovAtel (Canada); Mitel (Canada); Trusted Positioning (Canada); Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Feature (linguistics); The Internet; Computer network; Traffic classification; Internet of Things; Artificial intelligence; Distributed computing; World Wide Web","score_opus":0.011215908441935379,"score_gpt":0.22349208721121702,"score_spread":0.21227617876928165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402401309","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002224198,0.00027972725,0.9944405,0.0014194214,0.00059228117,0.00030886463,0.000002456122,0.00027401926,0.0004585358],"genre_scores_gemma":[0.9886675,0.00014145869,0.009008845,0.00045111513,0.00013181442,0.000049229908,0.00002954291,0.000019041969,0.0015014344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986328,0.000069966285,0.00030318246,0.00047672258,0.00020038843,0.00031697488],"domain_scores_gemma":[0.9994612,0.00014728852,0.000087920096,0.00016686469,0.000076430086,0.00006033291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046077947,0.00019583467,0.00023341874,0.00011026237,0.00018743833,0.00022437045,0.00036156076,0.00009660404,0.000020757856],"category_scores_gemma":[7.344286e-7,0.00017900272,0.00015636883,0.00077468157,0.000022798062,0.0002290928,0.00000957492,0.00028335216,0.000020338899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027073454,0.000039745915,4.253492e-7,0.00020779374,0.00029557606,0.000003988697,0.0008920132,0.86853915,0.000004319288,0.025430806,0.0048609837,0.099698104],"study_design_scores_gemma":[0.0001825004,0.00010725679,0.00001718097,0.00029195184,0.0001550635,0.0000033784584,0.0001333417,0.9764009,0.000010966515,0.000039663733,0.022492733,0.00016504354],"about_ca_topic_score_codex":0.000004158173,"about_ca_topic_score_gemma":0.000024315692,"teacher_disagreement_score":0.98644334,"about_ca_system_score_codex":0.00004107629,"about_ca_system_score_gemma":0.000013438978,"threshold_uncertainty_score":0.7299519},"labels":[],"label_agreement":null},{"id":"W4402436629","doi":"10.1109/tnsm.2024.3457858","title":"Exploring QUIC Security and Privacy: A Comprehensive Survey on QUIC Security and Privacy Vulnerabilities, Threats, Attacks, and Future Research Directions","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Computer security; Internet privacy; Information privacy; Cloud computing security; Security analysis; Cloud computing","score_opus":0.1246770513392024,"score_gpt":0.32338217032381433,"score_spread":0.19870511898461193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402436629","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95515925,0.010764716,0.015249927,0.012728202,0.002596494,0.002037111,0.00008338432,0.0006258364,0.000755067],"genre_scores_gemma":[0.9692443,0.029540494,0.00020598054,0.00039300905,0.00021822973,0.0002097678,0.000010115639,0.000026805039,0.00015127819],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969513,0.00067665573,0.00035723436,0.0010304404,0.00047894183,0.0005054528],"domain_scores_gemma":[0.99823207,0.00060152763,0.000044118802,0.00071010104,0.00015152682,0.00026064442],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010960397,0.00032556304,0.00033244828,0.0003561502,0.0010468655,0.00088842027,0.00032944253,0.00010467245,0.000010800354],"category_scores_gemma":[0.0000024752158,0.0003076773,0.000049738814,0.001195395,0.0001237197,0.0006076361,0.00008271805,0.0006414082,0.000012127844],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049591594,0.0014886695,0.0019687677,0.008557312,0.001821489,0.0001596655,0.5313062,0.00073681877,0.000030657844,0.05256816,0.004223093,0.39664322],"study_design_scores_gemma":[0.0035097392,0.001411849,0.098859064,0.002389418,0.00038821192,0.00016056077,0.017949613,0.404054,0.00010509946,0.02949218,0.4392066,0.0024737052],"about_ca_topic_score_codex":0.00073250214,"about_ca_topic_score_gemma":0.0018012269,"teacher_disagreement_score":0.5133566,"about_ca_system_score_codex":0.000082969425,"about_ca_system_score_gemma":0.00003305471,"threshold_uncertainty_score":0.99993753},"labels":[],"label_agreement":null},{"id":"W4402569568","doi":"10.1109/tnsm.2024.3462831","title":"Adaptive Feature Selection for Predicting Application Performance Degradation in Edge Cloud Environments","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); Concordia University","funders":"","keywords":"Computer science; Cloud computing; Degradation (telecommunications); Feature selection; Selection (genetic algorithm); Enhanced Data Rates for GSM Evolution; Feature (linguistics); Artificial intelligence; Telecommunications; Operating system","score_opus":0.009337657165588544,"score_gpt":0.20652106274653523,"score_spread":0.19718340558094669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402569568","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029573057,0.00010675089,0.9668474,0.0011586449,0.0006589375,0.00080065586,0.0000014746166,0.00019509062,0.0006579798],"genre_scores_gemma":[0.9870686,0.00019242703,0.0107581075,0.00041781433,0.00019894178,0.00032962125,0.000004428436,0.000017580462,0.0010124675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880534,0.00003900919,0.00018455235,0.0005137362,0.00018381726,0.00027354888],"domain_scores_gemma":[0.9996298,0.0000615122,0.000042439886,0.00020997105,0.000012227152,0.000044076358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003218776,0.00016493065,0.000110186615,0.00016501329,0.0003022309,0.00013362375,0.00021588951,0.00006195038,0.0000011655965],"category_scores_gemma":[1.9060968e-7,0.0001619486,0.000043079886,0.00071706774,0.000009844655,0.00007965935,0.000012520302,0.00018550496,0.000010919315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023793527,0.000050824015,0.000043628454,0.00015335537,0.000051553445,9.4529554e-7,0.0002655758,0.5611048,0.00000790425,0.0013054096,0.00021315983,0.43677908],"study_design_scores_gemma":[0.0003041909,0.00011878615,0.00092522096,0.00017591756,0.000042019656,0.0000028174725,0.000090731744,0.9824862,0.00008413932,0.00021767194,0.015395782,0.00015654987],"about_ca_topic_score_codex":0.00002363581,"about_ca_topic_score_gemma":0.00008479605,"teacher_disagreement_score":0.95749557,"about_ca_system_score_codex":0.00012266889,"about_ca_system_score_gemma":0.00000752007,"threshold_uncertainty_score":0.66040725},"labels":[],"label_agreement":null},{"id":"W4403022266","doi":"10.1109/tnsm.2024.3470989","title":"Metis: Selecting Diverse Atlas Vantage Points","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Historical Geography and Cartography","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metis; Computer science; Atlas (anatomy); World Wide Web; Medicine","score_opus":0.013570473438099681,"score_gpt":0.25465879533961017,"score_spread":0.24108832190151047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403022266","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026683643,0.0040394273,0.34543446,0.011028085,0.0123818,0.0019215136,0.0000372084,0.0022171529,0.5962567],"genre_scores_gemma":[0.99127066,0.0041858656,0.0007418257,0.0013621307,0.0003066138,0.000058452788,0.0000018692668,0.000017534061,0.0020550224],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99877226,0.00011907853,0.00014051856,0.00031792492,0.00029193953,0.0003582528],"domain_scores_gemma":[0.99960655,0.00008808495,0.000018975104,0.00012256298,0.000028155115,0.00013566126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040436955,0.00013062845,0.00011645623,0.00017411727,0.0010903756,0.00015260883,0.00013302182,0.00006838455,0.00022723016],"category_scores_gemma":[4.077138e-7,0.00012976468,0.00012841952,0.0017574761,0.00005605872,0.00014829155,0.000002832896,0.00019802907,0.00008162029],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012735277,0.00034613267,0.00023624285,0.0005961401,0.0012729997,0.0002180665,0.028512152,0.011054226,0.000012911157,0.06162904,0.019424697,0.87657005],"study_design_scores_gemma":[0.00024476022,0.000079538644,0.00017621982,0.00017288458,0.00035743785,0.0000013521188,0.007055586,0.0011531437,0.000014580097,0.0024453653,0.9879695,0.00032961133],"about_ca_topic_score_codex":0.0007292134,"about_ca_topic_score_gemma":0.0031389107,"teacher_disagreement_score":0.96854484,"about_ca_system_score_codex":0.000048841535,"about_ca_system_score_gemma":0.000013434728,"threshold_uncertainty_score":0.8386398},"labels":[],"label_agreement":null},{"id":"W4403390883","doi":"10.1109/tnsm.2024.3479246","title":"Monarch: Monitoring Architecture for 5G and Beyond Network Slices","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Rogers Communications (Canada); University of Regina; University of Waterloo","funders":"","keywords":"Computer science; Architecture; Computer network; Network architecture; Computer architecture; Distributed computing","score_opus":0.016818608223311212,"score_gpt":0.23689469584401632,"score_spread":0.2200760876207051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403390883","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022678033,0.0035831763,0.9858777,0.0038368532,0.0023211665,0.0005465002,0.0000049082964,0.00044397984,0.0011178919],"genre_scores_gemma":[0.8677891,0.006944668,0.11820044,0.0037936165,0.0019237036,0.00054931355,0.0000057701723,0.000079708705,0.000713668],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983815,0.00004055794,0.00022253412,0.00063236296,0.00020501652,0.00051801535],"domain_scores_gemma":[0.9991217,0.00032035305,0.000032703894,0.00035763555,0.000029391706,0.00013816252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028374122,0.0002603523,0.00020260992,0.000114288705,0.0005103429,0.00047070708,0.0003287079,0.0000839414,0.000004299476],"category_scores_gemma":[3.119067e-7,0.0002349952,0.00007938043,0.00074401137,0.00002568762,0.00021156503,0.000013770722,0.00024742278,0.000006559604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034784134,0.00002770711,0.0000350222,0.00033631665,0.0001760607,0.000018322724,0.0005055803,0.4400641,0.0000019590639,0.00546148,0.0015783996,0.55176026],"study_design_scores_gemma":[0.0010310203,0.00045894555,0.001439804,0.0009572204,0.00031030373,0.000043999917,0.00021026867,0.735208,0.00006879083,0.036873233,0.222427,0.0009714444],"about_ca_topic_score_codex":0.000020970125,"about_ca_topic_score_gemma":0.00006826202,"teacher_disagreement_score":0.8676773,"about_ca_system_score_codex":0.000022167143,"about_ca_system_score_gemma":0.000011592181,"threshold_uncertainty_score":0.9582826},"labels":[],"label_agreement":null},{"id":"W4403447053","doi":"10.1109/tnsm.2024.3481662","title":"Dynamic Policy Decision/Enforcement Security Zoning Through Stochastic Games and Meta Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Enforcement; Zoning; Computer security; Security policy; Policy learning; Law enforcement; Machine learning; Law","score_opus":0.011360762144891571,"score_gpt":0.251661662731051,"score_spread":0.24030090058615944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403447053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027435357,0.0016527027,0.990681,0.0019869162,0.000772862,0.00034978456,0.000002517035,0.0003697972,0.0014409036],"genre_scores_gemma":[0.9852504,0.0040757055,0.008415136,0.0016266751,0.00009271839,0.000094766336,0.0000019474671,0.000022081964,0.00042053065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828506,0.00008450316,0.0002904913,0.0006341008,0.00030467845,0.00040119],"domain_scores_gemma":[0.99930686,0.00017980432,0.00004819531,0.0003220754,0.000034934987,0.00010814468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003386923,0.00027228467,0.00026146907,0.00021646338,0.0006591137,0.0005245216,0.00026190447,0.00008073685,0.00006934608],"category_scores_gemma":[0.0000011368007,0.00024237868,0.00010182705,0.0010844627,0.000034641314,0.00054572866,0.000040060037,0.00041119786,0.000028443705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003672645,0.0000490129,2.1986362e-7,0.00020418267,0.00076051004,0.000021800153,0.002182842,0.7174043,0.0000032846438,0.03266155,0.00021926202,0.24645631],"study_design_scores_gemma":[0.00026259816,0.0001675998,0.00001150982,0.00014432428,0.0005272416,0.000026998512,0.00017972762,0.9538638,0.000011801418,0.0203966,0.02413457,0.00027325176],"about_ca_topic_score_codex":0.00014362545,"about_ca_topic_score_gemma":0.00026694595,"teacher_disagreement_score":0.98250693,"about_ca_system_score_codex":0.000074218246,"about_ca_system_score_gemma":0.000021306723,"threshold_uncertainty_score":0.9883916},"labels":[],"label_agreement":null},{"id":"W4403511241","doi":"10.1109/tnsm.2024.3483013","title":"A Data Completion Algorithm Based on Low-Rank Prior Knowledge for Data-Driven Applications","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Algorithm; Data mining","score_opus":0.04574966949544952,"score_gpt":0.2960385690265718,"score_spread":0.25028889953112227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403511241","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000012078785,0.00017110769,0.9904932,0.0056492663,0.00048865395,0.001608512,0.00043232035,0.0003659695,0.0007789102],"genre_scores_gemma":[0.19827032,0.0024275184,0.77433467,0.012409505,0.0019821203,0.00591191,0.0030024438,0.0001694126,0.001492067],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981294,0.000043390704,0.00026872673,0.0010486699,0.00019275605,0.0003170467],"domain_scores_gemma":[0.9973929,0.00026120414,0.000043844117,0.0021416899,0.000050748313,0.000109627654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002939013,0.00021653873,0.0001717251,0.00012249639,0.0005717467,0.00036734273,0.0016078253,0.00005462898,0.000012109021],"category_scores_gemma":[2.3326405e-7,0.00020411686,0.000048397884,0.00094631844,0.00002573445,0.00033685833,0.000061045605,0.00018096727,0.00008268328],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012193013,0.00026971987,1.698479e-7,0.00025435418,0.00007750692,0.0000025833538,0.00003866714,0.13027191,0.0000037669154,0.008104058,0.012304769,0.8486603],"study_design_scores_gemma":[0.00023822045,0.000040944575,0.00001367118,0.00010641152,0.00007455426,0.000001336025,0.0000079849615,0.6868424,0.0000038351336,0.00038044585,0.3121414,0.00014881401],"about_ca_topic_score_codex":0.000011526033,"about_ca_topic_score_gemma":0.00011538236,"teacher_disagreement_score":0.84851146,"about_ca_system_score_codex":0.000035666206,"about_ca_system_score_gemma":0.000033190547,"threshold_uncertainty_score":0.8323644},"labels":[],"label_agreement":null},{"id":"W4403864140","doi":"10.1109/tnsm.2024.3486288","title":"Sustainable Task Offloading in Secure UAV-Assisted Smart Farm Networks: A Multi-Agent DRL With Action Mask Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada); University of Ottawa","funders":"Mitacs","keywords":"Computer science; Task (project management); Computer network; Embedded system; Action (physics); Distributed computing; Systems engineering","score_opus":0.010734784760253122,"score_gpt":0.20478679537137434,"score_spread":0.1940520106111212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403864140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049838256,0.0004569161,0.9895825,0.0001394241,0.00027225722,0.0008408386,0.0000039952292,0.00045483813,0.003265423],"genre_scores_gemma":[0.98421997,0.0021610912,0.011175778,0.00016980394,0.000083549494,0.0006606743,0.000040912946,0.00007456954,0.0014136452],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880344,0.000029413695,0.00024139669,0.00037539308,0.0001396169,0.00041073406],"domain_scores_gemma":[0.9996067,0.000023237591,0.00002162394,0.00023902307,0.00003201634,0.00007739466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016783555,0.00024756082,0.00017263016,0.00023504322,0.0002094044,0.00019049311,0.000098387834,0.00010206458,0.000017675748],"category_scores_gemma":[9.19946e-8,0.00023661106,0.000041437706,0.0014057093,0.00001404975,0.00017272882,0.000004040864,0.0002880494,0.000010358319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030755233,0.000100342666,0.000012686403,0.0006744017,0.00015803153,0.000017294415,0.0004842673,0.9666391,0.0000074375507,0.0004370014,0.00019350255,0.031245172],"study_design_scores_gemma":[0.0005085665,0.000032997516,0.00042050472,0.00016133851,0.00013648586,0.00000800173,0.0017093153,0.9835533,0.000010466112,0.00001987857,0.013175949,0.0002632149],"about_ca_topic_score_codex":0.00010516541,"about_ca_topic_score_gemma":0.0010339597,"teacher_disagreement_score":0.9792361,"about_ca_system_score_codex":0.00020637632,"about_ca_system_score_gemma":0.000010058676,"threshold_uncertainty_score":0.9648719},"labels":[],"label_agreement":null},{"id":"W4404057060","doi":"10.1109/tnsm.2024.3491432","title":"QoE-Oriented Dependent Task Scheduling Under Multi-Dimensional QoS Constraints Over Distributed Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Science Foundation of Fujian Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Scheduling (production processes); Quality of service; Distributed computing; Computer network; Processor scheduling; Task (project management); Resource (disambiguation)","score_opus":0.007862726144893627,"score_gpt":0.21581186756987972,"score_spread":0.20794914142498608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404057060","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047601,0.0007880691,0.9898268,0.00013591076,0.0028561132,0.0004965742,0.000049398386,0.0008429278,0.00024413165],"genre_scores_gemma":[0.98249114,0.0016707698,0.014608009,0.00050877914,0.00027842599,0.00011286372,0.00012634427,0.000095605916,0.00010805869],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983428,0.00003974175,0.000361575,0.0004737146,0.00025989965,0.0005222757],"domain_scores_gemma":[0.9994161,0.00009214831,0.00003048415,0.0002609705,0.000045113225,0.00015519712],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001361646,0.00035843762,0.00023320963,0.00013224836,0.0002653915,0.000116044954,0.000112304275,0.00014891112,0.00012418628],"category_scores_gemma":[3.034835e-7,0.00037839988,0.0000776757,0.000775837,0.00004872477,0.00020990935,0.000008034087,0.00042994157,0.00003034434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003122656,0.000055025863,0.0000054732404,0.00014773871,0.00037584596,0.00003858796,0.00003964439,0.97515047,0.000020892297,0.00052969065,0.00029996524,0.023305416],"study_design_scores_gemma":[0.00072552555,0.000021025757,0.0001450095,0.00034585927,0.00016637707,0.000010120681,0.00009472969,0.99635184,0.000030996176,0.000055187655,0.0016822155,0.00037113775],"about_ca_topic_score_codex":0.000007035719,"about_ca_topic_score_gemma":0.000103698694,"teacher_disagreement_score":0.97773105,"about_ca_system_score_codex":0.00015999436,"about_ca_system_score_gemma":0.000011076186,"threshold_uncertainty_score":0.9998668},"labels":[],"label_agreement":null},{"id":"W4404132848","doi":"10.1109/tnsm.2024.3493758","title":"FeD-TST: Federated Temporal Sparse Transformers for QoS Prediction in Dynamic IoT Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"CHIST-ERA; Agence Nationale de la Recherche","keywords":"Computer science; Quality of service; Computer network; Transformer; Internet of Things; Distributed computing; Embedded system; Electrical engineering; Voltage","score_opus":0.011949670142203094,"score_gpt":0.22899511207526793,"score_spread":0.21704544193306483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404132848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058403057,0.00024846554,0.9795691,0.0019181072,0.010319978,0.0008116506,0.0000017482744,0.0003889977,0.00090168155],"genre_scores_gemma":[0.9900886,0.00039677531,0.0065067625,0.0012962989,0.00079586275,0.00020744756,0.000019311181,0.00004036777,0.00064856606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982948,0.000053023818,0.00036347008,0.00058269664,0.00017731501,0.0005287488],"domain_scores_gemma":[0.9995799,0.00008267443,0.00003306687,0.00017335976,0.000033226068,0.00009775699],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041092132,0.00025062737,0.00020637392,0.00024078782,0.00043078407,0.00040840657,0.00022299397,0.00011219449,0.0000044907],"category_scores_gemma":[1.735122e-7,0.00025005869,0.0000967906,0.0011375047,0.000016816466,0.00024935778,0.000006222405,0.0002853715,0.00001274774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101231715,0.00011148898,0.000021704867,0.00036323912,0.00016516534,0.000035034554,0.0005707438,0.49969393,0.000006031113,0.00023941234,0.0031224524,0.4955696],"study_design_scores_gemma":[0.0005887444,0.00015169078,0.00025259564,0.0002784782,0.000055312736,0.0000071971526,0.000067461275,0.9801681,0.000009907767,0.00024750776,0.017938733,0.00023429174],"about_ca_topic_score_codex":0.000066259854,"about_ca_topic_score_gemma":0.00041948835,"teacher_disagreement_score":0.9842483,"about_ca_system_score_codex":0.00011286226,"about_ca_system_score_gemma":0.0000279392,"threshold_uncertainty_score":0.9999952},"labels":[],"label_agreement":null},{"id":"W4404486483","doi":"10.1109/tnsm.2024.3490734","title":"Incentive Mechanism Design for Trust-Driven Resources Trading in Computing Force Networks: Contract Theory Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Key Research and Development Program of China; Natural Science Foundation of Beijing Municipality","keywords":"Incentive; Computer science; Mechanism (biology); Mechanism design; Contract theory; Distributed computing; Computer security; Microeconomics","score_opus":0.014202429506682955,"score_gpt":0.2254746344237377,"score_spread":0.21127220491705473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404486483","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025128163,0.00040326058,0.9938552,0.0007247901,0.00030469373,0.001247227,0.0000027994101,0.00039705553,0.0005522075],"genre_scores_gemma":[0.91991377,0.00025575116,0.07870926,0.0006401467,0.000061314706,0.00033813203,0.0000020266502,0.000018903409,0.000060684022],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842435,0.0001290607,0.00028514746,0.00062141375,0.00012130933,0.00041873698],"domain_scores_gemma":[0.9991345,0.00038100005,0.000053046107,0.00034778728,0.000026130032,0.000057544225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008925612,0.00021167198,0.00022114375,0.00021633726,0.00042149457,0.00019765043,0.0005061742,0.00013888763,0.000002910288],"category_scores_gemma":[5.205045e-7,0.00020581321,0.00007233901,0.00091635383,0.000035895617,0.00015328226,0.000016472306,0.0003229485,0.0000018256989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004230077,0.00011160293,0.0000018950897,0.00012306475,0.00013315385,0.00000805789,0.0014764876,0.48881173,0.0000053555455,0.373701,0.00005730704,0.13552806],"study_design_scores_gemma":[0.00039714423,0.000069967755,0.000029995943,0.00012467451,0.00005386252,0.0000053121685,0.00033877618,0.9523505,0.000044818815,0.045700025,0.00068553165,0.00019942278],"about_ca_topic_score_codex":0.000012029851,"about_ca_topic_score_gemma":0.000026228425,"teacher_disagreement_score":0.91740096,"about_ca_system_score_codex":0.000062117855,"about_ca_system_score_gemma":0.000011048248,"threshold_uncertainty_score":0.8392819},"labels":[],"label_agreement":null},{"id":"W4404529510","doi":"10.1109/tnsm.2024.3502239","title":"Hypergraph Attention Recurrent Network for Cellular Traffic Prediction","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Hypergraph; Computer network; Distributed computing","score_opus":0.008352052660506046,"score_gpt":0.19897247551567226,"score_spread":0.19062042285516623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404529510","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028994684,0.0015974862,0.98829955,0.00012926916,0.004590126,0.0009379243,0.000031146872,0.00089503493,0.0006200105],"genre_scores_gemma":[0.9800927,0.004891459,0.012455825,0.00012937191,0.0008660001,0.00082344405,0.000117223295,0.00012317352,0.00050079974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896026,0.00002259927,0.0002803696,0.0003122934,0.000112386995,0.0003120978],"domain_scores_gemma":[0.9996652,0.00003904314,0.000019985806,0.00018038215,0.000029797397,0.00006560062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001619436,0.00020974809,0.00015257184,0.000119799515,0.00020410567,0.00008345592,0.000068215544,0.00008478207,0.000013145109],"category_scores_gemma":[1.2072222e-7,0.00022347027,0.00008904234,0.00057135447,0.0000091650545,0.00016680988,0.0000012635682,0.00013944073,0.000017244749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024067655,0.000019952056,9.702524e-7,0.00075708574,0.00016797251,0.000002328609,0.00006584287,0.9049683,0.000029798926,0.00024950266,0.0022088476,0.09150529],"study_design_scores_gemma":[0.00031634566,0.00007747703,0.000019049428,0.00041148523,0.0002214706,0.0000027226085,0.00007442352,0.97085804,0.000025657042,0.00017371366,0.027619343,0.00020025995],"about_ca_topic_score_codex":0.000001745955,"about_ca_topic_score_gemma":0.00005040993,"teacher_disagreement_score":0.97719324,"about_ca_system_score_codex":0.0000760645,"about_ca_system_score_gemma":0.0000036031774,"threshold_uncertainty_score":0.91128534},"labels":[],"label_agreement":null},{"id":"W4405521288","doi":"10.1109/tnsm.2024.3514894","title":"A Network Connectivity-Aware Reinforcement Learning Method for Task Exploration and Allocation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Computer science; Reinforcement learning; Task (project management); Artificial intelligence; Computer network; Distributed computing; Machine learning","score_opus":0.023259519210626575,"score_gpt":0.2619093332318497,"score_spread":0.2386498140212231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405521288","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017736336,0.00020865953,0.9911921,0.003450651,0.003588398,0.0005852428,1.7531312e-7,0.00029783126,0.0004995602],"genre_scores_gemma":[0.8585094,0.0012524602,0.13197498,0.0038894652,0.002523875,0.00061441056,0.00002623936,0.00005941719,0.0011497296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872625,0.000091656184,0.00022274171,0.00047715622,0.00015373266,0.00032846638],"domain_scores_gemma":[0.9993917,0.00024234285,0.000047369074,0.00019862111,0.000051753508,0.0000682614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068195246,0.0001794491,0.00015287973,0.00010488437,0.0006278641,0.0004011454,0.00014432243,0.00005635872,0.000001466224],"category_scores_gemma":[6.9424544e-7,0.0001799752,0.000045658977,0.000559459,0.000009137241,0.00045341457,0.000015882804,0.00017214594,0.000007149481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017481076,0.000012320681,0.0000010010767,0.00021033305,0.00007262114,0.0000025721597,0.00085431075,0.6727087,0.000005755786,0.0034811955,0.0012109965,0.32142273],"study_design_scores_gemma":[0.00026646524,0.00014868603,0.000016235497,0.00019266336,0.00007048941,0.0000041927497,0.00010446936,0.9520002,0.000026250309,0.0037020813,0.043276656,0.00019162151],"about_ca_topic_score_codex":0.000030851745,"about_ca_topic_score_gemma":0.000030183706,"teacher_disagreement_score":0.8592171,"about_ca_system_score_codex":0.00004575394,"about_ca_system_score_gemma":0.000014380779,"threshold_uncertainty_score":0.7339176},"labels":[],"label_agreement":null},{"id":"W4405968140","doi":"10.1109/tnsm.2024.3525004","title":"Joint Optimization of Completion Ratio and Latency of Offloaded Tasks With Multiple Priority Levels in 5G Edge","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Joint (building); Latency (audio); Computer network; Enhanced Data Rates for GSM Evolution; Telecommunications","score_opus":0.018471076468589573,"score_gpt":0.21980706517324106,"score_spread":0.20133598870465147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405968140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049557406,0.00003865539,0.94832546,0.0004218008,0.0005780227,0.00032834033,6.1622137e-7,0.000028343484,0.00072134077],"genre_scores_gemma":[0.97030073,0.000086824075,0.02938578,0.00014981459,0.00002672109,0.000010948588,0.0000015580274,0.0000040618775,0.000033532484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916965,0.000054696804,0.00027920527,0.00023408288,0.00011676894,0.00014561362],"domain_scores_gemma":[0.9995712,0.00004857759,0.00008562681,0.0001995746,0.00006972594,0.00002530624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022015921,0.0001073894,0.0001913993,0.00016344119,0.000113362454,0.00003127111,0.00012429507,0.00003853269,0.0000012439478],"category_scores_gemma":[5.5347437e-7,0.00010101925,0.000019982386,0.00067253195,0.00002394991,0.00013662888,0.0000105569725,0.0000894016,2.8954008e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051744566,0.00015623479,0.00079398975,0.00041486058,0.00006214767,0.0000017427384,0.00084896665,0.9521805,0.00010512996,0.0009048446,0.0000471217,0.044432692],"study_design_scores_gemma":[0.0010430132,0.00008597114,0.04666613,0.0003094519,0.00003246292,9.203632e-7,0.00005670908,0.95072013,0.00066626817,0.00020435397,0.000103411825,0.00011117515],"about_ca_topic_score_codex":0.0001462923,"about_ca_topic_score_gemma":0.00017309321,"teacher_disagreement_score":0.92074335,"about_ca_system_score_codex":0.000021594107,"about_ca_system_score_gemma":0.0000176767,"threshold_uncertainty_score":0.41194454},"labels":[],"label_agreement":null},{"id":"W4406754375","doi":"10.1109/tnsm.2025.3530432","title":"Self-Adaptive Dynamic In-Band Network Telemetry Orchestration for Balancing Accuracy and Stability","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Time Synchronization Technologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Orchestration; Telemetry; Stability (learning theory); Network topology; Distributed computing; Computer network; Telecommunications","score_opus":0.008836933470170146,"score_gpt":0.23183780087204567,"score_spread":0.22300086740187552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406754375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070335506,0.0003115564,0.98831844,0.0015399304,0.000378964,0.0011154765,0.0000024700507,0.00034616597,0.0009534653],"genre_scores_gemma":[0.8812251,0.00079644716,0.11701439,0.0006612028,0.000028125369,0.00021520379,0.0000026535035,0.000010481371,0.000046368197],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985224,0.00007651856,0.00032589084,0.00055744685,0.00012862115,0.0003890984],"domain_scores_gemma":[0.9990268,0.0003559787,0.00008529938,0.0004225537,0.00006742553,0.0000419778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048276276,0.0002070326,0.00021721292,0.00013415344,0.00035967506,0.0001538532,0.0003190451,0.000105800784,0.0000036765637],"category_scores_gemma":[0.0000027312649,0.00021517905,0.000034190896,0.0014559352,0.000031403157,0.00030824955,0.000020335929,0.00019138436,0.0000015428025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011378842,0.00021528534,0.00052706554,0.0006784983,0.00024319328,0.000005541241,0.00045069182,0.3133786,0.000012096123,0.02699052,0.00037215295,0.6570126],"study_design_scores_gemma":[0.00088731834,0.00011801854,0.0029475796,0.0001628348,0.000060152335,0.0000013627139,0.0002401581,0.979382,0.00015374423,0.014427029,0.0013657658,0.00025405132],"about_ca_topic_score_codex":0.000017033919,"about_ca_topic_score_gemma":0.000663964,"teacher_disagreement_score":0.8741916,"about_ca_system_score_codex":0.0001532763,"about_ca_system_score_gemma":0.000041492498,"threshold_uncertainty_score":0.8774747},"labels":[],"label_agreement":null},{"id":"W4407450972","doi":"10.1109/tnsm.2025.3541977","title":"Spectrum Sharing in Internet-of-Vehicles Networks: Digital Twin-Empowered Proactive Interference Management Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; École de Technologie Supérieure","funders":"","keywords":"Computer science; Computer network; The Internet; Interference (communication); Spectrum management; Network management; Telecommunications; Distributed computing; World Wide Web; Cognitive radio; Wireless","score_opus":0.011340854756048041,"score_gpt":0.2070060122532433,"score_spread":0.19566515749719526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407450972","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015582858,0.00006969092,0.7470836,0.000084387466,0.000478919,0.0006509552,0.000005293191,0.0001858128,0.23585849],"genre_scores_gemma":[0.9976996,0.000350515,0.0008481174,0.00011592104,0.000018231704,0.0001701998,0.00000984825,0.000025876912,0.00076167553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869245,0.00001345976,0.00045856414,0.00033255466,0.00014658734,0.00035636075],"domain_scores_gemma":[0.9995724,0.00003191834,0.0000363762,0.00028672052,0.000017434657,0.000055143257],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011670548,0.00026400364,0.00024491825,0.00032284824,0.000050700746,0.00016637552,0.00031763746,0.00009436641,0.000015823462],"category_scores_gemma":[1.5465125e-7,0.000285066,0.00006114949,0.00090390176,0.00003424701,0.00041299063,0.000016843072,0.0003218844,0.0000070603537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009033003,0.00016998216,0.00016552043,0.00078646065,0.0004874865,0.0000063790967,0.00039767197,0.8955083,6.9656517e-7,0.004171529,0.00018558776,0.098030075],"study_design_scores_gemma":[0.0014287723,0.000069455746,0.00217923,0.0013156967,0.0001318988,0.0000031125762,0.0024103108,0.98811543,0.00028280227,0.0016489626,0.0018951822,0.00051917566],"about_ca_topic_score_codex":0.000011229399,"about_ca_topic_score_gemma":0.000029402778,"teacher_disagreement_score":0.98211676,"about_ca_system_score_codex":0.00011441103,"about_ca_system_score_gemma":0.000004110082,"threshold_uncertainty_score":0.9999601},"labels":[],"label_agreement":null},{"id":"W4407782977","doi":"10.1109/tnsm.2025.3539183","title":"Deterministic and Dynamic Joint Placement and Scheduling of VNF-FGs for Remote Robotic Surgery","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Space Satellite Systems and Control","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Scheduling (production processes); Joint (building); Distributed computing; Real-time computing; Operations management; Engineering","score_opus":0.01006193040620373,"score_gpt":0.21037337244184737,"score_spread":0.20031144203564363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407782977","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059614982,0.0014678191,0.93685603,0.00025574001,0.00061473093,0.00065381406,0.0000051902225,0.00007083518,0.00046087118],"genre_scores_gemma":[0.9946651,0.0024938758,0.0024097594,0.00016582073,0.00001890466,0.000044423392,0.0000017321751,0.000017776994,0.0001826219],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992563,0.000016863678,0.00026512597,0.00019271948,0.000067054316,0.00020194742],"domain_scores_gemma":[0.99963856,0.000113821276,0.00003222527,0.00014695273,0.000021628628,0.000046790912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018740275,0.00015302487,0.00027313115,0.00013036717,0.00010939852,0.00003646116,0.000034033175,0.00004867561,0.0000030628923],"category_scores_gemma":[5.1527775e-7,0.00015513503,0.000044132572,0.000160452,0.000015697655,0.000033943932,0.000002772558,0.00006713251,5.563945e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055917175,0.000016562775,0.0000102241,0.0023812156,0.00029215816,0.0000023351645,0.00014068562,0.8370475,0.00010611338,0.00014095161,0.000026003388,0.15978031],"study_design_scores_gemma":[0.0006044219,0.000039006973,0.0005850927,0.00058766373,0.00025599715,0.0000023742675,0.00043015112,0.99655503,0.000046297508,0.00017599229,0.00056141254,0.00015657194],"about_ca_topic_score_codex":0.000018467406,"about_ca_topic_score_gemma":0.00015851692,"teacher_disagreement_score":0.93505013,"about_ca_system_score_codex":0.000025505684,"about_ca_system_score_gemma":0.0000055653436,"threshold_uncertainty_score":0.6326223},"labels":[],"label_agreement":null},{"id":"W4408716403","doi":"10.1109/tnsm.2025.3553417","title":"Cost Optimization of FlexEthernet Over Elastic Optical Network Fronthaul Design","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Passive optical network; Computer network; Optical Transport Network; Integrated optics; Wavelength-division multiplexing; Electronic engineering; Engineering; Optoelectronics","score_opus":0.013480316582980151,"score_gpt":0.2248496584921874,"score_spread":0.21136934190920725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408716403","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006788005,0.00019418447,0.99147195,0.00009352077,0.00077051774,0.0005547078,0.0000036259255,0.0001808809,0.006051793],"genre_scores_gemma":[0.5965784,0.00316133,0.39709586,0.0012818038,0.00020789947,0.00026794255,0.000014581986,0.00012590193,0.0012662897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990052,0.00003389348,0.00028341592,0.00022485561,0.0001377491,0.00031487117],"domain_scores_gemma":[0.99945563,0.0001662777,0.000029593424,0.00025495427,0.000032109892,0.00006146035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000120605,0.00020364043,0.00022585293,0.000088097986,0.00009971578,0.000024083674,0.0001159073,0.00009425056,0.000081828104],"category_scores_gemma":[5.9861173e-7,0.00021815488,0.000043809727,0.0005465742,0.0000284575,0.00007290101,0.0000035369394,0.00016521713,0.000009343098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084348634,0.000035711346,0.0000041274875,0.00013987078,0.00022367087,0.0000033369522,0.000056540335,0.9793011,0.0000059521653,0.00043781058,0.0008152876,0.018892244],"study_design_scores_gemma":[0.00061636214,0.000038728136,0.00013865734,0.00018962269,0.00020804026,8.681539e-7,0.00007420026,0.9961844,0.0001250101,0.00029987315,0.001939449,0.00018480676],"about_ca_topic_score_codex":0.0000056851477,"about_ca_topic_score_gemma":0.000030046895,"teacher_disagreement_score":0.5958996,"about_ca_system_score_codex":0.00006437942,"about_ca_system_score_gemma":0.0000065374925,"threshold_uncertainty_score":0.88960975},"labels":[],"label_agreement":null},{"id":"W4409474267","doi":"10.1109/tnsm.2025.3561098","title":"Decentralization in PoS Blockchain Consensus: Quantification and Advancement","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Research Foundation","keywords":"Blockchain; Decentralization; Computer science; Computer security; Political science; Law","score_opus":0.00834678288512185,"score_gpt":0.23231828577653924,"score_spread":0.22397150289141737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409474267","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02439729,0.0002582869,0.961234,0.012739385,0.00017075245,0.00053928385,0.0000015497637,0.00012726652,0.00053222466],"genre_scores_gemma":[0.98369026,0.0016461046,0.012500659,0.0018448387,0.000005988512,0.00017530826,0.0000018339769,0.0000047435205,0.00013023824],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990403,0.00004682873,0.0002303956,0.00039208564,0.00008546701,0.00020493781],"domain_scores_gemma":[0.9994655,0.000048618946,0.00004469916,0.0003713647,0.000035638364,0.000034169898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021820492,0.0001208699,0.000117638556,0.00020376247,0.00023026731,0.000051687046,0.00019856627,0.00007212384,0.0000030888175],"category_scores_gemma":[4.9193403e-7,0.00012941867,0.000015201253,0.00094839325,0.00003357025,0.000035770874,0.000011503447,0.00011407413,0.0000030067208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032721502,0.0003000708,0.00020174377,0.00015413149,0.00006960235,0.000005024792,0.00036162525,0.09239763,0.000051703835,0.5023437,0.00022103531,0.403861],"study_design_scores_gemma":[0.0012798174,0.000058950147,0.0038701482,0.00017992397,0.000053684238,0.000004623803,0.0004039364,0.95449,0.00063102937,0.02228736,0.016432162,0.0003083456],"about_ca_topic_score_codex":0.000037499103,"about_ca_topic_score_gemma":0.00045916418,"teacher_disagreement_score":0.959293,"about_ca_system_score_codex":0.000036671343,"about_ca_system_score_gemma":0.0000109071825,"threshold_uncertainty_score":0.527754},"labels":[],"label_agreement":null},{"id":"W4410359239","doi":"10.1109/tnsm.2025.3570052","title":"Toward Intelligent Intent-Based Network Slicing for IoT Systems: Enabling Technologies, Challenges, and Vision","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Slicing; Internet of Things; Network Functions Virtualization; Distributed computing; Computer network; Embedded system; Cloud computing; World Wide Web; Operating system","score_opus":0.030480317565462243,"score_gpt":0.25586451595589627,"score_spread":0.22538419839043403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410359239","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00031253495,0.015190013,0.9740733,0.0067933453,0.0015802617,0.0009971533,0.000001993642,0.00063969713,0.0004117287],"genre_scores_gemma":[0.90270346,0.0336632,0.05738867,0.0048424443,0.00023225101,0.0008175026,0.0000059235613,0.00004909638,0.00029743905],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809325,0.00005887663,0.00038901562,0.0007167715,0.0001853132,0.0005567741],"domain_scores_gemma":[0.99883765,0.00033439533,0.00009351721,0.00057184324,0.000089485664,0.00007311786],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005382448,0.0002985677,0.00033342012,0.0002345207,0.00048269323,0.0002367127,0.0004909888,0.00015706355,0.0000012020369],"category_scores_gemma":[0.0000021199905,0.00027625024,0.000076859455,0.00078836654,0.000033059692,0.00008596517,0.00004328957,0.00023723835,0.0000021996407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000566713,0.000060193408,0.000005409042,0.0005241744,0.00011769087,0.0000059582458,0.00011338834,0.4196959,8.280236e-7,0.017512757,0.0005536364,0.5613534],"study_design_scores_gemma":[0.0009744443,0.00029855405,0.00006347708,0.0017503486,0.00014327573,0.0000046473647,0.0009176724,0.92858845,0.00004244897,0.0041333768,0.06267779,0.000405489],"about_ca_topic_score_codex":0.000040984345,"about_ca_topic_score_gemma":0.00012955324,"teacher_disagreement_score":0.9166846,"about_ca_system_score_codex":0.00007686889,"about_ca_system_score_gemma":0.000024810712,"threshold_uncertainty_score":0.99996895},"labels":[],"label_agreement":null},{"id":"W4410639593","doi":"10.1109/tnsm.2025.3573246","title":"Probabilistic Analysis of Validator Lifecycle and Fork Resolution in Ethereum 2.0-Like PoS System","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Validator; Computer science; Fork (system call); Probabilistic logic; Artificial intelligence; Operating system; World Wide Web","score_opus":0.005680022844788004,"score_gpt":0.20139040997565616,"score_spread":0.19571038713086816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410639593","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3712762,0.000952132,0.6163174,0.00029974565,0.0015500827,0.0014754641,0.000025879433,0.00041540543,0.0076877126],"genre_scores_gemma":[0.9993683,0.00017136091,0.00010571069,0.0000950614,0.00001280042,0.00011164256,0.0000016186301,0.0000094260495,0.00012407526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992225,0.00005662779,0.0002870036,0.00018130605,0.00009386797,0.00015868538],"domain_scores_gemma":[0.9996787,0.000045234832,0.000027893737,0.00019213454,0.000019469115,0.000036610043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020020081,0.000120239536,0.00025679354,0.00033093969,0.00007299398,0.000026968984,0.000058689286,0.00006561113,0.0000036823856],"category_scores_gemma":[2.8536246e-7,0.00012222723,0.000052968873,0.0012216785,0.000010463713,0.00004049436,0.0000020090292,0.00009063024,0.0000012554674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048014557,0.000025712838,0.0000618689,0.00084049354,0.0007036934,0.0000014608333,0.00010783876,0.9878806,0.000021554424,0.00045471988,0.00003899462,0.009815057],"study_design_scores_gemma":[0.0005286684,0.00002363653,0.0023637358,0.00027352056,0.0006923215,6.8604265e-7,0.00059628923,0.99402314,0.000020953754,0.000028453047,0.0013407341,0.000107849446],"about_ca_topic_score_codex":0.00019774753,"about_ca_topic_score_gemma":0.0016043823,"teacher_disagreement_score":0.6280921,"about_ca_system_score_codex":0.00007333253,"about_ca_system_score_gemma":0.0000035829328,"threshold_uncertainty_score":0.49842817},"labels":[],"label_agreement":null},{"id":"W4410809602","doi":"10.1109/tnsm.2025.3574581","title":"Paxos With Priorities for Blockchain Applications","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Blockchain; Computer science; Distributed computing; Computer security; Computer network","score_opus":0.006570591962358869,"score_gpt":0.21697782893483317,"score_spread":0.2104072369724743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410809602","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009302316,0.00011010091,0.9869429,0.007804994,0.00008588298,0.0012619898,0.000005413214,0.00030246886,0.00255603],"genre_scores_gemma":[0.85112906,0.00033545183,0.13488773,0.0056053377,0.000048392198,0.0064128274,0.0000022343534,0.000016112264,0.0015628673],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911356,0.000015683723,0.00015615657,0.00039287526,0.00008497712,0.00023672094],"domain_scores_gemma":[0.99921036,0.00008192667,0.00003718513,0.0005653635,0.00006647225,0.000038709375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013578772,0.00013947715,0.00013133006,0.00013712217,0.0005882468,0.000065337605,0.00041809794,0.00007231634,0.0000022702297],"category_scores_gemma":[1.20078e-7,0.00012835897,0.00003344189,0.0008522243,0.000042314095,0.000028767552,0.000009813637,0.00012129832,0.0000038082003],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028080669,0.00014211186,0.000011998822,0.00019185805,0.00016599409,0.0000010054492,0.00011317228,0.02603137,0.000004050551,0.62700564,0.00047649225,0.3458282],"study_design_scores_gemma":[0.0026255937,0.00032586948,0.0004753166,0.00021718917,0.00032665074,0.0000102837785,0.00056428433,0.47568518,0.0007531584,0.11593173,0.40229574,0.000789002],"about_ca_topic_score_codex":0.000012304822,"about_ca_topic_score_gemma":0.00018800628,"teacher_disagreement_score":0.8520552,"about_ca_system_score_codex":0.000023572284,"about_ca_system_score_gemma":0.000020502399,"threshold_uncertainty_score":0.5234327},"labels":[],"label_agreement":null},{"id":"W4411446663","doi":"10.1109/tnsm.2025.3581463","title":"THREATIFY: APT Threat Variant Generation Using Graph-Based Machine Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Terrorism, Counterterrorism, and Political Violence","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Ericsson (Canada)","funders":"","keywords":"Computer science; Graph; Artificial intelligence; Theoretical computer science","score_opus":0.031232212976141746,"score_gpt":0.29337365067610066,"score_spread":0.2621414376999589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411446663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05566486,0.00024627088,0.9322014,0.0036156785,0.0013292654,0.00046683542,0.000007848311,0.00016802881,0.006299855],"genre_scores_gemma":[0.9944644,0.000845161,0.00047454,0.0032374072,0.00019008835,0.000039137794,0.0000067002866,0.000012362241,0.00073023303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859697,0.00021170285,0.00021889147,0.0003295248,0.00024456918,0.00039836165],"domain_scores_gemma":[0.99952805,0.00007874051,0.000046706853,0.00018927829,0.00005530738,0.00010194159],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003690849,0.00017574828,0.00017027665,0.00014052486,0.0013136817,0.00013539282,0.00015530761,0.000097820026,0.000092000904],"category_scores_gemma":[0.0000010568428,0.00017284467,0.00007030035,0.0006019627,0.000069514586,0.00012683448,0.0000035830888,0.00021185879,0.000009301348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020749691,0.00046918003,0.00070622616,0.00033935768,0.0004642879,0.000052636507,0.014834034,0.77977425,0.00007200328,0.013744668,0.0004937994,0.18884207],"study_design_scores_gemma":[0.0017059419,0.0001786825,0.00087540864,0.0006471253,0.000795221,0.000002027986,0.019092899,0.9488987,0.00012745097,0.0029891368,0.023923265,0.0007641892],"about_ca_topic_score_codex":0.0031561658,"about_ca_topic_score_gemma":0.0066244956,"teacher_disagreement_score":0.9387995,"about_ca_system_score_codex":0.00009726933,"about_ca_system_score_gemma":0.000047010908,"threshold_uncertainty_score":0.99998647},"labels":[],"label_agreement":null},{"id":"W4411472147","doi":"10.1109/tnsm.2025.3581557","title":"Black Hole Prediction in Backbone Networks: A Comprehensive and Type-Independent Forecasting Model","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Type (biology); Computer network; Distributed computing","score_opus":0.018541268150596535,"score_gpt":0.228685507324601,"score_spread":0.21014423917400446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411472147","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08964789,0.00018998409,0.90724474,0.00046746465,0.00063951337,0.0004949625,0.000001431775,0.000110164685,0.0012038372],"genre_scores_gemma":[0.99397004,0.00084302766,0.0036588712,0.0010598837,0.000033286728,0.00005013356,0.0000016360173,0.0000074071795,0.0003757201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880147,0.000051785555,0.00028198276,0.00043412865,0.0001492555,0.000281373],"domain_scores_gemma":[0.99943966,0.00006166294,0.000045955636,0.00033113916,0.00006517679,0.00005637962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002421667,0.0001652499,0.00019145905,0.00014291606,0.00021775051,0.00009088587,0.00018430653,0.000085804015,0.0000024344215],"category_scores_gemma":[3.699329e-7,0.0001556019,0.000030357245,0.0007934363,0.000028349667,0.0002227383,0.000023142848,0.00019893101,0.000004964318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004023011,0.00005783745,0.00030572532,0.00020414781,0.0000455164,0.0000042564548,0.00038994127,0.9592803,0.0000012853702,0.00031166908,0.00016871293,0.03919035],"study_design_scores_gemma":[0.0006586567,0.00005355168,0.0030614098,0.00026404968,0.000030094006,0.000002021362,0.00017013136,0.9947385,0.000005313706,0.0004863489,0.00040777653,0.00012210343],"about_ca_topic_score_codex":0.000048690737,"about_ca_topic_score_gemma":0.00021079526,"teacher_disagreement_score":0.90432215,"about_ca_system_score_codex":0.000058971433,"about_ca_system_score_gemma":0.000018236406,"threshold_uncertainty_score":0.63452613},"labels":[],"label_agreement":null},{"id":"W4411551141","doi":"10.1109/tnsm.2025.3582146","title":"Retraction Notice Communication-Efficient Personalized Federated Meta-Learning in Edge Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Computer network; Enhanced Data Rates for GSM Evolution; Distributed computing; Computer architecture; Artificial intelligence","score_opus":0.025236802262261326,"score_gpt":0.2607734287200888,"score_spread":0.23553662645782747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411551141","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008941305,0.0006839536,0.9609994,0.031593446,0.00065857574,0.00046742725,0.0000014464839,0.000596039,0.004105525],"genre_scores_gemma":[0.9180703,0.0034713468,0.07323662,0.004154548,0.000025246,0.00030812927,0.00001379759,0.000021894668,0.000698082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811095,0.00030236266,0.00036786732,0.00057966454,0.00023990947,0.00039926206],"domain_scores_gemma":[0.9969772,0.00032354935,0.0001067108,0.0024652504,0.00007973703,0.00004755266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089009176,0.0002389462,0.00029939387,0.0002968616,0.0006772012,0.00029920202,0.004058563,0.00018834494,0.00002466189],"category_scores_gemma":[0.00004013516,0.00023853692,0.00007852871,0.0021818054,0.00005219807,0.0002702788,0.0008056177,0.0008826456,0.000010927983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005580438,0.00021679906,0.000016409434,0.00008196315,0.0005740327,0.000010500378,0.00011270733,0.92528987,0.0000036641786,0.0037551355,0.008791556,0.061091576],"study_design_scores_gemma":[0.000580619,0.000026977641,0.00035445308,0.0001210297,0.00023115061,0.0000020308746,0.00020784991,0.9863775,0.000028682442,0.002185593,0.009677171,0.00020697196],"about_ca_topic_score_codex":0.00015229614,"about_ca_topic_score_gemma":0.00040389592,"teacher_disagreement_score":0.9171762,"about_ca_system_score_codex":0.00013556278,"about_ca_system_score_gemma":0.00002253578,"threshold_uncertainty_score":0.9727253},"labels":[],"label_agreement":null},{"id":"W4411600798","doi":"10.1109/tnsm.2025.3582121","title":"Retraction Notice A Hybrid Multistage DNN-Based Collaborative IDPS for High-Risk Smart Factory Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Advanced Computing and Algorithms","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Notice; Computer science; Factory (object-oriented programming); Computer network","score_opus":0.008668286659327867,"score_gpt":0.2643565288631353,"score_spread":0.2556882422038075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411600798","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00469105,0.0001044377,0.9869436,0.0014169272,0.0029653967,0.0010203354,0.000043967986,0.00021134612,0.0026029572],"genre_scores_gemma":[0.9817099,0.0007453168,0.01285098,0.002028716,0.0003310935,0.00020078619,0.000018450655,0.00002160363,0.002093179],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984461,0.0002180232,0.00025292125,0.00043192453,0.00021909857,0.00043195233],"domain_scores_gemma":[0.99878335,0.00059259625,0.00012915376,0.00021980095,0.00017813823,0.00009695778],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005364779,0.00020311141,0.000214658,0.00011231706,0.0018868883,0.00012482832,0.0001646285,0.00011974475,0.000018144367],"category_scores_gemma":[0.0000052496976,0.00021798282,0.00006309357,0.0007833186,0.00007380805,0.0001227876,0.0000033224467,0.00033068995,0.000004946583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018771664,0.00012112098,0.000023204446,0.00006556353,0.0001407941,0.0000040137725,0.00035427653,0.7951511,8.687293e-7,0.0013844982,0.0011776192,0.20138921],"study_design_scores_gemma":[0.0032093124,0.00028185255,0.0013747164,0.00034323498,0.00062927086,2.7002628e-7,0.005115823,0.7650739,0.000102231366,0.0021985301,0.22097191,0.00069897267],"about_ca_topic_score_codex":0.000839353,"about_ca_topic_score_gemma":0.0033705449,"teacher_disagreement_score":0.97701883,"about_ca_system_score_codex":0.00016412047,"about_ca_system_score_gemma":0.00006746805,"threshold_uncertainty_score":0.99941254},"labels":[],"label_agreement":null},{"id":"W4411600806","doi":"10.1109/tnsm.2025.3582223","title":"FR-SFCO: Energy-Aware Offloading on Data Plane for Delay-Sensitive SFC","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Major Research Plan; National Natural Science Foundation of China","keywords":"Computer science; Forwarding plane; Energy consumption; Computer network; Network packet; Electrical engineering","score_opus":0.022041244304607605,"score_gpt":0.2585684639979712,"score_spread":0.23652721969336357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411600806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000060716597,0.000039642295,0.9914562,0.0020151455,0.00056704145,0.00029322767,0.000020651416,0.00036651484,0.0051808734],"genre_scores_gemma":[0.7967901,0.0009903326,0.18286793,0.017046334,0.00014987377,0.00013799821,0.00008767548,0.000027681863,0.0019020488],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874735,0.00005623098,0.00021052768,0.00057259377,0.00014023189,0.00027304547],"domain_scores_gemma":[0.99890494,0.00016920918,0.00005611273,0.00075036683,0.000063845844,0.000055510205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023456682,0.0001818589,0.0001710477,0.00016826365,0.0004500645,0.00014872772,0.0006495817,0.00006436358,0.0000025411334],"category_scores_gemma":[5.011122e-7,0.0001821612,0.00003836827,0.00052698527,0.000012919681,0.0001535409,0.00003492534,0.00010964114,0.00000411325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010412779,0.00014767765,0.0000018179684,0.00011519023,0.00025252817,0.00001372053,0.00014653821,0.6680531,0.0000031911572,0.028256165,0.019166945,0.283739],"study_design_scores_gemma":[0.00040798626,0.000090594345,0.000015111703,0.00018904528,0.000053896147,0.0000020236862,0.000034827855,0.9631724,0.0002740105,0.0010803617,0.034488767,0.00019099342],"about_ca_topic_score_codex":0.00005113008,"about_ca_topic_score_gemma":0.00011575149,"teacher_disagreement_score":0.80858827,"about_ca_system_score_codex":0.00003449516,"about_ca_system_score_gemma":0.00001746612,"threshold_uncertainty_score":0.74283177},"labels":[],"label_agreement":null},{"id":"W4413073828","doi":"10.1109/tnsm.2025.3589901","title":"A Game Theoretic Model for Strategic Defence Selection Against DDoS Attacks in IoT Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University; Ontario Tech University; University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Denial-of-service attack; Application layer DDoS attack; Computer security; Game theory; Selection (genetic algorithm); Computer network; Botnet; Trinoo; Distributed computing; Artificial intelligence; The Internet","score_opus":0.01522612685071359,"score_gpt":0.23866267540140737,"score_spread":0.22343654855069378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413073828","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01632458,0.00012105814,0.9790532,0.0008542508,0.0006152691,0.00082792295,0.0000012917911,0.00017055175,0.002031865],"genre_scores_gemma":[0.9872006,0.001272519,0.0066082575,0.0041944957,0.00006353223,0.0003234298,0.0000024507176,0.000013693607,0.00032099005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833435,0.00009175616,0.00034865312,0.0005913105,0.00016181373,0.00047214172],"domain_scores_gemma":[0.9993174,0.000104192804,0.000090723865,0.00034546613,0.00006977439,0.000072463976],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040380526,0.00024072465,0.00021775566,0.00025159365,0.00037394508,0.00019030405,0.0003890145,0.00014026742,0.0000052716564],"category_scores_gemma":[4.7669246e-7,0.00024897995,0.000082986386,0.0015003632,0.000030390771,0.00017844416,0.000013469991,0.0003150869,0.000003903976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000094558774,0.00009780809,0.0000055787336,0.00011099618,0.000040249633,0.0000017123328,0.00015931742,0.91445786,0.000005005082,0.032059107,0.00010704647,0.052860774],"study_design_scores_gemma":[0.00072594406,0.00009510971,0.000047515514,0.00021445531,0.000039080587,0.000001601403,0.000057114237,0.9750124,0.000029416404,0.023085343,0.00047259554,0.00021942252],"about_ca_topic_score_codex":0.000020991156,"about_ca_topic_score_gemma":0.00091136526,"teacher_disagreement_score":0.97244495,"about_ca_system_score_codex":0.00009605317,"about_ca_system_score_gemma":0.00003296518,"threshold_uncertainty_score":0.99999624},"labels":[],"label_agreement":null},{"id":"W4413212556","doi":"10.1109/tnsm.2025.3594954","title":"An Edge-Based Adaptive Event-Triggered Network Transmission Scheme for Fully Distributed Power and Frequency Control of Islanded AC Microgrids","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Sichuan Association for Science and Technology; National Natural Science Foundation of China","keywords":"Computer science; Scheme (mathematics); Transmission (telecommunications); Automatic frequency control; Power control; Enhanced Data Rates for GSM Evolution; Transmission network; Power (physics); Electronic engineering; Computer network; Distributed computing; Telecommunications; Engineering","score_opus":0.004024045374749316,"score_gpt":0.19768872460990128,"score_spread":0.19366467923515196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413212556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003901198,0.001646651,0.99223727,0.0003562397,0.00032445867,0.0010829687,0.00011853603,0.0001340561,0.0001985952],"genre_scores_gemma":[0.98521894,0.0008886829,0.013041908,0.00048168236,0.0000536727,0.00019589295,0.00006700485,0.000028355922,0.000023832132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989306,0.000047465808,0.00031950697,0.00028409337,0.00009436758,0.0003239713],"domain_scores_gemma":[0.99948907,0.00007060026,0.00005044767,0.00021156766,0.00009167173,0.0000866688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016700725,0.00024628136,0.00031048743,0.00009917092,0.00020215703,0.000044814686,0.00011427383,0.00012146209,0.000026669133],"category_scores_gemma":[2.9210383e-7,0.00024131352,0.00008801032,0.0004199205,0.000024445397,0.0000952095,0.0000012579371,0.00012544217,5.593015e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059789023,0.00013151848,0.000022645376,0.00029746952,0.00037826216,0.000001337006,0.000055647914,0.922339,0.0005681081,0.00021021068,0.0003802393,0.075017676],"study_design_scores_gemma":[0.004341784,0.00023209381,0.0004906249,0.0002534202,0.00036929932,4.0124445e-7,0.00007190771,0.9909451,0.0003657199,0.00034878057,0.0023266857,0.0002541619],"about_ca_topic_score_codex":0.000016317164,"about_ca_topic_score_gemma":0.00007610909,"teacher_disagreement_score":0.98131776,"about_ca_system_score_codex":0.000035804438,"about_ca_system_score_gemma":0.000018208619,"threshold_uncertainty_score":0.98404795},"labels":[],"label_agreement":null},{"id":"W4413822213","doi":"10.1109/tnsm.2025.3604352","title":"eSlice: Elastic Inter-Slice Resource Allocation for Smart City Applications","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Computer science; Resource management (computing); Resource allocation; Distributed computing; Computer network","score_opus":0.00726543281391674,"score_gpt":0.21410420386770362,"score_spread":0.2068387710537869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413822213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014398398,0.00019261101,0.9850848,0.0009267552,0.0004024684,0.0012986241,0.000010633704,0.0003905543,0.010253742],"genre_scores_gemma":[0.9873065,0.0006368844,0.0051862523,0.0024261775,0.00019541859,0.0022564747,0.00004895468,0.000060332408,0.0018830246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998892,0.00002807477,0.00027586744,0.00033009378,0.00011267922,0.00036124577],"domain_scores_gemma":[0.9993093,0.00012727255,0.000029545836,0.00040164063,0.0000491035,0.00008314196],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019902409,0.00022190105,0.00018294776,0.00013521589,0.0003057399,0.000073372496,0.00020020039,0.00009822717,0.00002010209],"category_scores_gemma":[4.240624e-7,0.00025171318,0.000071982795,0.0006630567,0.000019705489,0.000075961696,0.000005518561,0.00020321607,0.00002020908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003259104,0.00005136152,0.0000043411283,0.00035843128,0.00023239478,5.577614e-7,0.00005258188,0.92092365,0.000013851871,0.0010662248,0.0038828005,0.073381215],"study_design_scores_gemma":[0.0004930769,0.00002624786,0.00014982389,0.0001340831,0.0002468945,0.0000013192267,0.00010130424,0.68634796,0.00006665531,0.00040044484,0.31182554,0.0002066555],"about_ca_topic_score_codex":0.000017257711,"about_ca_topic_score_gemma":0.00044720687,"teacher_disagreement_score":0.98586667,"about_ca_system_score_codex":0.0001006785,"about_ca_system_score_gemma":0.0000083244795,"threshold_uncertainty_score":0.9999935},"labels":[],"label_agreement":null},{"id":"W4413977927","doi":"10.1109/tnsm.2025.3606343","title":"Extending WebAssembly for Deep-Learning Inference Across the Cloud Continuum","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; Innovation and Economic Development Trois Rivières","funders":"","keywords":"Computer science; Cloud computing; Inference; Deep learning; Artificial intelligence; Computer security; Operating system","score_opus":0.05248531674182655,"score_gpt":0.36900331859385355,"score_spread":0.316518001852027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413977927","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013228198,0.00012011701,0.97085357,0.0040579303,0.003852531,0.0006693734,0.000009095537,0.00010799166,0.0071012196],"genre_scores_gemma":[0.98032266,0.00020322218,0.0018968426,0.003152881,0.00014243182,0.00014701817,0.000004752249,0.000011855522,0.014118358],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99743843,0.00018212345,0.00051670615,0.00079421577,0.0005582708,0.00051025924],"domain_scores_gemma":[0.99696046,0.001799296,0.00014187385,0.00085773465,0.00017103682,0.000069601345],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0045408397,0.0001974207,0.0002309565,0.00016349935,0.0019516251,0.0011239618,0.00094441645,0.00004871222,0.0000451421],"category_scores_gemma":[0.000040646493,0.00013785863,0.00011328363,0.0016512492,0.00006290037,0.000158954,0.000060882074,0.00021438893,0.000055507702],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008157425,0.000061636594,0.00011900209,0.00004909225,0.00011549353,0.0000024633803,0.0005801563,0.19609553,0.000004290667,0.004479394,0.010573482,0.78783786],"study_design_scores_gemma":[0.0008556611,0.000067883084,0.003141537,0.00014325432,0.00012514922,9.921529e-7,0.006277075,0.31805122,0.00003365943,0.007962188,0.66308457,0.00025680216],"about_ca_topic_score_codex":0.00003555893,"about_ca_topic_score_gemma":0.0009451629,"teacher_disagreement_score":0.9689567,"about_ca_system_score_codex":0.000033466855,"about_ca_system_score_gemma":0.000012121362,"threshold_uncertainty_score":0.999913},"labels":[],"label_agreement":null},{"id":"W4414165801","doi":"10.1109/tnsm.2025.3608088","title":"Waris-Chain: The Blockchain Driven Transformation of Inheritance Solutions","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Scalability; Inheritance (genetic algorithm); Database transaction; Blockchain; Transaction processing; Throughput","score_opus":0.010218044058821119,"score_gpt":0.2160120320781123,"score_spread":0.20579398801929116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414165801","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040386003,0.00019032053,0.9689068,0.022489224,0.00021161842,0.00051801896,0.000003768652,0.00015022901,0.0034913938],"genre_scores_gemma":[0.9914651,0.00061370264,0.00581816,0.0017336959,0.000011062506,0.00022327705,8.36966e-7,0.0000045463594,0.00012962858],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905986,0.000055039036,0.00026771723,0.00025382938,0.00012884304,0.00023471951],"domain_scores_gemma":[0.9991912,0.00006970885,0.00006177332,0.00058810535,0.0000617218,0.00002743778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029101165,0.00012715797,0.0001375775,0.00012698218,0.0006102219,0.000035362256,0.0005630681,0.000085901745,0.000004168679],"category_scores_gemma":[3.31146e-7,0.00010618097,0.0000545797,0.0012187588,0.000072348375,0.00006316856,0.000013939833,0.00020502138,0.0000041999715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001540096,0.00019547371,0.0000071852796,0.00012181475,0.00016067554,9.5095936e-7,0.0011844967,0.14310141,0.000029013387,0.65247625,0.00054812484,0.20215923],"study_design_scores_gemma":[0.0006782624,0.00007040089,0.000578259,0.00012669388,0.000110247165,0.0000038240532,0.00069983484,0.9422982,0.0005183313,0.03576446,0.018925048,0.00022648246],"about_ca_topic_score_codex":0.000040019437,"about_ca_topic_score_gemma":0.0003597922,"teacher_disagreement_score":0.9874265,"about_ca_system_score_codex":0.000025341487,"about_ca_system_score_gemma":0.000016687385,"threshold_uncertainty_score":0.4693395},"labels":[],"label_agreement":null},{"id":"W4414538403","doi":"10.1109/tnsm.2025.3614632","title":"Cost-Aware VNF Decomposition for VNF Forwarding Graph Embedding","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); Concordia University","funders":"","keywords":"Network virtualization; Virtual network; Network topology; Decomposition; Embedding; Heuristics; Integer programming; Graph","score_opus":0.018232364039516936,"score_gpt":0.2855688188463212,"score_spread":0.26733645480680424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414538403","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00054478244,0.00024566738,0.9933227,0.0021264453,0.0015047031,0.0008828546,0.000006135429,0.00031292348,0.0010538207],"genre_scores_gemma":[0.87347484,0.0021821025,0.110936455,0.011491615,0.00022689463,0.0010679705,0.000021192061,0.000042157484,0.00055676047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985284,0.000043566528,0.00027763032,0.00053448405,0.00016241943,0.0004534878],"domain_scores_gemma":[0.99914277,0.00020862874,0.00006284804,0.00042338044,0.00007482894,0.000087534274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026723422,0.00023139076,0.00022388985,0.00022134824,0.0007784588,0.00025003857,0.00039580074,0.00008687618,0.000007168363],"category_scores_gemma":[2.616906e-7,0.00023699731,0.00016076844,0.0009895314,0.00001531837,0.00026279342,0.000016950911,0.00015132825,0.0000068670465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070824455,0.00008955099,0.00001919492,0.0001917491,0.00025303673,0.0000054336706,0.00016207417,0.5461414,0.000003873978,0.011170145,0.0047511715,0.43714154],"study_design_scores_gemma":[0.0019668872,0.00015254892,0.0003699016,0.0005786051,0.00024762744,0.000005942055,0.0002069811,0.93119425,0.00015009678,0.011506488,0.053079274,0.00054141093],"about_ca_topic_score_codex":0.000015821128,"about_ca_topic_score_gemma":0.00006414195,"teacher_disagreement_score":0.8823862,"about_ca_system_score_codex":0.000058844802,"about_ca_system_score_gemma":0.000012674059,"threshold_uncertainty_score":0.966447},"labels":[],"label_agreement":null},{"id":"W4415179262","doi":"10.1109/tnsm.2025.3621126","title":"Reinforcement Learning-Based In-Network Load Balancing","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Load balancing (electrical power); Reinforcement learning; Granularity; Load management; Retraining; Control (management)","score_opus":0.00702319938631418,"score_gpt":0.21234189081223354,"score_spread":0.20531869142591935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415179262","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00071693613,0.0002765155,0.98626316,0.0020218901,0.0010810519,0.0004119665,2.4439515e-7,0.00027089927,0.008957339],"genre_scores_gemma":[0.9785193,0.00066202437,0.008681901,0.010469262,0.00010234551,0.00015697893,0.000002840894,0.000017382252,0.0013879316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981147,0.000090592024,0.00036545942,0.0005374648,0.00030147823,0.0005903247],"domain_scores_gemma":[0.9991119,0.00017337264,0.000071689574,0.00050107314,0.0000560291,0.0000859392],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005120928,0.00025404492,0.00025215175,0.00016422501,0.0004143729,0.00016504251,0.0004441609,0.000090080015,0.000024142242],"category_scores_gemma":[9.837884e-7,0.00025751858,0.000073472875,0.0017256966,0.000018330684,0.00014544523,0.000021306727,0.00035204212,0.000024605064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045536708,0.000053345953,0.00023218071,0.00008994699,0.00005791122,0.0000162297,0.000082685765,0.93656933,3.530408e-7,0.0028039226,0.001356197,0.05869238],"study_design_scores_gemma":[0.001269136,0.000107774635,0.0017015759,0.0004800141,0.000048027996,0.0000010300919,0.000040413954,0.9589282,0.0000143987645,0.001038847,0.036067948,0.00030259922],"about_ca_topic_score_codex":0.00010350677,"about_ca_topic_score_gemma":0.00055461115,"teacher_disagreement_score":0.9778024,"about_ca_system_score_codex":0.00014754533,"about_ca_system_score_gemma":0.000058031022,"threshold_uncertainty_score":0.9999877},"labels":[],"label_agreement":null},{"id":"W4415256969","doi":"10.1109/tnsm.2025.3622149","title":"Active Learning for Transformer-Based Fault Diagnosis in 5G and Beyond Mobile Networks","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Rogers Communications (Canada); University of Regina; University of Waterloo","funders":"","keywords":"Interpretability; Exploit; Dependency (UML); Novelty; Transformer; Artificial neural network; Active learning (machine learning); Fault detection and isolation","score_opus":0.011274635602358916,"score_gpt":0.24012425179767458,"score_spread":0.22884961619531566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415256969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006376349,0.0014807226,0.98372453,0.0020468305,0.00092433335,0.0020382716,0.0000100871175,0.00011306223,0.0032858152],"genre_scores_gemma":[0.9870916,0.0064955372,0.00089908263,0.003649465,0.000087039945,0.0014675539,0.0000071315826,0.000031131047,0.00027142826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969702,0.00018221494,0.0006156219,0.0010833121,0.00023533248,0.0009133348],"domain_scores_gemma":[0.99845165,0.00078573363,0.00013224088,0.00035610856,0.00010212933,0.00017211188],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005792199,0.0005017585,0.0005296071,0.00042755972,0.0011746091,0.0003826796,0.0004058702,0.00022124988,0.00001738619],"category_scores_gemma":[0.0000020531052,0.0005599991,0.00015694776,0.001968144,0.00007782826,0.00036383752,0.000008197922,0.0006654085,0.0000022391353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024541554,0.0001860542,0.00012131568,0.0004478795,0.00011548481,0.00000754964,0.00043286887,0.50870734,0.0000012795473,0.00020607038,0.00003586923,0.48971373],"study_design_scores_gemma":[0.0026173044,0.0004872644,0.0010435531,0.0010569934,0.000343393,0.000001629543,0.0009823634,0.9867139,0.00008412247,0.0004100061,0.005741335,0.0005181738],"about_ca_topic_score_codex":0.00017506997,"about_ca_topic_score_gemma":0.0012621656,"teacher_disagreement_score":0.98282546,"about_ca_system_score_codex":0.0001177624,"about_ca_system_score_gemma":0.00006321328,"threshold_uncertainty_score":0.99968517},"labels":[],"label_agreement":null},{"id":"W4415971079","doi":"10.1109/tnsm.2025.3629642","title":"Design, Implementation, and Deployment of Multi-Task Neural Networks in Programmable Data-Planes","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scalability; Software deployment; Inference; Artificial neural network; Feature (linguistics); Resource (disambiguation); Memory management; Networking hardware","score_opus":0.029672207869636136,"score_gpt":0.27731106724398086,"score_spread":0.2476388593743447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415971079","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020555817,0.0007367527,0.9951544,0.0007650749,0.00037228785,0.00076599367,0.000004595724,0.000096179036,0.000049147806],"genre_scores_gemma":[0.9336235,0.0025083306,0.061353344,0.0021400207,0.000036107183,0.00021214156,0.000025553361,0.000017903922,0.00008308713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850535,0.000103001184,0.00036681647,0.00051817356,0.00014609075,0.00036055734],"domain_scores_gemma":[0.9991253,0.00012967426,0.00007657946,0.00057113566,0.000036555048,0.000060777547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040497072,0.0001916201,0.00022077232,0.00015268623,0.00018248336,0.000121994046,0.00047973692,0.000053820626,0.000005087203],"category_scores_gemma":[2.4767758e-7,0.00018378689,0.00002027102,0.0008782232,0.000025589685,0.00025154115,0.000047873647,0.00013707683,7.784598e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046672983,0.00013523716,0.00057193026,0.00010030443,0.00010110957,0.000010158494,0.00012954112,0.6034302,0.0000012757149,0.00057628105,0.0004242452,0.39447302],"study_design_scores_gemma":[0.0013640049,0.00009276447,0.0022989702,0.00008823012,0.000073788164,0.0000032451444,0.00021637657,0.99381393,0.00001724921,0.00020252663,0.001651231,0.00017770671],"about_ca_topic_score_codex":0.00023360846,"about_ca_topic_score_gemma":0.0015118534,"teacher_disagreement_score":0.93380105,"about_ca_system_score_codex":0.00002172369,"about_ca_system_score_gemma":0.000015188467,"threshold_uncertainty_score":0.7494612},"labels":[],"label_agreement":null},{"id":"W4415971084","doi":"10.1109/tnsm.2025.3630045","title":"A Multi-Objective Framework for Power-Aware Scheduling in Kubernetes","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Scalability; Workload; Scheduling (production processes); Energy consumption; Dynamic priority scheduling; Efficient energy use; Fair-share scheduling; Power optimization; Load balancing (electrical power)","score_opus":0.0126677955307427,"score_gpt":0.2541052872322405,"score_spread":0.2414374917014978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415971084","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012288632,0.00015632878,0.98182017,0.0027130404,0.0008371346,0.000768454,0.0000014797128,0.00018952161,0.0012252128],"genre_scores_gemma":[0.8169197,0.000078254416,0.17920633,0.003017839,0.000038628168,0.00020516502,5.9843575e-7,0.00001385077,0.0005196387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849194,0.000061006016,0.0002722287,0.0006007067,0.00014996465,0.0004241665],"domain_scores_gemma":[0.9991356,0.00024370835,0.000055834706,0.0004598727,0.00004708341,0.00005791216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032767293,0.00021912371,0.00021739518,0.00026924085,0.00034728108,0.00016299056,0.00049213815,0.00008741744,0.0000032516864],"category_scores_gemma":[0.0000016394761,0.00021793597,0.00008509795,0.0011234189,0.000018340595,0.000033213633,0.000034380842,0.00024202834,0.0000066933303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003944443,0.00021126049,0.00005691022,0.00018869177,0.00015686029,0.000007872555,0.00071558304,0.8809787,0.000001234884,0.022003358,0.00006752084,0.09557255],"study_design_scores_gemma":[0.0010331699,0.000086232205,0.0014851451,0.00060032407,0.00004702646,7.328554e-7,0.0006896702,0.98614734,0.000029705297,0.007331189,0.0022898903,0.00025955465],"about_ca_topic_score_codex":0.000050067483,"about_ca_topic_score_gemma":0.00016068555,"teacher_disagreement_score":0.80463105,"about_ca_system_score_codex":0.000074462754,"about_ca_system_score_gemma":0.000015252517,"threshold_uncertainty_score":0.8887171},"labels":[],"label_agreement":null},{"id":"W4416366392","doi":"10.1109/tnsm.2025.3635529","title":"Dynamic Task Scheduling and Adaptive GPU Resource Allocation in the Cloud","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); Concordia University","funders":"","keywords":"Scheduling (production processes); Workload; Cloud computing; Human multitasking; Resource allocation; Task (project management); Processor scheduling; Dynamic priority scheduling; Graphics","score_opus":0.009893587027645187,"score_gpt":0.22425552582271557,"score_spread":0.21436193879507037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416366392","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040248223,0.0017033501,0.9239833,0.023706915,0.0012252787,0.0013650696,0.0000024096337,0.00010720331,0.0076582744],"genre_scores_gemma":[0.9843699,0.0013918342,0.0041942485,0.008658404,0.0000937622,0.00010156352,0.0000016720307,0.000019886322,0.0011687778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99663275,0.0005218263,0.0006082267,0.0010947676,0.00045432954,0.0006880772],"domain_scores_gemma":[0.9984402,0.00031697226,0.00015137848,0.0009392503,0.000057328452,0.0000948802],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014829045,0.00046089554,0.0003544396,0.00039889486,0.0011889125,0.000566899,0.00094570464,0.00014399253,0.0000043357727],"category_scores_gemma":[0.000001204156,0.00040449665,0.00009605586,0.0024327273,0.00010860463,0.000056679237,0.00008555021,0.0006686554,0.000012186924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008321972,0.00020523822,0.000008792056,0.00024969128,0.00019652362,0.000021099544,0.002663836,0.6761245,8.624618e-7,0.0066508516,0.00016624991,0.31362912],"study_design_scores_gemma":[0.0010612394,0.00016649064,0.0016912945,0.0010134346,0.0002669214,0.0000049944597,0.0045795417,0.97508645,0.000002198401,0.0012143159,0.014542956,0.0003701701],"about_ca_topic_score_codex":0.00018006495,"about_ca_topic_score_gemma":0.0006387447,"teacher_disagreement_score":0.9441216,"about_ca_system_score_codex":0.00013509598,"about_ca_system_score_gemma":0.000031595246,"threshold_uncertainty_score":0.9998407},"labels":[],"label_agreement":null},{"id":"W4416960860","doi":"10.1109/tnsm.2025.3640098","title":"On Scalability Power of Payment Channel Networks","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scalability; Payment; Channel (broadcasting); Routing (electronic design automation); Network topology; Routing table","score_opus":0.00687678409036197,"score_gpt":0.21942135889861947,"score_spread":0.2125445748082575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416960860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008783181,0.00056055613,0.9719861,0.009790849,0.0016458564,0.0013918042,0.0000128855145,0.00016725235,0.005661486],"genre_scores_gemma":[0.9905461,0.0024684835,0.0014118175,0.0048279124,0.000028963155,0.00027987873,0.0000018617075,0.000016305888,0.00041865115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969476,0.00017163478,0.0007590621,0.001126674,0.00033066416,0.0006643821],"domain_scores_gemma":[0.9975227,0.00021433929,0.00020116587,0.0017672903,0.00016062576,0.00013385707],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007425242,0.0004635879,0.0005240244,0.00033942368,0.0007589249,0.00009962602,0.00097293174,0.00036838336,0.0000654923],"category_scores_gemma":[9.1420776e-7,0.0004731235,0.00017843638,0.0025034477,0.00020697188,0.00009607347,0.000056828878,0.0006656119,0.000016552214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000216545,0.0020148666,0.000013825782,0.0004116598,0.0006067201,0.0000061946175,0.00049634173,0.56352013,0.0000014474238,0.16113812,0.0013321667,0.27024198],"study_design_scores_gemma":[0.0015007405,0.00060315063,0.0016741438,0.0007126274,0.00033275265,0.0000021504825,0.00030140294,0.95877916,0.00021540518,0.030390896,0.0049190917,0.0005684601],"about_ca_topic_score_codex":0.000059363927,"about_ca_topic_score_gemma":0.00014301097,"teacher_disagreement_score":0.98176295,"about_ca_system_score_codex":0.000108109685,"about_ca_system_score_gemma":0.000040046052,"threshold_uncertainty_score":0.9997721},"labels":[],"label_agreement":null},{"id":"W4417170218","doi":"10.1109/tnsm.2025.3642067","title":"Visibility-Aware User Association and Resource Allocation in Multi-Slice LEO Satellite Networks","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Satellite Communication Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"C-Com Satellite Systems (Canada); Carleton University","funders":"National Research Council Canada; Mitacs","keywords":"Resource allocation; Bandwidth (computing); Bandwidth allocation; Scalability; Performance metric; Metric (unit); Resource management (computing); Greedy algorithm; Communications satellite","score_opus":0.017298100326791688,"score_gpt":0.24705719169317514,"score_spread":0.22975909136638345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417170218","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01376649,0.019081907,0.94956386,0.0042895204,0.002041179,0.003412243,0.000019140585,0.00042925097,0.0073964098],"genre_scores_gemma":[0.9246077,0.06960888,0.0011554406,0.0016709407,0.00008598922,0.00026143642,0.000028803657,0.00006896286,0.0025118499],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99651206,0.0005971976,0.0010866396,0.000795455,0.00032478574,0.0006838453],"domain_scores_gemma":[0.9978543,0.00057005434,0.00020125422,0.0010899355,0.00014184733,0.00014261945],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015590204,0.00052287866,0.00054538803,0.00044584143,0.00044962784,0.0003260311,0.0003825901,0.00045432508,0.000024056963],"category_scores_gemma":[0.0000032044084,0.00064803637,0.0000936587,0.0021056149,0.000035430603,0.0002932346,0.00002783318,0.0007967609,0.00002446023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011738625,0.00022853396,0.0023415913,0.0011595747,0.00051834213,0.0000032451503,0.001333528,0.81579596,0.000008436892,0.0002610194,0.00014414912,0.17808825],"study_design_scores_gemma":[0.0022923558,0.000045463446,0.038673487,0.001645286,0.00038110645,0.0000015413074,0.002219327,0.83162355,0.000029974879,0.000048846738,0.12238824,0.00065079663],"about_ca_topic_score_codex":0.00032807604,"about_ca_topic_score_gemma":0.0044273175,"teacher_disagreement_score":0.9484084,"about_ca_system_score_codex":0.0007445256,"about_ca_system_score_gemma":0.000021057867,"threshold_uncertainty_score":0.9995971},"labels":[],"label_agreement":null},{"id":"W7082659851","doi":"10.1109/tnsm.2025.3612425","title":"Spectrum and RAN Sharing: How to Avoid Cross-Subsidization While Taking Full Advantage of Massive MU-MIMO?","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Isolation (microbiology); Telecommunications link; A priori and a posteriori; Operator (biology); Spectral efficiency; Base station; Resource management (computing); Resource (disambiguation); Exploit","score_opus":0.012626395425164682,"score_gpt":0.23505274026777231,"score_spread":0.22242634484260762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7082659851","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024434684,0.000055073542,0.9543259,0.009506806,0.00033953943,0.0003282548,0.0000023806463,0.00007829042,0.01092906],"genre_scores_gemma":[0.9925127,0.00015431296,0.0047349385,0.0010078182,0.00002964647,0.000034517685,0.000002107444,0.0000034311938,0.0015205096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989405,0.000025838586,0.00018736765,0.0004604536,0.00012881607,0.0002569985],"domain_scores_gemma":[0.9993649,0.000048819198,0.00009033339,0.00037045465,0.000059260994,0.00006624014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020214831,0.00015455378,0.00016721462,0.000109652865,0.00029371204,0.00017399398,0.0003252959,0.00005628764,0.000021140873],"category_scores_gemma":[0.000001954085,0.00015865332,0.000034421144,0.0006947517,0.00002274647,0.00017177408,0.00003939253,0.000116564144,0.0000025518762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020008696,0.00021491885,0.0015094436,0.0018855373,0.00031339668,0.000039825794,0.001749067,0.90916556,0.0015769388,0.03417083,0.00048763276,0.048686773],"study_design_scores_gemma":[0.0041391463,0.000534643,0.026394786,0.0018032447,0.00031577886,0.000023438055,0.002656965,0.88009566,0.01827581,0.017632395,0.046648476,0.0014796434],"about_ca_topic_score_codex":0.000013895214,"about_ca_topic_score_gemma":0.00013618034,"teacher_disagreement_score":0.968078,"about_ca_system_score_codex":0.00002056547,"about_ca_system_score_gemma":0.00000875368,"threshold_uncertainty_score":0.64696944},"labels":[],"label_agreement":null},{"id":"W7117323654","doi":"10.1109/tnsm.2025.3648360","title":"Data Driven Deep Neural Network Based Task Offloading on Edge Cloud Continuum","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Cloud computing; Computation offloading; Artificial neural network; Edge computing; Edge device; Enhanced Data Rates for GSM Evolution; Task (project management); Computation; Data-driven","score_opus":0.023105047975758325,"score_gpt":0.24685309072544626,"score_spread":0.22374804274968793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117323654","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022951884,0.00062751095,0.9211506,0.006285161,0.060295526,0.0011701711,0.000009450385,0.0003156213,0.007850774],"genre_scores_gemma":[0.9269096,0.0011112943,0.017404666,0.04042355,0.011464857,0.000118834214,0.00008913948,0.00013233145,0.0023457003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942914,0.0004597727,0.0009842222,0.0020407992,0.00057600584,0.0016477562],"domain_scores_gemma":[0.99601316,0.000524748,0.00027966095,0.0027254457,0.0001417251,0.00031524297],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011167936,0.0008324296,0.0007395371,0.0003656299,0.002306015,0.0010440933,0.0027678718,0.00025678956,0.000025871313],"category_scores_gemma":[0.0000022382812,0.0009063188,0.0002121822,0.0028977436,0.00009996582,0.000494847,0.00021738329,0.00097637123,0.00009733035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019952514,0.00029482064,0.000039568386,0.00037413836,0.00043231124,0.000059159644,0.0002552106,0.6571127,0.0000022879453,0.0004519881,0.036888544,0.3038897],"study_design_scores_gemma":[0.0015851426,0.00022820721,0.0005223344,0.0011203205,0.00050072867,0.0000031657992,0.000059686146,0.8957855,0.000012956719,0.0002904977,0.09920018,0.0006912723],"about_ca_topic_score_codex":0.00006592133,"about_ca_topic_score_gemma":0.00043423535,"teacher_disagreement_score":0.9246144,"about_ca_system_score_codex":0.0001581169,"about_ca_system_score_gemma":0.000086454485,"threshold_uncertainty_score":0.9999929},"labels":[],"label_agreement":null},{"id":"W7117536868","doi":"10.1109/tnsm.2025.3620249","title":"Guest Editors’ Introduction: Special Issue on Resilient Communication Networks for an Hyper-Connected World","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Satellite Communication Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Resilience (materials science); Field (mathematics); Set (abstract data type); Telecommunications network; Special section","score_opus":0.016069840421266593,"score_gpt":0.2500188956363102,"score_spread":0.2339490552150436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117536868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011034125,0.00711967,0.61983424,0.035414085,0.21652476,0.011906417,0.000112821814,0.0015906568,0.10639391],"genre_scores_gemma":[0.52373666,0.06508039,0.009607933,0.00738585,0.35976556,0.0040667523,0.0007502242,0.00054185267,0.029064804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964318,0.0005129677,0.0011085875,0.00088820915,0.00035205937,0.00070632144],"domain_scores_gemma":[0.99596196,0.00053526845,0.00019701378,0.002803618,0.0002946869,0.00020744572],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00082708953,0.00065276964,0.0006142153,0.0006220504,0.0013481899,0.00038966985,0.0009274935,0.00030358072,0.00024184851],"category_scores_gemma":[0.000003020592,0.00078099186,0.0001681627,0.0023008052,0.00008778194,0.0003116834,0.00002561471,0.000813645,0.000083545194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005646777,0.00041782198,0.000006550256,0.0005237184,0.0005301575,7.2380914e-7,0.00047311574,0.73922676,0.0000050539943,0.0021189756,0.12880504,0.12732738],"study_design_scores_gemma":[0.0012902117,0.00020490358,0.00017087198,0.0006720639,0.00039248084,0.0000014474035,0.0007429995,0.20067555,0.00009246788,0.00010485347,0.79514796,0.0005042316],"about_ca_topic_score_codex":0.000077342665,"about_ca_topic_score_gemma":0.0022493298,"teacher_disagreement_score":0.66634285,"about_ca_system_score_codex":0.0004222851,"about_ca_system_score_gemma":0.000022059665,"threshold_uncertainty_score":0.9999519},"labels":[],"label_agreement":null}]}