{"meta":{"query_hash":"20acd20d0682","filters":{"venue":"IEEE Transactions on Mobile Computing"},"cohort_total":317,"direct_labels_cover":0,"predictions_cover":317,"exported":317,"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/20acd20d0682","api":"https://metacan.xera.ac/api/v1/cohort?venue=IEEE+Transactions+on+Mobile+Computing"},"results":[{"id":"W1609070905","doi":"10.1109/tmc.2015.2393856","title":"Decentralized and Parallel Constructionsfor Optimally Rigid Graphs in","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":10,"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":"Ministero dell’Istruzione, dell’Università e della Ricerca; Canadian Pain Society; National Science Foundation","keywords":"Computer science; Bidding; Cardinality (data modeling); Mathematical optimization; Rigidity (electromagnetism); Enhanced Data Rates for GSM Evolution; Greedy algorithm; Constant (computer programming); Graph; Theoretical computer science; Distributed computing; Algorithm; Mathematics","score_opus":0.022193717385069273,"score_gpt":0.2601645338487912,"score_spread":0.2379708164637219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1609070905","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032615993,0.000063380154,0.9640728,0.00013556206,0.00001776033,0.00006944703,0.000045548037,0.0002544568,0.0027250876],"genre_scores_gemma":[0.4170236,0.00018749232,0.57788265,0.00007475938,0.000036025205,0.0002003481,0.0002845892,0.00018966987,0.0041208756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990288,0.00028125965,0.000040164497,0.00026461488,0.00027404507,0.00011115821],"domain_scores_gemma":[0.9985273,0.0005238823,0.00019941147,0.0005459507,0.00012596398,0.00007748214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000930126,0.00076150504,0.0010959259,0.00070924987,0.00096805434,0.001143746,0.0014907442,0.0009954832,0.0041077733],"category_scores_gemma":[0.0027806698,0.0005910047,0.001032086,0.0010265422,0.0012212691,0.0024397064,0.0021323205,0.0015175873,0.00075968396],"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":[0.00013056888,0.00011650108,0.00046979636,0.00010979222,0.00003310552,0.00017604716,0.00014829932,0.82767487,0.009630091,0.1000442,0.0012159703,0.060250726],"study_design_scores_gemma":[0.00002337336,0.000066253044,0.00016214659,0.0000072256294,0.000010592815,0.00009455005,0.000061603096,0.92919594,0.0038462065,0.06393497,0.0025852416,0.000011864726],"about_ca_topic_score_codex":0.001178438,"about_ca_topic_score_gemma":0.0021238695,"teacher_disagreement_score":0.0041077733,"about_ca_system_score_codex":0.00081803685,"about_ca_system_score_gemma":0.00093013706,"threshold_uncertainty_score":0.013741851},"labels":[],"label_agreement":null},{"id":"W1827158947","doi":"10.1109/tmc.2015.2409882","title":"Delay Analysis of Multichannel Opportunistic Spectrum Access MAC Protocols","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":23,"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; Channel (broadcasting); Bottleneck; Access control; Queueing theory; Cognitive radio; Markov chain; Media access control; Control channel; Aloha; Queue; Queuing delay; Throughput; Protocol (science); Wireless; Base station; Telecommunications; Embedded system","score_opus":0.0678461264804908,"score_gpt":0.33429202545879594,"score_spread":0.2664458989783051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1827158947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10420324,0.0031276245,0.87756115,0.0003911942,0.0001526233,0.00016203278,0.00023680527,0.00026230307,0.013902953],"genre_scores_gemma":[0.9631592,0.0011669233,0.031698737,0.0000858525,0.00007827157,0.00015924394,0.00008294283,0.00005793265,0.0035107366],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882907,0.00021449006,0.00005464733,0.00013879458,0.00050790503,0.00025500273],"domain_scores_gemma":[0.99556714,0.0029543594,0.00035104668,0.00024341048,0.0007948677,0.00008910257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019979142,0.0009221266,0.0005345341,0.0012203872,0.0006017519,0.0012753152,0.001084213,0.00043498987,0.0018095236],"category_scores_gemma":[0.0073824674,0.00037789092,0.00042188546,0.0007249684,0.0006791225,0.0016243568,0.00082933356,0.000707283,0.00020374241],"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":[0.0001154205,0.000058194757,0.00095721334,0.00012549419,0.00003509742,0.00011752424,0.00011438979,0.8801431,0.0062152515,0.0934535,0.00078889896,0.017875938],"study_design_scores_gemma":[0.0000022619793,0.000017891027,0.00012291157,0.0000044931194,0.000005313141,0.00002413595,0.000010740765,0.99380255,0.0006057033,0.005005201,0.0003941559,0.0000046288155],"about_ca_topic_score_codex":0.004802292,"about_ca_topic_score_gemma":0.002854822,"teacher_disagreement_score":0.004802292,"about_ca_system_score_codex":0.002906984,"about_ca_system_score_gemma":0.0017121165,"threshold_uncertainty_score":0.0210917},"labels":[],"label_agreement":null},{"id":"W1964687629","doi":"10.1109/tmc.2011.92","title":"FESCIM: Fair, Efficient, and Secure Cooperation Incentive Mechanism for Multihop Cellular Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":50,"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; Computer network; Network packet; Overhead (engineering); Relay; Collusion; Computer security; Payment; Node (physics); Incentive","score_opus":0.032046914871176994,"score_gpt":0.252424370414075,"score_spread":0.220377455542898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964687629","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0546041,0.0007012388,0.9349001,0.0006714129,0.00032096985,0.00057729636,0.00020543578,0.0016245134,0.006394965],"genre_scores_gemma":[0.8995123,0.00023949915,0.09384679,0.00018893344,0.000089097106,0.00037154008,0.00014187662,0.000028722508,0.0055812607],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987521,0.0003691165,0.000067237575,0.00011940643,0.00041018944,0.0002819134],"domain_scores_gemma":[0.9987078,0.0003984903,0.00019597783,0.00024723317,0.00031960956,0.00013093279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026530507,0.0006526473,0.0007353528,0.0011379855,0.0011982785,0.0008161025,0.0021916493,0.0011149925,0.0023460959],"category_scores_gemma":[0.004465522,0.00018955053,0.00048141243,0.0007413844,0.00083698105,0.0015709836,0.0017158099,0.0007368276,0.0003624157],"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":[0.0014490639,0.0005025647,0.0024509626,0.0004705812,0.00015979327,0.0011374677,0.0004944076,0.25324324,0.024715584,0.32717994,0.022123136,0.36607322],"study_design_scores_gemma":[0.00025459056,0.00046216694,0.0007838756,0.00004405645,0.0000521338,0.00055635744,0.00006208501,0.8891328,0.010114549,0.07659991,0.021843327,0.00009406377],"about_ca_topic_score_codex":0.0018146303,"about_ca_topic_score_gemma":0.0017024793,"teacher_disagreement_score":0.0026530507,"about_ca_system_score_codex":0.0013442053,"about_ca_system_score_gemma":0.0020729199,"threshold_uncertainty_score":0.014030814},"labels":[],"label_agreement":null},{"id":"W1964718088","doi":"10.1109/tmc.2012.100","title":"Underwater Localization with Time-Synchronization and Propagation Speed Uncertainties","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":141,"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":"","keywords":"Computer science; Underwater; Benchmark (surveying); Synchronization (alternating current); Node (physics); Underwater acoustic communication; Global Positioning System; Radio propagation; Real-time computing; Propagation delay; Underwater acoustics; Network packet; Algorithm; Channel (broadcasting); Telecommunications; Computer network; Acoustics","score_opus":0.010979478833190939,"score_gpt":0.20775461919342883,"score_spread":0.19677514036023788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964718088","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013005103,0.00009208472,0.98585355,0.000042299445,0.000017188513,0.000011738013,0.000014537289,0.00026273934,0.0007007384],"genre_scores_gemma":[0.64496124,0.00032416705,0.35145998,0.00004126271,0.000057589205,0.000085582185,0.00012299424,0.00007266016,0.0028745094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937457,0.00015043562,0.00003448598,0.00012086551,0.0002690576,0.000050713927],"domain_scores_gemma":[0.99888974,0.0005362861,0.00020197048,0.00014713647,0.00019785618,0.000027039076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007492154,0.000665808,0.0007174309,0.000565127,0.00041103657,0.0006697359,0.0009858186,0.00082764134,0.00062200404],"category_scores_gemma":[0.0032807027,0.0003597893,0.0003621688,0.0009578094,0.00055255217,0.0012955952,0.0012405253,0.00057038636,0.00022016047],"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":[0.00007814129,0.000015322808,0.00062897336,0.000051108575,0.000024018662,0.000070666305,0.00006434216,0.9261615,0.005009678,0.008121194,0.00042257004,0.059352405],"study_design_scores_gemma":[0.0000115774155,0.000026578342,0.00014222732,0.0000027327012,0.0000062307695,0.00003883861,0.000008536789,0.99486333,0.002299107,0.0019733235,0.0006218654,0.000005646812],"about_ca_topic_score_codex":0.005559357,"about_ca_topic_score_gemma":0.0032159889,"teacher_disagreement_score":0.005559357,"about_ca_system_score_codex":0.0006104291,"about_ca_system_score_gemma":0.0010390066,"threshold_uncertainty_score":0.011053979},"labels":[],"label_agreement":null},{"id":"W1972961267","doi":"10.1109/tmc.2015.2416181","title":"Energy and Throughput Trade-Offs in Cellular Networks Using Base Station Switching","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":48,"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; Base station; Scheduling (production processes); Bottleneck; Benchmark (surveying); Computer network; Efficient energy use; Cellular network; Real-time computing; Embedded system; Engineering","score_opus":0.018088609770751916,"score_gpt":0.23416101860080127,"score_spread":0.21607240883004936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972961267","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81129724,0.0016008879,0.17128968,0.00048291963,0.000050823543,0.0000705615,0.00015562685,0.00028050915,0.014771782],"genre_scores_gemma":[0.9969867,0.00014862778,0.0024470168,0.000015661533,0.000007860663,0.00001015301,0.000013348146,0.00001106769,0.00035966226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987801,0.000558732,0.000032806325,0.000094691444,0.0002238882,0.0003098397],"domain_scores_gemma":[0.9978162,0.0016650787,0.0001596485,0.00014016495,0.00013830572,0.00008060103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017326395,0.00096791744,0.0008144171,0.0006235987,0.0006381667,0.0014445316,0.00075811933,0.0006651192,0.0011070368],"category_scores_gemma":[0.004810768,0.00037516686,0.00040778937,0.001345986,0.0009057999,0.001279917,0.0007685295,0.00043979514,0.00010029403],"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":[0.0001262827,0.00003367489,0.00061998615,0.000018004475,0.000016981538,0.000039038783,0.0000146817265,0.9891166,0.00111132,0.0037678513,0.00012715338,0.005008501],"study_design_scores_gemma":[0.000007822159,0.00008761243,0.00042261824,0.0000037659845,0.000016333452,0.00002451425,0.000026759551,0.99526656,0.0008760671,0.0031727473,0.00008898881,0.0000061541273],"about_ca_topic_score_codex":0.003655068,"about_ca_topic_score_gemma":0.0045863776,"teacher_disagreement_score":0.003655068,"about_ca_system_score_codex":0.0022760124,"about_ca_system_score_gemma":0.0006225767,"threshold_uncertainty_score":0.016513765},"labels":[],"label_agreement":null},{"id":"W1975668354","doi":"10.1109/tmc.2003.1233528","title":"Modeling and analysis of wap performance over wireless links","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Wireless Communication Networks Research","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 Waterloo","funders":"","keywords":"Goodput; Computer science; Wireless Application Protocol; Wireless; Channel (broadcasting); Computer network; Rayleigh fading; Network packet; Wireless network; Fading; General Packet Radio Service; Throughput; Telecommunications","score_opus":0.02438602890010434,"score_gpt":0.28429300523409035,"score_spread":0.259906976333986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975668354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07899815,0.0010409047,0.91304624,0.0003164507,0.00005911327,0.00007286057,0.00011880505,0.00063436065,0.0057130856],"genre_scores_gemma":[0.9482888,0.0025837864,0.044166084,0.000080945436,0.00009673434,0.0001900112,0.00016062497,0.00013771332,0.0042954143],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917233,0.00018464003,0.000035017958,0.00010024784,0.00039031115,0.000117433185],"domain_scores_gemma":[0.9983241,0.0008809384,0.00024631232,0.00017965719,0.0003327851,0.000036254543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079851376,0.0011495162,0.00062463275,0.0008574386,0.00050871604,0.0012530437,0.0015416478,0.0013227535,0.0009036086],"category_scores_gemma":[0.0047235,0.0004806886,0.00043408375,0.0008910444,0.0008108813,0.0028049438,0.00062218367,0.001188768,0.000607638],"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":[0.00002238576,0.000027154847,0.00060216134,0.0000598148,0.0000197278,0.00015894262,0.00005672806,0.9687427,0.0056336117,0.016203824,0.00027589564,0.008196949],"study_design_scores_gemma":[0.0000011285994,0.000013515319,0.000077748446,0.0000033128483,0.0000037475604,0.00003298981,0.0000063446596,0.9965293,0.0006352373,0.0024587512,0.00023433057,0.0000035934643],"about_ca_topic_score_codex":0.003619026,"about_ca_topic_score_gemma":0.0012816937,"teacher_disagreement_score":0.003619026,"about_ca_system_score_codex":0.0008424657,"about_ca_system_score_gemma":0.00078595796,"threshold_uncertainty_score":0.00719589},"labels":[],"label_agreement":null},{"id":"W1976864402","doi":"10.1109/tmc.2015.2412940","title":"Opportunistic Channel Selection by Cognitive Wireless Nodes Under Imperfect Observations and Limited Memory: A Repeated Game Model","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":18,"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; Academy of Finland; Science Foundation Ireland; National Science Foundation","keywords":"Computer science; Channel (broadcasting); Imperfect; Stochastic game; Distributed computing; Computer network; Cognitive radio; Wireless; Automaton; Telecommunications; Theoretical computer science","score_opus":0.04986149910598351,"score_gpt":0.262378937578177,"score_spread":0.21251743847219348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976864402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45921403,0.00016987915,0.5332787,0.00074433273,0.000048165897,0.00014555582,0.00015159737,0.00019531725,0.006052395],"genre_scores_gemma":[0.9889003,0.000055702712,0.008964386,0.00004572377,0.000013470919,0.000078803874,0.000021720394,0.000011506387,0.0019085087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822956,0.00069675647,0.00006466688,0.00030140724,0.00026965793,0.0004380722],"domain_scores_gemma":[0.99184376,0.0056257555,0.0012517497,0.0004183706,0.0003899769,0.00047042168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021459127,0.0012499022,0.0015658984,0.00069250073,0.00066409307,0.0017200862,0.0027603328,0.0018511298,0.0015314741],"category_scores_gemma":[0.0074875695,0.0006673762,0.0008958115,0.00061067875,0.0027941428,0.0020986982,0.001511866,0.0013685054,0.0002051336],"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":[0.00016994195,0.000083574,0.00085021765,0.00003241052,0.000065486725,0.0003555603,0.00017027353,0.9693443,0.0017892185,0.02479808,0.00019686407,0.0021440412],"study_design_scores_gemma":[0.000027189753,0.000043785494,0.00009686826,0.0000021993862,0.000010784532,0.000021000138,0.000020211415,0.99383575,0.00019129971,0.0056875357,0.000053472933,0.000009859015],"about_ca_topic_score_codex":0.010595498,"about_ca_topic_score_gemma":0.005985256,"teacher_disagreement_score":0.010595498,"about_ca_system_score_codex":0.001771802,"about_ca_system_score_gemma":0.0014512403,"threshold_uncertainty_score":0.021067679},"labels":[],"label_agreement":null},{"id":"W1982821438","doi":"10.1109/tmc.2015.2413782","title":"Robust Ergodic Uplink Resource Allocation in Underlay OFDMA Cognitive Radio Networks","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":45,"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 Manitoba; Manitoba Beekeepers' Association","funders":"","keywords":"Computer science; Mathematical optimization; Cognitive radio; Telecommunications link; Transmitter power output; Underlay; Constraint (computer-aided design); Channel state information; Resource allocation; Optimization problem; Quality of service; Channel (broadcasting); Signal-to-noise ratio (imaging); Algorithm; Wireless; Mathematics; Computer network; Telecommunications; Transmitter","score_opus":0.025062099688905094,"score_gpt":0.23680806309269856,"score_spread":0.21174596340379345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982821438","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0333599,0.00089803874,0.9627057,0.0001425912,0.00002794553,0.000025188845,0.000050967174,0.00012782232,0.0026618426],"genre_scores_gemma":[0.96333903,0.0006354642,0.034349564,0.000063153326,0.00004285808,0.00006752799,0.000041460866,0.000035645036,0.0014252242],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990453,0.00034382733,0.000044990567,0.00019955088,0.00018786266,0.00017850527],"domain_scores_gemma":[0.9986533,0.0009109718,0.00024191538,0.000056145527,0.000090815214,0.00004685193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012999715,0.0008172647,0.0010805659,0.00043248056,0.00036671013,0.0014780281,0.0009016155,0.00082202285,0.00065515726],"category_scores_gemma":[0.0035408225,0.00044886273,0.0006427385,0.0007976574,0.0009501469,0.000964158,0.0011629028,0.0006910965,0.000102919425],"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":[0.00003233896,0.000014856915,0.00022827755,0.000053840962,0.000037233633,0.000099917845,0.000034095276,0.97601694,0.0013499103,0.011467137,0.00021709272,0.010448448],"study_design_scores_gemma":[0.0000026529758,0.000013741693,0.00007742403,0.0000037730995,0.0000067355204,0.0000154942,0.000008641918,0.9963199,0.00032533164,0.0031001943,0.000121458965,0.000004605068],"about_ca_topic_score_codex":0.0048548873,"about_ca_topic_score_gemma":0.0026419908,"teacher_disagreement_score":0.0048548873,"about_ca_system_score_codex":0.0008519852,"about_ca_system_score_gemma":0.001077806,"threshold_uncertainty_score":0.00965327},"labels":[],"label_agreement":null},{"id":"W1982868438","doi":"10.1109/tmc.2012.173","title":"Quality Prediction-Based Dynamic Content Adaptation Framework Applied to Collaborative Mobile Presentations","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Multimedia Communication and Technology","field":"Social Sciences","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":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Transcoding; Adaptation (eye); Content adaptation; Multimedia; Dynamic web page; Quality (philosophy); Quality of experience; The Internet; Mobile device; On the fly; World Wide Web; Human–computer interaction; Ubiquitous computing; Computer network; Quality of service; Web page; Operating system","score_opus":0.0648451575247619,"score_gpt":0.3805682967868894,"score_spread":0.31572313926212753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982868438","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011317645,0.00027016297,0.98662984,0.00008585101,0.000034447843,0.00005379951,0.000033109784,0.0006022889,0.00097281625],"genre_scores_gemma":[0.6952487,0.0006511768,0.29985228,0.000094848125,0.00013119198,0.0001801897,0.00017667013,0.00016419982,0.0035007172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932086,0.00014437201,0.00003602934,0.00018154821,0.0002439385,0.00007325682],"domain_scores_gemma":[0.99906343,0.00032243974,0.00010411873,0.00010163487,0.000345007,0.000063447296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015449359,0.00083776703,0.00093984586,0.00089111103,0.00038276633,0.0010371513,0.0017316623,0.00083883543,0.0017287963],"category_scores_gemma":[0.0032006192,0.00033400176,0.000628238,0.00084134826,0.00058085826,0.001055484,0.00083729264,0.00093439536,0.000404],"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":[0.00026847472,0.00019757703,0.0013558741,0.000108430664,0.00009705602,0.00021543704,0.00020280753,0.7044989,0.018484704,0.008804757,0.0023789478,0.26338702],"study_design_scores_gemma":[0.0000063895795,0.000029094663,0.0002491668,0.000003973901,0.000013879858,0.00003331364,0.000008815811,0.9973296,0.0010012819,0.000903155,0.0004120241,0.000009342217],"about_ca_topic_score_codex":0.010466676,"about_ca_topic_score_gemma":0.0063755237,"teacher_disagreement_score":0.010466676,"about_ca_system_score_codex":0.0009807621,"about_ca_system_score_gemma":0.0009225953,"threshold_uncertainty_score":0.020811498},"labels":[],"label_agreement":null},{"id":"W1983109928","doi":"10.1109/tmc.2015.2425396","title":"Secondary VoIP Capacity in Opportunistic Spectrum Access Networks with Friendly Scheduling","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cognitive Radio Networks and Spectrum Sensing","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":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Scheduling (production processes); Voice over IP; Dynamic priority scheduling; Network packet; Quality of service; Schedule; Cognitive radio; Distributed computing; The Internet; Mathematical optimization; Telecommunications","score_opus":0.04603838214109765,"score_gpt":0.26290670296934326,"score_spread":0.21686832082824561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983109928","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.837014,0.0003273954,0.15585321,0.00006769946,0.000017113383,0.000045353718,0.000053777112,0.00021794379,0.006403431],"genre_scores_gemma":[0.996487,0.000028299512,0.003304575,0.000007184448,0.0000043454334,0.000007925565,0.000010343828,0.0000072263024,0.00014297897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993636,0.00023898607,0.000016541228,0.00006351079,0.00013894186,0.00017849778],"domain_scores_gemma":[0.99707913,0.00201129,0.0002887979,0.00024740855,0.00021254145,0.00016087979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013115372,0.00041776514,0.00033336013,0.00054900435,0.00041332873,0.0006097822,0.0005130847,0.00035117034,0.00070306234],"category_scores_gemma":[0.0051980997,0.0002198127,0.00022297139,0.00044252884,0.00069152476,0.00088061,0.00060239463,0.00025205308,0.00007842383],"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":[0.00037855897,0.00010878848,0.002572515,0.000039590977,0.000020784199,0.00019031765,0.000096320146,0.96183306,0.009873696,0.0056078904,0.00022180809,0.019056583],"study_design_scores_gemma":[0.000012697335,0.000188285,0.0013034076,0.000004508653,0.000009636904,0.0000792619,0.00004425858,0.9905398,0.0039699194,0.0036307909,0.00020781807,0.000009647985],"about_ca_topic_score_codex":0.0028488243,"about_ca_topic_score_gemma":0.0024347699,"teacher_disagreement_score":0.0028488243,"about_ca_system_score_codex":0.0008911849,"about_ca_system_score_gemma":0.00059245806,"threshold_uncertainty_score":0.0069361925},"labels":[],"label_agreement":null},{"id":"W1986895073","doi":"10.1109/tmc.2014.2307330","title":"Concurrent Multipath Transfer Using SCTP: Modelling and Congestion Window Management","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Multihoming; Stream Control Transmission Protocol; Transport layer; Computer network; Scalability; Throughput; Distributed computing; Markov process; Markov chain; Window (computing); Network congestion; Multipath propagation; Sliding window protocol; Session (web analytics); Channel (broadcasting); Layer (electronics); Wireless; Internet Protocol; The Internet; Telecommunications; Path (computing)","score_opus":0.018901233409715137,"score_gpt":0.23557447165189802,"score_spread":0.21667323824218288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986895073","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17648509,0.0004225376,0.8144728,0.0005817655,0.000083501036,0.00019489443,0.00021761109,0.00072028226,0.006821535],"genre_scores_gemma":[0.93741196,0.0003471801,0.059413347,0.00003189751,0.000041970936,0.000181932,0.0001026512,0.000047973055,0.0024209935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995413,0.00012617972,0.000028293925,0.000075372365,0.00014704568,0.00008189523],"domain_scores_gemma":[0.9983766,0.0009906562,0.00018428107,0.000103778875,0.00028019818,0.00006452584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009894331,0.00084015896,0.00051565946,0.0007544093,0.0005968072,0.0011251629,0.0016269579,0.0013293296,0.0010250718],"category_scores_gemma":[0.0028213032,0.0004988432,0.0006326809,0.0006951712,0.00085068727,0.0014261989,0.00058030157,0.0011548443,0.00013686345],"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":[0.00001399205,0.000020036954,0.00031095895,0.000010883135,0.0000048492343,0.000028716751,0.000026990789,0.9925292,0.0006501831,0.0038051084,0.00010534126,0.0024937463],"study_design_scores_gemma":[0.0000017985927,0.000004811485,0.000024896595,9.5232645e-7,0.0000016193255,0.0000036236784,0.0000026879065,0.9992079,0.00013860525,0.00054256833,0.0000688384,0.0000016761752],"about_ca_topic_score_codex":0.022999298,"about_ca_topic_score_gemma":0.01021818,"teacher_disagreement_score":0.022999298,"about_ca_system_score_codex":0.0015266213,"about_ca_system_score_gemma":0.0017453857,"threshold_uncertainty_score":0.04573083},"labels":[],"label_agreement":null},{"id":"W1987479549","doi":"10.1109/tmc.2015.2404791","title":"Virtual Servers Co-Migration for Mobile Accesses: Online versus Off-Line","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Caching and Content Delivery","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":"Ontario Tech University; University of New Brunswick","funders":"","keywords":"Server; Computer science; Online algorithm; Lambda; Computer network; Wireless network; Mathematics; Wireless; Combinatorics; Discrete mathematics; Algorithm; Operating system; Physics","score_opus":0.06621270087283197,"score_gpt":0.3247944025949143,"score_spread":0.25858170172208234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987479549","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2602759,0.0029333415,0.71983635,0.0024332118,0.0009434194,0.0005396573,0.00030120875,0.0019086818,0.010828247],"genre_scores_gemma":[0.91173804,0.00041438203,0.08332546,0.00027701026,0.00020263663,0.00012906508,0.00018176151,0.00018503913,0.0035466712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980526,0.0005924749,0.00008057309,0.00053073285,0.00026589513,0.00047779718],"domain_scores_gemma":[0.99570245,0.001880657,0.00053701026,0.0011017085,0.00032990222,0.00044828892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017279637,0.0017140099,0.0017468642,0.00037352205,0.00157792,0.0022898396,0.0036448922,0.0020687063,0.0049142796],"category_scores_gemma":[0.0055386173,0.00057018036,0.0007338326,0.00092904456,0.0012510016,0.003946494,0.0023536202,0.0015985956,0.0010525109],"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":[0.0016562839,0.00083116436,0.004783963,0.00052720367,0.00018895784,0.0008545947,0.00039876282,0.7441003,0.012738896,0.05340525,0.015663475,0.16485114],"study_design_scores_gemma":[0.0000410742,0.00014485818,0.0003202395,0.000016573602,0.000027226093,0.00023439838,0.00016585279,0.985679,0.0014812314,0.009859308,0.0020166072,0.000013726719],"about_ca_topic_score_codex":0.0036451695,"about_ca_topic_score_gemma":0.0040347017,"teacher_disagreement_score":0.0049142796,"about_ca_system_score_codex":0.0013540231,"about_ca_system_score_gemma":0.0016732805,"threshold_uncertainty_score":0.016439915},"labels":[],"label_agreement":null},{"id":"W1988792675","doi":"10.1109/tmc.2011.94","title":"On Reliable Broadcast in Low Duty-Cycle Wireless Sensor Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":80,"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":"","keywords":"Computer science; Atomic broadcast; Computer network; Wireless sensor network; Distributed computing; Broadcast radiation; Broadcast domain; Scalability; Broadcast communication network; Broadcasting (networking)","score_opus":0.01245002267926181,"score_gpt":0.21754064166890275,"score_spread":0.20509061898964093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988792675","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017698912,0.0055303858,0.96539724,0.0008128982,0.00028882516,0.00012166599,0.00006894409,0.00029617397,0.009784981],"genre_scores_gemma":[0.7545554,0.019097377,0.21400568,0.0007778228,0.0009374249,0.0006456666,0.0003717971,0.0003623973,0.009246433],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988839,0.00033727987,0.00004645994,0.00014773895,0.0004821399,0.00010252989],"domain_scores_gemma":[0.99773526,0.001683162,0.0001300313,0.00015243822,0.00025590585,0.000043221895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014081388,0.0009969547,0.00078038237,0.00097645586,0.00061733887,0.0010281873,0.0015129743,0.0009360754,0.0015612185],"category_scores_gemma":[0.0066095544,0.00039112422,0.0004851385,0.0016276707,0.0012772408,0.0021947392,0.0011433924,0.0014843491,0.00047734304],"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":[0.00018874684,0.00006657084,0.00038946414,0.00048818503,0.000033017463,0.00015427444,0.00019554711,0.79736984,0.0064131375,0.09243372,0.003936423,0.098331116],"study_design_scores_gemma":[0.000022282007,0.0001264587,0.00013856306,0.000052400163,0.000016294405,0.00007491946,0.000044572153,0.9336772,0.0013461531,0.058154937,0.0063296636,0.00001658499],"about_ca_topic_score_codex":0.00232072,"about_ca_topic_score_gemma":0.0014190051,"teacher_disagreement_score":0.00232072,"about_ca_system_score_codex":0.0011184174,"about_ca_system_score_gemma":0.0007964407,"threshold_uncertainty_score":0.008114755},"labels":[],"label_agreement":null},{"id":"W2001084946","doi":"10.1109/tmc.2011.246","title":"EMAP: Expedite Message Authentication Protocol for Vehicular Ad Hoc Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":218,"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":"Hash-based message authentication code; Computer science; Revocation list; Message authentication code; Computer network; Public key infrastructure; Authentication (law); Public-key cryptography; Revocation; Computer security; Communication source; Vehicular ad hoc network; Message broker; Wireless ad hoc network; Cryptography; Wireless; Encryption; Overhead (engineering); Telecommunications","score_opus":0.024196094358240473,"score_gpt":0.25790238593029796,"score_spread":0.2337062915720575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001084946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016049402,0.0011911375,0.96866107,0.0003987909,0.00029277927,0.0006312012,0.00029956718,0.0038040122,0.008672047],"genre_scores_gemma":[0.6716285,0.0018337988,0.30001253,0.0005149762,0.00021310683,0.0015232514,0.0019186961,0.00023647292,0.022118669],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867505,0.00039881104,0.00011711869,0.00013903361,0.00050570845,0.00016418291],"domain_scores_gemma":[0.99909985,0.0001624169,0.00013772817,0.00021568473,0.00032063553,0.00006378573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012177379,0.00057891494,0.00063277496,0.000948043,0.0008759279,0.0009700518,0.0017714512,0.0008792748,0.0024422517],"category_scores_gemma":[0.0020304923,0.0002339341,0.00044881183,0.00097209046,0.00054737757,0.0019270026,0.0026544693,0.001139578,0.001251988],"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":[0.0022016207,0.0003250137,0.002685111,0.0014544826,0.00030341564,0.001981145,0.00081349106,0.06812281,0.075784646,0.21074934,0.03649733,0.59908164],"study_design_scores_gemma":[0.0004106028,0.0013218937,0.0018570606,0.0002015946,0.0002496291,0.004001996,0.0004168422,0.5072859,0.093006484,0.052439764,0.33858168,0.00022659061],"about_ca_topic_score_codex":0.00080161256,"about_ca_topic_score_gemma":0.00087136356,"teacher_disagreement_score":0.0024422517,"about_ca_system_score_codex":0.00045047875,"about_ca_system_score_gemma":0.001077402,"threshold_uncertainty_score":0.008170128},"labels":[],"label_agreement":null},{"id":"W2015003673","doi":"10.1109/tmc.2013.83","title":"Optimal Design of the Spectrum Sensing Parameters in the Overlay Spectrum Sharing","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cognitive Radio Networks and Spectrum Sensing","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":"Carleton University; University of British Columbia","funders":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Overlay; Transmitter; Fading; Cognitive radio; Interference (communication); Mathematical optimization; Transmission (telecommunications); Computer network; Algorithm; Topology (electrical circuits); Wireless; Channel (broadcasting); Telecommunications; Mathematics","score_opus":0.019236901457483458,"score_gpt":0.23046850890383408,"score_spread":0.2112316074463506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015003673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032880377,0.00035468058,0.96287,0.00014947278,0.00002015465,0.00009284571,0.000037401885,0.000081828584,0.0035132756],"genre_scores_gemma":[0.916926,0.00026570036,0.08207623,0.000041310483,0.000014684096,0.00009528258,0.000020329497,0.00001804625,0.00054240256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912196,0.0003171099,0.000036322534,0.00019184095,0.0001993752,0.00013339415],"domain_scores_gemma":[0.9986487,0.00080724375,0.00023416095,0.00007276396,0.00017552887,0.000061718994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013926997,0.0010582992,0.0009675357,0.00055150694,0.00045862648,0.0013243633,0.0010198097,0.0010815246,0.0011995347],"category_scores_gemma":[0.0054898732,0.00047231856,0.00043711663,0.00050666346,0.0012574531,0.0017460778,0.0014226713,0.0011038339,0.00016645457],"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":[0.00016761375,0.000107210406,0.0008220836,0.00023900133,0.00005114569,0.00017226444,0.00021976026,0.89287937,0.014692099,0.04588658,0.000783015,0.043979917],"study_design_scores_gemma":[0.000012773736,0.00004479321,0.00015389248,0.0000150220585,0.000012993554,0.000039218354,0.000060495146,0.9897518,0.0016117324,0.007905353,0.00037622487,0.00001563236],"about_ca_topic_score_codex":0.0018627385,"about_ca_topic_score_gemma":0.0018883941,"teacher_disagreement_score":0.0018627385,"about_ca_system_score_codex":0.0010552251,"about_ca_system_score_gemma":0.0015204259,"threshold_uncertainty_score":0.0076562166},"labels":[],"label_agreement":null},{"id":"W2015398759","doi":"10.1109/tmc.2015.2417880","title":"Downlink Power Control in Self-Organizing Dense Small Cells Underlaying Macrocells: A Mean Field Game","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":70,"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; Mathematical optimization; Nash equilibrium; Base station; Telecommunications link; Power control; Relaxation (psychology); Cellular network; Power (physics); Mathematics; Computer network","score_opus":0.012104384029354551,"score_gpt":0.21801056353718837,"score_spread":0.20590617950783383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015398759","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051602807,0.00013650997,0.9448957,0.00028157386,0.00003198798,0.000052738866,0.000038687966,0.00005789122,0.002902139],"genre_scores_gemma":[0.96735615,0.0001480058,0.030160721,0.00009131087,0.00002741205,0.000092058806,0.000022156335,0.000014241718,0.0020878902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996151,0.00014652792,0.000011449284,0.00006878362,0.000090886555,0.000067183064],"domain_scores_gemma":[0.99928504,0.00045845503,0.0000881753,0.000031143307,0.00007250654,0.00006473069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008165276,0.0006229316,0.0007743096,0.00027861077,0.00034519823,0.0008566309,0.0011141432,0.0008079411,0.00092670135],"category_scores_gemma":[0.0013483481,0.00031699645,0.000464708,0.00037689073,0.0010116083,0.00094160484,0.0009140043,0.00080729043,0.00009804236],"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":[0.000047323483,0.000035869525,0.00027026166,0.000025968548,0.000022578955,0.00009785839,0.000049606504,0.9571997,0.0031311759,0.03245253,0.00042744988,0.0062396154],"study_design_scores_gemma":[0.000007749752,0.000020962141,0.000038391852,0.0000012907338,0.0000027155897,0.000010395534,0.000006663378,0.99505347,0.0001418826,0.004589373,0.00012431809,0.0000028945233],"about_ca_topic_score_codex":0.002844527,"about_ca_topic_score_gemma":0.0021081353,"teacher_disagreement_score":0.002844527,"about_ca_system_score_codex":0.0010366802,"about_ca_system_score_gemma":0.0008657521,"threshold_uncertainty_score":0.0075216293},"labels":[],"label_agreement":null},{"id":"W2020662527","doi":"10.1109/tmc.2010.152","title":"Distributed Multi-Interface Multichannel Random Access Using Convex Optimization","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Ad Hoc Networks","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 British Columbia","funders":"","keywords":"Computer science; Channel (broadcasting); Node (physics); Wireless ad hoc network; Channel allocation schemes; Optimization problem; Interface (matter); Convex optimization; Distributed algorithm; Mathematical optimization; Algorithm; Random access; Throughput; Wireless; Distributed computing; Computer network; Regular polygon; Mathematics","score_opus":0.02669440846908288,"score_gpt":0.30193889635787863,"score_spread":0.27524448788879574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020662527","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035826655,0.00016401416,0.9947955,0.000098258686,0.000010383052,0.000029920375,0.000014729972,0.00015681746,0.0011478319],"genre_scores_gemma":[0.55860925,0.00077436725,0.4364338,0.00013557894,0.00007094888,0.00047011508,0.0001484335,0.00015592783,0.003201565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99777526,0.0014534083,0.00005073292,0.00023201708,0.00033571257,0.0001528744],"domain_scores_gemma":[0.9976078,0.0017960398,0.000199744,0.00016597302,0.0001788565,0.00005146805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025901007,0.0013438341,0.0012873545,0.00050428545,0.00038721264,0.0014125584,0.0012386858,0.0007345228,0.0013117072],"category_scores_gemma":[0.004652358,0.0004497645,0.00069020625,0.00094775675,0.0010491823,0.0013417362,0.0011435105,0.0011864399,0.00047862448],"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":[0.000039522663,0.000038527563,0.00014439608,0.00003760196,0.000020895297,0.000025086363,0.000021708036,0.9600296,0.0005206974,0.017526146,0.0006710534,0.020924702],"study_design_scores_gemma":[0.000006664212,0.000010227519,0.000016335225,0.0000015315894,0.0000022577558,0.000005669349,0.000002521939,0.9964449,0.00015487023,0.003178599,0.00017438902,0.0000020642246],"about_ca_topic_score_codex":0.002482789,"about_ca_topic_score_gemma":0.0023145073,"teacher_disagreement_score":0.0025901007,"about_ca_system_score_codex":0.0013808893,"about_ca_system_score_gemma":0.0012227335,"threshold_uncertainty_score":0.013697922},"labels":[],"label_agreement":null},{"id":"W2025416284","doi":"10.1109/tmc.2012.206","title":"Background Subtraction for Online Calibration of Baseline RSS in RF Sensing Networks","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Beijing University of Posts and Telecommunications","keywords":"RSS; Computer science; Background subtraction; Calibration; Baseline (sea); Artificial intelligence; Pixel; Computer vision; Mathematics","score_opus":0.020730493715700477,"score_gpt":0.2583825845539969,"score_spread":0.2376520908382964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025416284","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025075153,0.00018559807,0.9727711,0.00003650129,0.000035866804,0.000017213039,0.000025174972,0.000869894,0.000983511],"genre_scores_gemma":[0.5262368,0.00048410424,0.47053537,0.000082514736,0.000041818468,0.00007290718,0.00026839948,0.00029050204,0.0019876088],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940455,0.00012896882,0.000023394885,0.00014571441,0.00023370636,0.00006370571],"domain_scores_gemma":[0.99915683,0.00036802623,0.00008665323,0.0001436026,0.00020790516,0.000036886286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086400704,0.00071215315,0.0006951613,0.0008993033,0.0004602337,0.0007017944,0.0010839228,0.00062582066,0.0010007767],"category_scores_gemma":[0.003335236,0.00033456873,0.0004412988,0.0010993965,0.0005019545,0.0012822523,0.00086107256,0.0006968098,0.00071311946],"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":[0.000522751,0.00020075188,0.0035655715,0.00018036723,0.00007113,0.00037737505,0.00027537756,0.27316266,0.1319898,0.009671333,0.0019717272,0.57801116],"study_design_scores_gemma":[0.000011621774,0.00009104319,0.0025666421,0.000013584834,0.000022436192,0.0002669159,0.000049048675,0.9318028,0.0586723,0.003562993,0.00291077,0.000029689694],"about_ca_topic_score_codex":0.0022684475,"about_ca_topic_score_gemma":0.002620374,"teacher_disagreement_score":0.0022684475,"about_ca_system_score_codex":0.0006172559,"about_ca_system_score_gemma":0.00055866153,"threshold_uncertainty_score":0.0045694113},"labels":[],"label_agreement":null},{"id":"W2031322911","doi":"10.1109/tmc.2014.2318700","title":"An Evolutionary Game for Distributed Resource Allocation in Self-Organizing Small Cells","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":147,"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; Resource allocation; Evolutionary game theory; Mathematical optimization; Macrocell; Game theory; Orthogonal frequency-division multiple access; Distributed computing; Base station; Computer network; Orthogonal frequency-division multiplexing; Mathematics","score_opus":0.007355821565909815,"score_gpt":0.21159318036079433,"score_spread":0.20423735879488453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031322911","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018621482,0.00009027465,0.97748613,0.00023016808,0.000034356075,0.000079044104,0.000029128938,0.00003529964,0.0033941637],"genre_scores_gemma":[0.8410963,0.00026715733,0.15190843,0.00019771274,0.000037768517,0.00041879428,0.000053091902,0.000019712654,0.006001094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994261,0.00027321704,0.000021348847,0.00008231684,0.0001327819,0.00006434392],"domain_scores_gemma":[0.9995529,0.000243344,0.00005288917,0.000025055706,0.00006962687,0.00005616402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008518749,0.0006400973,0.0007524345,0.00032183272,0.000493625,0.00081818964,0.0014550997,0.0011843516,0.0017706848],"category_scores_gemma":[0.0015683037,0.00025265722,0.0005690777,0.000417713,0.0010316584,0.0008979081,0.00089431053,0.0009011818,0.00016140753],"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":[0.00004592427,0.000050392173,0.00031582545,0.000045187797,0.000034350618,0.00020841767,0.00010376609,0.8681103,0.0031743096,0.115600914,0.0007613081,0.011549397],"study_design_scores_gemma":[0.000012726439,0.000021583612,0.00003652738,0.0000026209,0.000004035005,0.000023618308,0.000009385946,0.98844284,0.0001723582,0.010740593,0.00052893953,0.000004683077],"about_ca_topic_score_codex":0.0022514788,"about_ca_topic_score_gemma":0.0018213714,"teacher_disagreement_score":0.0022514788,"about_ca_system_score_codex":0.0010846623,"about_ca_system_score_gemma":0.00086275453,"threshold_uncertainty_score":0.00786978},"labels":[],"label_agreement":null},{"id":"W2032997440","doi":"10.1109/tmc.2004.1261818","title":"QoS-oriented packet scheduling for wireless multimedia CDMA communications","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":54,"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; Computer network; Quality of service; Transmission delay; Network packet; Scheduling (production processes); Code division multiple access; Packet loss; Wireless; Telecommunications","score_opus":0.03757708550607677,"score_gpt":0.3231624036712443,"score_spread":0.2855853181651676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032997440","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029820357,0.0026223012,0.9619385,0.00039092448,0.00040259844,0.00012449521,0.000043122414,0.0005944293,0.0040632505],"genre_scores_gemma":[0.6518335,0.0023834445,0.34110317,0.00031815527,0.000442255,0.00016152828,0.00012737376,0.000078769684,0.003551852],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995803,0.00016022584,0.000025555557,0.000042691354,0.00015240644,0.000038801216],"domain_scores_gemma":[0.9993187,0.0003102378,0.00006911088,0.00009103377,0.00016830588,0.000042644864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010334773,0.00042456778,0.00023180309,0.00047591672,0.000599693,0.0006578436,0.0006133339,0.00033950404,0.00078525615],"category_scores_gemma":[0.0025370882,0.00015330315,0.00015595916,0.0005416106,0.00044888607,0.00063851045,0.00031677468,0.000512008,0.00023776287],"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":[0.00056003046,0.00017669682,0.0015586036,0.00029405128,0.000054690583,0.00028442065,0.00027430654,0.29147682,0.053907685,0.1588258,0.007144823,0.48544207],"study_design_scores_gemma":[0.00005205643,0.0001663948,0.00029934963,0.000022986487,0.00002320217,0.00013956676,0.000037704034,0.9403004,0.011954856,0.03237235,0.014610432,0.000020614054],"about_ca_topic_score_codex":0.0018890064,"about_ca_topic_score_gemma":0.0019933318,"teacher_disagreement_score":0.0018890064,"about_ca_system_score_codex":0.000902899,"about_ca_system_score_gemma":0.0010384287,"threshold_uncertainty_score":0.006551087},"labels":[],"label_agreement":null},{"id":"W2037324986","doi":"10.1109/tmc.2011.251","title":"Coalition-Based Cooperative Packet Delivery under Uncertainty: A Dynamic Bayesian Coalitional Game","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":64,"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 Manitoba","funders":"","keywords":"Computer science; Bayesian game; Node (physics); Computer network; Network packet; Nash equilibrium; Markov decision process; Game theory; Wireless network; Core (optical fiber); Best response; Distributed computing; Wireless; Sequential game; Markov process; Mathematical optimization; Mathematical economics; Telecommunications","score_opus":0.02807585781632486,"score_gpt":0.25090287717687665,"score_spread":0.2228270193605518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037324986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028901251,0.00017838847,0.9630004,0.00070683454,0.000038575312,0.00013915771,0.000107184314,0.0000854248,0.0068427334],"genre_scores_gemma":[0.88674515,0.0005306747,0.10630118,0.00023018254,0.00006851391,0.00040458323,0.00013802189,0.00003401827,0.0055477247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821776,0.0007590509,0.000071871626,0.0002747871,0.00046156056,0.00021494241],"domain_scores_gemma":[0.99752253,0.0016391136,0.00027329908,0.00009236524,0.00026373763,0.00020899047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025671108,0.00085622736,0.0013539818,0.0006664323,0.0008512225,0.0015760964,0.0025361704,0.0019105442,0.0013581875],"category_scores_gemma":[0.0071011134,0.0005474185,0.0008795819,0.00088111236,0.0018085686,0.0029129453,0.0017943606,0.0016582222,0.0001959557],"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":[0.00013451875,0.00009790247,0.00085863774,0.00009773381,0.00008611959,0.00038078605,0.0004290882,0.7112695,0.0022165296,0.2643159,0.0018648582,0.01824834],"study_design_scores_gemma":[0.000020705456,0.000026500116,0.00010109889,0.0000074419886,0.000014999148,0.000038636692,0.000030789903,0.96361226,0.00017041733,0.035174783,0.00078802765,0.000014277673],"about_ca_topic_score_codex":0.00937498,"about_ca_topic_score_gemma":0.0055960356,"teacher_disagreement_score":0.00937498,"about_ca_system_score_codex":0.0021412075,"about_ca_system_score_gemma":0.00232746,"threshold_uncertainty_score":0.018640816},"labels":[],"label_agreement":null},{"id":"W2040714707","doi":"10.1109/tmc.2013.36","title":"Two-Tier HetNets with Cognitive Femtocells: Downlink Performance Modeling and Analysis in a Multichannel Environment","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":154,"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":"Femtocell; Heterogeneous network; Computer science; Femto-; Macro; Stochastic geometry; Interference (communication); Computer network; Telecommunications link; Macrocell; Cognitive radio; LTE Advanced; Base station; Rayleigh fading; Transmission (telecommunications); Wireless; Fading; Wireless network; Telecommunications; Channel (broadcasting)","score_opus":0.00782092328080329,"score_gpt":0.20280593295384494,"score_spread":0.19498500967304166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040714707","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4685225,0.00087548874,0.51848525,0.00031278483,0.00006715009,0.00005433149,0.00022009334,0.00025492153,0.011207463],"genre_scores_gemma":[0.9923149,0.0002653463,0.006342392,0.0000412482,0.00002062417,0.00001830329,0.000031573472,0.000011160442,0.0009544954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950266,0.00018277552,0.00001195531,0.000060776983,0.00010061521,0.00014131477],"domain_scores_gemma":[0.9992993,0.0003237976,0.00012077552,0.00006076218,0.00014304882,0.00005232875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069899036,0.0010129831,0.00059112185,0.00040115468,0.00039818743,0.0010802678,0.00072620687,0.0009495495,0.00048902526],"category_scores_gemma":[0.0012356044,0.00038947017,0.000580553,0.00045683532,0.0009619353,0.0008790381,0.00080177677,0.00046812612,0.00019616881],"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":[0.00003684583,0.000025207437,0.0012633214,0.000013126952,0.000020501828,0.00012945461,0.000037733524,0.9896813,0.002115727,0.0047503943,0.00014280196,0.0017836273],"study_design_scores_gemma":[0.0000014163385,0.000017581884,0.0003030627,0.0000014446645,0.0000048791935,0.000024314792,0.000013653989,0.99866986,0.00023263672,0.0006766449,0.00004976844,0.000004713934],"about_ca_topic_score_codex":0.009387224,"about_ca_topic_score_gemma":0.006509041,"teacher_disagreement_score":0.009387224,"about_ca_system_score_codex":0.0009303883,"about_ca_system_score_gemma":0.000550118,"threshold_uncertainty_score":0.018665195},"labels":[],"label_agreement":null},{"id":"W2072676840","doi":"10.1109/tmc.2012.62","title":"Resource Allocation with Flexible Channel Cooperation in Cognitive Radio Networks","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":72,"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; Cognitive radio; Resource allocation; Benchmark (surveying); Channel allocation schemes; Channel (broadcasting); Relay; Computer network; Heuristic; Resource management (computing); Optimization problem; Distributed computing; Mathematical optimization; Telecommunications; Algorithm; Wireless; Artificial intelligence","score_opus":0.03409540344574342,"score_gpt":0.2798092778000625,"score_spread":0.24571387435431907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072676840","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09763239,0.00060794,0.8944962,0.00044723134,0.000054636497,0.000048241465,0.000028180675,0.00010238805,0.006582803],"genre_scores_gemma":[0.9702908,0.0002237839,0.028306022,0.00006873071,0.000030574232,0.00006775697,0.000010883037,0.000014423347,0.0009870707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858356,0.00075502536,0.000027835162,0.00013760554,0.00022467825,0.00027138047],"domain_scores_gemma":[0.99749666,0.0019305705,0.00020354381,0.00013135096,0.00013165498,0.00010622386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023003533,0.0008464314,0.00081513915,0.000519437,0.00064170774,0.0013018729,0.001239447,0.0012958631,0.000569559],"category_scores_gemma":[0.004874814,0.0004491797,0.00045630155,0.0010405559,0.0020528613,0.0014656278,0.0013016757,0.0008827224,0.00011212232],"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":[0.000023724333,0.000019541865,0.000097453514,0.000011665521,0.000011099983,0.000046998735,0.000030238212,0.98319083,0.00036457067,0.013172666,0.0001698422,0.002861368],"study_design_scores_gemma":[0.000013265257,0.000018593268,0.000035868256,0.0000018641429,0.000004727925,0.000010689024,0.00001432434,0.98825336,0.0001490852,0.011380063,0.00011362722,0.0000044952567],"about_ca_topic_score_codex":0.004431295,"about_ca_topic_score_gemma":0.003210097,"teacher_disagreement_score":0.004431295,"about_ca_system_score_codex":0.0011156178,"about_ca_system_score_gemma":0.0014461935,"threshold_uncertainty_score":0.012165546},"labels":[],"label_agreement":null},{"id":"W2075829594","doi":"10.1109/tmc.2013.56","title":"Uplink Scheduling in Wireless Networks with Successive Interference Cancellation","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":80,"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; Telecommunications link; Scheduling (production processes); Single antenna interference cancellation; Computational complexity theory; Wireless; Distributed computing; Computer network; Job shop scheduling; Fair-share scheduling; Wireless network; Mathematical optimization; Algorithm; Quality of service; Channel (broadcasting); Telecommunications; Mathematics","score_opus":0.01743833570027899,"score_gpt":0.25568265323445905,"score_spread":0.23824431753418004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075829594","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03336369,0.001778747,0.95682484,0.0006600245,0.00014743565,0.00013373696,0.000093770876,0.00024447637,0.0067531937],"genre_scores_gemma":[0.7299465,0.0029758373,0.26124018,0.00032971008,0.00042864602,0.00026900554,0.00020250604,0.000108804525,0.004498858],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997971,0.00094032247,0.00007325162,0.00022348146,0.0004374935,0.00035449277],"domain_scores_gemma":[0.9957795,0.0033097642,0.0003549418,0.00020870152,0.0002564843,0.00009058083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023240314,0.0009917454,0.0015734349,0.00060382043,0.0011846963,0.0016057465,0.0012677865,0.0009669736,0.0016442037],"category_scores_gemma":[0.0056808977,0.0005667703,0.0006097758,0.0019417384,0.0014347873,0.0019256859,0.0011047391,0.0012328242,0.0003426642],"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":[0.000118691365,0.000064472944,0.00023542254,0.00014625206,0.000027718599,0.00010033647,0.00008236052,0.9265336,0.0013112483,0.043357916,0.0019762681,0.02604576],"study_design_scores_gemma":[0.000031668365,0.00003402241,0.000069462396,0.000007192382,0.0000097917,0.000033985703,0.00003335532,0.9754826,0.0005937534,0.02275734,0.000938852,0.000008001499],"about_ca_topic_score_codex":0.0066763423,"about_ca_topic_score_gemma":0.005451071,"teacher_disagreement_score":0.0066763423,"about_ca_system_score_codex":0.0020394167,"about_ca_system_score_gemma":0.002521038,"threshold_uncertainty_score":0.0147970915},"labels":[],"label_agreement":null},{"id":"W2087478559","doi":"10.1109/tmc.2012.212","title":"Designing Truthful Spectrum Double Auctions with Local Markets","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":59,"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":"Shanghai Jiao Tong University","keywords":"Spectrum auction; Computer science; Common value auction; License; Locality; Profit (economics); Revenue; Double auction; Auction theory; Business; Microeconomics; Revenue equivalence; Economics","score_opus":0.05241344568600851,"score_gpt":0.3322993702205266,"score_spread":0.27988592453451805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087478559","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049470995,0.00013745703,0.9466044,0.0001630048,0.000029391907,0.00015761465,0.00004424501,0.0002641548,0.0031288061],"genre_scores_gemma":[0.8372318,0.00016948002,0.15879323,0.00010921049,0.000051232855,0.00026901503,0.000049486538,0.00007022711,0.003256408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964715,0.0016410535,0.00023613856,0.00056806335,0.0006458523,0.00043736407],"domain_scores_gemma":[0.9921292,0.004664061,0.0010079051,0.0011595984,0.00064245315,0.00039685142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065140817,0.0009467248,0.001706954,0.0006443694,0.0006805887,0.0035587514,0.0028567486,0.0019815238,0.00335161],"category_scores_gemma":[0.014510055,0.0012302616,0.0010140791,0.00086228445,0.002055526,0.005292222,0.0026921174,0.0017191414,0.00068415963],"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":[0.00089007965,0.0004142882,0.0012401894,0.00039089005,0.00016135724,0.00068174576,0.0004414331,0.6546899,0.013516269,0.27149808,0.0017804232,0.054295395],"study_design_scores_gemma":[0.00011158698,0.00011455152,0.00007079749,0.000012777214,0.000023813996,0.000103506434,0.00003810383,0.9311536,0.0019291868,0.06570101,0.00072075485,0.000020283664],"about_ca_topic_score_codex":0.000371952,"about_ca_topic_score_gemma":0.00040171668,"teacher_disagreement_score":0.0065140817,"about_ca_system_score_codex":0.0010488044,"about_ca_system_score_gemma":0.0013778934,"threshold_uncertainty_score":0.034450173},"labels":[],"label_agreement":null},{"id":"W2089946312","doi":"10.1109/tmc.2013.143","title":"Reducing the Positional Error of Connectivity-Based Positioning Algorithms Through Cooperation Between Neighbors","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","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":"York University","funders":"","keywords":"Computer science; Wireless sensor network; Algorithm; Probabilistic logic; Range (aeronautics); Set (abstract data type); Brooks–Iyengar algorithm; Distributed algorithm; Real-time computing; Distributed computing; Key distribution in wireless sensor networks; Computer network; Wireless; Wireless network; Artificial intelligence; Telecommunications","score_opus":0.013968955136682141,"score_gpt":0.24610582933579284,"score_spread":0.2321368741991107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089946312","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04579984,0.00020278398,0.95211333,0.00008513194,0.000021673692,0.00002339798,0.000018370698,0.00041319392,0.001322249],"genre_scores_gemma":[0.73366463,0.00026469174,0.26418468,0.000048063797,0.000034554345,0.00007236704,0.00012230698,0.00007345865,0.0015352712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891996,0.00031883808,0.00005552688,0.00014725406,0.00049165275,0.000066826164],"domain_scores_gemma":[0.9975358,0.0012522804,0.0002500668,0.00056630885,0.00034934908,0.00004613902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090839533,0.0006605938,0.0006685978,0.00075074524,0.0005321524,0.0005226917,0.0011124559,0.00069257105,0.0006804986],"category_scores_gemma":[0.0058918004,0.0003999233,0.00038913504,0.0007146754,0.00056165026,0.0012981447,0.0014121641,0.0004430157,0.00024156747],"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":[0.00013354748,0.00003150021,0.0017237532,0.00006182011,0.00004859327,0.000093594004,0.00017882253,0.7819641,0.0125681395,0.008314586,0.00061850686,0.19426297],"study_design_scores_gemma":[0.000024258898,0.00015692916,0.00083659607,0.0000094976385,0.000022491555,0.00015290319,0.000035487326,0.9875141,0.00513557,0.004576105,0.001518262,0.000017968963],"about_ca_topic_score_codex":0.0029661409,"about_ca_topic_score_gemma":0.002873704,"teacher_disagreement_score":0.0029661409,"about_ca_system_score_codex":0.00038356462,"about_ca_system_score_gemma":0.0009071879,"threshold_uncertainty_score":0.0058977604},"labels":[],"label_agreement":null},{"id":"W2094667486","doi":"10.1109/tmc.2013.96","title":"Pricing, Spectrum Sharing, and Service Selection in Two-Tier Small Cell Networks: A Hierarchical Dynamic Game Approach","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":136,"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 Manitoba","funders":"","keywords":"Stackelberg competition; Computer science; Macrocell; Computer network; Game theory; Service provider; Service (business); Resource allocation; Evolutionary game theory; Nash equilibrium; Mathematical optimization; Base station; Business; Microeconomics","score_opus":0.006133857085514785,"score_gpt":0.21001357910320648,"score_spread":0.2038797220176917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094667486","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08738971,0.0006690424,0.889604,0.0014160123,0.000090868045,0.00018733602,0.00014727414,0.00010070904,0.020395057],"genre_scores_gemma":[0.96391475,0.00048617527,0.029130194,0.00015636152,0.0000682378,0.00015936916,0.000047967347,0.000018432245,0.006018563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998735,0.00054458936,0.000033589673,0.00017780719,0.00022186615,0.00028710323],"domain_scores_gemma":[0.9989109,0.00063576613,0.00012210847,0.00003234988,0.00012410701,0.00017488514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012636255,0.0011182997,0.0011356712,0.000716959,0.00093902193,0.0020612236,0.002225275,0.002066943,0.002879346],"category_scores_gemma":[0.0023061212,0.0007099552,0.0009568439,0.0007369502,0.0023434088,0.002106612,0.0016707025,0.0013963624,0.00025844463],"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":[0.00005234041,0.00006891956,0.0007385775,0.000041015122,0.00003950481,0.00030999357,0.00014272785,0.88526136,0.0014778449,0.106446296,0.00080069364,0.004620693],"study_design_scores_gemma":[0.000010366088,0.00001858937,0.00011440936,0.000003229997,0.000007999452,0.00002431367,0.0000305278,0.98251075,0.000059928872,0.016914964,0.00029683852,0.000008078979],"about_ca_topic_score_codex":0.016289636,"about_ca_topic_score_gemma":0.010796452,"teacher_disagreement_score":0.016289636,"about_ca_system_score_codex":0.0034338604,"about_ca_system_score_gemma":0.0020754605,"threshold_uncertainty_score":0.03238964},"labels":[],"label_agreement":null},{"id":"W2094800057","doi":"10.1109/tmc.2012.156","title":"Outage Performance of the Primary Service in Spectrum Sharing Networks","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cognitive Radio Networks and Spectrum Sensing","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":"Carleton University","funders":"","keywords":"Outage probability; Computer science; Stochastic geometry; Rayleigh fading; Transmitter; Throughput; Coverage probability; Transmitter power output; Power control; Constraint (computer-aided design); Fading; Node (physics); Computer network; Probability density function; Wireless network; Channel (broadcasting); Wireless; Topology (electrical circuits); Power (physics); Telecommunications; Mathematics; Statistics; Physics; Electrical engineering; Engineering","score_opus":0.01258942923578825,"score_gpt":0.22139247657882974,"score_spread":0.2088030473430415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094800057","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4173224,0.0012203698,0.57114387,0.00051892304,0.000062807245,0.00005531108,0.00030789996,0.0004599068,0.0089085605],"genre_scores_gemma":[0.99656844,0.00016882902,0.0028088384,0.00003010904,0.000017229577,0.000015045901,0.00003772938,0.000019247562,0.0003345529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978277,0.00080275437,0.0000655298,0.0002266902,0.000636176,0.0004411683],"domain_scores_gemma":[0.99345833,0.0044817524,0.000657166,0.00045756678,0.0007408324,0.00020442002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002555222,0.0010857665,0.0012448545,0.00079733034,0.0008002891,0.0013518764,0.0009205666,0.0008148766,0.0012326109],"category_scores_gemma":[0.009528595,0.00045533717,0.0005814648,0.0007382304,0.0017609106,0.0013987168,0.0013161377,0.00060110446,0.00031017105],"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":[0.00015403939,0.000039867173,0.0022151452,0.000084321975,0.00004761884,0.00035267833,0.00015127003,0.95555806,0.0063838353,0.026903283,0.00069472485,0.0074152444],"study_design_scores_gemma":[0.000006298469,0.000057025216,0.000697815,0.000006368042,0.000011362942,0.00015386197,0.00004116117,0.98871887,0.0011362389,0.008989801,0.00016892552,0.000012218917],"about_ca_topic_score_codex":0.0030352874,"about_ca_topic_score_gemma":0.0015815474,"teacher_disagreement_score":0.0030352874,"about_ca_system_score_codex":0.0022829333,"about_ca_system_score_gemma":0.0011037303,"threshold_uncertainty_score":0.016563892},"labels":[],"label_agreement":null},{"id":"W2095143852","doi":"10.1109/tmc.2014.2343636","title":"Joint Indoor Localization and Radio Map Construction with Limited Deployment Load","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":139,"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; University of New Brunswick","funders":"Mitacs","keywords":"Computer science; RSS; Real-time computing; Bottleneck; Floor plan; Software deployment; Construct (python library); Radio propagation; Set (abstract data type); Data mining; Computer network; Embedded system; Telecommunications","score_opus":0.007109315937893406,"score_gpt":0.18885269386622355,"score_spread":0.18174337792833015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095143852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049001675,0.00006430261,0.9473867,0.000045890454,0.000013053258,0.000041592422,0.00004558929,0.0023657868,0.0010353785],"genre_scores_gemma":[0.7301886,0.00008955444,0.26777628,0.000025717873,0.000022067861,0.000106123014,0.00027098297,0.00011675904,0.0014038804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99856085,0.00042425495,0.0000683743,0.0002570744,0.00051123643,0.00017829878],"domain_scores_gemma":[0.9978332,0.0004462349,0.00023168518,0.0011054926,0.0003137628,0.00006961554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008948339,0.00094803894,0.0013873264,0.00084855326,0.00039426223,0.0007797052,0.001188219,0.00056776736,0.0015669634],"category_scores_gemma":[0.0044482276,0.0005092611,0.00050657947,0.0013786041,0.00060615514,0.0020766458,0.002330893,0.00061761664,0.0014374118],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005327788,0.0002491627,0.003856744,0.0001888702,0.00009110696,0.00026749313,0.00023372332,0.49201426,0.045842763,0.0057746405,0.0017729646,0.44917548],"study_design_scores_gemma":[0.00002920523,0.00028110956,0.0024809053,0.000008580321,0.00002729079,0.0002672806,0.000078463905,0.958193,0.03370671,0.002841144,0.002051739,0.000034548964],"about_ca_topic_score_codex":0.0018952732,"about_ca_topic_score_gemma":0.0016261123,"teacher_disagreement_score":0.0018952732,"about_ca_system_score_codex":0.00031155514,"about_ca_system_score_gemma":0.0008084725,"threshold_uncertainty_score":0.0052420497},"labels":[],"label_agreement":null},{"id":"W2095691144","doi":"10.1109/tmc.2006.85","title":"Queue-aware uplink bandwidth allocation and rate control for polling service in IEEE 802.16 broadband wireless networks","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":148,"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 Winnipeg; University of Manitoba","funders":"","keywords":"Polling; Computer network; Computer science; Wireless broadband; Quality of service; Bandwidth allocation; Wireless network; Wireless; Telecommunications","score_opus":0.005773359184752195,"score_gpt":0.2109810775830105,"score_spread":0.2052077183982583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095691144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025927134,0.0015774537,0.96908647,0.00025580963,0.00015757087,0.000056990943,0.000021951231,0.0005416156,0.0023749727],"genre_scores_gemma":[0.8889361,0.0011623941,0.10720097,0.00018690068,0.0001758278,0.000111731075,0.00003865361,0.00007381114,0.0021136242],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872786,0.00038640836,0.00007362056,0.00018185815,0.00047652583,0.0001537664],"domain_scores_gemma":[0.9986558,0.0006481717,0.00023620363,0.00015648702,0.0002589819,0.000044445347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018547031,0.00043107022,0.0006598856,0.00050437823,0.0007201871,0.0013020817,0.0014626287,0.0008068402,0.00082831277],"category_scores_gemma":[0.0058357795,0.00035961458,0.00042753277,0.00073494256,0.000685188,0.00125832,0.0005156735,0.00095967366,0.00018500141],"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":[0.00022644683,0.00022020919,0.0020622604,0.00021956267,0.000091314265,0.00025507613,0.00036387745,0.66543454,0.03307112,0.13507065,0.004041996,0.15894291],"study_design_scores_gemma":[0.00001704916,0.000053230815,0.0002318981,0.000008379954,0.000023382736,0.00005672879,0.000013720985,0.9880205,0.0026194542,0.007337951,0.0015956433,0.000022103393],"about_ca_topic_score_codex":0.005305117,"about_ca_topic_score_gemma":0.004047935,"teacher_disagreement_score":0.005305117,"about_ca_system_score_codex":0.0015548421,"about_ca_system_score_gemma":0.0017341495,"threshold_uncertainty_score":0.011281252},"labels":[],"label_agreement":null},{"id":"W2096391112","doi":"10.1109/tmc.2009.148","title":"Extended Knowledge-Based Reasoning Approach to Spectrum Sensing for Cognitive Radio","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":42,"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":"Institute for Information Industry, Ministry of Science and Technology, Taiwan; Natural Sciences and Engineering Research Council of Canada","keywords":"Cognitive radio; Computer science; Overhead (engineering); Channel (broadcasting); Transmission (telecommunications); Markov chain; Computation; Channel state information; Data transmission; Real-time computing; Algorithm; Computer network; Wireless; Telecommunications; Machine learning","score_opus":0.016887801965010347,"score_gpt":0.26773668433774356,"score_spread":0.25084888237273323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096391112","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003269451,0.00016534247,0.9953117,0.00013549796,0.000023052244,0.000032047814,0.00002003899,0.00008350686,0.00095949444],"genre_scores_gemma":[0.47560677,0.000530018,0.52162606,0.0002424055,0.00010211031,0.00020925446,0.00010917584,0.000028362636,0.0015457802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982608,0.00057022204,0.00013746586,0.00032159255,0.0005630807,0.00014680716],"domain_scores_gemma":[0.99637175,0.0026139526,0.00024933188,0.00032715348,0.0003439582,0.000093929506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025012626,0.00078216917,0.0011206489,0.0009132273,0.00074050634,0.0016489319,0.002992742,0.0013814784,0.0017768702],"category_scores_gemma":[0.006414084,0.0005440901,0.0013177351,0.0009581987,0.0014244146,0.0032209589,0.0016463803,0.0022369088,0.00027312312],"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":[0.00031520074,0.00027514697,0.00076442875,0.00050368794,0.00022872808,0.0007361124,0.0007926126,0.6310025,0.0067289458,0.17184362,0.0017161333,0.1850929],"study_design_scores_gemma":[0.000024252922,0.000040032133,0.00009328865,0.00001908556,0.000029701476,0.00008373696,0.000037886304,0.9396503,0.0011515399,0.057843912,0.001003879,0.000022394912],"about_ca_topic_score_codex":0.005843026,"about_ca_topic_score_gemma":0.004548838,"teacher_disagreement_score":0.005843026,"about_ca_system_score_codex":0.0011310364,"about_ca_system_score_gemma":0.0013636495,"threshold_uncertainty_score":0.013228118},"labels":[],"label_agreement":null},{"id":"W2099737489","doi":"10.1109/tmc.2013.129","title":"Mobility and Intruder Prior Information Improving the Barrier Coverage of Sparse Sensor Networks","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":181,"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; Huawei Technologies (Canada)","funders":"","keywords":"Patrolling; Computer science; Wireless sensor network; Heuristic; Scheduling (production processes); Distributed computing; Real-time computing; Location awareness; Computer network; Artificial intelligence; Mathematical optimization","score_opus":0.006099576699461959,"score_gpt":0.19951007876226198,"score_spread":0.19341050206280003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099737489","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20982592,0.0009086431,0.78654265,0.00035940428,0.000038444057,0.000035583267,0.000055136254,0.000347775,0.001886372],"genre_scores_gemma":[0.9525315,0.00042689612,0.046231855,0.00004716492,0.000031796997,0.000034586552,0.00007192449,0.00003009308,0.00059424725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953914,0.00015359631,0.00001943578,0.000082657345,0.00012592795,0.00007926845],"domain_scores_gemma":[0.9979215,0.0012245481,0.0003306318,0.00025406425,0.00016446727,0.0001048147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066173024,0.0006778842,0.00090730767,0.00054836186,0.00038641857,0.0004748339,0.0011681891,0.0005434096,0.00048074176],"category_scores_gemma":[0.004735014,0.00035543268,0.000475357,0.0006338219,0.0005297854,0.001244912,0.0012276033,0.0006774944,0.00010901691],"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":[0.00022822797,0.00007186788,0.0029013306,0.0001463907,0.000046495166,0.00022529581,0.00021206505,0.9115276,0.017446242,0.008914838,0.00086567766,0.057414006],"study_design_scores_gemma":[0.000010263226,0.0000624797,0.0004852635,0.0000047034387,0.000012581785,0.00004797905,0.000027880787,0.99504083,0.0019041381,0.002020902,0.00037771877,0.00000525642],"about_ca_topic_score_codex":0.0022922407,"about_ca_topic_score_gemma":0.0019092642,"teacher_disagreement_score":0.0022922407,"about_ca_system_score_codex":0.00041843834,"about_ca_system_score_gemma":0.0006880147,"threshold_uncertainty_score":0.004557729},"labels":[],"label_agreement":null},{"id":"W2108337720","doi":"10.1109/tmc.2006.25","title":"Power saving access points for IEEE 802-11 wireless network infrastructure","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Blackberry (Canada); McMaster University","funders":"Indian Council of Agricultural Research; McMaster University","keywords":"Computer science; Computer network; Inter-Access Point Protocol; Wireless distribution system; IEEE 802.11; Wireless; IEEE 802.11u; Wireless network; IEEE 802.11b-1999; IEEE 802; Backward compatibility; Software deployment; Network allocation vector; Wi-Fi; Telecommunications; Quality of service","score_opus":0.012041216543759309,"score_gpt":0.2713476528080319,"score_spread":0.25930643626427263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108337720","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0914556,0.0008911689,0.891065,0.0004400249,0.0001375685,0.00025386133,0.000049719678,0.0014515084,0.014255467],"genre_scores_gemma":[0.8644605,0.0007808181,0.12366809,0.00016772455,0.0001213379,0.00030256066,0.00014009835,0.000043090386,0.010315778],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992853,0.0001931667,0.00004120495,0.000086505745,0.0003064185,0.00008741625],"domain_scores_gemma":[0.9992912,0.00020972811,0.00011503974,0.00016785355,0.00017859756,0.00003752956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007334841,0.0005371947,0.00032321177,0.0003575053,0.00060537976,0.0010292507,0.0012204348,0.0006188287,0.0041188975],"category_scores_gemma":[0.0016743611,0.00024945123,0.0002548679,0.0004130467,0.0004088234,0.0015853959,0.0009579942,0.0010386904,0.0012817974],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001032038,0.00037151846,0.0045269146,0.0006932682,0.00014106711,0.0016962119,0.0005833031,0.06454125,0.24921165,0.24287087,0.013375383,0.42095652],"study_design_scores_gemma":[0.00032201014,0.0029245676,0.0039878907,0.00016651792,0.00032656614,0.0046116295,0.00031947164,0.58379984,0.20566475,0.057746854,0.13999663,0.00013319934],"about_ca_topic_score_codex":0.0004758482,"about_ca_topic_score_gemma":0.0008745352,"teacher_disagreement_score":0.0041188975,"about_ca_system_score_codex":0.00050371233,"about_ca_system_score_gemma":0.0003941854,"threshold_uncertainty_score":0.013779044},"labels":[],"label_agreement":null},{"id":"W2109294155","doi":"10.1109/tmc.2006.95","title":"Discovering the architecture of geo-located web services for next generation mobile networks","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Service-Oriented Architecture and Web Services","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":"Polytechnique Montréal","funders":"","keywords":"Computer science; Web service; World Wide Web; Mobile computing; Computer network; Architecture; Middleware (distributed applications); Mobile Web; Database; Mobile technology","score_opus":0.010912450971467157,"score_gpt":0.22634484326684073,"score_spread":0.2154323922953736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109294155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0867703,0.00067307625,0.89879274,0.0010241852,0.000052698648,0.000181193,0.000110795925,0.0024738668,0.009921089],"genre_scores_gemma":[0.49703094,0.00089599995,0.49321887,0.0001335155,0.000027648872,0.00013083646,0.00063303363,0.00021543141,0.0077136266],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995338,0.0001371893,0.000044344473,0.0000676817,0.00013813915,0.00007898053],"domain_scores_gemma":[0.9995492,0.00005589637,0.00004927895,0.000116662355,0.00016266065,0.00006621653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011206933,0.00026419703,0.00023169017,0.0010061628,0.0011788326,0.0025196031,0.0011650837,0.0011747552,0.0009447096],"category_scores_gemma":[0.0019118511,0.00057343516,0.00046540197,0.00079504703,0.0008668152,0.0028777379,0.001256645,0.0007811446,0.0008053339],"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":[0.0002822275,0.00019805373,0.01266717,0.00030262928,0.00011886867,0.0026424397,0.0031332127,0.10252386,0.06723313,0.59500337,0.009715224,0.20617983],"study_design_scores_gemma":[0.000030169354,0.00006866216,0.0033126534,0.00010642406,0.000086368054,0.00081543304,0.0015489844,0.71306586,0.026324142,0.16335444,0.09122325,0.00006368436],"about_ca_topic_score_codex":0.008792531,"about_ca_topic_score_gemma":0.017543156,"teacher_disagreement_score":0.008792531,"about_ca_system_score_codex":0.0013637007,"about_ca_system_score_gemma":0.0014451762,"threshold_uncertainty_score":0.017482698},"labels":[],"label_agreement":null},{"id":"W2111586256","doi":"10.1109/tmc.2010.41","title":"Channel Assignment for Multihop Cellular Networks: Minimum Delay","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":14,"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 Lethbridge; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Cellular network; Network packet; Code division multiple access; Benchmark (surveying); Heuristic; Channel (broadcasting); Duplex (building); Throughput; Transmission delay; Wireless; Telecommunications","score_opus":0.007393418480356151,"score_gpt":0.21856210153641858,"score_spread":0.21116868305606243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111586256","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019625323,0.00115756,0.9734438,0.0002936281,0.00014816978,0.000077441415,0.000102341284,0.00019429595,0.004957501],"genre_scores_gemma":[0.76294315,0.0017118285,0.22876486,0.00022205882,0.00024036676,0.0003111519,0.00019383282,0.00011181078,0.005500941],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946576,0.00016741283,0.0000111363,0.000079932994,0.00017926276,0.00009651668],"domain_scores_gemma":[0.9993616,0.00030709195,0.000119009295,0.000055474717,0.000101052654,0.000055854918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042036118,0.0007040412,0.00045467704,0.00044252104,0.00048777828,0.00072705094,0.00076198636,0.00062212284,0.002519464],"category_scores_gemma":[0.001919032,0.00019750264,0.0001966801,0.00082165946,0.00050414825,0.00077009865,0.0006584056,0.0006433912,0.00043428736],"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":[0.00011935724,0.00008367628,0.0004319023,0.00023572319,0.000026183072,0.00006366508,0.00004984431,0.8513532,0.005818625,0.04284251,0.0048372084,0.094138116],"study_design_scores_gemma":[0.000026812082,0.00011151997,0.00017744629,0.000016062208,0.000012464341,0.000084226995,0.000026213545,0.97392184,0.0025553338,0.01914928,0.0039035208,0.000015273743],"about_ca_topic_score_codex":0.0014574148,"about_ca_topic_score_gemma":0.0019682504,"teacher_disagreement_score":0.002519464,"about_ca_system_score_codex":0.00092146563,"about_ca_system_score_gemma":0.0010072447,"threshold_uncertainty_score":0.008428454},"labels":[],"label_agreement":null},{"id":"W2113234775","doi":"10.1109/tmc.2010.173","title":"Flexible Broadcasting of Scalable Video Streams to Heterogeneous Mobile Devices","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Multimedia Communication and Technology","field":"Social Sciences","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":"Simon Fraser University","funders":"","keywords":"Computer science; Scalability; Testbed; Broadcasting (networking); Mobile device; Computer network; Scalable Video Coding; Energy consumption; Channel (broadcasting); Efficient energy use; Bitstream; Real-time computing; Decoding methods; Telecommunications","score_opus":0.02226645719882147,"score_gpt":0.327763971506339,"score_spread":0.30549751430751754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113234775","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30050364,0.00075730734,0.6949865,0.00027437982,0.000060826318,0.00010314118,0.000052708005,0.00024902166,0.003012553],"genre_scores_gemma":[0.9410774,0.0004134588,0.05758348,0.000039660805,0.000049858074,0.000046030866,0.00004549077,0.000018248104,0.0007263486],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973303,0.00008757293,0.000011280197,0.00004112156,0.00008107233,0.00004590023],"domain_scores_gemma":[0.9990381,0.000616838,0.00009078958,0.000116965006,0.000098120334,0.000039240553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005805079,0.0004445043,0.0005285159,0.0002852396,0.0004256938,0.00057744497,0.0006086279,0.0005175849,0.000514899],"category_scores_gemma":[0.0021265978,0.00018066063,0.00023484835,0.00056333054,0.00058177026,0.0010130714,0.0005820409,0.00056294916,0.00011110665],"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":[0.0007810338,0.0001941215,0.0018454657,0.0003084971,0.0001297115,0.0009713308,0.00056324655,0.65834165,0.17152673,0.030229248,0.0013909292,0.13371803],"study_design_scores_gemma":[0.000057973582,0.00021143437,0.00037631765,0.000008958413,0.00002421026,0.00013861009,0.00010323383,0.971894,0.020419717,0.005668742,0.0010795911,0.00001717115],"about_ca_topic_score_codex":0.0011799689,"about_ca_topic_score_gemma":0.0010082844,"teacher_disagreement_score":0.0011799689,"about_ca_system_score_codex":0.0004163359,"about_ca_system_score_gemma":0.00021115287,"threshold_uncertainty_score":0.0030700564},"labels":[],"label_agreement":null},{"id":"W2113996774","doi":"10.1109/tmc.2010.251","title":"Wireless Fountain Coding with IEEE 802.11e Block ACK for Media Streaming in Wireline-cum-WiFi Networks: A Performance Study","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cooperative Communication and Network Coding","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 Manitoba","funders":"","keywords":"Computer science; Computer network; Network packet; Wireless network; Automatic repeat request; Wireless; Linear network coding; Wireline; Real-time computing; Telecommunications link; Hybrid automatic repeat request; Telecommunications","score_opus":0.044597579841522475,"score_gpt":0.2693348494283819,"score_spread":0.2247372695868594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113996774","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5885023,0.0018769683,0.39330631,0.0006888658,0.00009195575,0.00019257268,0.00028719488,0.0008454529,0.014208394],"genre_scores_gemma":[0.9869292,0.00038728447,0.011794637,0.0000365908,0.0000114326185,0.000035487552,0.00006304201,0.000024026785,0.0007182311],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911505,0.00022042044,0.00003351333,0.000070076225,0.0003534459,0.00020744216],"domain_scores_gemma":[0.9968346,0.001681891,0.00042585802,0.00017198901,0.0008147172,0.00007099285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013051598,0.0008259117,0.0005460857,0.00070342456,0.0004232888,0.00066976267,0.00095740013,0.0006943948,0.0009015395],"category_scores_gemma":[0.004977041,0.00021363971,0.00035587195,0.0007313075,0.0005911593,0.0012990797,0.0006568387,0.0007077388,0.00015327099],"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":[0.000067254376,0.000054392625,0.0013296932,0.00003826393,0.000018731322,0.000085296386,0.000040235675,0.97998947,0.002764197,0.0060871528,0.00029143065,0.00923388],"study_design_scores_gemma":[0.0000014686917,0.000019577852,0.00008057582,0.000002366566,0.000002394088,0.000009595531,0.000004349976,0.999175,0.0004584323,0.00018993509,0.000053575906,0.0000026601065],"about_ca_topic_score_codex":0.025390837,"about_ca_topic_score_gemma":0.01472963,"teacher_disagreement_score":0.025390837,"about_ca_system_score_codex":0.002091361,"about_ca_system_score_gemma":0.0011578486,"threshold_uncertainty_score":0.050486088},"labels":[],"label_agreement":null},{"id":"W2114499779","doi":"10.1109/tmc.2006.29","title":"Call admission control for voice/data integration, in.broadband, wireless networks","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":48,"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; Computer network; Handover; Call Admission Control; Quality of service; Wireless broadband; Bandwidth (computing); Mobile broadband; Bandwidth allocation; Broadband networks; Wireless; Wireless network; Broadband; Telecommunications","score_opus":0.026935046007550024,"score_gpt":0.3015962590009492,"score_spread":0.2746612129933992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114499779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04383842,0.0043754717,0.93591654,0.0012347971,0.00073264213,0.00018271932,0.00008094353,0.0011674296,0.012471086],"genre_scores_gemma":[0.91865045,0.0013538899,0.07004507,0.00047364994,0.00059238885,0.00019126167,0.00008583979,0.000064012966,0.008543492],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991424,0.00026551358,0.00004045401,0.00014114102,0.00027709387,0.00013334295],"domain_scores_gemma":[0.998928,0.0005655907,0.000120556106,0.00012865616,0.00017208775,0.00008512579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093841064,0.00063958263,0.00048222003,0.000492981,0.0013235252,0.0015869961,0.0011095585,0.00082655094,0.0029211643],"category_scores_gemma":[0.0037483182,0.00014468638,0.00022058889,0.00064265303,0.0012942238,0.0011278216,0.00096362765,0.0012741206,0.00052458764],"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":[0.00068885734,0.00024936057,0.001595254,0.00033533378,0.00006793729,0.00047068074,0.00058055704,0.15392959,0.039720256,0.20881678,0.013317281,0.58022803],"study_design_scores_gemma":[0.000056202905,0.000121639445,0.0006736857,0.00003827573,0.00005401425,0.00033751215,0.00009920729,0.9125001,0.008771602,0.058160085,0.019141274,0.000046476063],"about_ca_topic_score_codex":0.0040363367,"about_ca_topic_score_gemma":0.0035257384,"teacher_disagreement_score":0.0040363367,"about_ca_system_score_codex":0.0014954181,"about_ca_system_score_gemma":0.0013014607,"threshold_uncertainty_score":0.010850072},"labels":[],"label_agreement":null},{"id":"W2114795108","doi":"10.1109/tmc.2007.70769","title":"Adaptive Cluster-Based Data Collection in Sensor Networks with Direct Sink Access","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":59,"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; Wireless sensor network; Energy consumption; Network packet; Cluster analysis; Computer network; Random access; Sink (geography); Robustness (evolution); Efficient energy use; Access control; Distributed computing; Data collection; Data access; Real-time computing","score_opus":0.039956022929606684,"score_gpt":0.26477423122594035,"score_spread":0.22481820829633367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114795108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14585555,0.00051897246,0.8514449,0.00016002286,0.000033194065,0.00012014534,0.00003786556,0.00047634647,0.0013530751],"genre_scores_gemma":[0.9256391,0.00030954188,0.073000595,0.000050403396,0.000025754662,0.00011143741,0.000034951525,0.000034801018,0.00079346093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904233,0.000315768,0.000048735692,0.00020252794,0.00028947418,0.00010117241],"domain_scores_gemma":[0.99736905,0.0015470189,0.00027979584,0.00033745766,0.00037998834,0.00008664349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017451122,0.0004607453,0.0006090985,0.0007998534,0.00066403835,0.0005474579,0.0012582965,0.0003927381,0.0003603416],"category_scores_gemma":[0.0054582236,0.00042560615,0.0003321955,0.0012403989,0.0010341839,0.0012894695,0.0010889511,0.00041704814,0.00008397442],"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":[0.00034863412,0.00012029661,0.0026562796,0.00014216955,0.00008585093,0.00016917575,0.00031818906,0.8913739,0.018817233,0.019704472,0.0007353756,0.065528475],"study_design_scores_gemma":[0.000029090794,0.00014167267,0.00085200655,0.000005904255,0.000022117732,0.00008829392,0.000053089923,0.98611987,0.0045079635,0.0074356366,0.0007287782,0.0000156036],"about_ca_topic_score_codex":0.0024643512,"about_ca_topic_score_gemma":0.0023982977,"teacher_disagreement_score":0.0024643512,"about_ca_system_score_codex":0.0010307442,"about_ca_system_score_gemma":0.0009245666,"threshold_uncertainty_score":0.009229183},"labels":[],"label_agreement":null},{"id":"W2115189853","doi":"10.1109/tmc.2010.43","title":"Optimal Cooperative Relaying Schemes in IR-UWB Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cooperative Communication and Network Coding","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":"University of British Columbia","funders":"University of Melbourne; University of Cambridge","keywords":"Computer science; Computer network; Network packet; Throughput; Fading; Overhead (engineering); Relay; Wireless; Wireless network; Cooperative diversity; Diversity gain; Interval (graph theory); Distributed computing; Channel (broadcasting); Power (physics); Telecommunications","score_opus":0.01948486199234165,"score_gpt":0.27817714473698946,"score_spread":0.2586922827446478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115189853","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13284993,0.002590319,0.8580385,0.00019269588,0.00003408012,0.000035053796,0.00003467426,0.00017306852,0.0060516377],"genre_scores_gemma":[0.9554885,0.0006238819,0.042989638,0.000031694854,0.000020963447,0.00003688024,0.000016651058,0.000009352055,0.00078254746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914813,0.00034026455,0.000040724353,0.00011073279,0.00025965276,0.00010043765],"domain_scores_gemma":[0.999154,0.00041925168,0.00018876529,0.000087466964,0.00012185965,0.000028702778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001169405,0.0005391645,0.0005806759,0.00061356253,0.00036259127,0.0007510104,0.000784691,0.00063644745,0.0003429356],"category_scores_gemma":[0.0023171029,0.00030098547,0.0002448306,0.00062386825,0.0008187294,0.00079884985,0.0004880369,0.000285491,0.00013437914],"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":[0.00014696186,0.000039252798,0.00052257464,0.0000924719,0.000041675263,0.00023140399,0.00021148991,0.87661606,0.011585155,0.06835089,0.00054574345,0.04161625],"study_design_scores_gemma":[0.000022753471,0.00009919995,0.00019995682,0.00001236284,0.000023217293,0.0001052694,0.0000555456,0.96555704,0.0025172029,0.030592702,0.00079750875,0.000017239123],"about_ca_topic_score_codex":0.00091609376,"about_ca_topic_score_gemma":0.00083350233,"teacher_disagreement_score":0.001169405,"about_ca_system_score_codex":0.00081066805,"about_ca_system_score_gemma":0.00052716024,"threshold_uncertainty_score":0.0061844587},"labels":[],"label_agreement":null},{"id":"W2119545161","doi":"10.1109/tmc.2010.254","title":"Lifetime Analysis of Random Event-Driven Clustered Wireless Sensor Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Energy Efficient Wireless Sensor Networks","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Wireless sensor network; Voronoi diagram; Network packet; Probabilistic logic; Energy consumption; Event (particle physics); Real-time computing; Computer network; Wireless network; Wireless; Telecommunications; Artificial intelligence; Engineering","score_opus":0.0160951400112579,"score_gpt":0.2362901031820996,"score_spread":0.2201949631708417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119545161","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16358118,0.0012740971,0.83049464,0.00040425133,0.000039450457,0.000071112074,0.00014060359,0.00017701711,0.0038175993],"genre_scores_gemma":[0.9818635,0.0007201117,0.015904935,0.00006226561,0.000026974913,0.0000683927,0.00011658569,0.000030292635,0.0012070361],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994836,0.00018792842,0.000022260627,0.00008247456,0.00015682433,0.00006689199],"domain_scores_gemma":[0.9974976,0.0014667977,0.000387458,0.00014631335,0.00041917982,0.00008262276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016470044,0.0004723244,0.00038590995,0.0007772534,0.00029250703,0.00042177652,0.0009835885,0.0005936312,0.0006965986],"category_scores_gemma":[0.0066622067,0.00030901315,0.0003951768,0.00056040037,0.0005259269,0.001186982,0.0004894585,0.00036833866,0.00009456203],"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":[0.000037346996,0.000017920343,0.00081847445,0.000046126534,0.0000331127,0.00007881185,0.00005219981,0.97290343,0.002108294,0.018946348,0.00024862064,0.0047092875],"study_design_scores_gemma":[0.0000017273612,0.000014353669,0.00019244655,0.000002926642,0.0000045650313,0.000026970403,0.00000987592,0.9953015,0.0002826529,0.004004519,0.00015502408,0.0000034665788],"about_ca_topic_score_codex":0.0012699872,"about_ca_topic_score_gemma":0.0006458385,"teacher_disagreement_score":0.0016470044,"about_ca_system_score_codex":0.0008805647,"about_ca_system_score_gemma":0.00044335154,"threshold_uncertainty_score":0.008710325},"labels":[],"label_agreement":null},{"id":"W2120051217","doi":"10.1109/tmc.2006.72","title":"Reliable packet transmissions in multipath routed wireless networks","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":65,"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; Network packet; Load balancing (electrical power); Source routing; Computer network; Erasure; Erasure code; Path (computing); Multipath propagation; Distributed computing; Algorithm; Decoding methods; Routing protocol; Routing table","score_opus":0.016994762535962084,"score_gpt":0.25754107185144776,"score_spread":0.24054630931548568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120051217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16370519,0.002337049,0.8304502,0.0005452471,0.000101250545,0.00007943147,0.00005593325,0.00057039177,0.0021553314],"genre_scores_gemma":[0.9318995,0.0012951621,0.065524064,0.0000589403,0.00008334886,0.00009818087,0.000033644526,0.00004846355,0.00095870066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893504,0.00047946494,0.000036495898,0.000081681435,0.0003367456,0.0001305877],"domain_scores_gemma":[0.99426454,0.0042284834,0.00062687194,0.0003619726,0.00041073468,0.000107395754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026413552,0.00079748273,0.0007657676,0.00091169804,0.0007431963,0.0007865693,0.0010079248,0.0008561923,0.000518218],"category_scores_gemma":[0.010974991,0.00053002813,0.0002677219,0.0012214438,0.0012890932,0.0016154927,0.0010691951,0.00060875237,0.00016879433],"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":[0.00009611304,0.000017516912,0.00049188925,0.00006470132,0.00002474305,0.000102100385,0.00010710707,0.96984005,0.0020154144,0.011204171,0.00040743928,0.015628764],"study_design_scores_gemma":[0.000028484164,0.00007363189,0.00012458932,0.000006919084,0.000010565769,0.00003957279,0.000023156334,0.986766,0.0007394669,0.011688714,0.0004895947,0.000009375298],"about_ca_topic_score_codex":0.0016091327,"about_ca_topic_score_gemma":0.0009862201,"teacher_disagreement_score":0.0026413552,"about_ca_system_score_codex":0.0008090841,"about_ca_system_score_gemma":0.0006189588,"threshold_uncertainty_score":0.013968945},"labels":[],"label_agreement":null},{"id":"W2120668076","doi":"10.1109/tmc.2010.68","title":"On the Design of Opportunistic MAC Protocols for Multihop Wireless ; Networks with Beamforming Antennas","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Wireless Networks and Protocols","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":"Toronto Metropolitan University","funders":"University of Toronto; Cairo University; University of Texas at Arlington","keywords":"Exponential backoff; Computer science; Computer network; Beamforming; Network packet; Overhead (engineering); Wireless; Wireless network; Throughput; Transmission (telecommunications); Reuse; Node (physics); Telecommunications; Engineering","score_opus":0.03523551401724262,"score_gpt":0.28385177730166516,"score_spread":0.24861626328442255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120668076","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034762034,0.0009772055,0.99119955,0.00024492957,0.00008333278,0.0001631726,0.000018866598,0.00014054637,0.0036962074],"genre_scores_gemma":[0.3193196,0.002738235,0.67210364,0.00041149312,0.00020900542,0.0012330645,0.000082164755,0.00009760227,0.00380521],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848986,0.0006577894,0.000107238215,0.00017002385,0.00045157413,0.00012349176],"domain_scores_gemma":[0.9975623,0.0012921355,0.000303365,0.00029095862,0.0004539998,0.00009716853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024909214,0.00090369344,0.00063340063,0.0007389963,0.0008617099,0.0018186503,0.0015657501,0.00079913833,0.0011142285],"category_scores_gemma":[0.0046588187,0.00056855695,0.00050044915,0.0006308961,0.0012528314,0.0014019548,0.00155575,0.0011028165,0.0003431292],"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":[0.00011721431,0.00013766457,0.00083809206,0.0005790564,0.00013415434,0.00031924137,0.00034241352,0.3690917,0.0205044,0.45980436,0.0047512334,0.14338039],"study_design_scores_gemma":[0.000036384183,0.00013416515,0.00015274243,0.00009577631,0.000055573248,0.00020647416,0.000047883797,0.922073,0.004805132,0.050579373,0.02177472,0.000038841656],"about_ca_topic_score_codex":0.001051086,"about_ca_topic_score_gemma":0.0017932763,"teacher_disagreement_score":0.0024909214,"about_ca_system_score_codex":0.0010406764,"about_ca_system_score_gemma":0.0019335587,"threshold_uncertainty_score":0.013173401},"labels":[],"label_agreement":null},{"id":"W2120869623","doi":"10.1109/tmc.2009.39","title":"Analysis of Enhanced Collision Avoidance Scheme Proposed for IEEE 802.11e-Enhanced Distributed Channel Access Protocol","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Wireless Networks and Protocols","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":"Western University","funders":"","keywords":"Computer science; Throughput; Computer network; Channel (broadcasting); Network packet; Protocol (science); Queue; Collision avoidance; Scheme (mathematics); Markov chain; Capture effect; Markov process; Collision; Real-time computing; Wireless; Telecommunications","score_opus":0.025073298572393283,"score_gpt":0.3333150148199676,"score_spread":0.30824171624757435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120869623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054132886,0.0024913477,0.9311424,0.00044521532,0.00025933553,0.00022577879,0.0001051105,0.00025177645,0.010946068],"genre_scores_gemma":[0.92300355,0.0015141043,0.07164191,0.00011830233,0.0000673235,0.00013602704,0.00012578562,0.000038573573,0.0033544125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989967,0.0002310107,0.00004072742,0.00010568435,0.0004939709,0.00013197647],"domain_scores_gemma":[0.99897647,0.00039152591,0.00010341185,0.000085702246,0.00042262726,0.000020277324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011544236,0.0005628754,0.0005054657,0.0007936505,0.0005585973,0.0009820374,0.00111616,0.0006018047,0.0019848165],"category_scores_gemma":[0.0027535942,0.00028287587,0.0005547514,0.0006224509,0.00048257504,0.0009983444,0.00043061227,0.0007678725,0.00022533233],"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":[0.00023755115,0.0001144937,0.0029076121,0.0003028452,0.00012420614,0.00039778044,0.00022014754,0.7840835,0.0248996,0.12403902,0.0025148974,0.06015842],"study_design_scores_gemma":[0.000009172527,0.000047455625,0.00028279208,0.00001036502,0.00001743943,0.00008987975,0.00001324949,0.99252534,0.0020569453,0.0040060706,0.00093003374,0.0000113045435],"about_ca_topic_score_codex":0.0041689654,"about_ca_topic_score_gemma":0.0025745793,"teacher_disagreement_score":0.0041689654,"about_ca_system_score_codex":0.00159494,"about_ca_system_score_gemma":0.0010930748,"threshold_uncertainty_score":0.011572182},"labels":[],"label_agreement":null},{"id":"W2123545238","doi":"10.1109/tmc.2013.106","title":"An Optimization Framework for XOR-Assisted Cooperative Relaying in Cellular Networks","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cooperative Communication and Network Coding","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; Linear network coding; Cooperative diversity; Relay; Maximization; Optimization problem; Bipartite graph; Resource allocation; Coding (social sciences); Mathematical optimization; Channel (broadcasting); Computer network; Theoretical computer science; Power (physics); Algorithm; Mathematics; Fading","score_opus":0.027734281654068244,"score_gpt":0.28793953124787675,"score_spread":0.26020524959380853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123545238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005192538,0.00039459518,0.98695403,0.000316176,0.00003994452,0.000035146048,0.00008317114,0.000047904676,0.006936483],"genre_scores_gemma":[0.6313717,0.0021494778,0.34721532,0.00031769904,0.00022668277,0.0004890738,0.00028459,0.0001224784,0.017822908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993224,0.00033124976,0.000021809963,0.000078083925,0.00016761661,0.00007887915],"domain_scores_gemma":[0.9994499,0.00036534737,0.000045107838,0.00003825694,0.000071249066,0.00003006805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015734626,0.0010632287,0.0008258518,0.00046779934,0.0004033779,0.0014021305,0.001217266,0.0009789324,0.0028927785],"category_scores_gemma":[0.001917178,0.00045686192,0.0005563325,0.00096418976,0.000920239,0.0017262225,0.0013893024,0.0013280736,0.0004973339],"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":[0.00003473731,0.000048521062,0.00012830866,0.000074079406,0.000023283128,0.00008244955,0.000065149296,0.6441886,0.0009143862,0.33307838,0.0027667163,0.018595455],"study_design_scores_gemma":[0.0000097762995,0.000021150121,0.0000396183,0.000008880919,0.000004802433,0.000019150724,0.000016678616,0.9168955,0.00016439475,0.080681026,0.002133492,0.0000055057594],"about_ca_topic_score_codex":0.002215364,"about_ca_topic_score_gemma":0.0022204816,"teacher_disagreement_score":0.0028927785,"about_ca_system_score_codex":0.0015975,"about_ca_system_score_gemma":0.0011373911,"threshold_uncertainty_score":0.01159066},"labels":[],"label_agreement":null},{"id":"W2124050329","doi":"10.1109/tmc.2011.77","title":"Gossip-Enabled Stochastic Channel Negotiation for Cognitive Radio Ad Hoc Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cognitive Radio Networks and Spectrum Sensing","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 Waterloo","funders":"","keywords":"Computer science; Wireless ad hoc network; Cognitive radio; Computer network; Control channel; Channel (broadcasting); Vehicular ad hoc network; Negotiation; Overhead (engineering); Markov chain; Wireless network; Distributed computing; Gossip; Mobile ad hoc network; Markov process; Wireless; Telecommunications; Network packet; Telecommunications link","score_opus":0.023960685889079986,"score_gpt":0.24128058583961667,"score_spread":0.2173198999505367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124050329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02751293,0.0004112448,0.9685661,0.00016958348,0.00007188818,0.000054380092,0.000024153629,0.00013695177,0.0030527299],"genre_scores_gemma":[0.94523245,0.0004810755,0.052611697,0.00006430046,0.00006226778,0.00013434561,0.000035359,0.000036624027,0.0013419407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921775,0.00035852546,0.000036571037,0.000089022054,0.0002153086,0.00008280092],"domain_scores_gemma":[0.9974746,0.0017470322,0.0003115748,0.00015329749,0.0001789169,0.00013456026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016932203,0.0005563277,0.00066554814,0.0004924508,0.0006490926,0.00080262945,0.0010719601,0.000643394,0.0009323584],"category_scores_gemma":[0.004658443,0.00035540914,0.00055124675,0.00044154326,0.0016232475,0.0013927149,0.00085576484,0.0008833021,0.00013863441],"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":[0.00007741692,0.00003309645,0.00038392615,0.00007778405,0.00003597578,0.00016280163,0.00009892345,0.90291184,0.002504683,0.08408283,0.00045533897,0.00917547],"study_design_scores_gemma":[0.000008239553,0.000022447955,0.000038869868,0.0000025947954,0.0000048426655,0.000015906862,0.000009214925,0.98453635,0.00019349254,0.014914576,0.0002482802,0.0000052508963],"about_ca_topic_score_codex":0.0021031068,"about_ca_topic_score_gemma":0.0019581376,"teacher_disagreement_score":0.0021031068,"about_ca_system_score_codex":0.0010538383,"about_ca_system_score_gemma":0.0012611499,"threshold_uncertainty_score":0.008954704},"labels":[],"label_agreement":null},{"id":"W2132128535","doi":"10.1109/tmc.2007.1051","title":"Optimal and Approximate Mobility-Assisted Opportunistic Scheduling in Cellular Networks","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":20,"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":"","keywords":"Computer science; Scheduling (production processes); Macrocell; Dynamic priority scheduling; Round-robin scheduling; Fair-share scheduling; Distributed computing; Algorithm; Computer network; Mathematical optimization; Quality of service; Base station; Mathematics","score_opus":0.011444725564159252,"score_gpt":0.23036018100810307,"score_spread":0.2189154554439438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132128535","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062287904,0.00083175895,0.93354607,0.00024102691,0.000042636544,0.00003222331,0.00004981903,0.00014216357,0.0028263463],"genre_scores_gemma":[0.94497347,0.00041423438,0.05367042,0.00004603605,0.00004213312,0.000053310094,0.000042392283,0.000022355893,0.0007355933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865913,0.0005961136,0.00004400719,0.00013056432,0.00034431543,0.00022586487],"domain_scores_gemma":[0.9973738,0.001760281,0.0003208723,0.000223949,0.00023674038,0.00008434318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015106112,0.0006828057,0.0009924538,0.00050406967,0.00054146064,0.0008797944,0.0009835112,0.000689535,0.00044277424],"category_scores_gemma":[0.0075712763,0.00048242835,0.0002801669,0.0010671741,0.0011589951,0.0013809464,0.00096930534,0.00049462845,0.00007978112],"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":[0.00003937384,0.000010052627,0.00022262447,0.000012944402,0.00000777046,0.000016214271,0.000016040567,0.9806803,0.00032481007,0.011204719,0.00017365879,0.0072914455],"study_design_scores_gemma":[0.0000037120783,0.000006252223,0.00003352143,0.0000012627205,0.0000014322533,0.00000614375,0.0000040375544,0.9947582,0.00009018063,0.004993124,0.00010041656,0.0000017079982],"about_ca_topic_score_codex":0.00729603,"about_ca_topic_score_gemma":0.0045443685,"teacher_disagreement_score":0.00729603,"about_ca_system_score_codex":0.0016850758,"about_ca_system_score_gemma":0.0016146816,"threshold_uncertainty_score":0.014507115},"labels":[],"label_agreement":null},{"id":"W2134298603","doi":"10.1109/tmc.2008.74","title":"Utility-Based Rate-Controlled Parallel Wireless Transmission of Multimedia Streams with Multiple Importance Levels","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Wireless Network 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 British Columbia","funders":"","keywords":"Computer science; Quality of service; Wireless; Computer network; Reliability (semiconductor); Resource allocation; Exploit; Multimedia; Distributed computing; Power (physics); Telecommunications","score_opus":0.013158122243363756,"score_gpt":0.21928404000324997,"score_spread":0.2061259177598862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134298603","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045607354,0.00022857841,0.952408,0.000096449505,0.000039492457,0.000054276188,0.000016670027,0.00018122974,0.0013679144],"genre_scores_gemma":[0.9169531,0.00023603204,0.08097329,0.00003325087,0.000045396013,0.000077806086,0.000028704177,0.000034967088,0.001617418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994312,0.00020427511,0.000028113489,0.00007683583,0.00019015746,0.000069443595],"domain_scores_gemma":[0.9980129,0.0010855286,0.00029860868,0.0001541445,0.00036684543,0.000081932536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014585917,0.0006212412,0.0006015374,0.0004862225,0.00036953925,0.0006501228,0.0014064065,0.00034602394,0.00076406007],"category_scores_gemma":[0.0047928714,0.00033134274,0.000292296,0.0005649143,0.00063976616,0.0010510825,0.0006881931,0.00066175644,0.0001342823],"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":[0.0004156048,0.00016899801,0.00086214184,0.000117546115,0.000049353093,0.0002405802,0.00016091336,0.8503754,0.025506424,0.022164254,0.0010906899,0.098848134],"study_design_scores_gemma":[0.000012465001,0.000035422017,0.000062451145,0.0000019300364,0.0000073718147,0.000034974302,0.000005675306,0.99639183,0.0020018339,0.001256578,0.000183475,0.0000059637714],"about_ca_topic_score_codex":0.0023117869,"about_ca_topic_score_gemma":0.0021209074,"teacher_disagreement_score":0.0023117869,"about_ca_system_score_codex":0.00081030896,"about_ca_system_score_gemma":0.0006683742,"threshold_uncertainty_score":0.0077138543},"labels":[],"label_agreement":null},{"id":"W2134537668","doi":"10.1109/tmc.2005.74","title":"A mobility prediction architecture based on contextual knowledge and spatial conceptual maps","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":134,"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; Component (thermodynamics); Context (archaeology); A priori and a posteriori; Process (computing); Data mining; Artificial intelligence; Dempster–Shafer theory; Information retrieval","score_opus":0.01696949112275252,"score_gpt":0.2824695090405268,"score_spread":0.2655000179177743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134537668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0245772,0.0002294483,0.968465,0.0004290053,0.000040587416,0.000065936445,0.00024865463,0.0033602533,0.002583988],"genre_scores_gemma":[0.64234084,0.00044663675,0.35292044,0.00011090802,0.00005350568,0.00014333453,0.0010136837,0.00007661061,0.0028938858],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996853,0.000044576278,0.000026261581,0.00011991589,0.0000851126,0.00003890156],"domain_scores_gemma":[0.9994881,0.00012430242,0.00005512931,0.00009013884,0.00019237268,0.000049857503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063673686,0.00045886284,0.0005039469,0.0009953257,0.0007598777,0.0011831417,0.0016677942,0.00062268495,0.0015496019],"category_scores_gemma":[0.0018083268,0.0003368086,0.00050585205,0.0009357811,0.0005670208,0.0026876915,0.0013508655,0.00078420626,0.00046353583],"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":[0.00033771747,0.0002896184,0.013467884,0.00020011158,0.00022804712,0.00045997385,0.00080280553,0.488733,0.007151746,0.07002599,0.00902421,0.409279],"study_design_scores_gemma":[0.000007630922,0.000029934994,0.0010575494,0.000013273923,0.000058997153,0.00006830703,0.000054160108,0.9814803,0.0014274501,0.012392592,0.0033887993,0.000021005611],"about_ca_topic_score_codex":0.02256063,"about_ca_topic_score_gemma":0.024875129,"teacher_disagreement_score":0.02256063,"about_ca_system_score_codex":0.0007893517,"about_ca_system_score_gemma":0.0014977083,"threshold_uncertainty_score":0.044858634},"labels":[],"label_agreement":null},{"id":"W2137663561","doi":"10.1109/tmc.2011.229","title":"Exploiting Spectrum Heterogeneity in Dynamic Spectrum Market","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":31,"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":"Defense Advanced Research Projects Agency; Harbin Institute of Technology; University of Toronto; University of Wisconsin-Madison; National Science Foundation","keywords":"Spectrum management; Computer science; Duopoly; Cognitive radio; Frequency allocation; Lease; Stochastic game; Wireless; Telecommunications; Computer network; Microeconomics; Business; Economics","score_opus":0.02058372274088885,"score_gpt":0.24191357805974723,"score_spread":0.22132985531885838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137663561","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4999153,0.00041305946,0.48185354,0.0007839239,0.00005671122,0.00006859677,0.00007476756,0.0001551476,0.016678913],"genre_scores_gemma":[0.9934362,0.00005707522,0.0058449404,0.000035914596,0.00001150103,0.000016422684,0.000008820088,0.000009520541,0.0005797126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914384,0.00036178666,0.000022303519,0.00012166437,0.00016066407,0.00018975591],"domain_scores_gemma":[0.9971463,0.0019368452,0.0003907352,0.00020563178,0.00016104429,0.0001594616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015541916,0.00036626344,0.0006598727,0.00047061683,0.00056471187,0.0017231429,0.0010151041,0.0010686925,0.0014087826],"category_scores_gemma":[0.0044896035,0.00040342868,0.00054628536,0.0004441301,0.0013421669,0.0027432642,0.001277141,0.00086361857,0.00010758741],"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":[0.000101978745,0.000116951756,0.0019288112,0.000043481683,0.000042100226,0.0005814955,0.00015135115,0.7845664,0.007864185,0.19600965,0.0005799504,0.008013661],"study_design_scores_gemma":[0.000007715436,0.00002044367,0.00030013602,0.0000020778198,0.000003948446,0.000044937635,0.000037841644,0.97322714,0.00031306598,0.025787074,0.0002472353,0.0000083094],"about_ca_topic_score_codex":0.0024611028,"about_ca_topic_score_gemma":0.0013641677,"teacher_disagreement_score":0.0024611028,"about_ca_system_score_codex":0.0015369493,"about_ca_system_score_gemma":0.0008019888,"threshold_uncertainty_score":0.011151433},"labels":[],"label_agreement":null},{"id":"W2137940221","doi":"10.1109/tmc.2010.40","title":"Localized Multicast: Efficient and Distributed Replica Detection in Large-Scale Sensor Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Security in Wireless Sensor Networks","field":"Computer Science","cited_by":95,"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":"Wuhan University; National University of Singapore; George Mason University","keywords":"Multicast; Computer science; Node (physics); Computer network; Replica; Distributed computing; Replication (statistics); Adversary; Wireless sensor network; Reliable multicast; Source-specific multicast; Computer security","score_opus":0.006467901101342321,"score_gpt":0.23652912507924323,"score_spread":0.2300612239779009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137940221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0487209,0.0011584455,0.9479195,0.00030976353,0.000058573747,0.0001038451,0.000028291532,0.00083726016,0.000863425],"genre_scores_gemma":[0.85126066,0.00046714564,0.14701486,0.00007964027,0.00010826501,0.00014125915,0.000046587607,0.0000462554,0.00083523476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836046,0.00067456736,0.00006709274,0.00017128923,0.00062914623,0.00009742879],"domain_scores_gemma":[0.9949473,0.0025652999,0.0006719369,0.0010441559,0.00063688424,0.00013434392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028068267,0.00057418935,0.0010599105,0.00089110126,0.0008378381,0.00073619804,0.0019589039,0.0011085491,0.0005315155],"category_scores_gemma":[0.0068778503,0.00032908868,0.00039510013,0.00088074896,0.0006873659,0.0020291584,0.0015186846,0.0005660194,0.00020335367],"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":[0.0007557674,0.00025886836,0.004368954,0.00040699655,0.00018327216,0.00060750725,0.0004577487,0.59055436,0.052719124,0.021496126,0.003473822,0.32471755],"study_design_scores_gemma":[0.000045914294,0.0001892969,0.0003746115,0.000012743363,0.000022580343,0.00021149621,0.00004682662,0.98364085,0.006194108,0.008009406,0.0012353866,0.000016825605],"about_ca_topic_score_codex":0.00064469804,"about_ca_topic_score_gemma":0.00074103684,"teacher_disagreement_score":0.0028068267,"about_ca_system_score_codex":0.00056318904,"about_ca_system_score_gemma":0.00051166717,"threshold_uncertainty_score":0.01484412},"labels":[],"label_agreement":null},{"id":"W2141435168","doi":"10.1109/tmc.2008.94","title":"Efficient Broadcasting in Mobile Ad Hoc Networks","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":69,"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":"","keywords":"Computer science; Flooding (psychology); Communication source; Broadcasting (networking); Wireless ad hoc network; Computer network; Algorithm; Mobile ad hoc network; Node (physics); Hop (telecommunications); Distributed computing; Theoretical computer science; Wireless; Network packet; Telecommunications","score_opus":0.015949714305613472,"score_gpt":0.240187802158681,"score_spread":0.2242380878530675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141435168","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013101959,0.0039611044,0.97703904,0.00040243458,0.00010847227,0.00011072652,0.000037345315,0.00061668543,0.004622173],"genre_scores_gemma":[0.43761557,0.008497455,0.54724884,0.0002110686,0.0005177061,0.00030001433,0.00022580352,0.00013714077,0.005246425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887174,0.00034632836,0.00007174718,0.00009839427,0.00054314954,0.00006865856],"domain_scores_gemma":[0.9986216,0.0007908437,0.0001435258,0.00018535327,0.00022738328,0.000031269392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012365163,0.00056609715,0.0008749594,0.0011209431,0.00064072054,0.00075853016,0.00093461614,0.00084460416,0.00069093413],"category_scores_gemma":[0.0039218976,0.00042864442,0.00040653607,0.0010443492,0.0006833008,0.001756539,0.00083173864,0.0005908335,0.00038450919],"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":[0.0002418209,0.000108551576,0.0012180799,0.0010132608,0.00012903377,0.00031855342,0.0006077061,0.22150631,0.044791207,0.1881418,0.007533241,0.53439045],"study_design_scores_gemma":[0.00015657471,0.0003835695,0.000766438,0.00010185413,0.00014511346,0.0010176199,0.000118019394,0.816842,0.024719736,0.09941672,0.05624254,0.00008979932],"about_ca_topic_score_codex":0.00063233485,"about_ca_topic_score_gemma":0.000538072,"teacher_disagreement_score":0.0012365163,"about_ca_system_score_codex":0.00069718214,"about_ca_system_score_gemma":0.000673351,"threshold_uncertainty_score":0.0065394044},"labels":[],"label_agreement":null},{"id":"W2143480613","doi":"10.1109/tmc.2006.17","title":"Cellular CDMA capacity with out-of-band multihop relaying","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cooperative Communication and Network Coding","field":"Computer Science","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":"McMaster University","funders":"","keywords":"Computer network; Computer science; Telecommunications link; Base station; Wireless ad hoc network; Soft handover; Relay; Near-far problem; Code division multiple access; Cellular network; Wireless; Telecommunications","score_opus":0.029343390558348233,"score_gpt":0.24747544139963834,"score_spread":0.21813205084129011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143480613","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38824707,0.006389795,0.48133498,0.0013915845,0.0005034019,0.00014988969,0.0005218982,0.00029788038,0.121163525],"genre_scores_gemma":[0.9892537,0.0008692137,0.005970641,0.000060473696,0.00009404139,0.000033414013,0.0000462461,0.000030686413,0.0036416282],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991105,0.0002530044,0.000015497715,0.00008921068,0.00022857849,0.0003032395],"domain_scores_gemma":[0.9980123,0.0011750158,0.00015776041,0.00015813834,0.0004193253,0.000077387915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085006095,0.00089093915,0.00055548735,0.00078119553,0.0006116623,0.0019706995,0.0012807949,0.001204061,0.0020610883],"category_scores_gemma":[0.005052708,0.00030071897,0.0005562079,0.0011215782,0.0012246112,0.0027361326,0.0008395611,0.00083504815,0.0002634914],"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":[0.000029982253,0.00003814876,0.00063619664,0.000046703408,0.000020175698,0.00015938214,0.000028443883,0.95261157,0.0012760765,0.038975682,0.0005021693,0.00567539],"study_design_scores_gemma":[0.0000035274209,0.00003053586,0.00024316848,0.00001268127,0.000019260982,0.00005387502,0.000027318185,0.99025786,0.0009865997,0.006923168,0.0014312095,0.000010761489],"about_ca_topic_score_codex":0.013310386,"about_ca_topic_score_gemma":0.0065474208,"teacher_disagreement_score":0.013310386,"about_ca_system_score_codex":0.002523231,"about_ca_system_score_gemma":0.0016153413,"threshold_uncertainty_score":0.026465774},"labels":[],"label_agreement":null},{"id":"W2144431033","doi":"10.1109/tmc.2013.2296502","title":"Maximum Stable Throughput of Network-Coded Multiple Broadcast Sessions for WirelessTandem Random Access Networks","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cooperative Communication and Network Coding","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 Toronto; University of New Brunswick","funders":"Iran Telecommunication Research Center","keywords":"Computer science; Linear network coding; Computer network; Network packet; Random access; Aloha; Throughput; Distributed computing; Wireless network; Wireless","score_opus":0.03701657566618761,"score_gpt":0.3012260340177361,"score_spread":0.2642094583515485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144431033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15024514,0.0005815445,0.8457588,0.00024221791,0.00002773383,0.000056629102,0.000051560055,0.00025405994,0.0027823918],"genre_scores_gemma":[0.98074603,0.0003107111,0.018068194,0.000027402943,0.00002246512,0.000065015265,0.000030371397,0.000033787943,0.0006961245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990331,0.00038745836,0.000026838597,0.000093851704,0.00029938348,0.00015947396],"domain_scores_gemma":[0.9943228,0.0044245464,0.00039747992,0.00019986011,0.0005640602,0.00009129761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026590999,0.0006173404,0.0006488715,0.0010945888,0.0005872015,0.000990876,0.00091257767,0.0005959592,0.000853053],"category_scores_gemma":[0.011337578,0.00029686198,0.00040718474,0.0011887493,0.0015646098,0.0019316265,0.0007691266,0.00061452936,0.0001403971],"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":[0.00017496664,0.00004061396,0.0011345275,0.00009585598,0.000033925302,0.00016269817,0.00022130263,0.889639,0.008040709,0.08632609,0.00040235824,0.013728007],"study_design_scores_gemma":[0.0000024563512,0.000024704883,0.000104626364,0.000003822696,0.0000045572137,0.000019355222,0.000017359973,0.98944783,0.00097389775,0.009296845,0.0001005824,0.0000040109485],"about_ca_topic_score_codex":0.0015582399,"about_ca_topic_score_gemma":0.0013082623,"teacher_disagreement_score":0.0026590999,"about_ca_system_score_codex":0.0025434787,"about_ca_system_score_gemma":0.0009849605,"threshold_uncertainty_score":0.018454313},"labels":[],"label_agreement":null},{"id":"W2144723957","doi":"10.1109/tmc.2007.1017","title":"Kernel-Based Positioning in Wireless Local Area Networks","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":428,"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":"RSS; Computer science; Hybrid positioning system; Wi-Fi; Local area network; Context (archaeology); Signal strength; Kernel (algebra); Location-based service; Wireless; Computer network; Wireless network; Histogram; Point (geometry); Location awareness; Ubiquitous computing; Wireless lan; Real-time computing; Positioning system; Telecommunications; Artificial intelligence; Geography; World Wide Web; Human–computer interaction","score_opus":0.00660713434968295,"score_gpt":0.21523298720016243,"score_spread":0.20862585285047947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144723957","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024694266,0.0006429434,0.97261864,0.00010837416,0.00004262782,0.000010595149,0.000017669809,0.0011051054,0.00075978355],"genre_scores_gemma":[0.7454436,0.0008192042,0.25093943,0.00004171163,0.000087948945,0.000028474227,0.0000930846,0.00007035982,0.0024761513],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991468,0.00034781575,0.000040763465,0.00013546742,0.0002817525,0.00004729249],"domain_scores_gemma":[0.9987232,0.0004505263,0.00016554505,0.00035567346,0.00027137337,0.0000337553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007516136,0.00023685172,0.0005120556,0.0005683597,0.00027468556,0.00066994206,0.00070615014,0.0004826905,0.0006824323],"category_scores_gemma":[0.0040838225,0.00018646439,0.00017888684,0.0009401199,0.0004963663,0.001224558,0.00073989294,0.0004157018,0.0006521605],"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":[0.0002561932,0.000082105216,0.0029554693,0.00012297813,0.000050784245,0.0001550701,0.00022062236,0.39386594,0.015194396,0.035260864,0.002252248,0.5495834],"study_design_scores_gemma":[0.0000062362424,0.000035776313,0.0006154704,0.000004180551,0.0000066838725,0.000071997994,0.000019322304,0.98844725,0.002785598,0.00625792,0.0017372454,0.00001229527],"about_ca_topic_score_codex":0.0026438574,"about_ca_topic_score_gemma":0.001699375,"teacher_disagreement_score":0.0026438574,"about_ca_system_score_codex":0.00043661485,"about_ca_system_score_gemma":0.00030926193,"threshold_uncertainty_score":0.0052570105},"labels":[],"label_agreement":null},{"id":"W2145552549","doi":"10.1109/tmc.2013.132","title":"Adaptive Context Dissemination in Heterogeneous Environments","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Blackberry (Canada)","funders":"","keywords":"Computer science; Middleware (distributed applications); Context (archaeology); Distributed computing; Overlay network; Overlay; Context model; Protocol (science); Computer network; Context awareness; Personalization; Ubiquitous computing; World Wide Web; Human–computer interaction; The Internet; Operating system; Artificial intelligence","score_opus":0.016194441889625353,"score_gpt":0.2438644258242591,"score_spread":0.22766998393463375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145552549","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19262102,0.0012624287,0.7999302,0.00028536873,0.000064978056,0.00013375895,0.00003960137,0.0013214521,0.0043411884],"genre_scores_gemma":[0.9044695,0.00047998977,0.09353409,0.00005963668,0.000028880357,0.00005778267,0.00006504392,0.00004946818,0.0012555696],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992518,0.00025216545,0.000044676726,0.0001472732,0.00022302601,0.00008114179],"domain_scores_gemma":[0.9984059,0.0006316802,0.00014326345,0.00046109888,0.00024339066,0.000114674935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012264048,0.0003996283,0.0005285738,0.0006080991,0.0007028329,0.0012115712,0.0009503953,0.0007236155,0.0005439467],"category_scores_gemma":[0.004399693,0.000295444,0.00026615872,0.00046858663,0.000494541,0.002219208,0.0023724998,0.000585785,0.00019052248],"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":[0.0004559576,0.00026315267,0.005990224,0.00030001433,0.0001819462,0.0013739044,0.0016998185,0.31283045,0.24286823,0.036632378,0.0026915679,0.39471233],"study_design_scores_gemma":[0.00007614662,0.00021881038,0.002967154,0.000035098346,0.00010721265,0.000595374,0.00059667026,0.9063236,0.04512518,0.028612366,0.015277146,0.00006516477],"about_ca_topic_score_codex":0.0018642525,"about_ca_topic_score_gemma":0.0012763962,"teacher_disagreement_score":0.0018642525,"about_ca_system_score_codex":0.00044608867,"about_ca_system_score_gemma":0.00038357617,"threshold_uncertainty_score":0.006485939},"labels":[],"label_agreement":null},{"id":"W2146940657","doi":"10.1109/tmc.2009.16","title":"On the Planning of Wireless Sensor Networks: Energy-Efficient Clustering under the Joint Routing and Coverage Constraint","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":167,"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":"Wireless sensor network; Computer science; Heuristic; Energy consumption; Network topology; Integer programming; Cluster analysis; Routing (electronic design automation); Distributed computing; Key distribution in wireless sensor networks; Topology (electrical circuits); Linear programming; Mathematical optimization; Computer network; Wireless network; Wireless; Algorithm; Mathematics; Engineering","score_opus":0.015664708098835488,"score_gpt":0.22736356686652276,"score_spread":0.21169885876768726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146940657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016078653,0.0011674977,0.9750611,0.0008193655,0.000040652245,0.00016132231,0.00022836826,0.00025965052,0.006183256],"genre_scores_gemma":[0.5253454,0.0029175163,0.4649829,0.00031957703,0.000105598396,0.0006494871,0.0006002456,0.00025172724,0.0048275427],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909174,0.00045055247,0.00003044051,0.00013317048,0.00019698351,0.00009700775],"domain_scores_gemma":[0.9983608,0.001214816,0.00014388532,0.00008352203,0.0001266715,0.00007036285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012659505,0.00122374,0.0011567324,0.0007387826,0.0007581403,0.0010782193,0.0012548323,0.0010396948,0.002621583],"category_scores_gemma":[0.004977077,0.0006559698,0.0006564201,0.0021729006,0.0012278792,0.0019200213,0.0010664546,0.0009962935,0.00042957132],"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":[0.000032223394,0.0000187049,0.00012890204,0.000072266965,0.000011515252,0.000036616726,0.00004880282,0.9676184,0.00042424467,0.01675061,0.0012530918,0.013604712],"study_design_scores_gemma":[0.000012804387,0.000020910613,0.00007260492,0.000013844764,0.0000056756194,0.00002039241,0.000023105578,0.97598547,0.00045159753,0.022432838,0.00095378264,0.0000068898366],"about_ca_topic_score_codex":0.008949426,"about_ca_topic_score_gemma":0.008040403,"teacher_disagreement_score":0.008949426,"about_ca_system_score_codex":0.0015038886,"about_ca_system_score_gemma":0.0020039405,"threshold_uncertainty_score":0.017794669},"labels":[],"label_agreement":null},{"id":"W2147411015","doi":"10.1109/tmc.2013.23","title":"On Quality of Monitoring for Multichannel Wireless Infrastructure Networks","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Wireless Networks and Protocols","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":"McMaster University","funders":"National Science Foundation","keywords":"Computer science; Wireless; Metric (unit); Wireless network; Frame (networking); Channel (broadcasting); Inference; Computer network; Data mining; Distributed computing; Artificial intelligence; Telecommunications","score_opus":0.022924980232338892,"score_gpt":0.29790909630370077,"score_spread":0.27498411607136186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147411015","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020666094,0.002197529,0.9746108,0.0008159996,0.00007547658,0.0000692307,0.000088596564,0.0002449951,0.0012311892],"genre_scores_gemma":[0.8674295,0.0034118206,0.12676407,0.00021616697,0.00029756912,0.00014049669,0.00020950561,0.00011141911,0.0014194162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959764,0.0017482752,0.00019687614,0.0007314794,0.0010074864,0.00033947566],"domain_scores_gemma":[0.9853764,0.011410975,0.001320684,0.0008057242,0.0008462846,0.00023983551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051964796,0.0015648453,0.0014731509,0.0014865403,0.0007576853,0.002064193,0.002029904,0.0013124633,0.0012374481],"category_scores_gemma":[0.019463312,0.0005746988,0.00068543194,0.0026370576,0.0019877695,0.0035864592,0.001656895,0.0017504976,0.00015206392],"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":[0.00014932815,0.0000664158,0.0020869544,0.00012829505,0.00005160288,0.000057273937,0.00007723701,0.8951368,0.0013708612,0.022943268,0.0013545419,0.07657734],"study_design_scores_gemma":[0.0000068461272,0.000031226136,0.00036961315,0.000008743045,0.000007485644,0.000030315325,0.0000112246225,0.9902547,0.00044126526,0.008490416,0.00034074267,0.0000073789574],"about_ca_topic_score_codex":0.0048560738,"about_ca_topic_score_gemma":0.003688405,"teacher_disagreement_score":0.0051964796,"about_ca_system_score_codex":0.0035428773,"about_ca_system_score_gemma":0.0014322577,"threshold_uncertainty_score":0.027481973},"labels":[],"label_agreement":null},{"id":"W2149136155","doi":"10.1109/tmc.2003.1195148","title":"Policy-driven personalized multimedia services for mobile users","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Agent-Based Network Management","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"National Research Council Canada","keywords":"Computer science; Provisioning; Service (business); World Wide Web; Mobile device; The Internet; Service provider; Presentation (obstetrics); Negotiation; Mobile computing; Multimedia; Work (physics); Telecommunications; Business","score_opus":0.015190335042676901,"score_gpt":0.26735585452956806,"score_spread":0.25216551948689114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149136155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.117385246,0.0021504108,0.7604097,0.0050088516,0.00053593185,0.0006027118,0.00042650025,0.008797861,0.104682766],"genre_scores_gemma":[0.87687975,0.0008962739,0.098362975,0.00058675715,0.00020417247,0.00027123862,0.00035558324,0.00025157293,0.02219162],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995165,0.00015576999,0.000030102245,0.000052311032,0.00017295474,0.0000722825],"domain_scores_gemma":[0.99949825,0.00015132946,0.0000451911,0.00011612605,0.00010609427,0.00008303224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080045493,0.00027703264,0.0003085201,0.00033633917,0.0009349986,0.0015435816,0.00078951265,0.0010238327,0.002709089],"category_scores_gemma":[0.0021137483,0.00026326318,0.0002326715,0.00037469363,0.00040502573,0.0014843874,0.001059271,0.0008028112,0.0013033288],"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":[0.00096728327,0.00076386076,0.0046126796,0.00037145964,0.000098958124,0.0013866188,0.0018975278,0.14831564,0.06202591,0.30825278,0.05182594,0.41948134],"study_design_scores_gemma":[0.000105089755,0.00009892937,0.0010577344,0.000047029847,0.000046523044,0.00040754915,0.00044319147,0.7974789,0.014645226,0.07208713,0.113515966,0.00006664646],"about_ca_topic_score_codex":0.003377556,"about_ca_topic_score_gemma":0.0050726314,"teacher_disagreement_score":0.003377556,"about_ca_system_score_codex":0.0008583656,"about_ca_system_score_gemma":0.0011736891,"threshold_uncertainty_score":0.009062827},"labels":[],"label_agreement":null},{"id":"W2149492558","doi":"10.1109/tmc.2007.1046","title":"Enhancing WLAN Capacity by Strategic Placement of Tetherless Relay Points","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":66,"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","keywords":"Computer science; Computer network; Relay; Wireless; Throughput; Wireless network; Rayleigh fading; Telecommunications; Channel (broadcasting); Fading","score_opus":0.023865911176451617,"score_gpt":0.27223506504425693,"score_spread":0.2483691538678053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149492558","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23320258,0.00066390535,0.7552216,0.0002721626,0.00007053092,0.00005076553,0.00006228144,0.0010697292,0.009386472],"genre_scores_gemma":[0.9650209,0.00018068928,0.034228787,0.000020846504,0.000019336652,0.000017912582,0.000017199704,0.000019953337,0.00047436653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952984,0.00018791042,0.000021397744,0.00006640585,0.00008420071,0.00011020713],"domain_scores_gemma":[0.99855417,0.00073640095,0.00021920189,0.00021629386,0.0001913095,0.00008271895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073429226,0.0009374625,0.00066658383,0.0006672954,0.00034182548,0.0007994708,0.0012073611,0.0007345725,0.0012450675],"category_scores_gemma":[0.0035304304,0.0003897584,0.00038726343,0.0005678927,0.000596708,0.0015903565,0.0013420953,0.0005599033,0.00061365875],"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":[0.00023681443,0.00009616634,0.0012115333,0.000086042375,0.000037795213,0.00038658414,0.00009677798,0.8920846,0.037952054,0.012209821,0.0006588098,0.054942977],"study_design_scores_gemma":[0.00003825965,0.0002796805,0.0003838863,0.0000135473465,0.000051735595,0.00027697312,0.00009450823,0.9760838,0.016551642,0.0049003237,0.0012984708,0.00002720869],"about_ca_topic_score_codex":0.0007892964,"about_ca_topic_score_gemma":0.0010529612,"teacher_disagreement_score":0.0012450675,"about_ca_system_score_codex":0.0005047668,"about_ca_system_score_gemma":0.00044921777,"threshold_uncertainty_score":0.004165232},"labels":[],"label_agreement":null},{"id":"W2149525424","doi":"10.1109/tmc.2010.42","title":"TDMA Scheduling with Optimized Energy Efficiency and Minimum Delay in Clustered Wireless Sensor Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":170,"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":"Time division multiple access; Computer science; Wireless sensor network; Scheduling (production processes); Computer network; Frequency-division multiple access; Energy consumption; Efficient energy use; Wireless; Power control; Wireless network; Frame (networking); Reliability (semiconductor); Distributed computing; Mathematical optimization; Power (physics); Orthogonal frequency-division multiplexing; Channel (broadcasting); Telecommunications","score_opus":0.006405290231647739,"score_gpt":0.21383205322392396,"score_spread":0.20742676299227622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149525424","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06438431,0.00038946923,0.93312943,0.00013699249,0.000033540375,0.0000328913,0.000036363075,0.00019339238,0.0016636176],"genre_scores_gemma":[0.8426574,0.00024467372,0.1553541,0.000048958358,0.000027500157,0.00007113054,0.000052440373,0.000074561445,0.0014691768],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997497,0.00008757865,0.000011323283,0.000052753785,0.000061307735,0.000037322265],"domain_scores_gemma":[0.999595,0.00019811296,0.00007054322,0.000045935838,0.00006724325,0.000023203434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006189384,0.0005038888,0.00043844475,0.00032711928,0.00037960615,0.00041412024,0.000857834,0.00040504851,0.00052958826],"category_scores_gemma":[0.001553258,0.00032543926,0.00025510346,0.00062311627,0.00038760394,0.0006608763,0.00040614465,0.0003589915,0.00012279478],"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":[0.0000617978,0.000023307091,0.00023836664,0.000047578487,0.000021113952,0.000028574208,0.000044807966,0.9656065,0.0048299613,0.012664459,0.0004931047,0.015940363],"study_design_scores_gemma":[0.000011907309,0.000033208737,0.00007198882,0.0000019497465,0.000006061364,0.000010119538,0.000012883383,0.9939614,0.0013812287,0.004126179,0.0003796456,0.000003436406],"about_ca_topic_score_codex":0.0030170397,"about_ca_topic_score_gemma":0.0035642819,"teacher_disagreement_score":0.0030170397,"about_ca_system_score_codex":0.00077054504,"about_ca_system_score_gemma":0.0009326986,"threshold_uncertainty_score":0.005998969},"labels":[],"label_agreement":null},{"id":"W2151667449","doi":"10.1109/tmc.2005.19","title":"Call admission control in wideband CDMA cellular networks by using fuzzy logic","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":54,"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":"Call blocking; Computer science; Call Admission Control; Code division multiple access; Computer network; Handover; Fuzzy logic; Cellular network; Wideband; Blocking (statistics); Base station; Scheme (mathematics); Telecommunications; Wireless; Wireless network; Mathematics","score_opus":0.022386736012835293,"score_gpt":0.2845824088462968,"score_spread":0.2621956728334615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151667449","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058299266,0.0008612999,0.9371476,0.00019175396,0.00007113807,0.00005196746,0.000022847535,0.0002567112,0.003097349],"genre_scores_gemma":[0.929271,0.0003904154,0.069143936,0.00008364581,0.000050304912,0.000047890542,0.00001874405,0.0000067474134,0.0009873634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995801,0.00008868008,0.000028823913,0.000081839244,0.00017073884,0.000049834518],"domain_scores_gemma":[0.9995541,0.0002394807,0.000060812697,0.000021285587,0.00010631412,0.000018020963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061641407,0.0003902034,0.00037706541,0.0003788509,0.0005744811,0.000801862,0.0006103852,0.00062575657,0.0004897843],"category_scores_gemma":[0.0013435718,0.00015372824,0.00033084338,0.0002890448,0.00065788056,0.00062973966,0.00029951124,0.0006059639,0.000081936436],"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":[0.0004809899,0.00017903959,0.002182572,0.00024369452,0.00010977262,0.00063662004,0.0004986011,0.6130341,0.061551396,0.052573703,0.001441922,0.26706758],"study_design_scores_gemma":[0.000027729344,0.00006297657,0.00023680569,0.000011931226,0.000030220554,0.000069922906,0.000020033094,0.9879274,0.0032095117,0.007648437,0.00073584425,0.000019278319],"about_ca_topic_score_codex":0.006902492,"about_ca_topic_score_gemma":0.00411878,"teacher_disagreement_score":0.006902492,"about_ca_system_score_codex":0.00082773954,"about_ca_system_score_gemma":0.0005998971,"threshold_uncertainty_score":0.013724625},"labels":[],"label_agreement":null},{"id":"W2155785578","doi":"10.1109/tmc.2006.130","title":"End-to-End Batch Transmission in a Multihop and Multirate Wireless Network: Latency, Reliability, and Throughput Analysis","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Ad Hoc Networks","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":"University of Manitoba; University of British Columbia","funders":"Asian Institute of Technology; Thammasat University; University of British Columbia; University of Manitoba","keywords":"Computer science; Computer network; Network packet; Latency (audio); Hybrid automatic repeat request; Wireless network; Automatic repeat request; Wireless; Transmission (telecommunications); Transmission Control Protocol; Selective Repeat ARQ; Node (physics); Reliability (semiconductor); End-to-end principle; Markov chain; Telecommunications; Engineering; Telecommunications link","score_opus":0.009240752330363608,"score_gpt":0.24402687829733063,"score_spread":0.23478612596696702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155785578","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13644648,0.00062261254,0.86073536,0.0001846529,0.00003162035,0.0000756088,0.000105222796,0.0003225492,0.001475944],"genre_scores_gemma":[0.9700815,0.00054166006,0.027625205,0.0000461492,0.000044948407,0.000117116855,0.000081767364,0.00006551903,0.0013961899],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989485,0.0002840636,0.00004798806,0.0001358996,0.0004126138,0.0001708811],"domain_scores_gemma":[0.9949216,0.0033721405,0.0006364816,0.00043180442,0.000516363,0.0001215553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002874974,0.0010032223,0.00090945454,0.00079371245,0.00040216342,0.0008104363,0.0016581828,0.0010037185,0.0007541735],"category_scores_gemma":[0.007413831,0.00053025497,0.0007959411,0.00051805034,0.00112911,0.002389586,0.0006337647,0.0011556789,0.00024140328],"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":[0.00016610508,0.00005695124,0.0017476003,0.00006131802,0.00003975367,0.00020242755,0.00011989181,0.9572641,0.009232518,0.025596308,0.00023322077,0.0052798255],"study_design_scores_gemma":[0.00000226842,0.000024010908,0.00017307716,0.0000023898542,0.000007195823,0.00002680307,0.0000069574303,0.9972389,0.0007799674,0.0016850999,0.000046808553,0.000006466813],"about_ca_topic_score_codex":0.0037576335,"about_ca_topic_score_gemma":0.0016243993,"teacher_disagreement_score":0.0037576335,"about_ca_system_score_codex":0.0016185751,"about_ca_system_score_gemma":0.00081581593,"threshold_uncertainty_score":0.015204489},"labels":[],"label_agreement":null},{"id":"W2155962735","doi":"10.1109/tmc.2010.229","title":"Adaptive Asynchronous Sleep Scheduling Protocols for Delay Tolerant Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":69,"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; Asynchronous communication; Sleep mode; Computer network; Energy consumption; Distributed computing; Efficient energy use; Scalability; Power management; Network packet; Scheduling (production processes); Power consumption; Power (physics)","score_opus":0.02207387829873364,"score_gpt":0.27644032573473315,"score_spread":0.2543664474359995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155962735","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023445968,0.0013760048,0.9695467,0.00023371965,0.00031690433,0.0001964374,0.000050121565,0.00044925595,0.0043849004],"genre_scores_gemma":[0.71752864,0.0019794165,0.2738825,0.00034074282,0.00035939316,0.00060354464,0.00019927091,0.000093369315,0.005013099],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965596,0.00009905057,0.00004096574,0.000055290162,0.00012181627,0.000026916507],"domain_scores_gemma":[0.99924314,0.00032345936,0.00012431628,0.00011680183,0.0001491501,0.00004315404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007318855,0.0004513504,0.00028534894,0.00057672005,0.0006270751,0.0006468857,0.0011232777,0.0003218404,0.0013201149],"category_scores_gemma":[0.0015830594,0.00016068443,0.00023124371,0.00051462196,0.00048964773,0.00096792885,0.0006042263,0.00063652964,0.00022214824],"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":[0.0005415163,0.00018858931,0.0012925066,0.000730222,0.000108640954,0.00032736314,0.00073851895,0.17665301,0.07820061,0.34639046,0.0069352966,0.38789332],"study_design_scores_gemma":[0.00017361081,0.00043940818,0.00046735475,0.00008216443,0.000111754365,0.00043625486,0.00021145478,0.83961207,0.025899211,0.08476686,0.047731847,0.00006799216],"about_ca_topic_score_codex":0.0006042654,"about_ca_topic_score_gemma":0.0010839108,"teacher_disagreement_score":0.0013201149,"about_ca_system_score_codex":0.0006096285,"about_ca_system_score_gemma":0.000591101,"threshold_uncertainty_score":0.0044231415},"labels":[],"label_agreement":null},{"id":"W2156371250","doi":"10.1109/tmc.2008.125","title":"A Tabu Search Algorithm for Cluster Building in Wireless Sensor Networks","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Energy Efficient Wireless Sensor 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":"Polytechnique Montréal","funders":"","keywords":"Tabu search; Computer science; Wireless sensor network; Cluster analysis; Energy consumption; Simulated annealing; Quality of service; Key distribution in wireless sensor networks; Distributed computing; Heuristic; Computer network; Wireless network; Wireless; Algorithm; Data mining; Machine learning","score_opus":0.01835153577391628,"score_gpt":0.25917700854078707,"score_spread":0.24082547276687077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156371250","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015316025,0.00072601327,0.9788898,0.00016987539,0.00006584105,0.00016302019,0.000121527715,0.0014754675,0.003072535],"genre_scores_gemma":[0.18308237,0.0004170687,0.8121917,0.00016484685,0.00004473442,0.0007146291,0.000439307,0.00034175176,0.002603513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992144,0.00041606577,0.000028613118,0.000112673646,0.00015889743,0.00006932612],"domain_scores_gemma":[0.99910235,0.0005113223,0.00008741767,0.00009785283,0.00016725925,0.00003391661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014445425,0.001045743,0.0012512178,0.001311302,0.0015017412,0.00095650705,0.0017338878,0.0013093032,0.0034168633],"category_scores_gemma":[0.0037315732,0.0005526435,0.000680895,0.00253333,0.00092350395,0.0011558844,0.00089580467,0.0010119823,0.0008285306],"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":[0.00011703376,0.00008892784,0.0005385356,0.00012538227,0.00007622275,0.00004736223,0.00016591835,0.8106369,0.0015904739,0.012328512,0.004774522,0.16951023],"study_design_scores_gemma":[0.00004211785,0.0000560277,0.00013326867,0.000014331528,0.000015388763,0.00003473359,0.000033933844,0.9907769,0.00065050443,0.0063865487,0.00184559,0.000010693675],"about_ca_topic_score_codex":0.0059990957,"about_ca_topic_score_gemma":0.005301759,"teacher_disagreement_score":0.0059990957,"about_ca_system_score_codex":0.0011192935,"about_ca_system_score_gemma":0.0015390591,"threshold_uncertainty_score":0.01192832},"labels":[],"label_agreement":null},{"id":"W2156919687","doi":"10.1109/tmc.2009.105","title":"Relay Node Deployment Strategies in Heterogeneous Wireless Sensor Networks","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":171,"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":"Software deployment; Computer science; Relay; Wireless sensor network; Computer network; Energy consumption; Base station; Network packet; Wireless; Distributed computing; Key distribution in wireless sensor networks; Wireless network; Telecommunications; Engineering; Power (physics)","score_opus":0.010550615405070081,"score_gpt":0.2410141750016289,"score_spread":0.23046355959655881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156919687","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29249337,0.0020243824,0.7016703,0.00031099343,0.00004105076,0.00008637169,0.00004065892,0.0003570827,0.0029758879],"genre_scores_gemma":[0.9680228,0.000531058,0.030857848,0.000042770156,0.000012125899,0.000037149086,0.000027759928,0.000025409305,0.00044304554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918956,0.0004909012,0.000040182917,0.000103368424,0.00010773871,0.00006818807],"domain_scores_gemma":[0.9973296,0.0017437888,0.00034609617,0.00029875047,0.00021028663,0.00007146118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019580575,0.00058706745,0.00038890995,0.0005382349,0.00032625566,0.0004331698,0.00094171386,0.00053947966,0.00028834434],"category_scores_gemma":[0.0059679556,0.0002394411,0.0003418866,0.000362934,0.00061434077,0.0012808266,0.0006971439,0.0002094523,0.00012917453],"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":[0.00024242185,0.00006592578,0.0036267159,0.00018482679,0.00009349556,0.0006903236,0.0003108,0.8847409,0.028810762,0.019685624,0.0007371971,0.060811087],"study_design_scores_gemma":[0.0000575117,0.0005457726,0.0017234221,0.00002941402,0.00012574907,0.00057347235,0.00025287902,0.9661095,0.016171165,0.01200988,0.0023611432,0.000040029667],"about_ca_topic_score_codex":0.00075946865,"about_ca_topic_score_gemma":0.00089358917,"teacher_disagreement_score":0.0019580575,"about_ca_system_score_codex":0.00053298625,"about_ca_system_score_gemma":0.000181741,"threshold_uncertainty_score":0.010355294},"labels":[],"label_agreement":null},{"id":"W2157753773","doi":"10.1109/tmc.2006.1599403","title":"Minimum-energy multicast in wireless ad hoc networks with adaptive antennas: MILP formulations and heuristic algorithms","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Ad Hoc Networks","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":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Multicast; Wireless ad hoc network; Heuristic; Integer programming; Wireless network; Wireless; Distributed computing; Computer network; Mobile ad hoc network; Linear programming; Algorithm; Mathematical optimization; Mathematics; Telecommunications","score_opus":0.009490260515106751,"score_gpt":0.21924146352580023,"score_spread":0.20975120301069347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157753773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070482446,0.0005913667,0.98754406,0.00037360544,0.000043613363,0.00007185185,0.00005392295,0.00015053508,0.004122842],"genre_scores_gemma":[0.34240997,0.0014604698,0.6521446,0.00025979645,0.00013307731,0.0005701544,0.0001754039,0.00010869556,0.002737826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943095,0.0003522522,0.000017605034,0.000041396877,0.000090731955,0.0000669802],"domain_scores_gemma":[0.9984149,0.0012404391,0.00014429975,0.00005383825,0.00010120968,0.00004544301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015200727,0.0014181309,0.0011847888,0.00074134057,0.0006220882,0.0014437181,0.0012656841,0.0016238588,0.0018743585],"category_scores_gemma":[0.0029752927,0.0007240667,0.00061451737,0.001187164,0.0008407219,0.0012718247,0.0010700113,0.0013667849,0.0003100404],"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":[0.000019271483,0.000023753753,0.00007742395,0.000045218185,0.000011352666,0.000029268567,0.000021410646,0.976307,0.00016975775,0.013340193,0.0006102374,0.009345153],"study_design_scores_gemma":[0.000009461172,0.00000873608,0.000013324182,0.000011025591,0.0000035888067,0.000006830887,0.000019036712,0.99024546,0.0001353963,0.0090300115,0.0005143573,0.000002788674],"about_ca_topic_score_codex":0.002380186,"about_ca_topic_score_gemma":0.002637163,"teacher_disagreement_score":0.002380186,"about_ca_system_score_codex":0.00095610897,"about_ca_system_score_gemma":0.0011313327,"threshold_uncertainty_score":0.008038998},"labels":[],"label_agreement":null},{"id":"W2159141994","doi":"10.1109/tmc.2007.49","title":"Distributed and Energy-Aware MAC for Differentiated Services Wireless Packet Networks: A General Queuing Analytical Framework","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Wireless Networks and Protocols","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":"University of Manitoba","funders":"","keywords":"Computer science; Computer network; Network packet; Queueing theory; Node (physics); Queue management system; Queue; Markovian arrival process; Wireless network; Real-time computing; Wireless","score_opus":0.011358742759590744,"score_gpt":0.27305192263304645,"score_spread":0.2616931798734557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159141994","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035044458,0.00043547014,0.9942649,0.00011460058,0.000041328785,0.000044928936,0.00003881329,0.00013054055,0.0014250969],"genre_scores_gemma":[0.48303717,0.0027037074,0.5061221,0.00036249383,0.00045549418,0.00045599663,0.00015863997,0.00017376148,0.006530601],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990476,0.0002896212,0.000047580386,0.00014083815,0.00037004333,0.000104307794],"domain_scores_gemma":[0.999316,0.00027936522,0.0000732302,0.000070761744,0.00022578292,0.000034885514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017499311,0.0011942699,0.00095153647,0.00097607274,0.0005439913,0.001240852,0.0022551334,0.00088149233,0.0009511636],"category_scores_gemma":[0.0023055363,0.00049097365,0.0011751944,0.0009713993,0.0009462905,0.0016106886,0.0011643935,0.00103465,0.00026498234],"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":[0.0000333865,0.00011508993,0.00039736825,0.00016145557,0.00005394239,0.00012029064,0.00016088995,0.72229594,0.008992615,0.23900272,0.0015392989,0.027126985],"study_design_scores_gemma":[0.0000026921591,0.000015598202,0.0000422005,0.000005228057,0.00000655826,0.000011882623,0.0000056607437,0.9902042,0.0002533013,0.008581618,0.00086454774,0.0000064575133],"about_ca_topic_score_codex":0.005934269,"about_ca_topic_score_gemma":0.004018459,"teacher_disagreement_score":0.005934269,"about_ca_system_score_codex":0.002363287,"about_ca_system_score_gemma":0.0020680213,"threshold_uncertainty_score":0.017146885},"labels":[],"label_agreement":null},{"id":"W2161062743","doi":"10.1109/tmc.2011.58","title":"Distributed Throughput Maximization in Wireless Networks via Random Power Allocation","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cooperative Communication and Network Coding","field":"Computer Science","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":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Computer science; Throughput; Distributed algorithm; Distributed computing; Wireless network; Mathematical optimization; Gossip; Wireless; Utility maximization; Maximization; Power (physics); Resource allocation; Power control; Computer network; Mathematics; Telecommunications","score_opus":0.026319829353325254,"score_gpt":0.25076834847825086,"score_spread":0.2244485191249256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161062743","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008450443,0.00014621977,0.989368,0.00014647757,0.000018327699,0.00002863146,0.000012941321,0.00012186062,0.0017069948],"genre_scores_gemma":[0.7322485,0.000552899,0.26388118,0.00016394477,0.00011212875,0.00031532903,0.000050341714,0.000114692935,0.0025610095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998968,0.0004983218,0.000034603174,0.00013989436,0.00026650043,0.00009264043],"domain_scores_gemma":[0.99849856,0.001037995,0.00017674509,0.0001223318,0.00012929048,0.000035078243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018465522,0.00079012976,0.0008730395,0.0004940223,0.0004884006,0.0008726378,0.0009434143,0.0007661183,0.0009814879],"category_scores_gemma":[0.004475806,0.00039307837,0.0005304362,0.0008271312,0.0015029444,0.0012853128,0.0009433284,0.0007936376,0.00034294114],"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":[0.000042953543,0.000029388952,0.00013037046,0.000047028618,0.000018765872,0.000049075214,0.000047062,0.92978173,0.003264133,0.05100329,0.0006726717,0.01491356],"study_design_scores_gemma":[0.000014365946,0.000022692575,0.000027815484,0.0000032314208,0.0000048785605,0.000014323209,0.0000039814076,0.9821868,0.0006951259,0.016674735,0.00034775276,0.0000043061523],"about_ca_topic_score_codex":0.00050728273,"about_ca_topic_score_gemma":0.00053102354,"teacher_disagreement_score":0.0018465522,"about_ca_system_score_codex":0.00094583444,"about_ca_system_score_gemma":0.00085346197,"threshold_uncertainty_score":0.009765625},"labels":[],"label_agreement":null},{"id":"W2162971298","doi":"10.1109/tmc.2005.26","title":"Fair resource allocation with guaranteed statistical QoS for multimedia traffic in wideband CDMA cellular network","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":49,"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; Quality of service; Computer network; Resource allocation; Weighting; Telecommunications link; Cellular network; Fading; Code division multiple access; Channel (broadcasting); Real-time computing","score_opus":0.016371654507289994,"score_gpt":0.2699364562629294,"score_spread":0.2535648017556394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162971298","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08213532,0.0005650981,0.91522515,0.00016012778,0.00010632987,0.00006564402,0.000025799895,0.00033809163,0.001378444],"genre_scores_gemma":[0.9466614,0.00020932012,0.051985923,0.00007076769,0.000099754194,0.00006955834,0.000028003542,0.000024899884,0.0008503805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99869317,0.0003394199,0.000066793786,0.0001678856,0.0004569331,0.0002757958],"domain_scores_gemma":[0.9986737,0.0005028063,0.00014137258,0.00023082194,0.00034556194,0.000105765605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016328146,0.00073411484,0.00080842787,0.0008430833,0.0013697846,0.0011714486,0.0017694463,0.0006888293,0.00055764004],"category_scores_gemma":[0.004367557,0.0003190658,0.00036123,0.0008250259,0.0012985065,0.0016634847,0.0011323716,0.00059100636,0.00012938582],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006892621,0.000232382,0.002377024,0.00013460143,0.00010789733,0.00044160974,0.0004059148,0.6583868,0.043287635,0.10022365,0.0032083427,0.19050488],"study_design_scores_gemma":[0.000034694553,0.00009535377,0.00022446476,0.000005233026,0.000026978767,0.00010143446,0.000026324742,0.9841987,0.0039771018,0.010073259,0.0012135324,0.000022904947],"about_ca_topic_score_codex":0.003971655,"about_ca_topic_score_gemma":0.005111876,"teacher_disagreement_score":0.003971655,"about_ca_system_score_codex":0.0017696316,"about_ca_system_score_gemma":0.0015803726,"threshold_uncertainty_score":0.012839615},"labels":[],"label_agreement":null},{"id":"W2163004199","doi":"10.1109/tmc.2008.26","title":"A Mobile-Directory Approach to Service Discovery in Wireless Ad Hoc Networks","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Ad Hoc Networks","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":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Forskningsrådet om Hälsa, Arbetsliv och Välfärd","keywords":"Computer science; Service discovery; Computer network; Directory; Directory service; Wireless ad hoc network; Mobile ad hoc network; Distributed computing; Network topology; Service (business); Wireless network; Heuristic; Wireless; World Wide Web; Web service; Artificial intelligence; Telecommunications","score_opus":0.0189154969990156,"score_gpt":0.23876511610395823,"score_spread":0.21984961910494263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163004199","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00905023,0.0009613567,0.9863831,0.00030197884,0.000056786103,0.00005535703,0.000028579236,0.00034170714,0.002820897],"genre_scores_gemma":[0.4672232,0.001558815,0.5262264,0.00016427934,0.00019314617,0.00021760269,0.000110414534,0.00005681843,0.0042493786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991891,0.00036035088,0.00003649049,0.00011895029,0.00022170214,0.00007348435],"domain_scores_gemma":[0.9993087,0.0003403465,0.00006715422,0.0001342446,0.00009096768,0.00005853604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010105993,0.00039924297,0.00059427513,0.00087487017,0.0010365023,0.0010509709,0.0013648148,0.0008552778,0.0014093395],"category_scores_gemma":[0.0020804894,0.00028568212,0.00035280336,0.001287977,0.0011287879,0.0017796101,0.0012529494,0.00074212725,0.0003764842],"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":[0.00015975017,0.00013347804,0.0013444443,0.00019477874,0.000054918157,0.00019411756,0.0002988452,0.43735167,0.0047437865,0.34240514,0.004173377,0.20894569],"study_design_scores_gemma":[0.00002373866,0.00009204381,0.00014616577,0.00001453503,0.000015443136,0.00019454832,0.0000569438,0.9317874,0.0013824338,0.05670863,0.0095626665,0.00001546197],"about_ca_topic_score_codex":0.0021403958,"about_ca_topic_score_gemma":0.0037903807,"teacher_disagreement_score":0.0021403958,"about_ca_system_score_codex":0.0009039303,"about_ca_system_score_gemma":0.0011453584,"threshold_uncertainty_score":0.0065585375},"labels":[],"label_agreement":null},{"id":"W2164562671","doi":"10.1109/tmc.2009.42","title":"Large Connectivity for Dynamic Random Geometric Graphs","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Ad Hoc Networks","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 Waterloo","funders":"","keywords":"Computer science; Random graph; Random geometric graph; Wireless ad hoc network; Geometric networks; Spatial network; Torus; Wireless network; Theoretical computer science; Wireless; Mathematics; Combinatorics; Complex network; Graph; Telecommunications","score_opus":0.010224502172423966,"score_gpt":0.25905162031996715,"score_spread":0.24882711814754319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164562671","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12008026,0.0021579014,0.84464806,0.002883249,0.00013445261,0.000092454735,0.00039523325,0.00067526835,0.028933074],"genre_scores_gemma":[0.96324205,0.0016384803,0.028888427,0.00042109846,0.00028309334,0.00022877067,0.0003649625,0.00019311199,0.0047400654],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897254,0.0003263867,0.000031384454,0.00016368237,0.00036356488,0.00014237323],"domain_scores_gemma":[0.9904254,0.006671222,0.0010586425,0.00065720355,0.000779782,0.00040778553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014498015,0.00068127015,0.0007573424,0.0021677199,0.00081246876,0.0014633399,0.0012885991,0.00091962074,0.0040872097],"category_scores_gemma":[0.0146752475,0.00048532675,0.0006105993,0.0011630129,0.0021479863,0.004317678,0.0014721791,0.0020381915,0.00041770042],"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":[0.00003111004,0.000026536816,0.00048302856,0.00009095524,0.000017401164,0.00018572148,0.00018165955,0.059936192,0.0021480757,0.92885333,0.0028251086,0.0052208044],"study_design_scores_gemma":[0.000021163043,0.000035488203,0.00047815414,0.000025579213,0.000014985507,0.00033689605,0.000058937403,0.28848985,0.0005738501,0.7063498,0.00359285,0.000022476972],"about_ca_topic_score_codex":0.0008772743,"about_ca_topic_score_gemma":0.0009687366,"teacher_disagreement_score":0.0040872097,"about_ca_system_score_codex":0.0016492876,"about_ca_system_score_gemma":0.0004980977,"threshold_uncertainty_score":0.013673067},"labels":[],"label_agreement":null},{"id":"W2165903656","doi":"10.1109/tmc.2006.132","title":"Maximizing the Lifetime of Wireless Sensor Networks through Optimal Single-Session Flow Routing","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":61,"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":"Office of Naval Research","keywords":"Computer science; Computer network; Routing (electronic design automation); Network packet; Flow control (data); Wireless sensor network; Routing table; Routing protocol; Static routing; Session (web analytics)","score_opus":0.012066663127931688,"score_gpt":0.22311169438859912,"score_spread":0.21104503126066743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165903656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070825696,0.0007939764,0.9254537,0.000313043,0.000039768693,0.00006844376,0.00005293542,0.0003585409,0.0020937533],"genre_scores_gemma":[0.8619614,0.0010194051,0.13519308,0.000090178524,0.000088876666,0.00016135607,0.000091386064,0.0001001641,0.0012940916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926025,0.00032292522,0.000033661487,0.00009948329,0.00016676179,0.00011680784],"domain_scores_gemma":[0.9984652,0.0008668075,0.00022677836,0.00017153083,0.00018164689,0.000088113135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002035684,0.0009138537,0.00083958433,0.00060979294,0.00069008424,0.0007215521,0.001065531,0.00074721326,0.00062679936],"category_scores_gemma":[0.0047093895,0.0004235178,0.0003960623,0.0005756883,0.0006860459,0.002500211,0.0012996967,0.0005117723,0.00014169075],"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":[0.00026258358,0.00009601469,0.00092897966,0.00014555486,0.00004917101,0.0000829527,0.00018343881,0.8627248,0.01875835,0.031100739,0.0017439728,0.083923385],"study_design_scores_gemma":[0.000013218723,0.00006580933,0.00013403737,0.000007943735,0.000013814763,0.000044034477,0.000026511283,0.9781895,0.0029945741,0.017633438,0.00086732267,0.000009725167],"about_ca_topic_score_codex":0.00068575825,"about_ca_topic_score_gemma":0.0009060411,"teacher_disagreement_score":0.002035684,"about_ca_system_score_codex":0.0007644949,"about_ca_system_score_gemma":0.0011140906,"threshold_uncertainty_score":0.010765791},"labels":[],"label_agreement":null},{"id":"W2168954868","doi":"10.1109/tmc.2005.42","title":"Information raining and optimal link-layer design for mobile hotspots","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cooperative Communication and Network Coding","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 Toronto","funders":"","keywords":"Computer science; Heuristics; Bipartite graph; Network packet; Repeater (horology); Computer network; Theoretical computer science; Artificial intelligence; Graph","score_opus":0.044549062107336765,"score_gpt":0.29518761822612294,"score_spread":0.25063855611878616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168954868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02747655,0.00031036808,0.97009844,0.00008139076,0.000020686024,0.000051588886,0.000029107137,0.00023137519,0.001700462],"genre_scores_gemma":[0.8668675,0.0003272818,0.13109586,0.00011303946,0.000048469014,0.00009667468,0.000049064303,0.00004781182,0.001354353],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991757,0.00029002628,0.000042176584,0.00012957894,0.00018742244,0.00017504762],"domain_scores_gemma":[0.9989417,0.00043904662,0.00019597217,0.00013434845,0.00023756771,0.00005136131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009585853,0.00068934937,0.0006022752,0.000511885,0.00043299064,0.00097489,0.0010411711,0.00045532177,0.0013628152],"category_scores_gemma":[0.0016906979,0.00041141352,0.00034930572,0.0004784304,0.000552816,0.0012876636,0.0007467705,0.00062064617,0.0002622808],"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":[0.0002558561,0.000100564976,0.001142196,0.00024807977,0.00014117917,0.00022401071,0.00025289893,0.81113845,0.032991387,0.034304317,0.0014784646,0.11772257],"study_design_scores_gemma":[0.00005077981,0.00021521025,0.0003928595,0.000014813325,0.000056958947,0.00013918307,0.00006106113,0.97177505,0.010601807,0.014745818,0.0019231989,0.0000232498],"about_ca_topic_score_codex":0.0008485476,"about_ca_topic_score_gemma":0.0011313505,"teacher_disagreement_score":0.0013628152,"about_ca_system_score_codex":0.0007673896,"about_ca_system_score_gemma":0.0006419078,"threshold_uncertainty_score":0.0055678487},"labels":[],"label_agreement":null},{"id":"W2170777543","doi":"10.1109/tmc.2007.70727","title":"A Noncooperative Game-Theoretic Framework for Radio Resource Management in 4G Heterogeneous Wireless Access Networks","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":181,"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 Manitoba","funders":"","keywords":"Computer network; Computer science; Wireless network; Quality of service; Bandwidth allocation; Dynamic bandwidth allocation; Heterogeneous wireless network; Bandwidth (computing); Resource allocation; Radio resource management; Handover; Call Admission Control; Heterogeneous network; Wireless; Telecommunications","score_opus":0.025249061896740706,"score_gpt":0.29745286295817197,"score_spread":0.27220380106143127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170777543","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004958995,0.0007999184,0.9790461,0.0007557011,0.000127833,0.000104947285,0.00006470913,0.00004450983,0.014097428],"genre_scores_gemma":[0.71435994,0.0027512885,0.25723502,0.00068655657,0.00045880832,0.0011324214,0.00014181645,0.00006605564,0.023167973],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985091,0.000795993,0.000053551226,0.00018230222,0.00030906222,0.00015002607],"domain_scores_gemma":[0.9992318,0.00043928553,0.000099857425,0.000030054951,0.00012411493,0.000074959426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020982958,0.0015143213,0.0014111948,0.00072650344,0.00086981157,0.0023538575,0.002441277,0.0018938612,0.0027412612],"category_scores_gemma":[0.0019109925,0.0005758788,0.0012234208,0.0009865246,0.002740616,0.0020309591,0.0016017373,0.0022839154,0.00044492443],"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":[0.000021303504,0.000050925024,0.00011577992,0.00007433136,0.00002671337,0.00021296974,0.00012732731,0.43065858,0.00091935694,0.56109285,0.001295794,0.0054041287],"study_design_scores_gemma":[0.000021360558,0.000040418174,0.000053476593,0.00001720855,0.000010961226,0.000033349148,0.000038592716,0.84836453,0.000114370225,0.1488652,0.0024242962,0.000016296908],"about_ca_topic_score_codex":0.006953156,"about_ca_topic_score_gemma":0.0053612944,"teacher_disagreement_score":0.006953156,"about_ca_system_score_codex":0.0028792378,"about_ca_system_score_gemma":0.0026988727,"threshold_uncertainty_score":0.020890415},"labels":[],"label_agreement":null},{"id":"W2172100059","doi":"10.1109/tmc.2013.147","title":"A Scalable Bandwidth-Efficient Hybrid Adaptive Service Discovery Protocol for Vehicular Networks with Infrastructure Support","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Ad Hoc Networks","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 Ottawa","funders":"","keywords":"Computer science; Computer network; Service discovery; Scalability; Correctness; Service provider; Bandwidth (computing); Routing protocol; Network packet; Vehicular ad hoc network; Service (business); Distributed computing; Wireless ad hoc network; Wireless; Web service; Telecommunications; World Wide Web; Database","score_opus":0.00934413978443698,"score_gpt":0.23763467440057165,"score_spread":0.22829053461613466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172100059","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018162938,0.0012956784,0.97341746,0.00060356036,0.00025867965,0.00052599865,0.00013795496,0.0016574604,0.003940277],"genre_scores_gemma":[0.71537155,0.0014088608,0.2763724,0.0003588111,0.00012699522,0.001149084,0.0007729412,0.000094487936,0.0043447586],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987086,0.00030316177,0.00014983871,0.00012640821,0.0005792168,0.00013272554],"domain_scores_gemma":[0.998691,0.0005274627,0.00014063908,0.00015021549,0.00041715743,0.00007358297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016548699,0.0005595392,0.00079850084,0.0013537575,0.0011180573,0.001059371,0.0019909258,0.0007847994,0.00082983583],"category_scores_gemma":[0.0038152626,0.00033663222,0.000551262,0.0014294079,0.0008854408,0.0015411878,0.0021707544,0.0010682849,0.00026391735],"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":[0.0008252289,0.000246902,0.0018532631,0.00078404445,0.00030433192,0.0018883473,0.0010400178,0.2709332,0.07934679,0.20718855,0.018318582,0.41727072],"study_design_scores_gemma":[0.00014131385,0.00030811699,0.00045762854,0.00004975127,0.0001174755,0.00092133565,0.00016031261,0.9188772,0.013568705,0.028051844,0.037228756,0.0001175238],"about_ca_topic_score_codex":0.0039475663,"about_ca_topic_score_gemma":0.0039806175,"teacher_disagreement_score":0.0039475663,"about_ca_system_score_codex":0.0011772739,"about_ca_system_score_gemma":0.001924003,"threshold_uncertainty_score":0.008751869},"labels":[],"label_agreement":null},{"id":"W2206237806","doi":"10.1109/tmc.2015.2460251","title":"Diffusion Adaptation over Multi-Agent Networks with Wireless Link Impairments","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":29,"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":"Division of Electrical, Communications and Cyber Systems; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Fading; Computer science; Channel state information; Path loss; Computer network; Channel (broadcasting); Wireless network; Wireless; Diffusion; Equalization (audio); Distributed computing; Telecommunications","score_opus":0.0247672620838634,"score_gpt":0.25323016102580603,"score_spread":0.22846289894194263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2206237806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10618656,0.0005437683,0.8916942,0.00033195032,0.000045098874,0.00003382446,0.000011263487,0.00013269449,0.0010206279],"genre_scores_gemma":[0.9683035,0.00028537938,0.030234667,0.000052558247,0.000025852503,0.000044315373,0.000016400816,0.000017021883,0.001020299],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994854,0.00021264596,0.000029245193,0.00008709471,0.00011981063,0.00006580526],"domain_scores_gemma":[0.9939135,0.004839122,0.00059205096,0.00014649019,0.0004029428,0.00010590974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019091417,0.0009320653,0.0009700116,0.00044667438,0.0003794171,0.0006858021,0.0008682842,0.00097558514,0.0004118383],"category_scores_gemma":[0.008038676,0.00040543947,0.00042446237,0.0004245706,0.0010619796,0.0013720603,0.0010358914,0.0011371835,0.00009440264],"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":[0.00004398358,0.00001743257,0.00047133185,0.000025031562,0.000030761847,0.000040368297,0.000047549966,0.9893099,0.0013981845,0.0024026958,0.00006402033,0.0061487365],"study_design_scores_gemma":[0.000003681626,0.000013385547,0.00003616301,0.0000010086068,0.000002241622,0.0000030693677,0.0000027075143,0.9992636,0.00014169926,0.00050404284,0.00002622108,0.0000021491617],"about_ca_topic_score_codex":0.0046157045,"about_ca_topic_score_gemma":0.0019575097,"teacher_disagreement_score":0.0046157045,"about_ca_system_score_codex":0.0007412356,"about_ca_system_score_gemma":0.0004694381,"threshold_uncertainty_score":0.01009661},"labels":[],"label_agreement":null},{"id":"W2320242814","doi":"10.1109/tmc.2016.2538227","title":"Energy Aware Offloading for Competing Users on a Shared Communication Channel","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Nash equilibrium; Upload; Cloud computing; Energy consumption; Base station; Game theory; Channel (broadcasting); Distributed computing; Computation offloading; Set (abstract data type); Computer network; Mathematical optimization; Edge computing; Operating system","score_opus":0.024778383875383145,"score_gpt":0.257888408299263,"score_spread":0.23311002442387987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2320242814","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35792178,0.00036393516,0.6272793,0.00038555544,0.00009898611,0.00017583102,0.000085814325,0.00016006407,0.013528701],"genre_scores_gemma":[0.9814435,0.00008506216,0.014912241,0.000042594947,0.0000228248,0.00004629745,0.00002395717,0.000027039081,0.0033964145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912554,0.00024335898,0.000021902579,0.0001337462,0.00016199629,0.00031344497],"domain_scores_gemma":[0.9986486,0.0008791136,0.00010395021,0.00008471798,0.00014382717,0.00013983295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000863449,0.0010900093,0.0016149445,0.0004292813,0.0011582788,0.0017767205,0.0013390611,0.001267006,0.0020954711],"category_scores_gemma":[0.002037652,0.00047885973,0.00069359253,0.0006188543,0.0011319154,0.001550136,0.001658534,0.0007262269,0.00022873818],"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":[0.00013989763,0.000089308145,0.0005612926,0.00003894123,0.000038190665,0.00025306182,0.00006232508,0.97694767,0.0035053212,0.008932178,0.00034314708,0.009088671],"study_design_scores_gemma":[0.0000074320487,0.000026589947,0.000100526995,0.0000017473945,0.000005547211,0.000025738625,0.00003505822,0.99683374,0.00036429844,0.0024397282,0.00015455656,0.000005052812],"about_ca_topic_score_codex":0.008364748,"about_ca_topic_score_gemma":0.009891244,"teacher_disagreement_score":0.008364748,"about_ca_system_score_codex":0.0012789347,"about_ca_system_score_gemma":0.0015178453,"threshold_uncertainty_score":0.01663208},"labels":[],"label_agreement":null},{"id":"W2333394547","doi":"10.1109/tmc.2016.2547867","title":"NoPSM: A Concurrent MAC Protocol over Low-Data-Rate Low-Power Wireless Channel without PRR-SINR Model","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Energy Efficient Wireless Sensor Networks","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":"Carleton University","funders":"Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Computer science; Computer network; Throughput; Concurrency; Network packet; Block (permutation group theory); Distributed computing; Wireless sensor network; Wireless","score_opus":0.02273045278110039,"score_gpt":0.2851860075487925,"score_spread":0.2624555547676921,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333394547","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019739632,0.00046527386,0.9736168,0.00020683571,0.00021188268,0.00018909831,0.000083783845,0.00258538,0.0029012915],"genre_scores_gemma":[0.7977148,0.000519244,0.19531128,0.0004451485,0.00017397158,0.0006214685,0.00031371738,0.00027256343,0.0046277815],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849415,0.00032565845,0.000118724354,0.00024686984,0.00067223416,0.00014240824],"domain_scores_gemma":[0.99753714,0.0007223061,0.00043527034,0.00049661176,0.0006721355,0.0001366348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013790578,0.0010841334,0.0006906919,0.0008058847,0.0007891155,0.0010490478,0.0022532362,0.0006507939,0.001188348],"category_scores_gemma":[0.0048839594,0.00034819322,0.000614996,0.00050198083,0.00078730803,0.0016054034,0.0019563772,0.001499615,0.00036756485],"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":[0.0011036568,0.0004642459,0.005140564,0.0009274691,0.00026701673,0.00073547947,0.0005609974,0.3838065,0.10721493,0.066740334,0.009847873,0.42319083],"study_design_scores_gemma":[0.000067363566,0.00028974688,0.000401479,0.000023996392,0.000056028268,0.0002591991,0.000032196058,0.97275823,0.013615227,0.006349731,0.006097559,0.0000491194],"about_ca_topic_score_codex":0.002277011,"about_ca_topic_score_gemma":0.0027402043,"teacher_disagreement_score":0.002277011,"about_ca_system_score_codex":0.0006756696,"about_ca_system_score_gemma":0.002250306,"threshold_uncertainty_score":0.0072932243},"labels":[],"label_agreement":null},{"id":"W2343343021","doi":"10.1109/tmc.2015.2513052","title":"Resource Allocation for an OFDMA Cloud-RAN of Small Cells Underlaying a Macrocell","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","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":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Macrocell; Computer science; Transmitter power output; Quality of service; Computer network; Cellular network; Telecommunications link; Radio access network; Resource allocation; Optimization problem; Base station; Mobile station; Algorithm; Transmitter","score_opus":0.0326695615136331,"score_gpt":0.253141921735149,"score_spread":0.2204723602215159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2343343021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.105058424,0.00092581956,0.88635004,0.0003685168,0.000098133845,0.00009685357,0.000084714295,0.00018096268,0.0068365145],"genre_scores_gemma":[0.91700685,0.00035105454,0.080390155,0.00009885616,0.00005428294,0.000086218606,0.000042943346,0.000026614438,0.0019429886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996124,0.00011452995,0.000010504262,0.00007984872,0.000069691865,0.000113082984],"domain_scores_gemma":[0.99963844,0.00017242065,0.000051310202,0.000026359738,0.00006566694,0.000045705143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006231394,0.0007245395,0.00066051894,0.0002370347,0.00058719923,0.001082526,0.0009972507,0.00064839795,0.0017594553],"category_scores_gemma":[0.0009930497,0.00023699494,0.0004608545,0.00045099622,0.0006488486,0.0005480784,0.00085108576,0.0006383297,0.00022922351],"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":[0.00010298458,0.000058542795,0.0004736306,0.000040934483,0.000024099347,0.00014127548,0.000028689241,0.9708576,0.0043882173,0.0067610377,0.00060068513,0.016522307],"study_design_scores_gemma":[0.0000034842817,0.000016164991,0.00004674124,0.0000013177178,0.000003164455,0.000007991651,0.0000057147417,0.9992495,0.00021304048,0.0003436591,0.000107163316,0.000001993632],"about_ca_topic_score_codex":0.012259232,"about_ca_topic_score_gemma":0.010540574,"teacher_disagreement_score":0.012259232,"about_ca_system_score_codex":0.0009916297,"about_ca_system_score_gemma":0.00134759,"threshold_uncertainty_score":0.024375796},"labels":[],"label_agreement":null},{"id":"W2345267774","doi":"10.1109/tmc.2016.2519343","title":"Relay-Assisted Device-to-Device Communication: A Stochastic Analysis of Energy Saving","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":44,"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 Manitoba","funders":"","keywords":"Relay; Computer science; Energy consumption; Poisson point process; Energy (signal processing); Monte Carlo method; Wireless; Probabilistic logic; Point (geometry); Stochastic geometry; Efficient energy use; Point process; Electronic engineering; Telecommunications; Electrical engineering; Mathematics; Engineering; Statistics; Artificial intelligence","score_opus":0.014241619449268514,"score_gpt":0.24980913974319308,"score_spread":0.23556752029392455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345267774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023545293,0.00069855474,0.970501,0.0004070901,0.000039924194,0.000046486777,0.00010708874,0.000092013375,0.0045624636],"genre_scores_gemma":[0.94305724,0.0016247043,0.05113886,0.00013999891,0.00009947055,0.0001292014,0.00011317742,0.000073493,0.0036238593],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872464,0.00046742917,0.000048478167,0.00017169506,0.00042596782,0.00016173867],"domain_scores_gemma":[0.99631965,0.002638728,0.00038896303,0.00023942621,0.00035581196,0.000057339992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002271828,0.00085963303,0.0009594301,0.00093113456,0.00046118387,0.0012182193,0.0014749244,0.0010588216,0.0024196],"category_scores_gemma":[0.0076841163,0.0005185503,0.0008764359,0.0012018249,0.0014101496,0.002233066,0.00094930344,0.00093136565,0.00036870333],"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":[0.000026420295,0.000015914275,0.00033785385,0.00005267549,0.0000229713,0.000078183686,0.000033323457,0.9217424,0.0013973771,0.07119862,0.00044070292,0.004653493],"study_design_scores_gemma":[0.00000187108,0.000015772921,0.0001612792,0.000007871007,0.0000090087115,0.00005256853,0.000011793514,0.9918194,0.0004567122,0.0071344236,0.00032217917,0.0000070433302],"about_ca_topic_score_codex":0.001972547,"about_ca_topic_score_gemma":0.0014600729,"teacher_disagreement_score":0.0024196,"about_ca_system_score_codex":0.0021048908,"about_ca_system_score_gemma":0.00083599676,"threshold_uncertainty_score":0.0152721405},"labels":[],"label_agreement":null},{"id":"W2415364921","doi":"10.1109/tmc.2015.2480063","title":"Optimizing Video Request Routing in Mobile Networks with Built-in Content Caching","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","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":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Server; Distributed computing; Static routing; Routing (electronic design automation); Routing protocol; Policy-based routing; Dynamic Source Routing","score_opus":0.037449273134749175,"score_gpt":0.25452774424474595,"score_spread":0.21707847110999678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2415364921","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18018802,0.0005568761,0.81416994,0.0003847269,0.000038634516,0.00018676564,0.00009864786,0.00080218347,0.0035742235],"genre_scores_gemma":[0.8278581,0.00029973808,0.17005908,0.000061408165,0.000023080036,0.00007523304,0.00012428401,0.000057103483,0.0014420485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996574,0.00014049123,0.0000126447285,0.0000522992,0.000054996435,0.000082183695],"domain_scores_gemma":[0.999243,0.00044292613,0.00011207009,0.00008064734,0.00008502806,0.00003624606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006113077,0.0007544664,0.00052787043,0.00046012932,0.00064629444,0.00082808384,0.0009455276,0.00079292816,0.0005978736],"category_scores_gemma":[0.0020257372,0.00031112263,0.00027370575,0.00071290485,0.00053392,0.0009889025,0.00046497674,0.0004302022,0.00016781557],"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":[0.00008689433,0.000063320396,0.00082217745,0.00005239647,0.00002481273,0.000070503054,0.00005111056,0.94430935,0.007343008,0.005947967,0.001045082,0.040183358],"study_design_scores_gemma":[0.0000072984794,0.000024395362,0.00009727631,0.0000022655868,0.000006431224,0.000019700896,0.000018542241,0.9952773,0.001942911,0.0023506007,0.00025028494,0.0000030710328],"about_ca_topic_score_codex":0.006247526,"about_ca_topic_score_gemma":0.012353989,"teacher_disagreement_score":0.006247526,"about_ca_system_score_codex":0.0016573225,"about_ca_system_score_gemma":0.0010396809,"threshold_uncertainty_score":0.012422323},"labels":[],"label_agreement":null},{"id":"W2461609381","doi":"10.1109/tmc.2016.2591527","title":"Characterizing the Instantaneous Connectivity of Large-Scale Urban Vehicular Networks","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":52,"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":"Seventh Framework Programme; Research Executive Agency","keywords":"Network topology; Computer science; Vehicular ad hoc network; Context (archaeology); Topology (electrical circuits); Navigability; Software deployment; Computer network; Reliability (semiconductor); Block (permutation group theory); Distributed computing; Wireless ad hoc network; Telecommunications; Wireless; Engineering; Power (physics)","score_opus":0.011533657406296588,"score_gpt":0.26491191103532524,"score_spread":0.25337825362902866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2461609381","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7058428,0.0006843441,0.28459483,0.00025141722,0.00002518039,0.00004023765,0.0006087852,0.00019494684,0.0077574905],"genre_scores_gemma":[0.99562484,0.00025677093,0.003632132,0.000008721768,0.000010808779,0.000016429633,0.00018937435,0.000010147344,0.00025077045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997533,0.00008434701,0.000010944734,0.000046125657,0.00006063809,0.000044600827],"domain_scores_gemma":[0.99880326,0.00070980657,0.0002256916,0.00011113492,0.000096030504,0.000053941585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040647815,0.0002858611,0.00022784183,0.0011932084,0.0003582819,0.0006537623,0.0005755093,0.00039221018,0.0004910146],"category_scores_gemma":[0.0035913289,0.00022887888,0.0002190171,0.0010129614,0.0005998491,0.0013232966,0.000546296,0.00033741072,0.000082674356],"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":[0.000019343026,0.00001209386,0.011159002,0.00004371007,0.000024694118,0.00022601408,0.00014410404,0.95131767,0.0021644144,0.028323831,0.0003607586,0.006204375],"study_design_scores_gemma":[0.0000025953002,0.00002924481,0.012028923,0.000014453392,0.00001845392,0.00023034633,0.0002859806,0.95649123,0.00089292065,0.028377974,0.0016116224,0.000016190768],"about_ca_topic_score_codex":0.0026359328,"about_ca_topic_score_gemma":0.0029971672,"teacher_disagreement_score":0.0026359328,"about_ca_system_score_codex":0.000650587,"about_ca_system_score_gemma":0.00022702583,"threshold_uncertainty_score":0.005241215},"labels":[],"label_agreement":null},{"id":"W2462138333","doi":"10.1109/tmc.2016.2582482","title":"Utility Maximization for Multimedia Data Dissemination in Large-Scale VANETs","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Dissemination; Scalability; Wireless ad hoc network; Computer network; Quality of service; Maximization; Path (computing); Taxis; Distributed computing; Multimedia; Telecommunications; Wireless; Mathematical optimization","score_opus":0.014978766361281494,"score_gpt":0.2626810506432161,"score_spread":0.24770228428193464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2462138333","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008282813,0.0006422437,0.9879147,0.00035621523,0.00003373168,0.00005138898,0.000050835828,0.00009058302,0.0025774362],"genre_scores_gemma":[0.85789573,0.0021658505,0.13326703,0.00022179617,0.00014010204,0.00031243032,0.00020744994,0.00016452746,0.005625088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869543,0.00071077916,0.00004669466,0.00016594748,0.00022294244,0.00015816723],"domain_scores_gemma":[0.99748766,0.0018795432,0.00017768981,0.00008535779,0.00026243253,0.00010737012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024002644,0.0012539728,0.0014604972,0.0009029077,0.000692953,0.0015806878,0.0018604216,0.0011178758,0.0017121466],"category_scores_gemma":[0.00695502,0.00065445673,0.0008282713,0.0017949055,0.0011999198,0.0020172046,0.0014294792,0.0014016097,0.00030453896],"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":[0.000032338623,0.000031086973,0.00024107215,0.0001022893,0.000030578958,0.00009653334,0.00005979725,0.95113087,0.00056303263,0.03853968,0.001072093,0.008100533],"study_design_scores_gemma":[0.0000039684114,0.000008995764,0.00003709025,0.0000046309847,0.0000041189696,0.000013135203,0.000014763094,0.9911007,0.00009502957,0.008427521,0.00028649822,0.0000035061378],"about_ca_topic_score_codex":0.005425039,"about_ca_topic_score_gemma":0.0036264877,"teacher_disagreement_score":0.005425039,"about_ca_system_score_codex":0.002442908,"about_ca_system_score_gemma":0.0013974428,"threshold_uncertainty_score":0.017724633},"labels":[],"label_agreement":null},{"id":"W2469954218","doi":"10.1109/tmc.2016.2585106","title":"Network Coding as a Performance Booster for Concurrent Multi-Path Transfer of Data in Multi-Hop Wireless Networks","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":18,"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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Computer network; Linear network coding; Network packet; Transport layer; Stream Control Transmission Protocol; Testbed; Wireless network; Distributed computing; Network layer; Wireless; Layer (electronics)","score_opus":0.09436529703555026,"score_gpt":0.3288500961123661,"score_spread":0.23448479907681585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2469954218","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08934602,0.0011635018,0.90460074,0.00052280846,0.00011213456,0.00007743216,0.000024339983,0.00080566446,0.0033473289],"genre_scores_gemma":[0.9220762,0.00045924366,0.07626745,0.000101439204,0.000060570612,0.000053326236,0.000023861368,0.00004694925,0.0009108955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994122,0.00018564302,0.000022743603,0.000060337094,0.0002567002,0.00006239946],"domain_scores_gemma":[0.99722517,0.001710318,0.00022047565,0.00026455917,0.00049808115,0.00008133929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012168984,0.00039492955,0.00033815365,0.0006534287,0.0004851394,0.0005289241,0.0008279829,0.000511696,0.00072829326],"category_scores_gemma":[0.0048539545,0.000159109,0.00017043002,0.0005370242,0.0008956578,0.001285194,0.00096406933,0.0009462937,0.00015513392],"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":[0.00041173855,0.00024008595,0.0016689794,0.00016183409,0.000034045745,0.000235049,0.00031117038,0.59586906,0.07491287,0.07193703,0.0018793728,0.25233877],"study_design_scores_gemma":[0.000007845209,0.00009009958,0.0001395584,0.000008407439,0.000007447882,0.0000608428,0.000015072711,0.9851917,0.008005183,0.005794427,0.00066778023,0.000011522312],"about_ca_topic_score_codex":0.0016774565,"about_ca_topic_score_gemma":0.0015458931,"teacher_disagreement_score":0.0016774565,"about_ca_system_score_codex":0.00069950515,"about_ca_system_score_gemma":0.0006904079,"threshold_uncertainty_score":0.0064356923},"labels":[],"label_agreement":null},{"id":"W2512240773","doi":"10.1109/tmc.2016.2604260","title":"How to Download More Data from Neighbors? A Metric for D2D Data Offloading Opportunity","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Opportunistic and Delay-Tolerant Networks","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 British Columbia","funders":"","keywords":"Computer science; Computer network; Mobile device; Metric (unit); Object (grammar); Download; Mobile computing; Distributed computing; Operating system","score_opus":0.10744815447260204,"score_gpt":0.3154034379914188,"score_spread":0.20795528351881676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2512240773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41104707,0.0048995735,0.5641713,0.0018866631,0.0002483118,0.00056621,0.0010215756,0.0009246704,0.01523452],"genre_scores_gemma":[0.94138795,0.00039926096,0.05697317,0.000056553632,0.00004226133,0.00013584428,0.00024378745,0.00004379392,0.0007174412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978363,0.0005786502,0.0003015455,0.00034950147,0.00074729446,0.00018683431],"domain_scores_gemma":[0.98959595,0.006380007,0.0011997193,0.00074380817,0.0010919985,0.0009884654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016237468,0.0008461052,0.00087057526,0.0019377051,0.00095526397,0.0017686842,0.0009341959,0.001036897,0.0014078438],"category_scores_gemma":[0.0135175735,0.000279473,0.00042162254,0.0021941294,0.0010163723,0.0038594236,0.0016007158,0.0006499202,0.00023301117],"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":[0.001880976,0.0006535404,0.065922044,0.00131354,0.00037291273,0.0008631992,0.0009116875,0.45372036,0.04925206,0.04998371,0.005785604,0.36934036],"study_design_scores_gemma":[0.00010083136,0.001446771,0.027702203,0.00008502098,0.00020666297,0.0021045418,0.0012652015,0.9102704,0.020774873,0.022832021,0.01304754,0.00016388255],"about_ca_topic_score_codex":0.0014573704,"about_ca_topic_score_gemma":0.002079742,"teacher_disagreement_score":0.0019377051,"about_ca_system_score_codex":0.0010419402,"about_ca_system_score_gemma":0.000703566,"threshold_uncertainty_score":0.008587301},"labels":[],"label_agreement":null},{"id":"W2517006165","doi":"10.1109/tmc.2016.2607748","title":"Delay Analysis and Routing for Two-Dimensional VANETs Using Carry-and-Forward Mechanism","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Flooding (psychology); Computer network; Propagation delay; Wireless ad hoc network; Path (computing); Shortest path problem; Node (physics); Routing protocol; Routing (electronic design automation); Transmission delay; Topology (electrical circuits); Wireless; Network packet; Telecommunications; Mathematics","score_opus":0.009755353520571488,"score_gpt":0.23498105210495276,"score_spread":0.22522569858438127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2517006165","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046246644,0.0026167657,0.94251925,0.00051418,0.00022356675,0.00008038791,0.00013649532,0.00016517023,0.0074974736],"genre_scores_gemma":[0.94544166,0.0032634034,0.045412645,0.00012516193,0.00014394677,0.00011433713,0.0001543836,0.000060180682,0.0052842665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966085,0.000074012685,0.00001848622,0.000058702713,0.00011777274,0.00007023316],"domain_scores_gemma":[0.9993948,0.00030448101,0.00009400413,0.000032821412,0.0001432323,0.0000306885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007667395,0.0007752761,0.00037503778,0.0015103316,0.00049981603,0.001000596,0.0007856738,0.0005088925,0.0011060075],"category_scores_gemma":[0.0022982135,0.00030359853,0.0005801768,0.0009143396,0.00060414424,0.001309204,0.00068068533,0.0005248472,0.00019101248],"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":[0.0000381483,0.000018149296,0.0006888667,0.00008525299,0.000022120357,0.00010880652,0.00005639147,0.92431545,0.003066966,0.060685318,0.0007524584,0.010161992],"study_design_scores_gemma":[0.0000022889876,0.000020414545,0.00010061183,0.0000049056434,0.000007009915,0.000029204693,0.000021358419,0.99128366,0.0003356257,0.0073135896,0.00087271305,0.000008636646],"about_ca_topic_score_codex":0.007252811,"about_ca_topic_score_gemma":0.0033143894,"teacher_disagreement_score":0.007252811,"about_ca_system_score_codex":0.0016130558,"about_ca_system_score_gemma":0.0010953528,"threshold_uncertainty_score":0.014421165},"labels":[],"label_agreement":null},{"id":"W2546966125","doi":"10.1109/tmc.2016.2624732","title":"PLP: Protecting Location Privacy Against Correlation Analyze Attack in Crowdsensing","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":42,"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":"National Natural Science Foundation of China","keywords":"Computer science; Crowdsensing; Publication; Context (archaeology); Computer security; Field (mathematics); Data mining; Internet privacy","score_opus":0.01840302831943537,"score_gpt":0.25890753243447356,"score_spread":0.2405045041150382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546966125","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02424001,0.00020633954,0.9709024,0.00058853725,0.000059694852,0.00013687345,0.00012867198,0.0015060109,0.002231488],"genre_scores_gemma":[0.89691246,0.0001917499,0.099280804,0.00040786198,0.00007435756,0.00017475487,0.00015708938,0.00009332587,0.002707672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99619496,0.0012977245,0.00016515292,0.0005873734,0.001265879,0.00048880116],"domain_scores_gemma":[0.99323964,0.003078305,0.0006179174,0.002289369,0.0005317475,0.0002430175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033813703,0.0008999119,0.0012810285,0.0008629375,0.0014331221,0.0012257746,0.002109197,0.0018199292,0.0012358456],"category_scores_gemma":[0.012344108,0.0005403643,0.0010670603,0.0010068038,0.0019715023,0.0030919444,0.0060674404,0.0019518093,0.00063092075],"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":[0.001019739,0.0002722967,0.005426679,0.00026337613,0.00019149276,0.0011429981,0.0006698565,0.6288368,0.030615022,0.07858686,0.0099661,0.2430088],"study_design_scores_gemma":[0.000038924136,0.00009054,0.00036907606,0.0000123047585,0.00001582559,0.00025501315,0.00005439484,0.9616121,0.006968944,0.028513094,0.0020426072,0.00002712457],"about_ca_topic_score_codex":0.0026747189,"about_ca_topic_score_gemma":0.0018910563,"teacher_disagreement_score":0.0033813703,"about_ca_system_score_codex":0.0014339453,"about_ca_system_score_gemma":0.0026655344,"threshold_uncertainty_score":0.017882645},"labels":[],"label_agreement":null},{"id":"W2550686015","doi":"10.1109/tmc.2016.2632715","title":"DV-maxHop: A Fast and Accurate Range-Free Localization Algorithm for Anisotropic Wireless Networks","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":124,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Wireless sensor network; Algorithm; Network topology; Node (physics); Isotropy; Wireless; Range (aeronautics); Distributed computing; Computer network; Telecommunications","score_opus":0.007684205611487831,"score_gpt":0.21500571793343587,"score_spread":0.20732151232194804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2550686015","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038833274,0.00019506614,0.99424756,0.00008291711,0.000033909215,0.0000223916,0.00002354609,0.00042618837,0.0010851078],"genre_scores_gemma":[0.20864445,0.000608266,0.78644013,0.000101189784,0.000049365888,0.0001140072,0.00022201997,0.00018994324,0.0036306316],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997372,0.000061153216,0.0000143917705,0.000035824614,0.00013002161,0.000021424441],"domain_scores_gemma":[0.99968195,0.00012251768,0.000046915306,0.000046682955,0.000083214465,0.000018673993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054872053,0.00045189846,0.00046212977,0.00068440614,0.00039029063,0.0005953591,0.001065729,0.00051021,0.0009782112],"category_scores_gemma":[0.0014738686,0.00025286135,0.00032096374,0.0007148873,0.0003738106,0.0009904484,0.0009583758,0.000666446,0.00033404093],"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":[0.00018074737,0.00005348612,0.0010822226,0.00018487782,0.000060266964,0.00018455693,0.00025725714,0.41888982,0.017901402,0.036847666,0.0103695365,0.51398814],"study_design_scores_gemma":[0.000028505045,0.000054789245,0.0001725452,0.0000107474725,0.000009041678,0.00014046286,0.000030578707,0.97956926,0.004682366,0.0056732562,0.009607177,0.00002135813],"about_ca_topic_score_codex":0.001853908,"about_ca_topic_score_gemma":0.0025653874,"teacher_disagreement_score":0.001853908,"about_ca_system_score_codex":0.0004732686,"about_ca_system_score_gemma":0.00061965646,"threshold_uncertainty_score":0.0036861897},"labels":[],"label_agreement":null},{"id":"W2552070661","doi":"10.1109/tmc.2016.2628034","title":"A Priority-Aware Truthful Mechanism for Supporting Multi-Class Delay-Sensitive Medical Packet Transmissions in E-Health Networks","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Wireless Body Area Networks","field":"Engineering","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; Network packet; Base station; Queueing theory; Body area network; Default gateway; Quality of service; Gateway (web page); Wireless; Scheduling (production processes); Wireless network; Transmission (telecommunications); Telecommunications; Wireless sensor network","score_opus":0.013108353161062373,"score_gpt":0.2700638260722778,"score_spread":0.25695547291121545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2552070661","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027300391,0.0006331259,0.9693823,0.00041454562,0.00022207499,0.0001733885,0.00004209481,0.000559856,0.0012722556],"genre_scores_gemma":[0.91630316,0.00034560834,0.081241675,0.0003711599,0.00026256515,0.00012550357,0.000042701664,0.000035993748,0.0012716573],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99715626,0.00080883916,0.0002751874,0.0006798282,0.00067817536,0.00040176077],"domain_scores_gemma":[0.98743266,0.0066869315,0.0021972614,0.0015062644,0.0015083675,0.00066846266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060855607,0.0008716368,0.0009793106,0.0011834387,0.001157762,0.0023693037,0.0040086024,0.0016563162,0.0018479581],"category_scores_gemma":[0.01410533,0.0006197347,0.00071082683,0.0007455262,0.001443839,0.0037816006,0.0018422017,0.001911149,0.00032176185],"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":[0.0027721655,0.0010231616,0.0053709727,0.0011335907,0.00041045187,0.0018501029,0.0025591496,0.26289433,0.10865094,0.34140033,0.006065721,0.2658692],"study_design_scores_gemma":[0.00019737467,0.0006356888,0.0005446619,0.000063500556,0.00017818976,0.00062866247,0.00012763207,0.92691267,0.013845284,0.05262508,0.004135441,0.000105779625],"about_ca_topic_score_codex":0.0009839381,"about_ca_topic_score_gemma":0.0006797681,"teacher_disagreement_score":0.0060855607,"about_ca_system_score_codex":0.0013861959,"about_ca_system_score_gemma":0.0018414408,"threshold_uncertainty_score":0.032183886},"labels":[],"label_agreement":null},{"id":"W2560891425","doi":"10.1109/tmc.2016.2645686","title":"Decoupled Uplink-Downlink User Association in Multi-Tier Full-Duplex Cellular Networks: A Two-Sided Matching Game","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Full-Duplex Wireless Communications","field":"Engineering","cited_by":71,"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; Telecommunications link; Cellular network; Base station; Karush–Kuhn–Tucker conditions; Computer network; Association scheme; Mathematical optimization; Provisioning; Optimization problem; Duplex (building); Distributed computing; Algorithm; Mathematics","score_opus":0.013142189507991882,"score_gpt":0.24286624955306113,"score_spread":0.22972406004506926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560891425","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08663585,0.00045782802,0.8914122,0.0013063586,0.00009248362,0.0001888801,0.00035908763,0.00013894906,0.01940836],"genre_scores_gemma":[0.95055115,0.00048165862,0.04085083,0.0003172328,0.000059975435,0.00018242355,0.00012895711,0.000034948946,0.0073928623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981877,0.0009041981,0.000054704193,0.00026552565,0.00025371116,0.00033409483],"domain_scores_gemma":[0.9982132,0.0011363691,0.00017332773,0.00007714739,0.00016276531,0.00023718874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019798656,0.0010895156,0.0016062724,0.0004719133,0.0006826245,0.002474149,0.0018430619,0.0025236306,0.0031728726],"category_scores_gemma":[0.0036823489,0.0006492646,0.00067951996,0.0010819425,0.00154207,0.0022275469,0.0021606754,0.0016188456,0.0004671521],"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":[0.00037059604,0.00020350046,0.0010383765,0.00012855475,0.000086777516,0.0005477088,0.00018271714,0.8190132,0.0023236196,0.15547378,0.0031991326,0.017432082],"study_design_scores_gemma":[0.000029906583,0.00004541079,0.00012189238,0.000007913166,0.000010700551,0.00007784501,0.00004404842,0.9706642,0.00018879023,0.02808378,0.00071173784,0.000013831189],"about_ca_topic_score_codex":0.0035581142,"about_ca_topic_score_gemma":0.002550819,"teacher_disagreement_score":0.0035581142,"about_ca_system_score_codex":0.0021268346,"about_ca_system_score_gemma":0.0016241705,"threshold_uncertainty_score":0.015431285},"labels":[],"label_agreement":null},{"id":"W2607164663","doi":"10.1109/tmc.2017.2690636","title":"Improving VANET Simulation with Calibrated Vehicular Mobility Traces","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":62,"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":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Vehicular ad hoc network; Network topology; Mobility model; Computer network; Reliability (semiconductor); Granularity; Wireless ad hoc network; Key (lock); Network simulation; Cluster analysis; Distributed computing; Graph; Trustworthiness; Topology (electrical circuits); Theoretical computer science; Computer security; Artificial intelligence; Telecommunications; Wireless","score_opus":0.009904312255489899,"score_gpt":0.230969378774905,"score_spread":0.2210650665194151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607164663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35958815,0.0004222203,0.6184215,0.0009596593,0.00043578216,0.0005271245,0.0021930768,0.009373628,0.008078877],"genre_scores_gemma":[0.8449317,0.00027922884,0.15040193,0.00009338796,0.000028249537,0.00026271204,0.0029350747,0.00037034962,0.0006973282],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965281,0.0014595393,0.0003314197,0.00030119755,0.0011352852,0.00024435236],"domain_scores_gemma":[0.9891412,0.005143632,0.0005458095,0.0020834997,0.0028278162,0.0002579941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041843355,0.0012734872,0.0009374901,0.0016581906,0.0005540224,0.0016197745,0.0020359822,0.0009170714,0.0011793049],"category_scores_gemma":[0.028582064,0.00051317766,0.0005986,0.0020442673,0.0005616763,0.0022147505,0.0013008751,0.0013687831,0.00034995002],"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":[0.000093774666,0.000109896966,0.0036558125,0.00007278578,0.000048668262,0.000090293055,0.00010443496,0.9720357,0.0017250745,0.0032205454,0.0011216933,0.01772134],"study_design_scores_gemma":[0.000013930523,0.000041689564,0.00033665219,0.000012274231,0.000009408206,0.000016480451,0.00005891063,0.99480337,0.002432727,0.0013024299,0.00095883704,0.000013395549],"about_ca_topic_score_codex":0.016980914,"about_ca_topic_score_gemma":0.010818679,"teacher_disagreement_score":0.016980914,"about_ca_system_score_codex":0.001203776,"about_ca_system_score_gemma":0.0016213708,"threshold_uncertainty_score":0.033764124},"labels":[],"label_agreement":null},{"id":"W2744996721","doi":"10.1109/tmc.2017.2737422","title":"Performance Analysis of Network Coding with IEEE 802.11 DCF in Multi-Hop Wireless Networks","year":2017,"lang":"en","type":"preprint","venue":"IEEE Transactions on Mobile Computing","topic":"Cooperative Communication and Network Coding","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":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Retransmission; Computer network; Linear network coding; Network packet; Wireless network; Distributed coordination function; IEEE 802.11; Queueing theory; Unicast; Wireless; Distributed computing; Telecommunications","score_opus":0.04734255140235394,"score_gpt":0.3000373913583815,"score_spread":0.25269483995602754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2744996721","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38866672,0.0044014202,0.58505255,0.0008973183,0.00016552937,0.00016623971,0.00018444736,0.00044253413,0.020023229],"genre_scores_gemma":[0.9843286,0.0006763723,0.013967573,0.000043325592,0.00002825958,0.000045854562,0.000052603154,0.000030063924,0.0008273705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817204,0.00054345356,0.000048135033,0.00014701099,0.0007413692,0.0003480055],"domain_scores_gemma":[0.9907348,0.006365585,0.0005677427,0.00046948856,0.001735284,0.00012705121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029483347,0.00081664784,0.0005146296,0.0015841993,0.0007157991,0.0010266775,0.00071475276,0.00083328586,0.00075342786],"category_scores_gemma":[0.013419994,0.00026308227,0.00037356373,0.0012674188,0.001203905,0.0014195626,0.000798596,0.00080717314,0.00011308417],"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":[0.00006504877,0.000044298366,0.00124987,0.00005486439,0.000016292946,0.00007202338,0.000064061656,0.9522632,0.0025661606,0.03000948,0.000477523,0.013117227],"study_design_scores_gemma":[0.0000016308899,0.000017526898,0.00021812775,0.000004926392,0.000003414232,0.000016182837,0.000010163176,0.9971192,0.0004988346,0.0020025722,0.00010259619,0.0000048302736],"about_ca_topic_score_codex":0.013812566,"about_ca_topic_score_gemma":0.0059936726,"teacher_disagreement_score":0.013812566,"about_ca_system_score_codex":0.0036490855,"about_ca_system_score_gemma":0.0017116341,"threshold_uncertainty_score":0.02746433},"labels":[],"label_agreement":null},{"id":"W2747746134","doi":"10.1109/tmc.2017.2741481","title":"An Incentive Mechanism Integrating Joint Power, Channel and Link Management for Social-Aware D2D Content Sharing and Proactive Caching","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":99,"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 Manitoba","funders":"","keywords":"Computer science; Computer network; Base station; Cache; Telecommunications link; Power control; Wireless; Scheduling (production processes); Distributed computing; Power (physics); Telecommunications; Mathematical optimization","score_opus":0.052788461821680135,"score_gpt":0.2837829283760228,"score_spread":0.2309944665543427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2747746134","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01721819,0.000101577134,0.97946435,0.0002605682,0.00005408764,0.00011611807,0.000023054845,0.00014528567,0.0026168185],"genre_scores_gemma":[0.8562742,0.00015386805,0.14078124,0.00011205728,0.000086503336,0.00020099204,0.000027619797,0.000034390836,0.0023292918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987037,0.00059433555,0.000056196255,0.00015365187,0.0003225543,0.00016955241],"domain_scores_gemma":[0.9983222,0.0008411008,0.00018645561,0.00022794725,0.0002452842,0.00017701341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001979455,0.00065685844,0.0009321571,0.0006920251,0.00072467036,0.0011744037,0.002322967,0.0014729517,0.0015761092],"category_scores_gemma":[0.0033228006,0.00028221027,0.00059522974,0.0008372044,0.0009830545,0.002079,0.0016572713,0.001037899,0.00022862328],"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":[0.0003290445,0.0008341023,0.0015589071,0.00024824523,0.00012468491,0.00055230013,0.00028803034,0.40868485,0.025603455,0.40442285,0.0040597003,0.1532939],"study_design_scores_gemma":[0.00004253849,0.0001187036,0.00015829282,0.000008333847,0.000021630132,0.0001553818,0.000029325916,0.96982455,0.0017407994,0.02570309,0.0021721004,0.000025240217],"about_ca_topic_score_codex":0.00084419665,"about_ca_topic_score_gemma":0.0009808677,"teacher_disagreement_score":0.002322967,"about_ca_system_score_codex":0.0011860525,"about_ca_system_score_gemma":0.0016598168,"threshold_uncertainty_score":0.010468483},"labels":[],"label_agreement":null},{"id":"W2767873707","doi":"10.1109/tmc.2017.2771353","title":"QoS-Aware Energy and Jitter-Efficient Downlink Predictive Scheduler for Heterogeneous Traffic LTE Networks","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Wireless Network Optimization","field":"Engineering","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":"Western University","funders":"","keywords":"Computer science; Jitter; Quality of service; Scheduling (production processes); Telecommunications link; Network packet; Computer network; Efficient energy use; Optimization problem; User equipment; Radio access network; Distributed computing; Real-time computing; Mathematical optimization; Base station; Algorithm","score_opus":0.008430268066044588,"score_gpt":0.22757831187119348,"score_spread":0.2191480438051489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767873707","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18456703,0.0008227623,0.8097112,0.00018603358,0.00009154688,0.00005006414,0.00006863204,0.00045977623,0.00404299],"genre_scores_gemma":[0.97865367,0.00013098466,0.020648856,0.000028485221,0.000017547998,0.000017157232,0.000031830154,0.000014942271,0.00045663887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971503,0.000056225752,0.000012401416,0.00003433211,0.0000971809,0.00008485893],"domain_scores_gemma":[0.99958307,0.00016118385,0.000079413054,0.00003559978,0.00009568599,0.000045077253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062915444,0.00043849827,0.0005358605,0.00031069492,0.0004493936,0.00066402316,0.0009826593,0.00028524612,0.00056587515],"category_scores_gemma":[0.0012551144,0.00019641398,0.00019838971,0.00044599682,0.0003043091,0.00042613698,0.00044403717,0.000463943,0.000104301216],"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":[0.00015295063,0.00006678707,0.0007540443,0.000022099066,0.00001562398,0.000046527348,0.000031408927,0.96124804,0.0062895366,0.003554266,0.0007020186,0.027116755],"study_design_scores_gemma":[0.0000030791325,0.000010916879,0.000061444094,7.142649e-7,0.000002357134,0.0000036672573,0.000004095333,0.9991917,0.00043800418,0.00022022575,0.00006224965,0.0000015369044],"about_ca_topic_score_codex":0.008879576,"about_ca_topic_score_gemma":0.011714482,"teacher_disagreement_score":0.008879576,"about_ca_system_score_codex":0.0011394667,"about_ca_system_score_gemma":0.0016557099,"threshold_uncertainty_score":0.01765579},"labels":[],"label_agreement":null},{"id":"W2768526075","doi":"10.1109/tmc.2017.2775221","title":"Dynamic SON-Enabled Location Management in LTE Networks","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","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":"Western University","funders":"Royal Commission for Jubail and Yanbu","keywords":"Computer science; Overhead (engineering); Computer network; Pooling; Control reconfiguration; Heuristic; Wireless network; Distributed computing; User equipment; Quality of experience; Network packet; Wireless; Quality of service; Base station; Telecommunications; Embedded system; Artificial intelligence","score_opus":0.0070609574632530805,"score_gpt":0.23927205647682184,"score_spread":0.23221109901356876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768526075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061671264,0.0013410249,0.93315977,0.00017678761,0.000055000626,0.000028732635,0.000039803264,0.00022953632,0.0032980386],"genre_scores_gemma":[0.9523984,0.00055980484,0.045359336,0.00004881159,0.00004958381,0.000036917027,0.000048665177,0.000010897125,0.0014875073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980944,0.00007067176,0.000006919597,0.000035413454,0.00003494031,0.000042684896],"domain_scores_gemma":[0.9998803,0.000048872633,0.000027939543,0.000010170637,0.000020398904,0.000012370626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032640406,0.00043396148,0.0005020485,0.00026659505,0.00031391377,0.00045734734,0.0006812341,0.00043847086,0.00048167864],"category_scores_gemma":[0.0005065191,0.00024372265,0.00023797948,0.0003570304,0.00044339508,0.0006386522,0.000565846,0.0002933021,0.00011362429],"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":[0.00009615219,0.0000324002,0.0006433378,0.000057584264,0.000015194142,0.00011593976,0.00006359,0.92256653,0.0059029525,0.0055819904,0.000988598,0.06393568],"study_design_scores_gemma":[0.0000038027397,0.000034965575,0.00010188735,0.000002545575,0.000005718478,0.000022803104,0.000027207388,0.9977215,0.00077238417,0.00093132496,0.00037245237,0.0000034568745],"about_ca_topic_score_codex":0.0047460874,"about_ca_topic_score_gemma":0.006044095,"teacher_disagreement_score":0.0047460874,"about_ca_system_score_codex":0.00054116297,"about_ca_system_score_gemma":0.0004523288,"threshold_uncertainty_score":0.009436905},"labels":[],"label_agreement":null},{"id":"W2769860519","doi":"10.1109/tmc.2017.2777481","title":"A Truthful Online Mechanism for Location-Aware Tasks in Mobile Crowd Sensing","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":77,"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":"National Natural Science Foundation of China","keywords":"Computer science; Auction algorithm; Competitive analysis; Combinatorial auction; Focus (optics); Mechanism design; Block (permutation group theory); Remuneration; Common value auction; Vickrey–Clarke–Groves auction; Online algorithm; Payment; Incentive compatibility; Mathematical optimization; Distributed computing; Incentive; Auction theory; Revenue equivalence; Algorithm; World Wide Web; Upper and lower bounds; Microeconomics","score_opus":0.02346257707735367,"score_gpt":0.28885113307263005,"score_spread":0.2653885559952764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769860519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054307487,0.0002080092,0.93965137,0.0005220456,0.00009877181,0.0002584816,0.00012631806,0.00055659085,0.0042709275],"genre_scores_gemma":[0.8871662,0.000109088214,0.11029284,0.000115934694,0.000050330087,0.00022747194,0.00005892275,0.000054164284,0.0019250148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966917,0.0014763258,0.00018918031,0.0005507698,0.0006352787,0.0004567708],"domain_scores_gemma":[0.9911283,0.0054060556,0.0010170041,0.0014129893,0.0005848462,0.0004507599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055301148,0.0009829173,0.0014905453,0.0006574302,0.0011668529,0.002655069,0.003354888,0.0022712476,0.0029792462],"category_scores_gemma":[0.016083613,0.0007225451,0.0008622795,0.0008809466,0.001812671,0.004260255,0.002427942,0.0016902318,0.00046401416],"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":[0.0007837443,0.00042364534,0.0013004638,0.00028963332,0.00010054444,0.0005171075,0.0005464727,0.75484365,0.011723071,0.16608325,0.0036700163,0.059718397],"study_design_scores_gemma":[0.00007411057,0.00009651397,0.00013151023,0.000011113071,0.000013543418,0.00009246609,0.00004692487,0.94340307,0.0010651073,0.05419989,0.0008407259,0.000025051288],"about_ca_topic_score_codex":0.0015901324,"about_ca_topic_score_gemma":0.0013419521,"teacher_disagreement_score":0.0055301148,"about_ca_system_score_codex":0.0012298006,"about_ca_system_score_gemma":0.002524944,"threshold_uncertainty_score":0.02924639},"labels":[],"label_agreement":null},{"id":"W2783749042","doi":"10.1109/tmc.2018.2793198","title":"Transmission Management of Delay-Sensitive Medical Packets in Beyond Wireless Body Area Networks: A Queueing Game Approach","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":45,"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 Manitoba","funders":"","keywords":"Computer science; Computer network; Queueing theory; Network packet; Body area network; Default gateway; Quality of service; Transmission (telecommunications); Wireless; Gateway (web page); Base station; Layered queueing network; Distributed computing; Telecommunications; Wireless sensor network","score_opus":0.007823626103067239,"score_gpt":0.2240175858149654,"score_spread":0.21619395971189814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783749042","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056448434,0.00034529145,0.93663293,0.00052957697,0.00010457955,0.00012947849,0.000067438166,0.00006568939,0.005676654],"genre_scores_gemma":[0.9664016,0.0004420887,0.029187404,0.00015720777,0.00007814715,0.000105503044,0.000030071635,0.00002064817,0.0035774233],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875164,0.00047736955,0.000050927385,0.00023674616,0.000226072,0.0002571998],"domain_scores_gemma":[0.9982822,0.0010705877,0.00022297095,0.000051705796,0.00023095844,0.00014147587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017209298,0.0010811939,0.0010848292,0.0005349034,0.00080135744,0.0015242035,0.0019442857,0.001376718,0.0016274978],"category_scores_gemma":[0.0031433841,0.00047934358,0.0006112328,0.0005069748,0.0012732678,0.0019521744,0.0012576567,0.0012534454,0.00016441905],"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":[0.00013525628,0.00014986664,0.0010945009,0.00011897969,0.00007221234,0.0004899962,0.00032513452,0.8604592,0.007289675,0.11858621,0.0010229383,0.01025605],"study_design_scores_gemma":[0.000008057519,0.000044071698,0.00010877351,0.000005488409,0.000013810496,0.00003684587,0.000038676426,0.9902602,0.00027201898,0.0088931145,0.0003096551,0.000009326024],"about_ca_topic_score_codex":0.006864158,"about_ca_topic_score_gemma":0.004819904,"teacher_disagreement_score":0.006864158,"about_ca_system_score_codex":0.0021850793,"about_ca_system_score_gemma":0.0020696495,"threshold_uncertainty_score":0.015853941},"labels":[],"label_agreement":null},{"id":"W2789414605","doi":"10.1109/tmc.2018.2812722","title":"Optimal Power Allocation and Scheduling for Non-Orthogonal Multiple Access Relay-Assisted Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":163,"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; National Natural Science Foundation of China","keywords":"Relay; Computer science; Time division multiple access; Computer network; Telecommunications link; Noma; Throughput; Scheduling (production processes); Cellular network; Transmitter power output; Relay channel; Base station; Spectral efficiency; Wireless; Channel (broadcasting); Power (physics); Telecommunications; Mathematical optimization; Transmitter; Mathematics","score_opus":0.018627063096252828,"score_gpt":0.27869569213543116,"score_spread":0.2600686290391783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789414605","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03699297,0.0010083861,0.9582324,0.00029857372,0.00008305535,0.000068458256,0.0000741605,0.000117739866,0.0031243276],"genre_scores_gemma":[0.8797178,0.0011458667,0.11566903,0.00009731032,0.000093413895,0.0001540787,0.00009008733,0.000059213155,0.0029731742],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991248,0.0004620831,0.000028147706,0.00011027306,0.00014347686,0.000131206],"domain_scores_gemma":[0.9987728,0.00083766686,0.00016579946,0.00004756068,0.00012201071,0.000054058954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001460404,0.0010491217,0.0010966669,0.00051994924,0.00053214305,0.001249068,0.0009065493,0.00075313536,0.0015053994],"category_scores_gemma":[0.0036417972,0.0006898913,0.00041374305,0.0008454956,0.00096170517,0.0010501316,0.0007851147,0.0006692063,0.00029730948],"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":[0.00003721835,0.000025709,0.00015876975,0.00003840543,0.000015957816,0.000049537914,0.000031042488,0.9799412,0.00062085927,0.009778111,0.00040461984,0.008898594],"study_design_scores_gemma":[0.0000070902283,0.00001254502,0.000035433193,0.000001923722,0.0000023647451,0.000005726546,0.000009095069,0.9959859,0.000102787126,0.0037061817,0.00012837588,0.0000024633796],"about_ca_topic_score_codex":0.005417532,"about_ca_topic_score_gemma":0.005755358,"teacher_disagreement_score":0.005417532,"about_ca_system_score_codex":0.0016753497,"about_ca_system_score_gemma":0.0017064583,"threshold_uncertainty_score":0.012155592},"labels":[],"label_agreement":null},{"id":"W2792845577","doi":"10.1109/tmc.2018.2810228","title":"Optimal Resource Allocations for Mobile Data Offloading via Dual-Connectivity","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Computer network; Small cell; Base station; Exploit; Telecommunications link; Cellular network; Optimization problem; Scheduling (production processes); Bandwidth allocation; Bandwidth (computing); Handover; Distributed computing; Mathematical optimization; Algorithm","score_opus":0.022023838797690504,"score_gpt":0.2738646612198756,"score_spread":0.25184082242218514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792845577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040306304,0.00036814404,0.9532118,0.0003088388,0.000047447815,0.00005770867,0.000055994806,0.00014572353,0.0054980125],"genre_scores_gemma":[0.84347004,0.00034966855,0.15241855,0.00016044668,0.00004766482,0.00017239865,0.00012393393,0.00008128204,0.0031760975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961346,0.00014653747,0.000009765145,0.00006046325,0.00006979411,0.00009994836],"domain_scores_gemma":[0.99955636,0.00028169496,0.000045082623,0.000025603978,0.000051799743,0.000039403447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062403624,0.0009820005,0.00091687916,0.0005176202,0.00038617806,0.0010214214,0.00073661486,0.0008768506,0.0021410985],"category_scores_gemma":[0.0015690041,0.00048722068,0.0005818673,0.00058216543,0.0007468381,0.0009097635,0.0012289042,0.0009395284,0.00027355365],"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":[0.00009190786,0.000066457134,0.00037639227,0.00004479265,0.00002051297,0.000072237126,0.00003845199,0.9642432,0.0018482425,0.013858231,0.0011081479,0.018231377],"study_design_scores_gemma":[0.0000056409244,0.000009485228,0.000038373983,0.0000021299256,0.0000019439708,0.0000080358805,0.0000067047586,0.9965138,0.00014487216,0.0031136894,0.00015315873,0.000002261895],"about_ca_topic_score_codex":0.0032070074,"about_ca_topic_score_gemma":0.0030179366,"teacher_disagreement_score":0.0032070074,"about_ca_system_score_codex":0.0008721153,"about_ca_system_score_gemma":0.00091270235,"threshold_uncertainty_score":0.0071626306},"labels":[],"label_agreement":null},{"id":"W2794838781","doi":"10.1109/tmc.2019.2908403","title":"Joint Communication, Computation, Caching, and Control in Big Data Multi-Access Edge Computing","year":2019,"lang":"en","type":"preprint","venue":"IEEE Transactions on Mobile Computing","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":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"National Research Foundation of Korea","keywords":"Computer science; Cloud computing; Server; Edge computing; Distributed computing; Overhead (engineering); Upper and lower bounds; Optimization problem; Computation offloading; Mobile edge computing; Computation; Bandwidth (computing); Computer network; Enhanced Data Rates for GSM Evolution; Algorithm; Operating system; Mathematics","score_opus":0.10252900560045566,"score_gpt":0.3338326797343236,"score_spread":0.23130367413386796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794838781","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0380343,0.001577123,0.9569422,0.00060159346,0.000077879595,0.00004392081,0.00003225089,0.00010863094,0.002582033],"genre_scores_gemma":[0.9371314,0.0008584052,0.059878517,0.00012609204,0.00009506714,0.00006308767,0.000040207575,0.000037963102,0.001769253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893147,0.00040446012,0.0000398601,0.00017993491,0.00025179365,0.00019251654],"domain_scores_gemma":[0.9991709,0.00054356223,0.00007188388,0.00006064231,0.00008843336,0.00006454491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015203839,0.00071283686,0.0010238264,0.000323724,0.00057709066,0.001706343,0.0010607666,0.0010016403,0.00064576865],"category_scores_gemma":[0.0018824885,0.00041564612,0.00044389477,0.0009451665,0.0012079144,0.00185796,0.0013347288,0.0011760079,0.00007591967],"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":[0.00020013828,0.00009144466,0.0006156644,0.00013430153,0.00005381109,0.00021423493,0.00009305972,0.91743064,0.0030826584,0.046690855,0.0011941221,0.030199083],"study_design_scores_gemma":[0.000005004849,0.00001715776,0.00008021478,0.0000025399017,0.000005524099,0.000012347903,0.000014952867,0.9924353,0.00051283505,0.006643941,0.00026622257,0.0000039580777],"about_ca_topic_score_codex":0.0061585666,"about_ca_topic_score_gemma":0.0052728886,"teacher_disagreement_score":0.0061585666,"about_ca_system_score_codex":0.0012075739,"about_ca_system_score_gemma":0.0014229816,"threshold_uncertainty_score":0.012245417},"labels":[],"label_agreement":null},{"id":"W2799292918","doi":"10.1109/tmc.2018.2831679","title":"Multipath Cooperative Routing with Efficient Acknowledgement for LEO Satellite Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":165,"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 Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer network; Computer science; Multipath routing; Dynamic Source Routing; Acknowledgement; Routing protocol; Distributed computing; Network packet; Linear network coding; Static routing; Retransmission; Multipath propagation; Channel (broadcasting)","score_opus":0.026441189963418432,"score_gpt":0.28621969294979666,"score_spread":0.2597785029863782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799292918","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.073427215,0.0016241526,0.9204314,0.00039089387,0.00006244244,0.00007104027,0.000034393084,0.0005932391,0.003365287],"genre_scores_gemma":[0.92127705,0.0008532453,0.07594434,0.00006795713,0.00004524323,0.00009433438,0.000044831333,0.000027861399,0.0016450161],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999539,0.00013198859,0.000020094258,0.000051329127,0.00019597773,0.00006149224],"domain_scores_gemma":[0.99880254,0.00052090234,0.00021615505,0.00016651508,0.0002549123,0.000038990725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008941154,0.00042055137,0.00029829302,0.0006172115,0.0005398052,0.00048761905,0.00074224576,0.00038985984,0.00051281875],"category_scores_gemma":[0.002445097,0.00020087254,0.00024886636,0.00070146145,0.00055440224,0.0010709893,0.0007856367,0.0005014273,0.00011084897],"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":[0.0001746729,0.00006810074,0.0015187016,0.00022803227,0.00005589588,0.0003383301,0.00036740696,0.73330545,0.048881866,0.057581227,0.0026685135,0.15481184],"study_design_scores_gemma":[0.000009451618,0.00010565336,0.00026247773,0.000011156287,0.000022785014,0.00011944097,0.000048787526,0.9812438,0.0059296694,0.009453299,0.0027704884,0.000023049772],"about_ca_topic_score_codex":0.003175602,"about_ca_topic_score_gemma":0.004346381,"teacher_disagreement_score":0.003175602,"about_ca_system_score_codex":0.00089035823,"about_ca_system_score_gemma":0.0011757761,"threshold_uncertainty_score":0.006460011},"labels":[],"label_agreement":null},{"id":"W2805491853","doi":"10.1109/tmc.2018.2842733","title":"Backup Battery Analysis and Allocation against Power Outage for Cellular Base Stations","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Battery Technologies Research","field":"Engineering","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":"Simon Fraser University","funders":"","keywords":"Backup; Battery (electricity); Base station; Computer science; Reliability engineering; Power (physics); Reliability (semiconductor); Service (business); IT service continuity; Computer network; Engineering; Operating system","score_opus":0.013907107570950083,"score_gpt":0.26796945171290276,"score_spread":0.25406234414195267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805491853","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9261239,0.0014640241,0.06119536,0.000784098,0.000084394276,0.00006839569,0.005736775,0.002320015,0.0022230912],"genre_scores_gemma":[0.98726815,0.00020725398,0.007557734,0.00006709079,0.000027371394,0.000022640454,0.004152817,0.00003619004,0.0006606668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997638,0.000041149302,0.000017977878,0.0000740263,0.00004936593,0.000053709307],"domain_scores_gemma":[0.9993249,0.00023341089,0.00007658012,0.00010764187,0.00019064582,0.00006685294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052016857,0.00068356853,0.00063444104,0.0009626444,0.0003042682,0.00061185064,0.0009997431,0.0006014245,0.0006722987],"category_scores_gemma":[0.0028199246,0.00019639356,0.000363329,0.00091186754,0.00026595476,0.0009384835,0.0005887667,0.000618609,0.00028727175],"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":[0.00074144354,0.00024017146,0.098210886,0.00023242591,0.00014186262,0.00039398318,0.00017315903,0.71660304,0.005660989,0.00087542296,0.013287447,0.16343915],"study_design_scores_gemma":[0.000015368216,0.000056542685,0.009685531,0.000009142295,0.000021609176,0.0000626987,0.00007583447,0.98668,0.0015128093,0.0009608856,0.0009113681,0.000008248669],"about_ca_topic_score_codex":0.016491214,"about_ca_topic_score_gemma":0.020720694,"teacher_disagreement_score":0.016491214,"about_ca_system_score_codex":0.0008293501,"about_ca_system_score_gemma":0.0005752561,"threshold_uncertainty_score":0.032790422},"labels":[],"label_agreement":null},{"id":"W2808881604","doi":"10.1109/tmc.2018.2848644","title":"Flexible and Efficient Authenticated Key Agreement Scheme for BANs Based on Physiological Features","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":59,"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":"Innovation-Driven Project of Central South University; Postdoctoral Science Foundation of Central South University; China Scholarship Council","keywords":"Computer science; Authentication (law); Computer security; Computer network; Key (lock); Scheme (mathematics); Session key; Access control; Session (web analytics); Cryptography; Secure communication; Encryption; World Wide Web","score_opus":0.015347708678058163,"score_gpt":0.2471728123893377,"score_spread":0.23182510371127954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808881604","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028914971,0.0003889788,0.96634054,0.0003427925,0.00019146792,0.00030742935,0.00011963772,0.00044560694,0.0029485086],"genre_scores_gemma":[0.86418957,0.0003759557,0.12976009,0.00020345605,0.00013348497,0.0005752585,0.00033419684,0.00004476989,0.0043831402],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99545836,0.0015122639,0.0005224201,0.00078518543,0.0011559409,0.00056592654],"domain_scores_gemma":[0.9951728,0.0011072627,0.0007146489,0.0017793778,0.000913048,0.00031293076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003475645,0.0008511336,0.0011658303,0.0008064029,0.0018023778,0.0016443375,0.002225367,0.0011521637,0.0021930835],"category_scores_gemma":[0.005157697,0.00049104996,0.00095708057,0.0012570327,0.0016953625,0.0050013047,0.005594125,0.0024529018,0.0008603271],"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":[0.0030192616,0.0004987543,0.002438164,0.0008485024,0.00036995043,0.0015142775,0.0031583344,0.1171231,0.15758415,0.49581268,0.006849167,0.21078369],"study_design_scores_gemma":[0.0005259583,0.0010606032,0.0007983232,0.00011681426,0.00022632579,0.0015106266,0.000564739,0.74185145,0.05780352,0.16603507,0.029159945,0.00034655674],"about_ca_topic_score_codex":0.00047924538,"about_ca_topic_score_gemma":0.00040127613,"teacher_disagreement_score":0.003475645,"about_ca_system_score_codex":0.000850695,"about_ca_system_score_gemma":0.0020566199,"threshold_uncertainty_score":0.018381238},"labels":[],"label_agreement":null},{"id":"W2809450641","doi":"10.1109/tmc.2018.2847350","title":"Reward or Penalty: Aligning Incentives of Stakeholders in Crowdsourcing","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Crowdsensing and Crowdsourcing","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":"University of Waterloo","funders":"","keywords":"Crowdsourcing; Incentive; Computer science; Payment; Crowdsourcing software development; Quality (philosophy); Key (lock); Perspective (graphical); Data science; Knowledge management; Computer security; World Wide Web; Artificial intelligence; Microeconomics; Economics","score_opus":0.045404160681853045,"score_gpt":0.28470959608749746,"score_spread":0.23930543540564442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809450641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0881876,0.00042162792,0.90229666,0.0013592637,0.00015607006,0.000533742,0.00008771442,0.00049241993,0.006464888],"genre_scores_gemma":[0.9095229,0.00012699148,0.08748855,0.00019046324,0.000053125805,0.00030386055,0.000031466636,0.00005498571,0.0022275087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99280614,0.003564684,0.0003800526,0.0011151687,0.0013397296,0.00079428597],"domain_scores_gemma":[0.98677325,0.0069311727,0.002222717,0.001172916,0.0016346385,0.0012652925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008740157,0.0017022686,0.0014937139,0.0012758578,0.0015216084,0.0025478045,0.0028227633,0.002168899,0.0026066096],"category_scores_gemma":[0.03254433,0.0006104486,0.0006851504,0.00118735,0.0020196931,0.0045000585,0.0038099724,0.001998618,0.0004030187],"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":[0.0012270785,0.0006986824,0.009906843,0.0006131661,0.00016859663,0.0005991466,0.0012219916,0.6206834,0.020344902,0.16868536,0.004212969,0.17163794],"study_design_scores_gemma":[0.0001292034,0.00033567252,0.001594785,0.000066605266,0.00007085753,0.00014409942,0.00029128822,0.9240439,0.0034827164,0.066615604,0.0031399603,0.000085319436],"about_ca_topic_score_codex":0.0019699757,"about_ca_topic_score_gemma":0.0017604715,"teacher_disagreement_score":0.008740157,"about_ca_system_score_codex":0.002563064,"about_ca_system_score_gemma":0.0029600395,"threshold_uncertainty_score":0.046222925},"labels":[],"label_agreement":null},{"id":"W2884365744","doi":"10.1109/tmc.2018.2857826","title":"How Expensive is Consistency? Performance Analysis of Consistent Rate Provisioning to Mobile Users in Cellular Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":28,"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; Provisioning; Consistency (knowledge bases); Class (philosophy); Independence (probability theory); Cellular network; Computer network; Distributed computing; Artificial intelligence","score_opus":0.010209619560298085,"score_gpt":0.22558963103681806,"score_spread":0.21538001147651997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884365744","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6580994,0.00412127,0.32145667,0.0031049375,0.00020791292,0.00021161448,0.000403507,0.00044048193,0.011954247],"genre_scores_gemma":[0.99041736,0.00064513716,0.008125282,0.000114370036,0.00007936102,0.000047281777,0.00006296232,0.000050632087,0.00045768198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.992161,0.0037506174,0.00024534445,0.00069907657,0.0015003722,0.0016436693],"domain_scores_gemma":[0.93754786,0.049259685,0.0050916984,0.0035651503,0.0035493567,0.0009863443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009078428,0.001600571,0.0020631824,0.0012492012,0.0013162544,0.002989784,0.0025678081,0.0019884242,0.0024649738],"category_scores_gemma":[0.055639192,0.000787911,0.0007659493,0.002128502,0.0025322083,0.00465694,0.0024693944,0.0022374836,0.00028808028],"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":[0.00090007565,0.00016302687,0.0041743494,0.00018116247,0.000087110086,0.00030023803,0.00016129574,0.94201684,0.008606153,0.024251388,0.0012461598,0.017912166],"study_design_scores_gemma":[0.000026689748,0.00033289916,0.0015937025,0.00002788028,0.000064374115,0.0002119177,0.00017091157,0.98933035,0.0020375943,0.005902,0.00026341935,0.00003827887],"about_ca_topic_score_codex":0.005014734,"about_ca_topic_score_gemma":0.0025723053,"teacher_disagreement_score":0.009078428,"about_ca_system_score_codex":0.0036714973,"about_ca_system_score_gemma":0.0021485863,"threshold_uncertainty_score":0.0480119},"labels":[],"label_agreement":null},{"id":"W2892472137","doi":"10.1109/tmc.2018.2871686","title":"Collision Avoidance Energy Efficient Multi-Channel MAC Protocol for UnderWater Acoustic Sensor Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":63,"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":"Handshaking; Computer science; Channel (broadcasting); Computer network; Throughput; Control channel; Network packet; Underwater acoustic communication; Collision; Multiple Access with Collision Avoidance for Wireless; Data transmission; Propagation delay; Underwater; Wireless; Telecommunications; Routing protocol; Telecommunications link; Computer security","score_opus":0.027231140662991815,"score_gpt":0.27519839053971307,"score_spread":0.24796724987672125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892472137","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038754452,0.004332303,0.9511859,0.00027803538,0.0002913019,0.0003535813,0.00005149596,0.00050361216,0.0042493534],"genre_scores_gemma":[0.7765219,0.0018523987,0.21576881,0.00034436755,0.00009323559,0.00082133594,0.00019956476,0.000050745875,0.004347673],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899095,0.00028056372,0.00007092286,0.00008528701,0.0004937288,0.00007852079],"domain_scores_gemma":[0.998887,0.00036994592,0.00016274802,0.00011980836,0.00042671367,0.0000337237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009772973,0.00041560823,0.00047228055,0.00054673746,0.00061001384,0.00065848435,0.0010781824,0.00046862505,0.0007616843],"category_scores_gemma":[0.0021625843,0.0001615646,0.00026626658,0.00048979337,0.00037367822,0.0008083497,0.0008695377,0.0006786288,0.00014066978],"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":[0.00061995455,0.00025782685,0.0026144572,0.0012922288,0.00028798234,0.00097602746,0.0006847814,0.27263042,0.25097108,0.07264071,0.009134873,0.38788965],"study_design_scores_gemma":[0.00006658949,0.00080127816,0.0012282544,0.00009651474,0.00011148366,0.00067121646,0.00011943763,0.90219235,0.059817776,0.008595616,0.026210016,0.000089416106],"about_ca_topic_score_codex":0.0006956722,"about_ca_topic_score_gemma":0.00077618426,"teacher_disagreement_score":0.0010781824,"about_ca_system_score_codex":0.00045783565,"about_ca_system_score_gemma":0.0008429909,"threshold_uncertainty_score":0.0051684976},"labels":[],"label_agreement":null},{"id":"W2896913059","doi":"10.1109/tmc.2018.2865340","title":"On Mutual Interference Analysis in Hybrid Interweave-Underlay Cognitive Communications","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":8,"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":"Underlay; Computer science; Interference (communication); Computer network; Cognitive radio; Telecommunications; Wireless; Signal-to-noise ratio (imaging); Channel (broadcasting)","score_opus":0.022253314654316386,"score_gpt":0.2880059926411514,"score_spread":0.265752677986835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896913059","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043947224,0.0009462605,0.9486696,0.00016449291,0.000033264863,0.000027192591,0.00004005909,0.00010709986,0.006064925],"genre_scores_gemma":[0.9686558,0.000891263,0.027937578,0.00014034117,0.00007706669,0.000063079126,0.000035094996,0.000052933276,0.0021468555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981036,0.0005899178,0.000059491867,0.00020879018,0.00072109693,0.0003171458],"domain_scores_gemma":[0.9957158,0.0029963397,0.0005093629,0.00024733198,0.0004413995,0.00008973554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020752372,0.0014748338,0.0008735745,0.0011302092,0.0006343636,0.0015267994,0.0014933435,0.0010761602,0.0013956517],"category_scores_gemma":[0.005949546,0.00047041706,0.00090221834,0.001079882,0.0018694852,0.0025099802,0.0018285912,0.0011992377,0.00025428826],"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":[0.000063910295,0.000039771876,0.000661835,0.000087394175,0.00008088477,0.00030968178,0.00016627686,0.9285398,0.0036104908,0.058657,0.0002600069,0.0075228787],"study_design_scores_gemma":[0.0000035109351,0.000025937128,0.00022647451,0.000008597146,0.000015779386,0.000080359554,0.000028802338,0.9849786,0.00060003565,0.013790781,0.00022972503,0.000011417676],"about_ca_topic_score_codex":0.0036732177,"about_ca_topic_score_gemma":0.0023557711,"teacher_disagreement_score":0.0036732177,"about_ca_system_score_codex":0.0015295,"about_ca_system_score_gemma":0.000994126,"threshold_uncertainty_score":0.011097372},"labels":[],"label_agreement":null},{"id":"W2904313714","doi":"10.1109/tmc.2018.2883451","title":"Nocturnal Epileptic Seizures Detection Using Inertial and Muscular Sensors","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","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 Waterloo","funders":"","keywords":"Accelerometer; Computer science; Gyroscope; Sliding window protocol; Inertial measurement unit; False alarm; ALARM; Constant false alarm rate; Pattern recognition (psychology); Artificial intelligence; Real-time computing; Computer vision; Control theory (sociology); Window (computing); Engineering","score_opus":0.026437092194500585,"score_gpt":0.2837980301438689,"score_spread":0.2573609379493683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904313714","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31035542,0.0035785816,0.67791206,0.00017418631,0.00024877262,0.00009870937,0.0007081676,0.0022232148,0.004700925],"genre_scores_gemma":[0.8737617,0.0011182483,0.12183796,0.00010997598,0.00021727038,0.00008671884,0.0007654225,0.000049727492,0.0020529425],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999747,0.000035624915,0.000024350722,0.00006948462,0.000097983786,0.000025640215],"domain_scores_gemma":[0.9997899,0.000055009074,0.000053373296,0.000022559063,0.00006277101,0.000016393671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019141416,0.0007765508,0.00051255536,0.001469429,0.00014234796,0.00031776165,0.00027527544,0.00037763955,0.0004336819],"category_scores_gemma":[0.00069383945,0.0001703082,0.00038932037,0.00072049943,0.000086755565,0.0004664717,0.0003921909,0.00020438158,0.00025129435],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051743886,0.00019949998,0.023328383,0.00032224174,0.00017158,0.00074450107,0.00011499765,0.014152423,0.16810882,0.0011575572,0.0026288242,0.7885538],"study_design_scores_gemma":[0.00008965345,0.00086122367,0.14000669,0.000097061435,0.0002780822,0.0036655222,0.00022785662,0.75951684,0.08039669,0.0033705616,0.011369268,0.00012057668],"about_ca_topic_score_codex":0.00076799834,"about_ca_topic_score_gemma":0.0014481202,"teacher_disagreement_score":0.001469429,"about_ca_system_score_codex":0.00009944143,"about_ca_system_score_gemma":0.00018285868,"threshold_uncertainty_score":0.001527071},"labels":[],"label_agreement":null},{"id":"W2909444676","doi":"10.1109/tmc.2019.2893917","title":"Cooperative Caching for Multiple Bitrate Videos in Small Cell Edges","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":52,"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":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Server; Quality of experience; Function (biology); Mobile device; Enhanced Data Rates for GSM Evolution; Constant bitrate; Mobile edge computing; Software deployment; Computer network; Variable bitrate; Bit rate; Quality of service; Artificial intelligence","score_opus":0.019722116296718113,"score_gpt":0.23126746341815335,"score_spread":0.21154534712143525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909444676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2729119,0.00093304215,0.7206033,0.0005277873,0.000053781707,0.00018639506,0.00020477978,0.0003885865,0.004190387],"genre_scores_gemma":[0.94889784,0.00029381647,0.0494221,0.00006791717,0.00002056045,0.000055838806,0.00007575755,0.000018610228,0.0011476293],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944514,0.00016405697,0.000023661507,0.000102751306,0.000076485536,0.0001878445],"domain_scores_gemma":[0.9979913,0.001345837,0.00017675877,0.00013560231,0.00022039794,0.00013006528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008791969,0.000706782,0.0010872968,0.00048659524,0.00075792,0.0010955011,0.0014726297,0.0011151709,0.0011556185],"category_scores_gemma":[0.003300842,0.00029130894,0.00054203096,0.0010205465,0.0005227303,0.0012367583,0.0010294366,0.0005891563,0.0001844348],"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":[0.00072289933,0.00022753343,0.002706036,0.00022592857,0.000071675946,0.0007909074,0.00025499452,0.9041959,0.015128826,0.019367157,0.0032494566,0.053058725],"study_design_scores_gemma":[0.00001658374,0.000052295618,0.00019805264,0.00000473706,0.000010917262,0.00006499884,0.000053445736,0.99508345,0.0011455255,0.0031462654,0.0002183317,0.000005496705],"about_ca_topic_score_codex":0.0075263963,"about_ca_topic_score_gemma":0.009684868,"teacher_disagreement_score":0.0075263963,"about_ca_system_score_codex":0.0011581503,"about_ca_system_score_gemma":0.00082206784,"threshold_uncertainty_score":0.014965177},"labels":[],"label_agreement":null},{"id":"W2916491361","doi":"10.1109/tmc.2019.2901474","title":"Near-Optimal and Truthful Online Auction for Computation Offloading in Green Edge-Computing Systems","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":134,"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":"Innovation-Driven Project of Central South University; International Science and Technology Cooperation Programme; Higher Education Discipline Innovation Project; Kuwait Foundation for the Advancement of Sciences; National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Mobile edge computing; Computation offloading; Lyapunov optimization; Computation; Edge computing; Enhanced Data Rates for GSM Evolution; Wireless; Task (project management); Distributed computing; Mobile device; Channel (broadcasting); Computer network; Algorithm; Artificial intelligence; Telecommunications; Operating system","score_opus":0.01632178714720007,"score_gpt":0.2634931279648677,"score_spread":0.24717134081766762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2916491361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059501406,0.00047362005,0.93484676,0.00031576282,0.000086632,0.00012990253,0.00006830261,0.00017208814,0.0044055628],"genre_scores_gemma":[0.9682962,0.00016822618,0.02953013,0.00008003132,0.000029432726,0.00006671249,0.0000316125,0.00003209903,0.0017656538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812824,0.00076963485,0.000086242166,0.00029318433,0.00031807442,0.00040462255],"domain_scores_gemma":[0.9963356,0.0025270262,0.00034490094,0.00020213454,0.0003581371,0.00023222428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002883408,0.0012219952,0.0021201305,0.00061622844,0.0007863063,0.002498076,0.001718946,0.0012776064,0.002035586],"category_scores_gemma":[0.006551699,0.00077014783,0.000692612,0.000677766,0.001455392,0.0022544607,0.0014322638,0.0013166957,0.00025886192],"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":[0.00022677048,0.00011903681,0.00047773877,0.00012743559,0.00005410122,0.00024026523,0.00009090421,0.9532152,0.0025291767,0.029894909,0.0008341748,0.012190311],"study_design_scores_gemma":[0.000009655008,0.000021136459,0.000047436046,0.0000030079536,0.000004535888,0.000018203378,0.000011234184,0.99315333,0.00016865706,0.0064771324,0.000080490834,0.000005152486],"about_ca_topic_score_codex":0.0023537064,"about_ca_topic_score_gemma":0.0021201435,"teacher_disagreement_score":0.002883408,"about_ca_system_score_codex":0.0014326082,"about_ca_system_score_gemma":0.0020143546,"threshold_uncertainty_score":0.0152490735},"labels":[],"label_agreement":null},{"id":"W2916760466","doi":"10.1109/tmc.2019.2901671","title":"On the Interaction of Charging-Aware Mobility and Wireless Capacity","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Energy Harvesting in Wireless Networks","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":"University of Alberta","funders":"","keywords":"Computer science; Computer network; Waypoint; Wireless; Node (physics); Inductive charging; Mobility model; Mobile device; Hotspot (geology); Wireless network; Telecommunications; Real-time computing; Engineering","score_opus":0.012440425616742024,"score_gpt":0.21354558190864387,"score_spread":0.20110515629190184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2916760466","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30247197,0.0041449824,0.60180825,0.015134715,0.00052295596,0.000118000695,0.0005261596,0.00041211516,0.07486093],"genre_scores_gemma":[0.98841363,0.00132728,0.0049901423,0.00021845939,0.00014451655,0.000038354694,0.00003869915,0.00005040413,0.004778568],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942255,0.000245926,0.000014535013,0.000078019395,0.000073837,0.00016513477],"domain_scores_gemma":[0.9948372,0.0037994008,0.0005061864,0.0003231385,0.00029602958,0.00023802802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010848116,0.0006071841,0.00063455687,0.0009704544,0.0009969439,0.0019539655,0.0013224279,0.0013864381,0.004574455],"category_scores_gemma":[0.009048601,0.00052728486,0.0006426359,0.00096576917,0.0024003626,0.0048242565,0.0021005333,0.001313827,0.0004465005],"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":[0.000091125425,0.000062159685,0.0029185594,0.00007833852,0.000039722905,0.00041497493,0.00033986865,0.55378646,0.0018922432,0.427143,0.0026706355,0.010562916],"study_design_scores_gemma":[0.000013626934,0.000040207276,0.0014288627,0.000040589377,0.000020128242,0.00021967203,0.00021261007,0.8236925,0.00024791912,0.17202672,0.0020078633,0.000049295035],"about_ca_topic_score_codex":0.007590707,"about_ca_topic_score_gemma":0.0067706164,"teacher_disagreement_score":0.007590707,"about_ca_system_score_codex":0.001806515,"about_ca_system_score_gemma":0.0007911963,"threshold_uncertainty_score":0.015303075},"labels":[],"label_agreement":null},{"id":"W2940299710","doi":"10.1109/tmc.2019.2911935","title":"<i>Razor</i>: Scaling Backend Capacity for Mobile Applications","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Wuhan University; National Natural Science Foundation of China","keywords":"Computer science; Burstiness; Schedule; Mobile device; Computer network; Real-time computing; Key (lock); Distributed computing; Term (time); Operating system; Network packet","score_opus":0.016744550679614365,"score_gpt":0.24322299262574373,"score_spread":0.22647844194612937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940299710","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10463462,0.002979437,0.7790868,0.001950013,0.00065094477,0.0007842093,0.0011883795,0.0964841,0.012241381],"genre_scores_gemma":[0.714573,0.0008564099,0.27377534,0.0014373667,0.00025126064,0.00031240296,0.0012637307,0.001979355,0.0055510476],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852496,0.00026538037,0.00012922894,0.0004019759,0.00040732138,0.00027106426],"domain_scores_gemma":[0.99651426,0.0006741302,0.00039887484,0.0011712331,0.0008599021,0.0003815445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017938893,0.0024075944,0.00075824105,0.0013214647,0.0007987699,0.0018245728,0.005741793,0.0012097481,0.004565156],"category_scores_gemma":[0.0057837004,0.0007095284,0.0006585788,0.000954377,0.001042319,0.0034736225,0.00244917,0.0016581116,0.0019966254],"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":[0.002517645,0.0007583281,0.012757721,0.00078810414,0.0002784141,0.0005436189,0.00058005186,0.15695582,0.11633108,0.013603994,0.071593516,0.62329173],"study_design_scores_gemma":[0.00015887848,0.0008146133,0.0023774488,0.00007405974,0.00008885355,0.0003444944,0.00011138825,0.9108654,0.057411063,0.0055884547,0.021986423,0.0001788504],"about_ca_topic_score_codex":0.0065128626,"about_ca_topic_score_gemma":0.0071007065,"teacher_disagreement_score":0.0065128626,"about_ca_system_score_codex":0.001392111,"about_ca_system_score_gemma":0.0014066215,"threshold_uncertainty_score":0.015271962},"labels":[],"label_agreement":null},{"id":"W2943828857","doi":"10.1109/tmc.2019.2911945","title":"Possibility-based trust for mobile wireless networks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Ad Hoc 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":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Reputation; Network packet; Computer network; Authentication (law); Wireless; Mobile ad hoc network; Mobile computing; Wireless network; Mobile wireless; Computer security; Telecommunications","score_opus":0.01048481649275894,"score_gpt":0.24871920237001316,"score_spread":0.2382343858772542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943828857","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013047306,0.0023418244,0.9761885,0.0018480279,0.00013373359,0.000076990334,0.00007671124,0.00011655154,0.006170368],"genre_scores_gemma":[0.8709355,0.0025305173,0.12259669,0.00016717485,0.00034072134,0.00026410184,0.00012147473,0.000026469323,0.0030173],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959214,0.0021013063,0.00022208499,0.0003386455,0.0012397377,0.00017682805],"domain_scores_gemma":[0.98959047,0.007873206,0.0008284284,0.0005559119,0.0008636568,0.00028834457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037602924,0.00070095557,0.00095882965,0.001753297,0.0011467427,0.003468152,0.0014559278,0.0018108626,0.0018993211],"category_scores_gemma":[0.017906994,0.0005566969,0.001483233,0.001750016,0.0034822477,0.0054410864,0.0022952864,0.0026938566,0.00032755898],"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":[0.00006538115,0.000030238385,0.00074511167,0.000191859,0.00009973125,0.0003258926,0.00037417116,0.28452232,0.0006485748,0.6809675,0.001179336,0.030849941],"study_design_scores_gemma":[0.000015429272,0.000038646882,0.0001615834,0.000041071024,0.000020101239,0.00010192434,0.000065887056,0.5371528,0.00017026177,0.46015733,0.0020438626,0.000030984596],"about_ca_topic_score_codex":0.0040677977,"about_ca_topic_score_gemma":0.0026009206,"teacher_disagreement_score":0.0040677977,"about_ca_system_score_codex":0.003368626,"about_ca_system_score_gemma":0.0015000742,"threshold_uncertainty_score":0.024441242},"labels":[],"label_agreement":null},{"id":"W2944741907","doi":"10.1109/tmc.2019.2915071","title":"A Seamless Mobility Management Protocol in 5G Locator Identificator Split Dense Small Cells","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","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":"Polytechnique Montréal","funders":"","keywords":"Computer science; Computer network; Mobility management; Handover; Packet loss; Network packet; Scalability; Routing protocol; Distributed computing; Operating system","score_opus":0.00920451216935654,"score_gpt":0.23602896495602468,"score_spread":0.22682445278666813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944741907","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10400958,0.0017336195,0.88774997,0.00059799856,0.00015638462,0.00019123055,0.00006880755,0.00055928924,0.00493307],"genre_scores_gemma":[0.9186023,0.00061431446,0.07876802,0.00011458318,0.000046156572,0.00016534665,0.0000755912,0.000014974473,0.0015988051],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957424,0.00016686114,0.000023166825,0.000049720962,0.00013869007,0.000047340614],"domain_scores_gemma":[0.9996393,0.00014489837,0.00006455159,0.000053111395,0.00008090994,0.000017261273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007862943,0.00029816106,0.0002854703,0.00049172,0.00047086863,0.0006827159,0.000555147,0.00039993037,0.00047539186],"category_scores_gemma":[0.0013643905,0.00016311764,0.00025105415,0.00043663336,0.00061533606,0.0009429111,0.00078474975,0.0004380228,0.00011650189],"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":[0.00033019795,0.00015037315,0.0023950743,0.00029116575,0.00008000891,0.001139284,0.0006603729,0.38284722,0.07957627,0.22373433,0.005448113,0.30334753],"study_design_scores_gemma":[0.000042847016,0.0003671695,0.0006509529,0.000027659522,0.00003891481,0.00038425898,0.00012314518,0.96440846,0.009292961,0.013307134,0.011317581,0.000038951646],"about_ca_topic_score_codex":0.0013990053,"about_ca_topic_score_gemma":0.0019211225,"teacher_disagreement_score":0.0013990053,"about_ca_system_score_codex":0.00056445383,"about_ca_system_score_gemma":0.0005946434,"threshold_uncertainty_score":0.004158318},"labels":[],"label_agreement":null},{"id":"W2948999504","doi":"10.1109/tmc.2019.2920819","title":"Optimal Mobile Computation Offloading with Hard Deadline Constraints","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computation offloading; Markov process; Markov chain; Distributed computing; Wireless; Markov decision process; Computation; Task (project management); Energy consumption; Channel (broadcasting); Mobile device; Real-time computing; Algorithm; Cloud computing; Computer network; Edge computing","score_opus":0.012421989687256843,"score_gpt":0.24502031423056905,"score_spread":0.2325983245433122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948999504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15574136,0.0005825571,0.8357374,0.0002526916,0.00009729677,0.000103094026,0.00011658345,0.00050000736,0.0068689943],"genre_scores_gemma":[0.9462369,0.00022825117,0.050881866,0.000045920864,0.000035649195,0.00006131146,0.00007303198,0.00007152017,0.0023655829],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995592,0.00006851516,0.000017630919,0.00009370004,0.00008783453,0.00017306709],"domain_scores_gemma":[0.99930274,0.00044288122,0.00008577906,0.00005489394,0.000055889406,0.000057869347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036339412,0.0008097532,0.0010313739,0.0003418421,0.00049935293,0.0008846338,0.00068789476,0.00048445963,0.0014519404],"category_scores_gemma":[0.0014854571,0.00038075744,0.00038510715,0.0005702757,0.0005556327,0.00088978285,0.00069579255,0.0006195339,0.00019781946],"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":[0.00018348277,0.00006939829,0.00051011785,0.000070653354,0.000013737645,0.00013327233,0.00004719747,0.95014673,0.0044272174,0.010675712,0.0008089982,0.03291346],"study_design_scores_gemma":[0.000009397903,0.00001899164,0.000111312846,0.000003197375,0.0000029057262,0.000016873722,0.000015082826,0.99485,0.0009618348,0.0037617725,0.0002451786,0.0000035020014],"about_ca_topic_score_codex":0.004147506,"about_ca_topic_score_gemma":0.0047652368,"teacher_disagreement_score":0.004147506,"about_ca_system_score_codex":0.00066616596,"about_ca_system_score_gemma":0.0016072693,"threshold_uncertainty_score":0.00824672},"labels":[],"label_agreement":null},{"id":"W2949089721","doi":"10.1109/tmc.2019.2922614","title":"Coverage Performance in MIMO-ZFBF Dense HetNets with Multiplexing and LOS/NLOS Path-Loss Attenuation","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Shenzhen University; National Natural Science Foundation of China","keywords":"MIMO; Non-line-of-sight propagation; Beamforming; Computer science; Multiplexing; Spatial multiplexing; Path loss; Robustness (evolution); Context (archaeology); Algorithm; Topology (electrical circuits); Telecommunications; Mathematics; Wireless","score_opus":0.005924428433289776,"score_gpt":0.2045398459508831,"score_spread":0.19861541751759332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949089721","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8263665,0.0009240387,0.1648806,0.00018969727,0.000051847688,0.000037940394,0.0002565263,0.00041251702,0.006880432],"genre_scores_gemma":[0.9973015,0.00010714205,0.0022469047,0.000030095922,0.0000051349766,0.000009248999,0.000044178778,0.0000038883904,0.00025192232],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993393,0.00020393982,0.000022487304,0.000081908525,0.00011938004,0.00023301945],"domain_scores_gemma":[0.99787056,0.0013418858,0.00023349591,0.00015700156,0.0002687829,0.00012822785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001194148,0.00094511785,0.00078389695,0.00059794687,0.0004436158,0.0007176142,0.0006358857,0.00070135016,0.000658112],"category_scores_gemma":[0.0027888916,0.00031693117,0.00031035743,0.0006014675,0.00094706845,0.00074475,0.0014708465,0.0003344386,0.00017786738],"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":[0.0003494318,0.00004336947,0.0026257008,0.000060056962,0.000051105868,0.00024005241,0.00008831768,0.9750645,0.0064768246,0.0038350504,0.00034604358,0.0108194435],"study_design_scores_gemma":[0.00002015938,0.00018628783,0.0016535404,0.000014612831,0.000022422255,0.00017988538,0.00010074213,0.9922456,0.0029005862,0.002546344,0.00011237996,0.000017391687],"about_ca_topic_score_codex":0.0065862085,"about_ca_topic_score_gemma":0.004255276,"teacher_disagreement_score":0.0065862085,"about_ca_system_score_codex":0.00088383333,"about_ca_system_score_gemma":0.0005580899,"threshold_uncertainty_score":0.0130957365},"labels":[],"label_agreement":null},{"id":"W2953406779","doi":"10.1109/tmc.2019.2926713","title":"Profit Maximization in 5G+ Networks with Heterogeneous Aerial and Ground Base Stations","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"UAV Applications and Optimization","field":"Engineering","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":"Carleton University; University of Toronto","funders":"","keywords":"Computer science; Base station; Orthogonal frequency-division multiple access; Computational complexity theory; Mathematical optimization; Wireless network; Integer programming; Profit maximization; Optimization problem; Heterogeneous network; Resource allocation; Linear programming; Cellular network; Wireless; Computer network; Orthogonal frequency-division multiplexing; Profit (economics); Channel (broadcasting); Algorithm; Mathematics; Telecommunications","score_opus":0.004667069890156266,"score_gpt":0.18774709183032942,"score_spread":0.18308002194017314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953406779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055770054,0.0009362977,0.9351203,0.00047357052,0.00005490937,0.00006298447,0.00012206564,0.00011092029,0.0073489407],"genre_scores_gemma":[0.93779385,0.00069078075,0.058792572,0.00010694548,0.000057694022,0.000067635316,0.00009175228,0.00003522587,0.002363526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993426,0.00029562952,0.000013277365,0.00012479862,0.00009150397,0.00013226515],"domain_scores_gemma":[0.9997118,0.00017690753,0.000043163203,0.000017547784,0.000026697788,0.000023887187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088579813,0.0011872103,0.0010244211,0.00034471496,0.00051647384,0.0014377608,0.0010798445,0.00078469317,0.0016243382],"category_scores_gemma":[0.0011703599,0.00036793455,0.00054353906,0.00080505235,0.0008981246,0.0015602353,0.0010429557,0.00060891977,0.00018689461],"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":[0.000042120573,0.000026203938,0.0003428481,0.00004193588,0.000023930283,0.0001512674,0.000022118473,0.95934546,0.0009191595,0.02771776,0.00068664795,0.010680461],"study_design_scores_gemma":[0.0000055798228,0.000017902748,0.00010185781,0.000003079435,0.000006370881,0.000025431133,0.000021316824,0.98996466,0.00021204284,0.009178283,0.00045985184,0.0000036267554],"about_ca_topic_score_codex":0.004207797,"about_ca_topic_score_gemma":0.0032530972,"teacher_disagreement_score":0.004207797,"about_ca_system_score_codex":0.0016210026,"about_ca_system_score_gemma":0.0009823901,"threshold_uncertainty_score":0.011761308},"labels":[],"label_agreement":null},{"id":"W2964280086","doi":"10.1109/tmc.2019.2908638","title":"Enabling Strong Privacy Preservation and Accurate Task Allocation for Mobile Crowdsensing","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":193,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; University of Waterloo","funders":"","keywords":"Computer science; Mobile device; Computer security; Mobile computing; Service provider; Information privacy; Mobile commerce; Encryption; Internet privacy; Computer network; Service (business); World Wide Web","score_opus":0.01993404008538232,"score_gpt":0.26755603159582275,"score_spread":0.24762199151044043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964280086","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038413234,0.0002014422,0.95724446,0.00045486406,0.000068325746,0.00014617517,0.00007992036,0.00052703946,0.002864613],"genre_scores_gemma":[0.91727024,0.00013677271,0.07967363,0.00016383982,0.000058048125,0.00013718878,0.00006661461,0.000037083973,0.0024564387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99705434,0.000796246,0.0001942571,0.00058272685,0.00087725493,0.0004952394],"domain_scores_gemma":[0.9961443,0.0011734854,0.0005229008,0.001520706,0.00037723052,0.00026137396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020042802,0.0007937503,0.00097574596,0.00055085204,0.0013425612,0.001388639,0.0015978699,0.0011859258,0.0010545664],"category_scores_gemma":[0.007073541,0.00043357405,0.0005674947,0.0008269906,0.0018266166,0.0025109332,0.006013461,0.001305458,0.00045314748],"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":[0.0018778081,0.00038828113,0.005109547,0.00050384045,0.00013935754,0.0015049558,0.002210479,0.40542582,0.11126393,0.1833426,0.007444296,0.28078914],"study_design_scores_gemma":[0.00008428503,0.00020650789,0.00065841374,0.00002776421,0.000028055822,0.00040056734,0.0002471828,0.8959698,0.020673076,0.07405545,0.0075905845,0.00005828495],"about_ca_topic_score_codex":0.0014256408,"about_ca_topic_score_gemma":0.0013244023,"teacher_disagreement_score":0.0020042802,"about_ca_system_score_codex":0.001055643,"about_ca_system_score_gemma":0.001597857,"threshold_uncertainty_score":0.010599792},"labels":[],"label_agreement":null},{"id":"W2964316170","doi":"10.1109/tmc.2018.2861861","title":"Seamless Resource Sharing in Wearable Networks by Application Function Virtualization","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","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":"University of Saskatchewan","funders":"","keywords":"Computer science; Virtualization; Network Functions Virtualization; Wearable computer; Computer network; Function (biology); Shared resource; Resource (disambiguation); Wearable technology; Mobile computing; Distributed computing; Cloud computing; Embedded system; Operating system","score_opus":0.009163064384485366,"score_gpt":0.232434279132622,"score_spread":0.22327121474813663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964316170","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17743647,0.0024004343,0.79565537,0.000675259,0.00040576307,0.000266935,0.00012288388,0.0067060464,0.01633087],"genre_scores_gemma":[0.9168051,0.00067979645,0.07800761,0.00035234093,0.00008614277,0.00024462424,0.0001322934,0.00031675218,0.0033753272],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986014,0.00048235097,0.00010066291,0.00018973954,0.00025578652,0.00036996853],"domain_scores_gemma":[0.99862015,0.00032430384,0.00012199741,0.00056866556,0.00017313549,0.00019181393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016352462,0.0007451014,0.0005779322,0.0004987823,0.00088629714,0.0023491962,0.0016784717,0.0005185167,0.0014410308],"category_scores_gemma":[0.002622734,0.00045234582,0.0005277575,0.0004130577,0.00089547015,0.0035034702,0.004358594,0.0010966678,0.00043159514],"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":[0.0022029236,0.00078440655,0.0138042625,0.00072670594,0.00040081106,0.0025833172,0.0046394463,0.17472404,0.13019109,0.1993396,0.027889837,0.4427135],"study_design_scores_gemma":[0.00010559909,0.00042626276,0.00365715,0.00020393907,0.00017892057,0.0011067147,0.00073127204,0.76458067,0.0541234,0.09774585,0.076935865,0.00020442395],"about_ca_topic_score_codex":0.0017458976,"about_ca_topic_score_gemma":0.0020189688,"teacher_disagreement_score":0.0023491962,"about_ca_system_score_codex":0.00068566576,"about_ca_system_score_gemma":0.0009861299,"threshold_uncertainty_score":0.008648157},"labels":[],"label_agreement":null},{"id":"W2981080014","doi":"10.1109/tmc.2019.2948014","title":"Optimal ADMM-Based Spectrum and Power Allocation for Heterogeneous Small-Cell Networks with Hybrid Energy Supplies","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":23,"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":"National Mobile Communications Research Laboratory, Southeast University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Science Foundation","keywords":"Mathematical optimization; Computer science; Maximization; Utility maximization problem; Convexity; Optimization problem; Grid; Convex optimization; Minification; Energy consumption; Lagrangian relaxation; Regular polygon; Mathematics; Utility maximization","score_opus":0.004130425254912459,"score_gpt":0.18181707689298812,"score_spread":0.17768665163807568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981080014","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006600155,0.0001748616,0.99096054,0.00014457823,0.000031946074,0.00002593213,0.000034107885,0.00009600321,0.0019318248],"genre_scores_gemma":[0.629921,0.0004991322,0.36348546,0.00027657757,0.00009245728,0.00025053142,0.0002418308,0.0000993698,0.005133509],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995536,0.00018632597,0.000020020183,0.0000809375,0.00010140748,0.00005767817],"domain_scores_gemma":[0.9993923,0.0003732093,0.000070104696,0.00004315632,0.00008927099,0.000032110966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011940906,0.0010956702,0.0011125279,0.00039877894,0.00037934785,0.001096959,0.0010317336,0.0010674868,0.0018811435],"category_scores_gemma":[0.001905284,0.0005070749,0.00067767035,0.0008343899,0.00090248015,0.0011420851,0.0010122018,0.0015222998,0.00042399147],"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":[0.00004578063,0.000028496193,0.00015003806,0.000048696107,0.000019173156,0.000059832037,0.00002977352,0.9690708,0.0010611005,0.00865856,0.00095834443,0.019869408],"study_design_scores_gemma":[0.000006516468,0.000009201523,0.000015349282,0.0000025798277,0.0000022114593,0.0000071191116,0.0000055835617,0.9974136,0.00015173752,0.0021712966,0.00021300382,0.0000017665678],"about_ca_topic_score_codex":0.0028430978,"about_ca_topic_score_gemma":0.003237797,"teacher_disagreement_score":0.0028430978,"about_ca_system_score_codex":0.0008552024,"about_ca_system_score_gemma":0.0011399422,"threshold_uncertainty_score":0.0063150525},"labels":[],"label_agreement":null},{"id":"W2983694339","doi":"10.1109/tmc.2019.2953163","title":"Software-Defined Cooperative Data Sharing in Edge Computing Assisted 5G-VANET","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":149,"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 Science Foundation of Beijing Municipality; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Vehicular ad hoc network; Computer network; Distributed computing; Mobile edge computing; Data sharing; Software-defined networking; Edge computing; Dedicated short-range communications; Wireless ad hoc network; Cloud computing; Server; Wireless; Operating system","score_opus":0.02471518104194979,"score_gpt":0.2561665114549453,"score_spread":0.2314513304129955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983694339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034770492,0.00024824077,0.9608123,0.00017974366,0.000059915037,0.00007315986,0.00005241545,0.00019409752,0.003609654],"genre_scores_gemma":[0.8951538,0.0002084761,0.10219528,0.00012230924,0.00003067267,0.00013776789,0.00012062197,0.000029506931,0.0020017016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888223,0.00028427917,0.000053541004,0.00026523366,0.00027101982,0.00024369868],"domain_scores_gemma":[0.9993975,0.00022110096,0.0000872128,0.00008071152,0.00014703687,0.00006646231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008081467,0.00067268615,0.00082039286,0.00052473234,0.0007504026,0.0011374142,0.001590274,0.0005553418,0.0011240954],"category_scores_gemma":[0.0013729341,0.000266654,0.0005010587,0.001025227,0.00068085303,0.0011589515,0.0014728856,0.00058441784,0.00018336902],"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":[0.00014568403,0.00007214331,0.0005262644,0.00007595402,0.00004035115,0.0002227241,0.00014042239,0.9054403,0.0067682625,0.03382825,0.0016379445,0.051101673],"study_design_scores_gemma":[0.0000075178496,0.00003588988,0.000052980125,0.0000033356469,0.0000057461716,0.00003538596,0.000034420915,0.99256986,0.00093049416,0.005510762,0.00080708734,0.000006505499],"about_ca_topic_score_codex":0.0048540304,"about_ca_topic_score_gemma":0.004383475,"teacher_disagreement_score":0.0048540304,"about_ca_system_score_codex":0.00095283205,"about_ca_system_score_gemma":0.0014053877,"threshold_uncertainty_score":0.009651542},"labels":[],"label_agreement":null},{"id":"W2984468147","doi":"10.1109/tmc.2019.2952848","title":"Optimal Scheduling for Unmanned Aerial Vehicle Networks With Flow-Level Dynamics","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Queueing theory; Scheduling (production processes); Wireless; Distributed computing; Wireless network; Fading; Dynamic priority scheduling; Vehicle dynamics; Throughput; Real-time computing; Channel (broadcasting); Computer network; Mathematical optimization; Quality of service; Telecommunications","score_opus":0.00745129455024186,"score_gpt":0.20656836843316156,"score_spread":0.19911707388291972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2984468147","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06850047,0.00045274125,0.92838526,0.00028449114,0.00007726706,0.00006316471,0.00008381359,0.00016241508,0.0019903479],"genre_scores_gemma":[0.9498671,0.0003533653,0.04769051,0.00006718558,0.00004142536,0.00006235704,0.00008699602,0.000037219965,0.0017938201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966776,0.00008887209,0.0000125197785,0.00006836764,0.00005807689,0.00010442581],"domain_scores_gemma":[0.9992623,0.00038775612,0.00014411443,0.00002763927,0.00009242077,0.000085735104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008745556,0.00097625924,0.00083949516,0.0005796026,0.0005527834,0.0008984486,0.00086732337,0.00062329444,0.001080093],"category_scores_gemma":[0.0021366288,0.00040766445,0.00036510918,0.00054035964,0.0006967584,0.00091807597,0.00080888136,0.00072097406,0.00013643436],"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":[0.000024120565,0.000013867136,0.00018901913,0.000019550387,0.0000072207617,0.00002207146,0.000018277267,0.9888461,0.00066730776,0.005754753,0.0002950561,0.0041426597],"study_design_scores_gemma":[0.0000021026501,0.0000062549143,0.000022221348,7.226082e-7,8.2534524e-7,0.0000016449899,0.0000031395557,0.9987471,0.00005351329,0.0011122891,0.00004912253,8.942528e-7],"about_ca_topic_score_codex":0.01050972,"about_ca_topic_score_gemma":0.00686085,"teacher_disagreement_score":0.01050972,"about_ca_system_score_codex":0.0021114212,"about_ca_system_score_gemma":0.0017958783,"threshold_uncertainty_score":0.02089709},"labels":[],"label_agreement":null},{"id":"W2991627768","doi":"10.1109/tmc.2019.2955948","title":"Energy Efficient Collaborative Beamforming for Reducing Sidelobe in Wireless Sensor Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Antenna Design and Optimization","field":"Engineering","cited_by":80,"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":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Beamforming; Wireless sensor network; Cuckoo search; Efficient energy use; Optimization problem; Node (physics); Transmission (telecommunications); Mathematical optimization; Distributed computing; Algorithm; Computer network; Particle swarm optimization; Telecommunications; Mathematics; Engineering; Electrical engineering","score_opus":0.005291760724860604,"score_gpt":0.20491713591642702,"score_spread":0.19962537519156642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991627768","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013595466,0.00026378542,0.98493224,0.00007820651,0.000016392163,0.000008950899,0.000007731657,0.00007682767,0.0010204455],"genre_scores_gemma":[0.81175524,0.0006905935,0.18532759,0.00011051129,0.00004456934,0.00010034941,0.000050187657,0.000044905537,0.0018760064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954957,0.00016734227,0.00001637186,0.00006646734,0.00016113358,0.00003920254],"domain_scores_gemma":[0.9995732,0.0002572278,0.000051503586,0.000031906377,0.00006953703,0.000016611635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005488225,0.0006068225,0.000449381,0.00032632376,0.00030352533,0.0003728589,0.00049462065,0.0005497013,0.0005750467],"category_scores_gemma":[0.0012215349,0.0002644125,0.00035837738,0.00058570964,0.00058006926,0.000806633,0.0006676409,0.0005164049,0.0001681479],"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":[0.00010204354,0.00005162081,0.0004342365,0.00010135947,0.000046318113,0.00005572541,0.00008300053,0.87152475,0.030590894,0.018560799,0.00083199417,0.077617295],"study_design_scores_gemma":[0.000007663183,0.00004481268,0.00009617071,0.0000037917857,0.0000064383803,0.000017088381,0.000011704398,0.9927274,0.0030606408,0.0036736885,0.00034529725,0.0000053387193],"about_ca_topic_score_codex":0.0009847493,"about_ca_topic_score_gemma":0.0012855588,"teacher_disagreement_score":0.0009847493,"about_ca_system_score_codex":0.0003591683,"about_ca_system_score_gemma":0.0005340954,"threshold_uncertainty_score":0.0029025078},"labels":[],"label_agreement":null},{"id":"W2997803755","doi":"10.1109/tmc.2019.2962126","title":"Load Management, Power and Admission Control in Downlink Cellular OFDMA Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Wireless Network 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":"University of Toronto; University of Manitoba","funders":"","keywords":"Telecommunications link; Computer science; Base station; Computer network; Load balancing (electrical power); Transmitter power output; Power control; Orthogonal frequency-division multiple access; Admission control; Cellular network; Resource management (computing); Orthogonal frequency-division multiplexing; Distributed computing; Power (physics); Transmitter; Mathematics; Quality of service","score_opus":0.0026288476344518328,"score_gpt":0.18963445289293476,"score_spread":0.18700560525848292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997803755","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051190775,0.0008332551,0.9440457,0.00023566402,0.000057191253,0.00007400189,0.000037317663,0.00020372936,0.0033222719],"genre_scores_gemma":[0.96734995,0.00034995333,0.031023655,0.00004969006,0.00007541476,0.00005777527,0.000018174836,0.00001937177,0.0010560129],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910307,0.00027435806,0.00003854137,0.00015156709,0.00028801794,0.00014442357],"domain_scores_gemma":[0.99932814,0.00032936851,0.0001013267,0.00005878658,0.0001249396,0.00005734483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008759541,0.00076721306,0.0008099728,0.0004521169,0.00082939595,0.0014010533,0.0012774956,0.0007546001,0.0006991086],"category_scores_gemma":[0.0024267675,0.00031035178,0.00035849866,0.00054221496,0.0012543441,0.001157793,0.0011305716,0.00076560554,0.00013329819],"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":[0.00004311178,0.00006589453,0.00043934694,0.000032359698,0.00001643862,0.00010332958,0.00009165421,0.9519523,0.004968858,0.022746475,0.0003281115,0.01921219],"study_design_scores_gemma":[0.0000029921105,0.000011787889,0.000056236124,0.0000016371538,0.0000033408478,0.000009597581,0.0000077334735,0.9965503,0.0003002292,0.002894876,0.00015704661,0.000004146712],"about_ca_topic_score_codex":0.007939409,"about_ca_topic_score_gemma":0.0043900204,"teacher_disagreement_score":0.007939409,"about_ca_system_score_codex":0.0015398102,"about_ca_system_score_gemma":0.001280421,"threshold_uncertainty_score":0.01578635},"labels":[],"label_agreement":null},{"id":"W2998898452","doi":"10.1109/tmc.2020.2965929","title":"Edge-Enabled V2X Service Placement for Intelligent Transportation Systems","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":181,"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":"Heuristic; Integer programming; Service (business); Quality of service; Linear programming; Enhanced Data Rates for GSM Evolution; Computation; Set (abstract data type); Resource allocation","score_opus":0.01853816813043831,"score_gpt":0.22755114404623097,"score_spread":0.20901297591579265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998898452","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050817292,0.0008527632,0.94057435,0.0003159438,0.00015146512,0.00012346763,0.00012440326,0.0005746863,0.0064656157],"genre_scores_gemma":[0.8566552,0.0005188772,0.13908099,0.00010918002,0.00003883156,0.000083654646,0.00019167249,0.000076302276,0.0032453835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949765,0.00016417984,0.000016613314,0.00008742899,0.00008754496,0.00014661958],"domain_scores_gemma":[0.9997421,0.000093934876,0.000031059884,0.000024960176,0.00005315978,0.00005472703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048547154,0.0009788452,0.00071350357,0.0005729221,0.0008241511,0.0009961003,0.0011343305,0.00092845346,0.0026631446],"category_scores_gemma":[0.00082977064,0.00035522028,0.00046256479,0.00091199746,0.00046145916,0.0010279896,0.0012686937,0.000757981,0.00042302796],"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":[0.00014294637,0.00007377831,0.00063772313,0.000089647925,0.000026879121,0.00014909571,0.00007423454,0.91097564,0.0050695566,0.014834163,0.0033626375,0.06456371],"study_design_scores_gemma":[0.0000057714055,0.000050604387,0.00009948542,0.000006738859,0.0000055277774,0.000033673703,0.000057757035,0.9924769,0.0009843287,0.0049603516,0.0013131533,0.000005755954],"about_ca_topic_score_codex":0.007964511,"about_ca_topic_score_gemma":0.0089246305,"teacher_disagreement_score":0.007964511,"about_ca_system_score_codex":0.0014181066,"about_ca_system_score_gemma":0.0015654713,"threshold_uncertainty_score":0.015836298},"labels":[],"label_agreement":null},{"id":"W2998910091","doi":"10.1109/tmc.2020.2965450","title":"Dynamic Model for Network Selection in Next Generation HetNets With Memory-Affecting Rational Users","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","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":"York University; University of Alberta","funders":"National Research Foundation of Korea","keywords":"Computer science; Heterogeneous network; Evolutionary game theory; Game theory; Wireless network; Network formation; Selection (genetic algorithm); Service (business); Distributed computing; Wireless; Artificial intelligence; Telecommunications; Mathematics","score_opus":0.023076380376729447,"score_gpt":0.23812649483682713,"score_spread":0.2150501144600977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998910091","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11202932,0.0009157545,0.8607557,0.0014249517,0.0001966602,0.00014131387,0.0004356497,0.00015064691,0.023950007],"genre_scores_gemma":[0.96232784,0.000757351,0.014236184,0.00027133437,0.0000750372,0.00024611704,0.00013855033,0.000035054672,0.021912515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921596,0.00023034129,0.00002677228,0.00017114198,0.00014434419,0.00021132367],"domain_scores_gemma":[0.99891543,0.0005233839,0.0002082943,0.000043250657,0.00019700793,0.000112751055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011533288,0.0012549142,0.0013502237,0.0007437599,0.0008554897,0.0018687084,0.002414601,0.0024038088,0.005873368],"category_scores_gemma":[0.003098081,0.00061808276,0.0010694661,0.00075464905,0.0014847167,0.002175374,0.0014395843,0.0016250684,0.0005676716],"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":[0.000081166945,0.000039545277,0.0011916788,0.00005885787,0.000054597047,0.00061393686,0.00020954193,0.88094646,0.0016667064,0.11007905,0.0011559876,0.003902469],"study_design_scores_gemma":[0.000013378843,0.00001941791,0.00014499672,0.0000061369396,0.000014819003,0.000048618567,0.000040831757,0.98968726,0.00007586891,0.009543777,0.00039494754,0.000009886452],"about_ca_topic_score_codex":0.011130507,"about_ca_topic_score_gemma":0.0063023213,"teacher_disagreement_score":0.011130507,"about_ca_system_score_codex":0.0017765828,"about_ca_system_score_gemma":0.0009894053,"threshold_uncertainty_score":0.022131443},"labels":[],"label_agreement":null},{"id":"W2999263473","doi":"10.1109/tmc.2020.2967038","title":"The Design of Dynamic Probabilistic Caching with Time-Varying Content Popularity","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":83,"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; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Probabilistic logic; Popularity; Dynamic web page; Markov chain; Exploit; Markov decision process; Markov process; Mathematical optimization; Artificial intelligence; Machine learning; Computer security; Mathematics; Statistics","score_opus":0.03543056412763705,"score_gpt":0.23232863268019663,"score_spread":0.1968980685525596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999263473","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0080547305,0.00010324935,0.99065065,0.000097616,0.000017443048,0.000049046273,0.000019776537,0.00021493417,0.00079264765],"genre_scores_gemma":[0.8606416,0.0002932613,0.13729398,0.000123414,0.000040377246,0.00023782205,0.00006349435,0.0000603145,0.0012457647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982817,0.00048433084,0.000100297075,0.00044981766,0.00047430195,0.00020953004],"domain_scores_gemma":[0.9969387,0.0012137133,0.0004692064,0.00036978215,0.0008533409,0.00015536427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015550673,0.0007951447,0.001021951,0.00046657288,0.0005288976,0.0010359919,0.0022890456,0.0010811507,0.0009294027],"category_scores_gemma":[0.007512134,0.0006947208,0.0005021715,0.00071788166,0.00081710285,0.0017447651,0.0010578779,0.0009612293,0.00026918334],"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":[0.00011301825,0.00007507188,0.001289092,0.00012694461,0.00004486339,0.00015334845,0.00010746803,0.9165619,0.011653093,0.028011063,0.0011517648,0.040712364],"study_design_scores_gemma":[0.000008817599,0.00003235717,0.00007548695,0.0000042642737,0.000011268947,0.000053009786,0.000007969874,0.9957098,0.0012536017,0.002371268,0.00046510552,0.0000070430415],"about_ca_topic_score_codex":0.003541837,"about_ca_topic_score_gemma":0.0031462768,"teacher_disagreement_score":0.003541837,"about_ca_system_score_codex":0.0012471848,"about_ca_system_score_gemma":0.0021328125,"threshold_uncertainty_score":0.009048998},"labels":[],"label_agreement":null},{"id":"W3004215161","doi":"10.1109/tmc.2020.2970902","title":"SST: Software Sonic Thermometer on Acoustic-Enabled IoT Devices","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":25,"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":"National Natural Science Foundation of China","keywords":"Thermometer; Computer science; Real-time computing; Temperature measurement; Software; Pipeline transport; Computer hardware; Embedded system; Environmental science","score_opus":0.014459423350645723,"score_gpt":0.22759690955681341,"score_spread":0.21313748620616768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004215161","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07784994,0.0013920177,0.8150878,0.00051433634,0.0009692841,0.0007119603,0.002937551,0.07172463,0.028812492],"genre_scores_gemma":[0.6503421,0.00082932046,0.31556287,0.0012038621,0.00034779886,0.0009126145,0.0038101037,0.0035865083,0.0234048],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992957,0.00009064548,0.000049000028,0.00013626354,0.0003729075,0.000055507837],"domain_scores_gemma":[0.99955565,0.00011265356,0.0000725962,0.00007716044,0.0001456496,0.000036319663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038785307,0.0011399899,0.0005425665,0.0007139702,0.00023662194,0.0006559094,0.0011853701,0.0005681571,0.00985884],"category_scores_gemma":[0.0019025438,0.0003596739,0.00041551836,0.0005742961,0.00035267422,0.0010972291,0.000992704,0.0005821011,0.0043505225],"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":[0.0012743835,0.00029382147,0.011083219,0.0014947663,0.00018265044,0.00086943916,0.0004501133,0.022345226,0.40310746,0.010579018,0.054927602,0.49339226],"study_design_scores_gemma":[0.0003007079,0.0011621289,0.0124013685,0.0003346162,0.0002610115,0.0017666776,0.00020695635,0.4036668,0.37462005,0.006779246,0.19821924,0.0002811561],"about_ca_topic_score_codex":0.00091945037,"about_ca_topic_score_gemma":0.001225058,"teacher_disagreement_score":0.00985884,"about_ca_system_score_codex":0.0003644841,"about_ca_system_score_gemma":0.00047797276,"threshold_uncertainty_score":0.032981157},"labels":[],"label_agreement":null},{"id":"W3007412953","doi":"10.1109/tmc.2020.2975792","title":"PROTECT: Efficient Password-Based Threshold Single-Sign-On Authentication for Mobile Users against Perpetual Leakage","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":112,"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":"National Natural Science Foundation of China","keywords":"Password; Computer science; Computer security; Security token; Authentication (law); S/KEY; Authentication server; Computer network; Mobile device; Server; One-time password; World Wide Web","score_opus":0.03566837131189985,"score_gpt":0.25863941984195776,"score_spread":0.22297104853005792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007412953","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086654566,0.00083219603,0.898687,0.00039185854,0.00019808243,0.00038073177,0.0002257961,0.006362603,0.006267124],"genre_scores_gemma":[0.9175832,0.00025473983,0.07723915,0.0001634574,0.0000583638,0.0001340866,0.00024731967,0.00010157377,0.004217932],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763894,0.00048099898,0.00019247823,0.00026161966,0.0010558505,0.00037020337],"domain_scores_gemma":[0.9970673,0.00043188262,0.0004522305,0.00146957,0.00039045783,0.00018862347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012712113,0.0006566525,0.0011012004,0.00096752326,0.00090622547,0.0010581893,0.0015723559,0.0011162956,0.0032705613],"category_scores_gemma":[0.0041183988,0.00042033393,0.0007867943,0.0007871692,0.001303356,0.0038925589,0.004151015,0.0012601214,0.0020774137],"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":[0.00443508,0.0004941476,0.0055522183,0.0009490421,0.00032003,0.0014675487,0.0011723471,0.0660129,0.2522607,0.15077823,0.019910477,0.4966472],"study_design_scores_gemma":[0.0005424732,0.0025032747,0.0023935451,0.00013077901,0.0002048333,0.0040265312,0.00030905288,0.7028949,0.18916577,0.061992846,0.035588983,0.00024694327],"about_ca_topic_score_codex":0.0004058511,"about_ca_topic_score_gemma":0.00034825466,"teacher_disagreement_score":0.0032705613,"about_ca_system_score_codex":0.0005785864,"about_ca_system_score_gemma":0.0013449225,"threshold_uncertainty_score":0.010941148},"labels":[],"label_agreement":null},{"id":"W3009727746","doi":"10.1109/tmc.2020.2977902","title":"Multi-Adversarial In-Car Activity Recognition Using RFIDs","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":51,"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; Activity recognition; Radio-frequency identification; Software deployment; Wearable computer; Domain (mathematical analysis); Wireless; Identification (biology); Wearable technology; Radio frequency; Deep learning; Human–computer interaction; Artificial intelligence; Real-time computing; Embedded system; Computer security; Telecommunications","score_opus":0.05087555828928229,"score_gpt":0.25995392903430276,"score_spread":0.20907837074502048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009727746","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07916189,0.00039452128,0.91591686,0.0002742526,0.000098782446,0.000045068016,0.000092875875,0.0012043075,0.0028113476],"genre_scores_gemma":[0.96533257,0.00014116918,0.030923879,0.00020793114,0.00003497926,0.000042418334,0.00019438902,0.000038300146,0.003084311],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99949324,0.0001274043,0.000021459737,0.00017323086,0.00009907403,0.00008553766],"domain_scores_gemma":[0.9992912,0.00036458857,0.000088262335,0.00010862128,0.0001022965,0.000044952227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007204641,0.0008010639,0.0007228126,0.0002631505,0.0001935899,0.00048879226,0.0010850106,0.00080703123,0.000975501],"category_scores_gemma":[0.0017138893,0.0003287983,0.0006518671,0.0002539837,0.0006188927,0.0006545297,0.0009713582,0.0011475475,0.00045222146],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017985477,0.000077985074,0.0020642618,0.00005037064,0.000057948888,0.00016211285,0.000048047867,0.92095935,0.0058615454,0.0027123925,0.0009854669,0.066840544],"study_design_scores_gemma":[0.0000020164596,0.0000141570135,0.00019267671,0.0000016666162,0.0000033963483,0.000019066192,0.0000031868672,0.998086,0.0010194124,0.0005280581,0.00012737267,0.0000031093139],"about_ca_topic_score_codex":0.0027145748,"about_ca_topic_score_gemma":0.002079467,"teacher_disagreement_score":0.0027145748,"about_ca_system_score_codex":0.0005107723,"about_ca_system_score_gemma":0.00038756276,"threshold_uncertainty_score":0.005397558},"labels":[],"label_agreement":null},{"id":"W3014297757","doi":"10.1109/tmc.2020.2984261","title":"LeaD: Large-Scale Edge Cache Deployment Based on Spatio-Temporal WiFi Traffic Statistics","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":144,"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":"National Key Research and Development Program of China; Higher Education Discipline Innovation Project; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Cache; Computer network; Software deployment; Backhaul (telecommunications); Bottleneck; Enhanced Data Rates for GSM Evolution; Telecommunications; Operating system; Embedded system; Base station","score_opus":0.023736029891642318,"score_gpt":0.2440994077803274,"score_spread":0.2203633778886851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014297757","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77993494,0.00073854957,0.2027857,0.0005215365,0.00012119295,0.00040010692,0.002864643,0.009113332,0.0035200329],"genre_scores_gemma":[0.96203375,0.00016676403,0.035229266,0.0000608595,0.00002116157,0.000096990814,0.001860916,0.000078177654,0.00045208144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938726,0.00010963231,0.000040152096,0.00018362408,0.00014774829,0.00013154071],"domain_scores_gemma":[0.99840826,0.00041620477,0.0001940449,0.00034387788,0.00039018082,0.00024751146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076341344,0.001197968,0.00077172334,0.0012562554,0.0007696834,0.0009957474,0.0016947243,0.0005868714,0.0004969891],"category_scores_gemma":[0.0042777397,0.0003725196,0.00041044594,0.0016982784,0.00043851958,0.0017164497,0.0013532424,0.00058184593,0.0002983462],"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":[0.0008310114,0.00068682595,0.21903603,0.0004791275,0.0003563719,0.0015189119,0.00082924357,0.56393063,0.04007931,0.004947465,0.016465608,0.15083948],"study_design_scores_gemma":[0.0000294576,0.00015005685,0.015372006,0.000013292517,0.000047314934,0.00028723545,0.0002662779,0.97653925,0.0046455236,0.0012493777,0.0013641901,0.000035954406],"about_ca_topic_score_codex":0.015079475,"about_ca_topic_score_gemma":0.032037336,"teacher_disagreement_score":0.015079475,"about_ca_system_score_codex":0.0008301087,"about_ca_system_score_gemma":0.0012175344,"threshold_uncertainty_score":0.029983401},"labels":[],"label_agreement":null},{"id":"W3016835749","doi":"10.1109/tmc.2019.2910074","title":"Energy Efficient Scheduling Algorithms for Sweep Coverage in Mobile Sensor Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Energy Efficient Wireless Sensor Networks","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 Victoria","funders":"National Key Research and Development Program of China Stem Cell and Translational Research; National Natural Science Foundation of China; Shanghai Science and Technology Development Foundation","keywords":"Computer science; Wireless sensor network; Algorithm; Scheduling (production processes); Scalability; Schedule; Wireless ad hoc network; Mobile device; Distributed computing; Real-time computing; Wireless; Mathematical optimization; Computer network; Telecommunications","score_opus":0.017319510006784035,"score_gpt":0.24554659227501807,"score_spread":0.22822708226823404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016835749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0425512,0.0005981216,0.952932,0.0002538538,0.000060277333,0.00015643983,0.00014340853,0.00075583736,0.0025488394],"genre_scores_gemma":[0.599635,0.0006350278,0.39625862,0.00014624021,0.00007492392,0.00029181555,0.00049148325,0.00015560555,0.002311265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994293,0.00015361796,0.00003293464,0.0001359357,0.00013017049,0.000118002106],"domain_scores_gemma":[0.99901485,0.00050690863,0.00013958773,0.00015868263,0.000104912324,0.00007498683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086703704,0.0009154306,0.00097254437,0.0008185605,0.00077208155,0.00066325377,0.0014038059,0.0006417083,0.0020481865],"category_scores_gemma":[0.0027850247,0.00035766434,0.00059557246,0.0013971126,0.0005795863,0.001253545,0.0010612812,0.0007272827,0.000381098],"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":[0.00025453776,0.00009903618,0.0005517119,0.00011427927,0.000030898434,0.0000631978,0.00012141181,0.89138067,0.0047181062,0.015704405,0.0027704248,0.08419129],"study_design_scores_gemma":[0.00003447676,0.000043824366,0.000090728296,0.00000470389,0.0000055000396,0.000023389028,0.000027949678,0.9919329,0.0010011285,0.005999308,0.000831634,0.000004428671],"about_ca_topic_score_codex":0.0029816905,"about_ca_topic_score_gemma":0.0032376477,"teacher_disagreement_score":0.0029816905,"about_ca_system_score_codex":0.0010826576,"about_ca_system_score_gemma":0.0012714311,"threshold_uncertainty_score":0.007855296},"labels":[],"label_agreement":null},{"id":"W3017967519","doi":"10.1109/tmc.2020.2983688","title":"Profit-Oriented Task Allocation for Mobile Crowdsensing With Worker Dynamics: Cooperative Offline Solution and Predictive Online Solution","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":41,"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; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Task (project management); Online and offline; Online algorithm; Crowdsourcing; Scale (ratio); Data mining; Machine learning; Algorithm; World Wide Web","score_opus":0.013444001855619246,"score_gpt":0.24202445204851356,"score_spread":0.2285804501928943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017967519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03497565,0.00051375024,0.9568458,0.000630163,0.0001142793,0.00012213862,0.00008254088,0.0002885263,0.0064270888],"genre_scores_gemma":[0.8938422,0.0002835406,0.10036053,0.00023339567,0.000096175594,0.0002313392,0.000102374586,0.00006916488,0.0047811694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936694,0.00013924357,0.000025200998,0.00018145445,0.000120768265,0.00016629201],"domain_scores_gemma":[0.9986744,0.0007569936,0.0001652094,0.00009829437,0.00016556571,0.00013950263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009314242,0.0012945826,0.0015883878,0.0005522451,0.0007767525,0.0014555155,0.0018643688,0.0016875466,0.0026023262],"category_scores_gemma":[0.0030618487,0.0005400659,0.000685092,0.0007050262,0.0009268783,0.0011510925,0.0015903254,0.0011341895,0.0003518469],"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":[0.00009997189,0.000098307995,0.00052934175,0.00008941839,0.00002503331,0.00012859778,0.00009470954,0.9653666,0.0012538969,0.007132181,0.001448176,0.023733811],"study_design_scores_gemma":[0.000008365346,0.0000145182485,0.000049918835,0.000003818086,0.000003737199,0.00001236271,0.000023930634,0.9966993,0.00013371554,0.0027852356,0.0002618128,0.0000033172864],"about_ca_topic_score_codex":0.008155917,"about_ca_topic_score_gemma":0.0060119648,"teacher_disagreement_score":0.008155917,"about_ca_system_score_codex":0.00101499,"about_ca_system_score_gemma":0.0022293483,"threshold_uncertainty_score":0.016216874},"labels":[],"label_agreement":null},{"id":"W3025029677","doi":"10.1109/tmc.2020.2994639","title":"Toward an Automated Auction Framework for Wireless Federated Learning Services Market","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":261,"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":"Dalian Science and Technology Innovation Fund; National Research Foundation of Korea; National Natural Science Foundation of China; National Research Foundation Singapore","keywords":"Computer science; Federated learning; Artificial intelligence; Machine learning; Server; Social Welfare; Computer security; World Wide Web","score_opus":0.03505927190919694,"score_gpt":0.30126244497585947,"score_spread":0.2662031730666625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025029677","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025054455,0.0001300826,0.96937484,0.0005257447,0.00006304893,0.00020315601,0.00009217388,0.00047959277,0.0040768534],"genre_scores_gemma":[0.8346932,0.00016436417,0.16033575,0.0001888696,0.00005433668,0.00026642048,0.00009594732,0.00008328118,0.004117896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973156,0.0012487058,0.000115899544,0.00041676927,0.00048525588,0.00041768645],"domain_scores_gemma":[0.9970999,0.001352865,0.0002819489,0.00043925093,0.0005143108,0.0003118245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004630624,0.000767095,0.001364284,0.00076131424,0.0012641636,0.0030999002,0.0036165821,0.0020156107,0.004680252],"category_scores_gemma":[0.007263586,0.0005690934,0.0010316692,0.0010430093,0.001617606,0.004545007,0.0027691636,0.002165279,0.0006324795],"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":[0.0003905425,0.00041136114,0.00088019593,0.00014224376,0.00007736183,0.0005291541,0.00022479614,0.52095836,0.003778918,0.4187654,0.005057306,0.048784435],"study_design_scores_gemma":[0.000033181663,0.000025418733,0.000040265386,0.000004903172,0.000005470734,0.00003790618,0.000022439806,0.9366468,0.0003320656,0.062011473,0.0008312903,0.000008701576],"about_ca_topic_score_codex":0.002493176,"about_ca_topic_score_gemma":0.0019260662,"teacher_disagreement_score":0.004680252,"about_ca_system_score_codex":0.0019653516,"about_ca_system_score_gemma":0.0034022727,"threshold_uncertainty_score":0.024489403},"labels":[],"label_agreement":null},{"id":"W3025507213","doi":"10.1109/tmc.2020.2994354","title":"Uplink Scheduling in Multi-Cell OFDMA Networks: A Comprehensive Study","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Telecommunications link; Scheduling (production processes); Goodput; Benchmark (surveying); Cellular network; Computer network; Distributed computing; Mathematical optimization; Telecommunications; Wireless","score_opus":0.02506381545907331,"score_gpt":0.25246457490581387,"score_spread":0.22740075944674054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025507213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13172285,0.13911799,0.67874694,0.002052446,0.0004274457,0.00013108527,0.00029608016,0.00033592794,0.047169328],"genre_scores_gemma":[0.8987058,0.049643926,0.04570171,0.00046444783,0.0014694392,0.000056811507,0.00017412344,0.00008640608,0.0036973066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994301,0.00016280981,0.000025952833,0.00009894531,0.00019548116,0.00008665949],"domain_scores_gemma":[0.9987173,0.0007529081,0.00013413752,0.00012476667,0.00019974427,0.00007107228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012275886,0.0006833653,0.0011636191,0.0009212243,0.0007065116,0.0019031914,0.00070315646,0.0008040365,0.0010442571],"category_scores_gemma":[0.0027762318,0.00039412413,0.00070729206,0.0018569662,0.0006121411,0.0024675052,0.00075462495,0.0010866305,0.000265601],"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":[0.00008255295,0.0002864968,0.004111489,0.00057959446,0.0001867858,0.00033550768,0.00013498,0.8021187,0.004855334,0.06308254,0.0035937012,0.120632306],"study_design_scores_gemma":[0.000007285297,0.00015577396,0.003589484,0.00014253755,0.0000670637,0.00030271354,0.00012807801,0.96076167,0.0015900038,0.021645747,0.011579037,0.00003052698],"about_ca_topic_score_codex":0.0034727643,"about_ca_topic_score_gemma":0.002825262,"teacher_disagreement_score":0.0034727643,"about_ca_system_score_codex":0.0012918666,"about_ca_system_score_gemma":0.0011395286,"threshold_uncertainty_score":0.009373248},"labels":[],"label_agreement":null},{"id":"W3038876156","doi":"10.1109/tmc.2020.3006713","title":"Prediction of Traffic Flow via Connected Vehicles","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":25,"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; Polytechnique Montréal","funders":"","keywords":"Mean squared error; Autoregressive integrated moving average; Computer science; Artificial neural network; Trajectory; Traffic flow (computer networking); Time series; Deep learning; Measure (data warehouse); Artificial intelligence; Intelligent transportation system; Flow (mathematics); Recurrent neural network; Machine learning; Data mining; Statistics; Mathematics; Engineering","score_opus":0.015966058535235583,"score_gpt":0.20195223583827507,"score_spread":0.18598617730303948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3038876156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46055144,0.0004569424,0.5297159,0.0005683544,0.00031385684,0.00006421186,0.0014627657,0.001908063,0.0049585546],"genre_scores_gemma":[0.9862298,0.0000938551,0.01196712,0.000023802817,0.000035335877,0.0000220823,0.00062622153,0.000017469787,0.0009842513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998209,0.000025308322,0.0000063518687,0.000073448675,0.000045410237,0.000028626771],"domain_scores_gemma":[0.99965286,0.0001135226,0.000049476246,0.000022652073,0.00012929794,0.00003218072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002885451,0.00080118835,0.00044005553,0.0011515816,0.00024789828,0.00051762315,0.00084628345,0.0006038777,0.0010223814],"category_scores_gemma":[0.00138202,0.00035405677,0.00040740473,0.00090064446,0.00028948553,0.0009155247,0.00042471537,0.00072767,0.0002465011],"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":[0.000050754177,0.000043014206,0.004062631,0.000012802351,0.000020587353,0.000028787275,0.000011817689,0.9708345,0.00064826716,0.0009841906,0.0007448094,0.022557914],"study_design_scores_gemma":[8.343077e-7,0.0000025809577,0.00021538156,7.168339e-7,0.0000010783892,0.0000013006791,9.764711e-7,0.9992685,0.000086987915,0.00036415114,0.00005651048,9.419612e-7],"about_ca_topic_score_codex":0.038330004,"about_ca_topic_score_gemma":0.0265126,"teacher_disagreement_score":0.038330004,"about_ca_system_score_codex":0.0008316677,"about_ca_system_score_gemma":0.00079492194,"threshold_uncertainty_score":0.07621384},"labels":[],"label_agreement":null},{"id":"W3049705125","doi":"10.1109/tmc.2021.3082927","title":"Computation Offloading in Heterogeneous Vehicular Edge Networks: On-Line and Off-Policy Bandit Solutions","year":2021,"lang":"en","type":"preprint","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Università di Bologna; Ministero dell’Istruzione, dell’Università e della Ricerca; Bundesministerium für Bildung und Forschung","keywords":"Computation offloading; Computer science; Base station; Mobile edge computing; Server; Edge computing; Computer network; Enhanced Data Rates for GSM Evolution; Latency (audio); Distributed computing; Cellular network; Network congestion; Computation; Edge device; Vehicular ad hoc network; Cloud computing; Wireless; Wireless ad hoc network; Artificial intelligence; Telecommunications","score_opus":0.030876974550178096,"score_gpt":0.28250189590348346,"score_spread":0.2516249213533054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3049705125","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10163859,0.00092762674,0.8890228,0.00062718196,0.00014934059,0.00008020474,0.00004744783,0.00039928322,0.0071074045],"genre_scores_gemma":[0.97700673,0.00025549953,0.020606454,0.00013807716,0.000050422033,0.000040522973,0.000039563238,0.000031686137,0.0018311485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994221,0.00013174013,0.000021403477,0.00011725952,0.000106404135,0.00020113947],"domain_scores_gemma":[0.9991642,0.0003793663,0.00013949719,0.00009045851,0.00012234894,0.00010416042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075578905,0.0010074812,0.0010000627,0.00036806066,0.0007201583,0.0013554695,0.0016078217,0.0010314024,0.0012466147],"category_scores_gemma":[0.0021148138,0.00028917548,0.0003751826,0.000495702,0.0008205328,0.0012524519,0.00147,0.0009620782,0.00027746797],"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":[0.00015487624,0.00010981186,0.00048671642,0.00003294912,0.000018381408,0.00009493958,0.00005854773,0.9596331,0.0011373975,0.0057911808,0.00080945756,0.03167273],"study_design_scores_gemma":[0.000003835237,0.000020653802,0.000038821283,0.0000022140666,0.0000024157923,0.000007863627,0.000015642567,0.99788696,0.0001863411,0.0017184835,0.00011487359,0.0000017854297],"about_ca_topic_score_codex":0.005738327,"about_ca_topic_score_gemma":0.0037662403,"teacher_disagreement_score":0.005738327,"about_ca_system_score_codex":0.00080393883,"about_ca_system_score_gemma":0.0009086815,"threshold_uncertainty_score":0.011409819},"labels":[],"label_agreement":null},{"id":"W3068675400","doi":"10.1109/tmc.2020.3017646","title":"Decoupled Uplink-Downlink Association in Full-Duplex Cellular Networks: A Contract-Theory Approach","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Full-Duplex Wireless Communications","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Telecommunications link; Computer science; Base station; Computer network; User equipment; Duplex (building); Cellular network; Association (psychology); Association scheme; Contract theory; Network topology; Channel (broadcasting); Wireless; Wireless network; Telecommunications; Microeconomics; Mathematics","score_opus":0.011627490306822896,"score_gpt":0.21018511727588324,"score_spread":0.19855762696906035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3068675400","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015860673,0.0008507818,0.9757273,0.0005662404,0.00006752904,0.00006844485,0.000056338424,0.00003452893,0.00676813],"genre_scores_gemma":[0.83778757,0.0023041794,0.1501274,0.00032466318,0.00024835914,0.00027914185,0.00013067405,0.000055943277,0.008742005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972933,0.0014432478,0.00007398168,0.0003146085,0.00057197595,0.00030289154],"domain_scores_gemma":[0.9955655,0.0029620165,0.00042787503,0.00028955325,0.00047721367,0.0002778595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003702622,0.000992914,0.0016855019,0.0008143282,0.0013063374,0.0030957386,0.002284847,0.0024337487,0.003090679],"category_scores_gemma":[0.00683966,0.0009923829,0.00095707003,0.0020615326,0.0032151316,0.0038092753,0.0023433028,0.0026454534,0.00059339165],"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":[0.00005352691,0.00009710476,0.00048454967,0.000095104246,0.000029202805,0.00019976296,0.00013319448,0.7679811,0.0007879409,0.21063718,0.0012710532,0.018230278],"study_design_scores_gemma":[0.000008616248,0.000023838053,0.00005974238,0.000009736801,0.0000045107454,0.000041958036,0.000030693525,0.9614207,0.0001383544,0.037533436,0.0007174228,0.000011001614],"about_ca_topic_score_codex":0.004525628,"about_ca_topic_score_gemma":0.002997032,"teacher_disagreement_score":0.004525628,"about_ca_system_score_codex":0.003136611,"about_ca_system_score_gemma":0.0029029646,"threshold_uncertainty_score":0.022757769},"labels":[],"label_agreement":null},{"id":"W3082183248","doi":"10.1109/tmc.2021.3073772","title":"Distributed Cooperation Under Uncertainty in Drone-Based Wireless Networks: A Bayesian Coalitional Game","year":2021,"lang":"en","type":"preprint","venue":"IEEE Transactions on Mobile Computing","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Drone; Pooling; Computer science; Unavailability; Game theory; Bayesian game; Wireless network; Wireless; Bayesian probability; Divergence (linguistics); Mathematical optimization; Operations research; Sequential game; Artificial intelligence; Economics; Telecommunications; Mathematical economics; Engineering; Mathematics","score_opus":0.009505673007928168,"score_gpt":0.2288612947084637,"score_spread":0.21935562170053552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082183248","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078659944,0.00032689952,0.9145965,0.00055823044,0.00003509236,0.00007759473,0.000069983835,0.000057765166,0.0056178914],"genre_scores_gemma":[0.9633508,0.0003829688,0.033112463,0.000103328086,0.000038605325,0.00014494135,0.000044345346,0.000020457275,0.0028020933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985261,0.00074259687,0.000049180646,0.00021956312,0.0002935382,0.0001690186],"domain_scores_gemma":[0.99653316,0.0025387234,0.0004100317,0.00012813552,0.00021250511,0.0001773932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023306587,0.0010824837,0.0014185004,0.0006350194,0.0006600844,0.0015377948,0.0018960113,0.0018405915,0.0012577208],"category_scores_gemma":[0.006266947,0.0005185486,0.0007216185,0.0008151326,0.0021247892,0.002672569,0.0020055233,0.001452663,0.000174155],"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":[0.000079805555,0.000044877914,0.00045161776,0.00005066383,0.000052569674,0.00025330565,0.00020583293,0.9282327,0.0009855767,0.06396965,0.0003609639,0.0053123706],"study_design_scores_gemma":[0.0000138939295,0.000022381224,0.0000704142,0.0000051239413,0.000007862425,0.000022592567,0.000030294215,0.9842643,0.00012874816,0.015179465,0.00024600024,0.000008826972],"about_ca_topic_score_codex":0.0053143394,"about_ca_topic_score_gemma":0.002814474,"teacher_disagreement_score":0.0053143394,"about_ca_system_score_codex":0.0014325503,"about_ca_system_score_gemma":0.00096722407,"threshold_uncertainty_score":0.012325883},"labels":[],"label_agreement":null},{"id":"W3088881913","doi":"10.1109/tmc.2020.3025116","title":"Partial Computation Offloading and Adaptive Task Scheduling for 5G-Enabled Vehicular Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":202,"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; Simon Fraser University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Computation offloading; Scheduling (production processes); Distributed computing; Computer network; Computation; Dynamic priority scheduling; Vehicular ad hoc network; Stochastic game; Mathematical optimization; Wireless; Wireless ad hoc network; Edge computing; Internet of Things; Quality of service; Algorithm; Computer security","score_opus":0.024726931463749238,"score_gpt":0.25081265574565154,"score_spread":0.2260857242819023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088881913","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048629552,0.00049837324,0.9461112,0.00022817991,0.00010558376,0.00006978926,0.00006363979,0.00022196314,0.004071714],"genre_scores_gemma":[0.95177007,0.0002753612,0.04598227,0.000060305894,0.00005294397,0.000054854965,0.000068418405,0.000032315333,0.0017033492],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993755,0.00015515162,0.000031054184,0.00013396631,0.00012543201,0.00017887772],"domain_scores_gemma":[0.9995467,0.00017580554,0.000060488976,0.00007663131,0.00008216339,0.00005807983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005825654,0.0007917197,0.00074159814,0.00039594906,0.00077544386,0.00074256235,0.0010141114,0.0004634413,0.0010740735],"category_scores_gemma":[0.001367299,0.00025313176,0.00044533963,0.00078299997,0.0005661149,0.00089390384,0.00091155217,0.0005314043,0.00016471892],"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":[0.0002358218,0.000070504066,0.0007520541,0.0001238752,0.000039795497,0.0001507687,0.00012749518,0.8709447,0.009436372,0.039036386,0.0025088524,0.07657341],"study_design_scores_gemma":[0.0000063730786,0.000039462564,0.00014144991,0.0000040684868,0.0000071109234,0.000033363835,0.000028452512,0.9881684,0.0008965092,0.009674569,0.0009935313,0.0000067296246],"about_ca_topic_score_codex":0.004163946,"about_ca_topic_score_gemma":0.0060300515,"teacher_disagreement_score":0.004163946,"about_ca_system_score_codex":0.00078638946,"about_ca_system_score_gemma":0.0015788662,"threshold_uncertainty_score":0.008279443},"labels":[],"label_agreement":null},{"id":"W3089314236","doi":"10.1109/tmc.2020.3025201","title":"Low-Latency and Fresh Content Provision in Information-Centric Vehicular Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Age of Information Optimization","field":"Computer Science","cited_by":63,"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; Toronto Metropolitan University","funders":"National Key Research and Development Program of China; Beijing Municipal Natural Science Foundation","keywords":"Computer science; Cache; Latency (audio); Content delivery; Computer network; Information-centric networking; Bandwidth (computing); Telecommunications","score_opus":0.012585359209285336,"score_gpt":0.20514561151961874,"score_spread":0.1925602523103334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089314236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26544273,0.0054073506,0.72162163,0.00045841304,0.00023032042,0.00014114697,0.00022537158,0.0008764516,0.0055966],"genre_scores_gemma":[0.98823464,0.0005289928,0.010489037,0.00003383961,0.000028016535,0.000017633884,0.000063350046,0.000015758835,0.0005886611],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999081,0.00019022249,0.000045083234,0.00013508592,0.000250131,0.0002984218],"domain_scores_gemma":[0.9983303,0.0006446214,0.00020684913,0.00021281425,0.0005125435,0.000092890565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009473645,0.0007462391,0.0008032305,0.00070608215,0.0009315849,0.001503278,0.001558593,0.000712033,0.0005585214],"category_scores_gemma":[0.0034083512,0.00029688972,0.00026686155,0.001333542,0.00067542016,0.0020270352,0.0009791707,0.00050951866,0.00017151085],"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":[0.00078709057,0.00011570304,0.0064249616,0.000674135,0.00013442463,0.00080524303,0.0005090613,0.7767847,0.034304474,0.04560734,0.003642934,0.13020985],"study_design_scores_gemma":[0.000013872138,0.00018794571,0.0007988977,0.000018928462,0.00005528356,0.0002558915,0.00023443528,0.97830325,0.007993691,0.008629026,0.003482515,0.000026185677],"about_ca_topic_score_codex":0.007394602,"about_ca_topic_score_gemma":0.009122414,"teacher_disagreement_score":0.007394602,"about_ca_system_score_codex":0.0017396815,"about_ca_system_score_gemma":0.001539057,"threshold_uncertainty_score":0.014703155},"labels":[],"label_agreement":null},{"id":"W3104711241","doi":"10.1109/tmc.2020.3038710","title":"Online Bitrate Selection for Viewport Adaptive 360-Degree Video Streaming","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Image and Video Quality Assessment","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 British Columbia","funders":"","keywords":"Viewport; Computer science; Variable bitrate; Quality of experience; Upload; Constant bitrate; Streaming algorithm; Video quality; Bandwidth (computing); Dynamic Adaptive Streaming over HTTP; Real-time computing; Multimedia; Computer vision; Quality of service; Bit rate; Computer network; Upper and lower bounds; Metric (unit)","score_opus":0.08319445523496753,"score_gpt":0.32464107369505935,"score_spread":0.2414466184600918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104711241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047852717,0.000839162,0.9491326,0.00011679339,0.000045125966,0.00006261094,0.00004956647,0.0007274087,0.001173977],"genre_scores_gemma":[0.8526815,0.00048428727,0.14545795,0.00009381123,0.00007633421,0.00008100959,0.000111439396,0.00009227459,0.00092143484],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994011,0.00014029416,0.000033102395,0.00015566158,0.00019476093,0.00007507658],"domain_scores_gemma":[0.99881566,0.0005799163,0.0001437182,0.00013583241,0.00022608411,0.00009882037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085631874,0.0008872337,0.0009135342,0.00050190307,0.0003377926,0.0006998872,0.0011557521,0.00046020673,0.00082868146],"category_scores_gemma":[0.0041835546,0.00032745002,0.00033068287,0.0004407077,0.000507503,0.0010636912,0.0007121412,0.0009183349,0.00031209408],"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":[0.0009409801,0.00035708083,0.0036579077,0.00018666327,0.000064009095,0.00020606838,0.00030259456,0.436831,0.0578487,0.0072417567,0.0032713923,0.4890919],"study_design_scores_gemma":[0.00001954106,0.00006139372,0.0003882153,0.000007211186,0.000008239864,0.00008325667,0.000014880025,0.9937645,0.0041310233,0.0011485426,0.00036148343,0.000011757448],"about_ca_topic_score_codex":0.003042321,"about_ca_topic_score_gemma":0.002390971,"teacher_disagreement_score":0.003042321,"about_ca_system_score_codex":0.00052260124,"about_ca_system_score_gemma":0.00077288505,"threshold_uncertainty_score":0.006049216},"labels":[],"label_agreement":null},{"id":"W3105504538","doi":"10.1109/tmc.2020.3036871","title":"Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systems","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":531,"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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Reinforcement learning; Mobile edge computing; Task (project management); Enhanced Data Rates for GSM Evolution; Edge computing; Node (physics); Mobile device; Edge device; Distributed computing; Exploit; Computer network; Real-time computing; Artificial intelligence; Cloud computing; Operating system","score_opus":0.02226726386392214,"score_gpt":0.2558794744901244,"score_spread":0.23361221062620224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105504538","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10697405,0.00080835016,0.88732725,0.000642511,0.00013080843,0.000060533883,0.00006960141,0.0007249163,0.0032619569],"genre_scores_gemma":[0.972148,0.00012044064,0.02575351,0.00018010722,0.000025803829,0.000044048447,0.00006231335,0.000031579715,0.0016341654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999673,0.000069516725,0.000015179643,0.000084110994,0.00005298074,0.00010516722],"domain_scores_gemma":[0.999288,0.0003969284,0.00007249667,0.0000406806,0.00013565128,0.00006611191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007866394,0.0007301296,0.000927952,0.00022917347,0.00036606117,0.0005716869,0.0011419904,0.0008112817,0.0014051247],"category_scores_gemma":[0.0021150522,0.00034536183,0.00027394152,0.0002956587,0.0006430544,0.0010045967,0.0008355659,0.0012650549,0.00019561518],"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":[0.00012665588,0.000100687794,0.0009353406,0.00005202515,0.000022098144,0.00006550406,0.000046160614,0.94892293,0.0015577849,0.0025795586,0.0012294943,0.044361666],"study_design_scores_gemma":[0.0000036763383,0.000008076895,0.00003790689,0.0000012997176,0.0000014180955,0.0000023597393,0.0000025076522,0.99889284,0.00011066988,0.0008902634,0.000047981266,0.0000010105997],"about_ca_topic_score_codex":0.00919464,"about_ca_topic_score_gemma":0.008662745,"teacher_disagreement_score":0.00919464,"about_ca_system_score_codex":0.0009690657,"about_ca_system_score_gemma":0.0012517972,"threshold_uncertainty_score":0.018282235},"labels":[],"label_agreement":null},{"id":"W3107627141","doi":"10.1109/tmc.2020.3040945","title":"A Reinforcement Learning Framework for Efficient Informative Sensing","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Crowdsensing and Crowdsourcing","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":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Computer science; Motion planning; Path (computing); Inference; Process (computing); Plan (archaeology); Mathematical optimization; Artificial intelligence; Robot; Distributed computing; Mathematics","score_opus":0.01973670981837335,"score_gpt":0.25837263188972825,"score_spread":0.2386359220713549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107627141","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053976597,0.00029701443,0.9909689,0.00022437367,0.000046979407,0.00003913476,0.00006882699,0.00035421105,0.0026028557],"genre_scores_gemma":[0.7419256,0.0005373155,0.24936984,0.00031183247,0.00012530606,0.00036061293,0.00026468994,0.0001585864,0.006946178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934393,0.0001935164,0.000028351053,0.00017796004,0.00014054218,0.00011562073],"domain_scores_gemma":[0.99871576,0.0008046067,0.00012444278,0.00009019262,0.00016110069,0.00010396672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011201601,0.0011922215,0.001346974,0.0005453442,0.00053318666,0.0008909039,0.0020106798,0.0012562985,0.0041244444],"category_scores_gemma":[0.003205427,0.0005996342,0.0008345442,0.00060043106,0.0011562075,0.001324065,0.0015003205,0.0022620212,0.000585288],"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":[0.00008334304,0.000062578765,0.00033380958,0.00008974464,0.000036597794,0.000094342904,0.000058519818,0.93223786,0.001367441,0.02989459,0.0019605246,0.03378064],"study_design_scores_gemma":[0.000014773348,0.000017513179,0.0000296001,0.0000048475877,0.0000042742863,0.00000996509,0.000004299339,0.98895186,0.00014654713,0.010281167,0.00053082436,0.000004320251],"about_ca_topic_score_codex":0.0077996356,"about_ca_topic_score_gemma":0.0066930605,"teacher_disagreement_score":0.0077996356,"about_ca_system_score_codex":0.001284387,"about_ca_system_score_gemma":0.0020337221,"threshold_uncertainty_score":0.015508473},"labels":[],"label_agreement":null},{"id":"W3111189508","doi":"10.1109/tmc.2020.3042925","title":"Online Altitude Control and Scheduling Policy for Minimizing AoI in UAV-assisted IoT Wireless Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Age of Information Optimization","field":"Computer Science","cited_by":126,"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; Markov decision process; Lyapunov optimization; Scheduling (production processes); Reinforcement learning; Computer network; Base station; Online algorithm; Optimization problem; Upload; Wireless; Channel state information; Software deployment; Wireless network; Real-time computing; Markov process; Distributed computing; Mathematical optimization; Telecommunications; Artificial intelligence","score_opus":0.017582990612235838,"score_gpt":0.2640407087262677,"score_spread":0.2464577181140319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111189508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0835347,0.00071808766,0.91066355,0.0004964635,0.000097763295,0.00008645115,0.00010008474,0.0002791109,0.0040238886],"genre_scores_gemma":[0.9770786,0.00019619557,0.021269504,0.00007938926,0.000026487009,0.000052812677,0.000048325088,0.000025623292,0.0012231075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995801,0.000094301366,0.000018943554,0.00011200039,0.000077811135,0.00011682963],"domain_scores_gemma":[0.99884975,0.00064142945,0.00023845329,0.000044067336,0.00013575403,0.00009048396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007004174,0.0008381846,0.00099219,0.00036016403,0.00042962638,0.00075841096,0.0008811279,0.00083736447,0.0013540514],"category_scores_gemma":[0.002329114,0.00043673304,0.00030472834,0.00033945986,0.00078731513,0.0008799654,0.00089630065,0.000896173,0.00017661307],"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":[0.000053571523,0.000042414184,0.00062243815,0.00005324757,0.00001732956,0.00007190552,0.000037722697,0.9832371,0.0013568216,0.0031331517,0.0004968019,0.010877505],"study_design_scores_gemma":[0.0000045196994,0.000020650576,0.00009593482,0.0000034439754,0.0000038714347,0.000007356683,0.000009180327,0.9986449,0.00016609614,0.0009510418,0.000090899026,0.0000021984151],"about_ca_topic_score_codex":0.0064663105,"about_ca_topic_score_gemma":0.0046700896,"teacher_disagreement_score":0.0064663105,"about_ca_system_score_codex":0.0008704786,"about_ca_system_score_gemma":0.0014754407,"threshold_uncertainty_score":0.012857318},"labels":[],"label_agreement":null},{"id":"W3112548650","doi":"10.1109/tmc.2020.3043736","title":"Joint Server Selection, Cooperative Offloading and Handover in Multi-access Edge Computing Wireless Network: A Deep Reinforcement Learning Approach","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":97,"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; Server; Mobile edge computing; Computation offloading; Reinforcement learning; Wireless network; Handover; Computer network; Distributed computing; Edge computing; Wireless; Enhanced Data Rates for GSM Evolution; Artificial intelligence","score_opus":0.04258256282409849,"score_gpt":0.2678853654331454,"score_spread":0.2253028026090469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112548650","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054463312,0.0005683399,0.9409902,0.0006520021,0.000059045353,0.000058362355,0.000038519614,0.0004439383,0.002726224],"genre_scores_gemma":[0.9484966,0.0001722817,0.048367746,0.00025863948,0.00003924581,0.000088433364,0.00007060198,0.000041005456,0.0024655173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995622,0.00011439201,0.000020013062,0.00010050046,0.000076750766,0.00012615252],"domain_scores_gemma":[0.99886715,0.00069206615,0.00012216218,0.00004547591,0.00016406017,0.00010910445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010062649,0.00081350264,0.0013863728,0.00033306598,0.00041793505,0.00080684706,0.001587653,0.0011701961,0.0016960711],"category_scores_gemma":[0.0022826153,0.00047470172,0.0004091064,0.0003669485,0.0009705593,0.0009934353,0.0011626829,0.0015551345,0.00020781215],"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":[0.000074169766,0.000068640904,0.00085749396,0.000035616205,0.000021050328,0.00006511483,0.000040530824,0.96751404,0.00061556045,0.0027265034,0.00064654514,0.027334856],"study_design_scores_gemma":[0.000003548594,0.0000068744525,0.000025528858,0.0000011860009,0.0000016429334,0.0000028060392,0.0000024125522,0.9995011,0.000046089972,0.0003752884,0.000032566448,9.907111e-7],"about_ca_topic_score_codex":0.012892303,"about_ca_topic_score_gemma":0.009576973,"teacher_disagreement_score":0.012892303,"about_ca_system_score_codex":0.0011299143,"about_ca_system_score_gemma":0.0017435272,"threshold_uncertainty_score":0.025634527},"labels":[],"label_agreement":null},{"id":"W3118639493","doi":"10.1109/tmc.2021.3051665","title":"Boosting Chirp Signal Based Aerial Acoustic Communication Under Dynamic Channel Conditions","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","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":"McMaster University","funders":"","keywords":"Computer science; Bit error rate; Fading; Robustness (evolution); Channel (broadcasting); Multipath propagation; Communications system; Chirp; Transmission (telecommunications); Real-time computing; Data transmission; Algorithm; Telecommunications; Electronic engineering; Computer network; Engineering","score_opus":0.01439244555626429,"score_gpt":0.2411125298944728,"score_spread":0.2267200843382085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118639493","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2246329,0.00096159696,0.76240176,0.00036464827,0.00013791873,0.000086131295,0.00007714238,0.0012720188,0.01006589],"genre_scores_gemma":[0.889268,0.0005925436,0.10547207,0.00018341481,0.00006800122,0.00006124059,0.00011886335,0.00004454439,0.0041914256],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997874,0.000029567944,0.0000071389686,0.00004078661,0.00011304596,0.000022145008],"domain_scores_gemma":[0.9995782,0.00013543056,0.0000719818,0.00006331179,0.00012687629,0.000024231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018852294,0.0005483755,0.00027555932,0.00043994503,0.00026050664,0.00024204007,0.000451839,0.0003504087,0.0010121415],"category_scores_gemma":[0.0008936525,0.00012347744,0.00014229068,0.00037588336,0.00037948496,0.0005945009,0.0005944006,0.00040461667,0.000640926],"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":[0.0004027134,0.000090379915,0.0018500991,0.00022138488,0.000032306692,0.0004234454,0.00026636609,0.060676094,0.5776135,0.006584541,0.001836858,0.35000223],"study_design_scores_gemma":[0.000046062953,0.00055853906,0.0024768151,0.000041765692,0.000051839335,0.0008107486,0.00013319646,0.7411677,0.24269804,0.0035179397,0.008441651,0.00005571837],"about_ca_topic_score_codex":0.00062865455,"about_ca_topic_score_gemma":0.0010115317,"teacher_disagreement_score":0.0010121415,"about_ca_system_score_codex":0.00018936573,"about_ca_system_score_gemma":0.0002653226,"threshold_uncertainty_score":0.003385961},"labels":[],"label_agreement":null},{"id":"W3132420509","doi":"10.1109/tmc.2021.3058787","title":"Measuring Roaming in Europe: Infrastructure and Implications on Users’ QoE","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Image and Video Quality Assessment","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":"Western University","funders":"Horizon 2020 Framework Programme; European Commission","keywords":"Roaming; Quality of experience; Computer science; Computer network; Telecommunications; Latency (audio); Order (exchange); Computer security; Quality of service; Business; Finance","score_opus":0.03407407743208634,"score_gpt":0.28650872601408955,"score_spread":0.2524346485820032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132420509","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98490936,0.00023619691,0.008978055,0.00012826332,0.000012898711,0.000050756928,0.00030066507,0.00007399895,0.005309732],"genre_scores_gemma":[0.9971199,0.00010548912,0.0024017275,0.00002770045,0.000005083601,0.000017575183,0.0002059103,0.000006688369,0.00010985558],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988518,0.0005273861,0.00006975516,0.0001681966,0.00019101369,0.00019180593],"domain_scores_gemma":[0.9976731,0.0007999264,0.0005621924,0.00032167672,0.00046765586,0.0001754812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016821144,0.00029486406,0.0002788385,0.0010572402,0.000334118,0.0012294227,0.00036648018,0.00046994849,0.0004692106],"category_scores_gemma":[0.0048082145,0.00013028228,0.00018329822,0.0019233128,0.0003883509,0.0014162954,0.001019243,0.00031692692,0.0001181247],"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":[0.00043862613,0.00029110754,0.7959513,0.00022115321,0.0002757656,0.00051302055,0.0017491733,0.040543012,0.020020159,0.0057864725,0.0014961321,0.13271411],"study_design_scores_gemma":[0.00002213296,0.0008367805,0.9285716,0.00009451725,0.00012623765,0.00089035335,0.0029633844,0.048404202,0.008797279,0.0023317516,0.006891622,0.0000701012],"about_ca_topic_score_codex":0.004457543,"about_ca_topic_score_gemma":0.003539482,"teacher_disagreement_score":0.004457543,"about_ca_system_score_codex":0.0003665796,"about_ca_system_score_gemma":0.0002514638,"threshold_uncertainty_score":0.008895993},"labels":[],"label_agreement":null},{"id":"W3135807060","doi":"10.1109/tmc.2021.3062775","title":"Privacy-Preserving Streaming Truth Discovery in Crowdsourcing With Differential Privacy","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Crowdsensing and Crowdsourcing","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","funders":"Higher Education Discipline Innovation Project; Natural Science Foundation of Hainan Province; National Natural Science Foundation of China","keywords":"Differential privacy; Crowdsourcing; Computer science; Internet privacy; Privacy protection; Information privacy; Privacy software; Computer security; World Wide Web; Data mining","score_opus":0.0116516055892134,"score_gpt":0.23471800594834075,"score_spread":0.22306640035912734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135807060","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019920932,0.0004761892,0.97462434,0.00085039774,0.00008640573,0.00019135099,0.00046258036,0.0007209352,0.0026669987],"genre_scores_gemma":[0.8472672,0.00050314516,0.14675912,0.0005456833,0.0002100733,0.000386276,0.0006049479,0.00011651289,0.003606975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9922816,0.0020405224,0.0004898356,0.0023445091,0.0021010053,0.0007424778],"domain_scores_gemma":[0.98410046,0.0087240925,0.0013269754,0.0037574836,0.0015448376,0.00054628844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060292482,0.001131002,0.0022185873,0.0011428414,0.0019639865,0.0032309315,0.0037810542,0.002223611,0.002383991],"category_scores_gemma":[0.025814585,0.0007768395,0.0015687362,0.0026022657,0.0022084685,0.005451322,0.0063124886,0.0026910857,0.00065368693],"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":[0.0015318984,0.00023166728,0.005862798,0.00076701894,0.00027194573,0.0016373827,0.0019770523,0.5097867,0.015795246,0.25912467,0.009024667,0.19398895],"study_design_scores_gemma":[0.000068106936,0.00007257998,0.0003943357,0.000029423709,0.000035427853,0.00023850247,0.00013877856,0.85042316,0.0031014169,0.14257894,0.0028804084,0.00003890046],"about_ca_topic_score_codex":0.0038032474,"about_ca_topic_score_gemma":0.0027921638,"teacher_disagreement_score":0.0060292482,"about_ca_system_score_codex":0.0026058485,"about_ca_system_score_gemma":0.0035427879,"threshold_uncertainty_score":0.0318861},"labels":[],"label_agreement":null},{"id":"W3159475051","doi":"10.1109/tmc.2021.3074917","title":"QoE-Aware Efficient Content Distribution Scheme For Satellite-Terrestrial Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":154,"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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Cache; Quality of experience; Computer network; Network topology; Distributed computing; Real-time computing; Quality of service","score_opus":0.035296122757390865,"score_gpt":0.2549687856965256,"score_spread":0.21967266293913473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159475051","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.114960045,0.0006389799,0.8794103,0.00024340251,0.000103796774,0.00016103749,0.00011558768,0.00072060514,0.003646126],"genre_scores_gemma":[0.96321905,0.00022540313,0.0350147,0.000067479654,0.000041014602,0.00005698801,0.000103396254,0.00002959574,0.0012423826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994911,0.00008931899,0.00003199684,0.00011488983,0.00016943415,0.00010319776],"domain_scores_gemma":[0.99890363,0.00030735251,0.00011352399,0.00012906358,0.0004442148,0.00010231537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005038965,0.00060949224,0.000838106,0.0007186568,0.00090329186,0.00074334966,0.0015415157,0.00040803998,0.0010284489],"category_scores_gemma":[0.002260271,0.00022335138,0.00031564117,0.0009684551,0.00042207938,0.0014550781,0.000945815,0.00043992887,0.00019477209],"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":[0.0010471508,0.00050432753,0.0061160997,0.00034734793,0.00014201952,0.0006741364,0.00070298364,0.51228946,0.10778216,0.024579903,0.00888894,0.33692545],"study_design_scores_gemma":[0.000022296528,0.00009000921,0.00043502852,0.0000048151933,0.000022625734,0.00011053725,0.000054710046,0.9916523,0.004295986,0.0023450488,0.0009525395,0.000014062258],"about_ca_topic_score_codex":0.0046674856,"about_ca_topic_score_gemma":0.006794728,"teacher_disagreement_score":0.0046674856,"about_ca_system_score_codex":0.0012506322,"about_ca_system_score_gemma":0.0009543456,"threshold_uncertainty_score":0.009280622},"labels":[],"label_agreement":null},{"id":"W3160147183","doi":"10.1109/tmc.2021.3080714","title":"Location Privacy-Preserving Task Recommendation With Geometric Range Query in Mobile Crowdsensing","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":83,"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; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Crowdsensing; Task (project management); Mobile computing; Privacy protection; Range query (database); Information privacy; Mobile device; Range (aeronautics); Information retrieval; Web search query; World Wide Web; Computer network; Sargable; Computer security; Search engine","score_opus":0.015299288162377552,"score_gpt":0.24793596530437467,"score_spread":0.23263667714199712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160147183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029499928,0.00082984235,0.9647079,0.0004716778,0.00009193383,0.0002623976,0.00033680216,0.001391769,0.0024077697],"genre_scores_gemma":[0.82174176,0.00051611767,0.17200257,0.0003884597,0.00013804644,0.00031229857,0.00046661793,0.0000977903,0.0043362477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9938334,0.0014452729,0.00043539202,0.0014962741,0.002050224,0.0007394993],"domain_scores_gemma":[0.9913659,0.0024536129,0.00080392574,0.0040928973,0.0008949468,0.00038861946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026293134,0.0011794498,0.0027204743,0.000870095,0.0018828256,0.0017056656,0.0035438957,0.0021775258,0.002461249],"category_scores_gemma":[0.01054019,0.0006207009,0.0013076233,0.0025765784,0.001394002,0.004884826,0.0049611693,0.0017950935,0.0013995004],"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":[0.0026669821,0.0006024001,0.0049783704,0.0009277552,0.00027766032,0.0014866347,0.001873614,0.4248998,0.04475236,0.085861474,0.0164381,0.41523483],"study_design_scores_gemma":[0.00015467522,0.00031328024,0.0006003887,0.000026414937,0.000051864085,0.00072070904,0.00030514356,0.94190365,0.010121527,0.039989606,0.0057131434,0.00009959163],"about_ca_topic_score_codex":0.004112182,"about_ca_topic_score_gemma":0.0031142416,"teacher_disagreement_score":0.004112182,"about_ca_system_score_codex":0.0014292989,"about_ca_system_score_gemma":0.002051701,"threshold_uncertainty_score":0.013905287},"labels":[],"label_agreement":null},{"id":"W3171631644","doi":"10.1109/tmc.2021.3086687","title":"Maximization of Value of Service for Mobile Collaborative Computing Through Situation-Aware Task Offloading","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","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":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computation offloading; Distributed computing; Mobile computing; Mobile device; Mobile edge computing; Quality of service; Mobile cloud computing; Provisioning; Partition (number theory); Computer network; Server; Cloud computing; Edge computing","score_opus":0.016706698316556964,"score_gpt":0.27598628281227816,"score_spread":0.2592795844957212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171631644","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024177395,0.00035791658,0.9723256,0.00020419346,0.00004024836,0.00005545022,0.00002762794,0.00008017413,0.0027313281],"genre_scores_gemma":[0.9032344,0.0003582005,0.094994284,0.00005187738,0.000047205893,0.00011920827,0.000049905455,0.000040518556,0.0011044084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991215,0.00032495163,0.00003394266,0.00013446108,0.00022378519,0.00016135529],"domain_scores_gemma":[0.99891174,0.00068098766,0.000108204695,0.00006422231,0.00013882514,0.000096001364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010706236,0.0009333467,0.0012410578,0.00057110767,0.0004628508,0.0012153358,0.0012418795,0.00080807664,0.0010230897],"category_scores_gemma":[0.0032422477,0.0003377757,0.000604642,0.0008518337,0.0007344808,0.0013841913,0.0015621865,0.00090762187,0.00017124924],"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":[0.00015190829,0.000119840544,0.00059857505,0.00015645588,0.000051760497,0.00015758001,0.00016148546,0.90273565,0.007075669,0.03706908,0.0013068209,0.050415095],"study_design_scores_gemma":[0.0000050588096,0.000024513422,0.00006771408,0.000004704809,0.0000047479584,0.000018656548,0.000019142886,0.99396956,0.00040402316,0.0051254216,0.0003513523,0.0000051274105],"about_ca_topic_score_codex":0.0018678788,"about_ca_topic_score_gemma":0.0017805006,"teacher_disagreement_score":0.0018678788,"about_ca_system_score_codex":0.0009821787,"about_ca_system_score_gemma":0.0012814112,"threshold_uncertainty_score":0.0071262717},"labels":[],"label_agreement":null},{"id":"W3176790016","doi":"10.1109/tmc.2021.3093259","title":"Acoustic Software Defined Platform: A Versatile Sensing and General Benchmarking Platform","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","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":"McMaster University","funders":"","keywords":"Computer science; Benchmarking; Software; Usability; Interface (matter); User interface; Focus (optics); Ubiquitous computing; Embedded system; Graphical user interface; Human–computer interaction; Software engineering; Operating system","score_opus":0.012017022441308268,"score_gpt":0.21554543594089662,"score_spread":0.20352841349958836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176790016","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07035136,0.0010454877,0.75860816,0.00068220636,0.00056981505,0.001744637,0.0022541676,0.14980052,0.014943596],"genre_scores_gemma":[0.6123695,0.00089360203,0.34502307,0.00085071987,0.00016958266,0.002587846,0.011477301,0.017867506,0.008760889],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9946673,0.0011572983,0.0005241862,0.0009288694,0.0019810796,0.0007412694],"domain_scores_gemma":[0.9959019,0.0008109362,0.00029773105,0.0013273751,0.001083185,0.0005787939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052268403,0.002049689,0.00092620385,0.0019036097,0.0005958226,0.0023826791,0.0039249873,0.001124171,0.0037484628],"category_scores_gemma":[0.009530503,0.0009581941,0.0009239581,0.00093562517,0.0010751899,0.0036730815,0.004136692,0.003073803,0.002216495],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004931975,0.002166722,0.02142292,0.0031221323,0.0006676912,0.0020246382,0.0023876007,0.078228824,0.20892689,0.08155886,0.101924926,0.49263677],"study_design_scores_gemma":[0.000752464,0.0029037085,0.010003954,0.0003846862,0.00030031306,0.0014308203,0.00049083825,0.54463476,0.18015169,0.018889243,0.23955181,0.00050574634],"about_ca_topic_score_codex":0.0023043314,"about_ca_topic_score_gemma":0.0009638279,"teacher_disagreement_score":0.0052268403,"about_ca_system_score_codex":0.0008901453,"about_ca_system_score_gemma":0.0021581908,"threshold_uncertainty_score":0.027642488},"labels":[],"label_agreement":null},{"id":"W3182470338","doi":"10.1109/tmc.2021.3093316","title":"Matrix Gaussian Mechanisms for Differentially-Private Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Differential privacy; Computer science; Scalability; Novelty; Gaussian; Dimensionality reduction; Information privacy; Theoretical computer science; Artificial intelligence; Machine learning; Data mining; Computer security","score_opus":0.021123104044203554,"score_gpt":0.282879726962925,"score_spread":0.2617566229187215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3182470338","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008793287,0.0003011231,0.9876853,0.00052288023,0.00006014553,0.00010935503,0.00014151282,0.000552004,0.0018344575],"genre_scores_gemma":[0.7675317,0.00073482504,0.22329424,0.0009109771,0.00030460456,0.00063279306,0.000327435,0.00020287787,0.006060515],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9898129,0.0040202406,0.000549084,0.0016049899,0.003032791,0.0009799502],"domain_scores_gemma":[0.98311526,0.007005359,0.0014427501,0.006334636,0.0015485943,0.0005533169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0112314895,0.0011888093,0.0014509257,0.0013998381,0.0014454218,0.0033434203,0.0048012943,0.0024988842,0.0044615506],"category_scores_gemma":[0.031688966,0.0006079864,0.0014741991,0.002532802,0.0031899286,0.0077313855,0.00639812,0.0038730411,0.0011642012],"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":[0.0003908907,0.00018891157,0.00083631417,0.00018836108,0.00009037664,0.00015741492,0.00025528166,0.09932221,0.004370196,0.79776293,0.005013441,0.09142376],"study_design_scores_gemma":[0.00006763937,0.00013620127,0.00019367668,0.000035391506,0.00003261326,0.0002045573,0.00003795184,0.50822246,0.005039395,0.48068386,0.0053027757,0.000043511292],"about_ca_topic_score_codex":0.00066455785,"about_ca_topic_score_gemma":0.0005800425,"teacher_disagreement_score":0.0112314895,"about_ca_system_score_codex":0.0027104935,"about_ca_system_score_gemma":0.00272109,"threshold_uncertainty_score":0.059398532},"labels":[],"label_agreement":null},{"id":"W3192883372","doi":"10.1109/tmc.2021.3076088","title":"Joint Observation and Transmission Scheduling in Agile Satellite Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Satellite Communication Systems","field":"Engineering","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":"University of Toronto","funders":"Natural Science Basic Research Program of Shaanxi Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Initialization; Agile software development; Scheduling (production processes); Data transmission; Integer programming; Real-time computing; Population; Transmission (telecommunications); Algorithm; Mathematical optimization; Computer network; Telecommunications","score_opus":0.02943505902220186,"score_gpt":0.24160857474381808,"score_spread":0.21217351572161622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192883372","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05518207,0.00020552916,0.942491,0.000120939985,0.000032989767,0.000059230963,0.00004927025,0.00015139702,0.0017076023],"genre_scores_gemma":[0.8394242,0.00025937086,0.15798762,0.000056844976,0.00004604922,0.00017653666,0.0000896755,0.00005735418,0.0019024132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922466,0.00030438273,0.000028239036,0.00015808677,0.00012309321,0.00016156166],"domain_scores_gemma":[0.9990458,0.00052414957,0.0001778481,0.0000656744,0.00009319555,0.00009333681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00122059,0.00066310994,0.0007457308,0.00031822702,0.0004919094,0.00064022915,0.0008638366,0.0005143243,0.0010551573],"category_scores_gemma":[0.001848433,0.00041109734,0.0004403621,0.0006809449,0.00060889655,0.00075373595,0.00079928496,0.00071052107,0.00015249864],"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":[0.000062488165,0.00002739851,0.00035039036,0.00003615779,0.000015179819,0.000076036165,0.00005841894,0.97545093,0.0017736829,0.0053773625,0.00038535,0.016386585],"study_design_scores_gemma":[0.000009986182,0.000030220577,0.00008263836,0.000001998286,0.000004196514,0.000015068938,0.000019457872,0.996639,0.00042819898,0.002514734,0.00025113483,0.000003401135],"about_ca_topic_score_codex":0.0046136873,"about_ca_topic_score_gemma":0.0034958257,"teacher_disagreement_score":0.0046136873,"about_ca_system_score_codex":0.0006626627,"about_ca_system_score_gemma":0.0012702474,"threshold_uncertainty_score":0.009173691},"labels":[],"label_agreement":null},{"id":"W3195136400","doi":"10.1109/tmc.2021.3106256","title":"Lightweight and Secure Face-based Active Authentication for Mobile Users","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":11,"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; Biometrics; Authentication (law); Mobile device; Overhead (engineering); Cloud computing; Smart card; Embedded system; Computer network; Computer security; Operating system","score_opus":0.015671604667771993,"score_gpt":0.26240497800053925,"score_spread":0.24673337333276726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195136400","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18471107,0.0010670373,0.7929667,0.00025727716,0.00021795052,0.0003271577,0.00031025606,0.013302342,0.006840204],"genre_scores_gemma":[0.9400616,0.00020276262,0.05501544,0.00012841987,0.000044236625,0.0000874714,0.00022986774,0.00004794592,0.004182119],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950135,0.00006653416,0.000026477099,0.00009287845,0.00021796807,0.000094901814],"domain_scores_gemma":[0.99954814,0.000049487586,0.000047716178,0.00017314176,0.00014485269,0.00003660023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003126802,0.0004487365,0.0004976848,0.00049178326,0.00054675783,0.0005699011,0.0010677055,0.00060335844,0.0031501213],"category_scores_gemma":[0.0008883127,0.00019123282,0.00034567132,0.00032872276,0.00023380252,0.0014416765,0.0013973614,0.0004802892,0.0016675474],"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":[0.0014625598,0.00040671806,0.0050844727,0.00020650857,0.000089243185,0.0006106627,0.00028310082,0.020083247,0.24799673,0.009395459,0.012067201,0.7023141],"study_design_scores_gemma":[0.00008184707,0.0005822901,0.0067171617,0.000036213376,0.000060534585,0.0010254422,0.00011768121,0.8540786,0.11792197,0.0037796684,0.015513779,0.00008482445],"about_ca_topic_score_codex":0.0021256611,"about_ca_topic_score_gemma":0.002020247,"teacher_disagreement_score":0.0031501213,"about_ca_system_score_codex":0.00039799107,"about_ca_system_score_gemma":0.00052609935,"threshold_uncertainty_score":0.01053828},"labels":[],"label_agreement":null},{"id":"W3198602495","doi":"10.1109/tmc.2021.3107458","title":"Mobility Load Management in Cellular Networks: A Deep Reinforcement Learning Approach","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","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":"University of Toronto","funders":"","keywords":"Computer science; Reinforcement learning; Cellular network; Load balancing (electrical power); Robustness (evolution); Distributed computing; Throughput; Cellular traffic; Telecommunications link; Computer network; Artificial intelligence; Wireless; Telecommunications","score_opus":0.008481284533606468,"score_gpt":0.21280502348375052,"score_spread":0.20432373895014405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198602495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06590623,0.0005711946,0.9284585,0.0008524877,0.000067612316,0.000032721888,0.000049802784,0.00034047692,0.0037209312],"genre_scores_gemma":[0.9717339,0.00016854145,0.025509723,0.00016461268,0.00003803082,0.00004437971,0.0000457959,0.000023840308,0.0022711277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998286,0.000054528886,0.0000064985193,0.00003173207,0.000030328833,0.000048253558],"domain_scores_gemma":[0.9995012,0.00028246493,0.000053887525,0.0000228562,0.000098696,0.00004097502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063598057,0.0006463902,0.0006520356,0.00022774149,0.0002640384,0.00055214146,0.0008851422,0.0007980775,0.0010474909],"category_scores_gemma":[0.0015801928,0.00030105692,0.0002641988,0.0002400427,0.00066939095,0.00063608936,0.0006739888,0.001234579,0.0001326586],"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":[0.000018406214,0.000016538577,0.00037469174,0.000009730086,0.000010749507,0.000017827004,0.000013619033,0.9877337,0.00035873798,0.0022326047,0.00030053078,0.008912927],"study_design_scores_gemma":[0.0000013378043,0.0000036458148,0.000019410763,8.1513764e-7,9.5345786e-7,0.000001078367,0.0000010856552,0.9993518,0.00003976401,0.000541235,0.000038196864,6.305266e-7],"about_ca_topic_score_codex":0.010689208,"about_ca_topic_score_gemma":0.008506745,"teacher_disagreement_score":0.010689208,"about_ca_system_score_codex":0.0010400291,"about_ca_system_score_gemma":0.00095737283,"threshold_uncertainty_score":0.021253943},"labels":[],"label_agreement":null},{"id":"W3204080079","doi":"10.1109/tmc.2021.3116157","title":"New Routing Protocol for Reliability to Intelligent Transportation Communication","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT Networks and Protocols","field":"Engineering","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":"Computer science; Computer network; Zone Routing Protocol; Enhanced Interior Gateway Routing Protocol; Link-state routing protocol; Dynamic Source Routing; Wireless Routing Protocol; Routing protocol; Static routing; Routing Information Protocol; Distributed computing; Network packet","score_opus":0.024520883626849517,"score_gpt":0.3108057120607756,"score_spread":0.2862848284339261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204080079","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074843937,0.014957742,0.9212563,0.0032743686,0.0065926355,0.00091371336,0.0011566033,0.003317775,0.041046456],"genre_scores_gemma":[0.30733916,0.021514857,0.56054306,0.004766003,0.0031830738,0.0037275103,0.0056524924,0.00074813294,0.092525594],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987435,0.00034853315,0.00017111962,0.00018979405,0.0004450526,0.000102126025],"domain_scores_gemma":[0.99892837,0.00019941454,0.00011209315,0.00015510511,0.00056207477,0.00004301843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001014599,0.00073834235,0.0006322155,0.0009846246,0.00093900535,0.0012885493,0.0013042861,0.000880905,0.0038479136],"category_scores_gemma":[0.002052868,0.00022864224,0.00068375416,0.0012601177,0.0005881136,0.0018545567,0.0011089832,0.001942215,0.0019365094],"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":[0.00034954934,0.00014741729,0.0007932724,0.0015113577,0.00019324174,0.0006556545,0.00049523456,0.019515552,0.03230437,0.32031205,0.118255794,0.5054665],"study_design_scores_gemma":[0.0001414495,0.00039581346,0.00068990554,0.0002883988,0.0002350112,0.001668711,0.00019493874,0.08851695,0.024345012,0.066010386,0.81737393,0.00013957816],"about_ca_topic_score_codex":0.0012554736,"about_ca_topic_score_gemma":0.0016000469,"teacher_disagreement_score":0.0038479136,"about_ca_system_score_codex":0.0009819331,"about_ca_system_score_gemma":0.0015332731,"threshold_uncertainty_score":0.012872577},"labels":[],"label_agreement":null},{"id":"W3208661763","doi":"10.1109/tmc.2020.3031319","title":"An Analysis of a Stochastic ON-OFF Queueing Mobility Model for Software-Defined Vehicle Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","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":"Carleton University","funders":"","keywords":"Queueing theory; Computer science; Layered queueing network; Node (physics); Exponential distribution; Mobility model; Poisson distribution; Queue; Distributed computing; Service (business); Computer network; Mathematical optimization; Mathematics","score_opus":0.015476768712646466,"score_gpt":0.24194751976982398,"score_spread":0.2264707510571775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208661763","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1592397,0.0013419911,0.8217492,0.0013222148,0.00021133274,0.00015771466,0.00032798256,0.00026439,0.015385443],"genre_scores_gemma":[0.9804822,0.0007272145,0.011214872,0.00011703182,0.00008213747,0.00009781788,0.00015509699,0.00005655464,0.007066931],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990256,0.0003107994,0.00003252439,0.00014996831,0.000206994,0.00027414688],"domain_scores_gemma":[0.9985392,0.0007242552,0.00022973571,0.000059586662,0.000319463,0.00012772507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016344119,0.0011620119,0.0012742152,0.00094979565,0.0007699559,0.0016973151,0.001724212,0.0015962404,0.0017813644],"category_scores_gemma":[0.0043110633,0.00062755815,0.0012322693,0.00063678174,0.0014616338,0.0014368457,0.00104701,0.0011109583,0.00020836451],"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":[0.000030104564,0.000026757378,0.00048280708,0.00002909906,0.000021042682,0.00011032436,0.00006390981,0.9676849,0.00088351243,0.02885933,0.0003841948,0.0014240964],"study_design_scores_gemma":[0.0000022578652,0.000007268598,0.000052472893,0.0000024855522,0.0000034672755,0.000007890079,0.0000086551,0.9979235,0.000039353225,0.0018438621,0.00010565027,0.000003068504],"about_ca_topic_score_codex":0.031733003,"about_ca_topic_score_gemma":0.01055149,"teacher_disagreement_score":0.031733003,"about_ca_system_score_codex":0.0033226458,"about_ca_system_score_gemma":0.002117052,"threshold_uncertainty_score":0.06309658},"labels":[],"label_agreement":null},{"id":"W4205093761","doi":"10.1109/tmc.2021.3136611","title":"Joint Client Selection and Bandwidth Allocation Algorithm for Federated Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":58,"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 Research Foundation","keywords":"Computer science; Markov decision process; Curse of dimensionality; Reinforcement learning; Scheduling (production processes); Mathematical optimization; Wireless; Algorithm; Bandwidth (computing); Distributed computing; Markov process; Computer network; Artificial intelligence; Mathematics; Telecommunications","score_opus":0.02474850513147081,"score_gpt":0.27059510866971886,"score_spread":0.24584660353824805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205093761","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028666299,0.00032400625,0.9666499,0.00034176707,0.00006843024,0.00012357319,0.00009736869,0.002110559,0.0016181621],"genre_scores_gemma":[0.7678399,0.00017476176,0.22743514,0.00031128043,0.000060481587,0.0003176393,0.00032305194,0.00014849218,0.0033891846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982496,0.0005258893,0.00008434208,0.0004709839,0.00031435,0.00035472514],"domain_scores_gemma":[0.9971271,0.0014454437,0.00024793774,0.00044250258,0.00047219498,0.00026482245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023119156,0.0010071022,0.0018713038,0.00070301373,0.0009093635,0.0011649112,0.0028618183,0.0015064277,0.0033858018],"category_scores_gemma":[0.005230605,0.00046544953,0.00077207637,0.0012266191,0.0009202137,0.0019268079,0.0017493672,0.0017307206,0.0008861742],"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":[0.00075241935,0.0004978038,0.0019533406,0.00012200091,0.00009101223,0.00012099897,0.0001482283,0.74530184,0.0031823535,0.012356334,0.00640454,0.22906913],"study_design_scores_gemma":[0.000019121077,0.000025706284,0.000078602134,0.0000034008538,0.0000051382854,0.000025792502,0.000011430354,0.9952336,0.00057545747,0.0037893436,0.00022760555,0.0000047645126],"about_ca_topic_score_codex":0.00543081,"about_ca_topic_score_gemma":0.004469916,"teacher_disagreement_score":0.00543081,"about_ca_system_score_codex":0.0015235672,"about_ca_system_score_gemma":0.0033056855,"threshold_uncertainty_score":0.012226701},"labels":[],"label_agreement":null},{"id":"W4205670437","doi":"10.1109/tmc.2021.3136236","title":"Utility-Aware Legitimacy Detection of Mobile Crowdsensing Tasks via Knowledge-Based Self Organizing Feature Map","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Crowdsensing and Crowdsourcing","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; Artificial intelligence; Task (project management); Crowdsensing; Feature (linguistics); Machine learning; Frame (networking); Data mining; Computer security","score_opus":0.010740745935985799,"score_gpt":0.24322912769872665,"score_spread":0.23248838176274084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205670437","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2886478,0.0005184409,0.70386004,0.00055122713,0.000096544376,0.00020724362,0.0003544927,0.0020751685,0.0036890807],"genre_scores_gemma":[0.97536224,0.00006596986,0.023618665,0.000040357772,0.000027165379,0.000058384336,0.00015669,0.000016985141,0.0006534388],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999199,0.00016682631,0.00004731436,0.00016969473,0.00028131937,0.0001358896],"domain_scores_gemma":[0.9974878,0.0011921406,0.00039321807,0.00027700167,0.00054133893,0.00010838948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088395795,0.00073801033,0.0009827039,0.001253369,0.0005821036,0.00091733923,0.001037859,0.000727245,0.000687625],"category_scores_gemma":[0.006137013,0.00023644963,0.0004710484,0.00090647716,0.0005945457,0.0014936109,0.0011396165,0.0006543367,0.00030605833],"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":[0.0010743805,0.00080220884,0.025646722,0.000298302,0.00014022463,0.00047135167,0.00043139982,0.33161286,0.017050259,0.0053208442,0.0044624573,0.612689],"study_design_scores_gemma":[0.000008749182,0.000056519548,0.0030489299,0.000007537111,0.000011930031,0.00005598192,0.000050355367,0.99000704,0.0038091948,0.0025106892,0.00041860223,0.000014399782],"about_ca_topic_score_codex":0.0039439453,"about_ca_topic_score_gemma":0.0034227348,"teacher_disagreement_score":0.0039439453,"about_ca_system_score_codex":0.00080233696,"about_ca_system_score_gemma":0.0008903968,"threshold_uncertainty_score":0.007842004},"labels":[],"label_agreement":null},{"id":"W4206480403","doi":"10.1109/tmc.2022.3141930","title":"On the Impact of Recharging Behavior on Mobility","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Node (physics); Probability density function; Bounded function; Distortion (music); Inversion (geology); Regular polygon; Topology (electrical circuits); Mathematical optimization; Mathematics; Computer network; Mathematical analysis; Physics; Geometry; Statistics; Acoustics; Bandwidth (computing)","score_opus":0.031142329604661827,"score_gpt":0.2882260434484933,"score_spread":0.25708371384383144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206480403","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5105304,0.0038346655,0.42525542,0.010103793,0.0004165657,0.00013720304,0.00036756953,0.00038345758,0.04897085],"genre_scores_gemma":[0.9935369,0.00096062524,0.0037199473,0.00014015657,0.00008330415,0.000024116775,0.000025892878,0.000033744313,0.0014752347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999273,0.00031642427,0.000021534586,0.00009319347,0.00012686802,0.00016893224],"domain_scores_gemma":[0.9925781,0.005759244,0.0006169181,0.0004491337,0.00040648793,0.00019015966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011507806,0.00067410443,0.0005893919,0.00072028185,0.00094630086,0.0013293979,0.0009536225,0.0012155547,0.00226684],"category_scores_gemma":[0.018694231,0.00027861103,0.00055823673,0.00070982095,0.001847359,0.0035686775,0.0014653458,0.0011115358,0.00028375644],"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":[0.0000960321,0.000050295457,0.0051313555,0.00010005996,0.000035993595,0.0006326361,0.00030879537,0.8665533,0.0039153583,0.10889513,0.0014967978,0.012784189],"study_design_scores_gemma":[0.000005617028,0.000038401384,0.001281812,0.000022310109,0.000017683473,0.00017586208,0.00011990521,0.97659063,0.0005939663,0.020390024,0.00074566377,0.000018172757],"about_ca_topic_score_codex":0.009724007,"about_ca_topic_score_gemma":0.0050908043,"teacher_disagreement_score":0.009724007,"about_ca_system_score_codex":0.0014286702,"about_ca_system_score_gemma":0.0007087482,"threshold_uncertainty_score":0.019334853},"labels":[],"label_agreement":null},{"id":"W4206666877","doi":"10.1109/tmc.2021.3135301","title":"Dual-Anonymous Off-Line Electronic Cash for Mobile Payment","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Blockchain Technology Applications and Security","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 Waterloo; University of Guelph; Queen's University","funders":"","keywords":"Mobile payment; Computer science; Payment; Computer security; Database transaction; Electronic cash; Electronic money; Dual (grammatical number); Payment service provider; Mobile computing; Scheme (mathematics); Internet privacy; Computer network; World Wide Web; Database","score_opus":0.011181069841244097,"score_gpt":0.26084045117917926,"score_spread":0.24965938133793517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206666877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0618716,0.0015467129,0.8889624,0.0017665811,0.00086133037,0.0007442706,0.00022982009,0.0009925894,0.043024752],"genre_scores_gemma":[0.87864393,0.0007612303,0.10160057,0.0004561555,0.0003972426,0.0002966007,0.00014858886,0.00005789199,0.017637663],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99493814,0.0021162473,0.00023856875,0.0005073254,0.001455721,0.000743996],"domain_scores_gemma":[0.9972058,0.0005576343,0.00039811947,0.0010776952,0.00051286217,0.00024794598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022351334,0.000562615,0.0007240557,0.0012285578,0.0023745133,0.002584593,0.0017806616,0.0019973998,0.0057312273],"category_scores_gemma":[0.0047588935,0.00028945407,0.0006729933,0.0016557272,0.0021986377,0.0048849233,0.0039069215,0.002199779,0.0021768203],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010866087,0.00023451573,0.0010236527,0.0003419018,0.00003957785,0.00072121527,0.00048673304,0.012551251,0.020077616,0.81374246,0.008840855,0.14085351],"study_design_scores_gemma":[0.00028333405,0.0010268942,0.0014633745,0.00032922789,0.00015153099,0.0041888873,0.00042482355,0.30789956,0.03517217,0.47283274,0.17586438,0.00036310998],"about_ca_topic_score_codex":0.00047516872,"about_ca_topic_score_gemma":0.0004199993,"teacher_disagreement_score":0.0057312273,"about_ca_system_score_codex":0.0015388252,"about_ca_system_score_gemma":0.0017291898,"threshold_uncertainty_score":0.019172847},"labels":[],"label_agreement":null},{"id":"W4214889552","doi":"10.1109/tmc.2022.3155657","title":"Optimized Controller Provisioning in Software-Defined LEO Satellite Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Satellite Communication Systems","field":"Engineering","cited_by":33,"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":"Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Computer science; Overhead (engineering); Network topology; Provisioning; Controller (irrigation); Rounding; Software-defined networking; Distributed computing; Computer network; Operating system","score_opus":0.012679508755944625,"score_gpt":0.22837622825145906,"score_spread":0.21569671949551444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214889552","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10073888,0.00082608557,0.8875314,0.0006104822,0.0000723407,0.000081459126,0.00014882527,0.0004888927,0.009501483],"genre_scores_gemma":[0.9814548,0.0002842523,0.015742958,0.00006014297,0.00003387814,0.000047930313,0.000054750824,0.000035800334,0.0022854647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989882,0.00021428379,0.000034763834,0.00018919136,0.00030005517,0.0002734781],"domain_scores_gemma":[0.99908686,0.00040194977,0.000206155,0.00008367432,0.00015801599,0.00006333949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008140424,0.00083377474,0.00073875027,0.00038319294,0.00044416712,0.0014673957,0.0011553658,0.00074890064,0.0011211731],"category_scores_gemma":[0.0026555539,0.00050918147,0.00026385946,0.0007081959,0.00081724214,0.001820261,0.00082391786,0.00083627156,0.00013795923],"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":[0.000023829214,0.00002049909,0.00018068738,0.000015484053,0.000005848707,0.000025642663,0.000024228004,0.9865979,0.00071415486,0.0069521093,0.00032156621,0.0051180306],"study_design_scores_gemma":[0.000001640572,0.0000059955573,0.000050372015,0.0000010841203,0.000001337685,0.0000027946323,0.0000055470787,0.99874914,0.000107740845,0.00095907744,0.000113678994,0.0000016360628],"about_ca_topic_score_codex":0.012536889,"about_ca_topic_score_gemma":0.011522376,"teacher_disagreement_score":0.012536889,"about_ca_system_score_codex":0.0023712511,"about_ca_system_score_gemma":0.0017508344,"threshold_uncertainty_score":0.024927795},"labels":[],"label_agreement":null},{"id":"W4226178516","doi":"10.1109/tmc.2022.3159697","title":"A Deep Learning Framework for Beam Selection and Power Control in Massive MIMO - Millimeter-Wave Communications","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","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":"École de Technologie Supérieure","funders":"Mitacs","keywords":"Computer science; MIMO; Base station; Channel state information; Extremely high frequency; Transmitter power output; User equipment; Power control; Channel (broadcasting); Ray tracing (physics); Transmission (telecommunications); Real-time computing; Power (physics); Electronic engineering; Telecommunications; Wireless; Transmitter; Engineering","score_opus":0.018006066301912677,"score_gpt":0.24508580799564716,"score_spread":0.2270797416937345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226178516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01465686,0.0009110364,0.9813705,0.00034000308,0.00006640068,0.000024149973,0.00020176164,0.0008375438,0.0015917323],"genre_scores_gemma":[0.7553444,0.000997851,0.23389499,0.0004875072,0.00014630795,0.00021490481,0.00089196186,0.00013441051,0.007887636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979144,0.000044826833,0.000009703473,0.00005406787,0.000049676546,0.000050379833],"domain_scores_gemma":[0.9996953,0.00013578036,0.00003460895,0.000024541469,0.00008519654,0.000024603662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005475385,0.001031,0.0007836672,0.00039853455,0.00034030507,0.0007055582,0.001640729,0.0011493783,0.0017242457],"category_scores_gemma":[0.001115204,0.00047511008,0.00050923927,0.0006492946,0.0005584134,0.00089734874,0.0009235022,0.0020277414,0.00042260718],"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":[0.000050440412,0.00005769724,0.0005066955,0.000047721147,0.000035075333,0.000051113602,0.000029833642,0.9131891,0.0014531051,0.005383462,0.0020316548,0.07716415],"study_design_scores_gemma":[0.0000021567005,0.000007997334,0.00003296084,0.0000023918278,0.0000021004714,0.000003521763,0.0000019864742,0.99810046,0.00020476249,0.0014854847,0.00015434867,0.0000017684048],"about_ca_topic_score_codex":0.01614913,"about_ca_topic_score_gemma":0.022278287,"teacher_disagreement_score":0.01614913,"about_ca_system_score_codex":0.00097125274,"about_ca_system_score_gemma":0.0013212457,"threshold_uncertainty_score":0.032110274},"labels":[],"label_agreement":null},{"id":"W4291653259","doi":"10.1109/tmc.2022.3199048","title":"HealthFort: A Cloud-Based eHealth System With Conditional Forward Transparency and Secure Provenance via Blockchain","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Blockchain Technology Applications and Security","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":"University of Waterloo","funders":"Putian University; National Natural Science Foundation of China","keywords":"Computer science; Computer security; Cloud computing; eHealth; Encryption; Transparency (behavior); Forward secrecy; Confidentiality; Delegate; Internet privacy; Key (lock); Verifiable secret sharing; Authentication (law); Delegation; Secrecy; Smart contract; Public-key cryptography; Server; Password; World Wide Web; Blockchain; Health care","score_opus":0.006905866799278625,"score_gpt":0.2242207163075572,"score_spread":0.21731484950827856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291653259","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07360198,0.0012537963,0.8957024,0.0026595206,0.00041058345,0.0014826416,0.0011209548,0.00998031,0.013787737],"genre_scores_gemma":[0.8073267,0.0009870251,0.17829004,0.00063704984,0.00016559642,0.0004447522,0.001563254,0.00023012875,0.010355345],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873406,0.00028290943,0.00013047254,0.00019951005,0.00044602316,0.00020699832],"domain_scores_gemma":[0.99815303,0.00038177214,0.00021217807,0.00066252303,0.0003137895,0.00027668435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019677933,0.00037103845,0.0004829197,0.00048194846,0.0011363653,0.0013717636,0.0015535997,0.0009298725,0.0038860932],"category_scores_gemma":[0.0030829299,0.00025956726,0.00042547475,0.0008118168,0.00075007416,0.0031155874,0.0036362095,0.00094920985,0.0010925219],"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":[0.0053730416,0.0012056271,0.018302493,0.0014043677,0.0002680229,0.0062343385,0.00169923,0.12452907,0.06712371,0.1807988,0.053609148,0.5394522],"study_design_scores_gemma":[0.0010393822,0.0005834486,0.0035356793,0.00016483627,0.00013352055,0.0018985795,0.00030645347,0.7884742,0.03720647,0.058686525,0.1077768,0.00019401278],"about_ca_topic_score_codex":0.004965321,"about_ca_topic_score_gemma":0.003605946,"teacher_disagreement_score":0.004965321,"about_ca_system_score_codex":0.0008388974,"about_ca_system_score_gemma":0.0036486816,"threshold_uncertainty_score":0.0130003095},"labels":[],"label_agreement":null},{"id":"W4292387182","doi":"10.1109/tmc.2022.3200104","title":"Electrocardiogram Based Group Device Pairing for Wearables","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"User Authentication and Security Systems","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 Windsor","funders":"Fundamental Research Funds for the Central Universities; Education Department of Shaanxi Province; National Natural Science Foundation of China","keywords":"Computer science; Group key; Wearable computer; Computer network; Pairing; Randomness; Protocol (science); Entropy (arrow of time); Computer security; Embedded system; Encryption","score_opus":0.018832111968408623,"score_gpt":0.2551343065351563,"score_spread":0.23630219456674767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292387182","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06110898,0.00063360715,0.9320021,0.0003024831,0.00027699128,0.0002214331,0.00009348398,0.0010350428,0.0043260143],"genre_scores_gemma":[0.89474964,0.00040710322,0.09962706,0.00012385813,0.00013869627,0.00017844656,0.00015740661,0.000042869422,0.004575],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988261,0.00041744232,0.00009657853,0.00019834268,0.0003582836,0.00010327614],"domain_scores_gemma":[0.99910563,0.00021004764,0.000163665,0.00032280936,0.00013036832,0.0000674427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009346086,0.00045625525,0.00056305807,0.0005491653,0.0004941306,0.0005999188,0.0006783505,0.0007050571,0.0021090505],"category_scores_gemma":[0.0025386515,0.00018019597,0.00038659485,0.0004790725,0.00045841053,0.0016002327,0.0018266384,0.0005708949,0.00082626514],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025784988,0.00037118644,0.009059651,0.0006854289,0.00023754119,0.0026719675,0.00095653994,0.05536471,0.27930212,0.11140726,0.007049765,0.53031534],"study_design_scores_gemma":[0.0003349395,0.0037361642,0.008583668,0.00015153212,0.00024488894,0.006957852,0.0004449249,0.6507839,0.22292818,0.05919425,0.046432696,0.00020698164],"about_ca_topic_score_codex":0.00013325244,"about_ca_topic_score_gemma":0.00011284869,"teacher_disagreement_score":0.0021090505,"about_ca_system_score_codex":0.00022295777,"about_ca_system_score_gemma":0.00030708304,"threshold_uncertainty_score":0.0070554614},"labels":[],"label_agreement":null},{"id":"W4293198317","doi":"10.1109/tmc.2022.3199876","title":"Multi-Objective Parallel Task Offloading and Content Caching in D2D-aided MEC Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":159,"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":"","keywords":"Computer science; Task (project management); Computer network; Distributed computing","score_opus":0.027613079849307456,"score_gpt":0.2406868773261701,"score_spread":0.21307379747686267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293198317","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1435231,0.0011620249,0.8470461,0.00049869565,0.00012424638,0.00011188366,0.00012805304,0.0005401094,0.006865805],"genre_scores_gemma":[0.92000556,0.00031233413,0.07602761,0.00011305818,0.00003112464,0.00009840254,0.00012528348,0.000045754317,0.0032408182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996007,0.000118059936,0.000018086837,0.00008405399,0.000074716976,0.00010434146],"domain_scores_gemma":[0.99923754,0.0004572199,0.00007148518,0.00004747597,0.00011874433,0.00006750875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064078247,0.0010368851,0.0013557154,0.00040067642,0.0007261311,0.001088553,0.0010596354,0.0011508282,0.0011459898],"category_scores_gemma":[0.0013279092,0.00050886004,0.00048211162,0.00083578326,0.00054787105,0.0010706307,0.000852105,0.0006895442,0.00016848672],"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":[0.000080952275,0.000041913863,0.00037716937,0.000044014043,0.000020164378,0.00008715794,0.00002735619,0.9794948,0.0013377409,0.0015350444,0.0007006614,0.016253017],"study_design_scores_gemma":[0.00000524606,0.000012755412,0.000055149092,0.0000015030487,0.000003106324,0.000008424817,0.000008953805,0.9991423,0.00021810016,0.00042615205,0.000116127776,0.0000022282852],"about_ca_topic_score_codex":0.015752362,"about_ca_topic_score_gemma":0.010923286,"teacher_disagreement_score":0.015752362,"about_ca_system_score_codex":0.0012510349,"about_ca_system_score_gemma":0.0011766439,"threshold_uncertainty_score":0.031321347},"labels":[],"label_agreement":null},{"id":"W4296707149","doi":"10.1109/tmc.2022.3208229","title":"When Virtual Network Operator Meets E-Commerce Platform: Advertising via Data Reward","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Digital Platforms and Economics","field":"Business, Management and Accounting","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":"Stackelberg competition; Computer science; Nash equilibrium; Operator (biology); Incentive; Game theory; Best response; Mathematical optimization; Microeconomics","score_opus":0.029193414529770454,"score_gpt":0.22923629145116706,"score_spread":0.2000428769213966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296707149","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6174962,0.00046042431,0.34827003,0.004888249,0.00028462222,0.00034467093,0.00019741207,0.00040754664,0.02765073],"genre_scores_gemma":[0.99216217,0.00005756361,0.0056280284,0.00010396102,0.000023197239,0.00002265244,0.00002701335,0.00001082552,0.0019646038],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977471,0.00077192206,0.00007437557,0.00048852456,0.00030508265,0.0006129973],"domain_scores_gemma":[0.99636924,0.0020024197,0.0004121639,0.00018683559,0.00045954474,0.00056985463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00277613,0.0005057484,0.0010049356,0.00066008343,0.0012425913,0.0028102547,0.0015177506,0.0023299262,0.0035422377],"category_scores_gemma":[0.010743076,0.00042518706,0.00054568396,0.00047955723,0.0012158209,0.0041714087,0.0014357934,0.0016049203,0.000302236],"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":[0.0023056674,0.0008636182,0.054093212,0.00038130357,0.00026039276,0.005432646,0.0012823644,0.46194586,0.0171512,0.3154374,0.015546956,0.12529936],"study_design_scores_gemma":[0.000056749534,0.00013615974,0.00207492,0.000011631722,0.00004969454,0.0005027747,0.00035897354,0.95182824,0.0020165031,0.041054234,0.0018694596,0.00004068145],"about_ca_topic_score_codex":0.0047157994,"about_ca_topic_score_gemma":0.0050014635,"teacher_disagreement_score":0.0047157994,"about_ca_system_score_codex":0.0016832958,"about_ca_system_score_gemma":0.0017187946,"threshold_uncertainty_score":0.0146817565},"labels":[],"label_agreement":null},{"id":"W4312305523","doi":"10.1109/tmc.2022.3226448","title":"Fine-Grained Spatio-Temporal Distribution Prediction of Mobile Content Delivery in 5G Ultra-Dense Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Caching and Content Delivery","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 British Columbia","funders":"Science, Technology and Innovation Commission of Shenzhen Municipality; National Natural Science Foundation of China","keywords":"Computer science; Provisioning; Distributed computing; Dependency (UML); Feature (linguistics); Data mining; Computer network; Artificial intelligence","score_opus":0.020296553853010362,"score_gpt":0.21829247684625547,"score_spread":0.19799592299324512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312305523","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40264964,0.0013518803,0.59176606,0.0005307017,0.00009466165,0.000050653318,0.00061433215,0.0012355112,0.0017065401],"genre_scores_gemma":[0.9713978,0.0003748448,0.026940186,0.00006822861,0.000042450898,0.00002109413,0.0004535521,0.000017634238,0.00068399566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998386,0.00002803382,0.000008687107,0.00005105847,0.00004035592,0.000033135148],"domain_scores_gemma":[0.9996306,0.00016442529,0.000060337854,0.000037780253,0.000081225895,0.000025657064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030616543,0.0005829486,0.00044931585,0.000607255,0.0002434059,0.0004070951,0.00075872353,0.00046720443,0.00027900975],"category_scores_gemma":[0.0013545287,0.00022669273,0.00028436037,0.0006974998,0.00028289424,0.00084561115,0.00042154876,0.0005901432,0.00013636713],"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":[0.00020643472,0.000099715224,0.016015952,0.00006829324,0.000050296283,0.00023769602,0.00010013242,0.84366786,0.006358559,0.0030715507,0.0023546715,0.1277689],"study_design_scores_gemma":[0.0000011514973,0.0000052463974,0.0007521438,0.000001299775,0.0000029161195,0.000011665101,0.0000062102004,0.9983912,0.0003143601,0.0004135042,0.00009859561,0.0000017199051],"about_ca_topic_score_codex":0.018169416,"about_ca_topic_score_gemma":0.015841702,"teacher_disagreement_score":0.018169416,"about_ca_system_score_codex":0.0006315001,"about_ca_system_score_gemma":0.00043501722,"threshold_uncertainty_score":0.03612727},"labels":[],"label_agreement":null},{"id":"W4312354825","doi":"10.1109/tmc.2022.3220720","title":"MOTO: Mobility-Aware Online Task Offloading With Adaptive Load Balancing in Small-Cell MEC","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":94,"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; University of Calgary","funders":"Key Research and Development Program of Hunan Province of China; Higher Education Discipline Innovation Project; Natural Science Foundation of Hainan Province; National Natural Science Foundation of China","keywords":"Computer science; Server; Task (project management); Load balancing (electrical power); Mobile edge computing; Enhanced Data Rates for GSM Evolution; Limiting; Artificial intelligence; Computer network; Mathematics","score_opus":0.014556747067839316,"score_gpt":0.22495368271572022,"score_spread":0.2103969356478809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312354825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.071626574,0.0004750042,0.9159961,0.0005245912,0.00019853159,0.00016306345,0.00012402715,0.0012054355,0.009686612],"genre_scores_gemma":[0.9570963,0.00015580529,0.03847195,0.00013467825,0.000040426214,0.000099401215,0.00006573773,0.000045571032,0.003890205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978524,0.000031774238,0.000011176423,0.00005868382,0.000047611888,0.000065569],"domain_scores_gemma":[0.9996126,0.00015831264,0.000053952157,0.000051742325,0.00006441353,0.000058891983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003535638,0.0006528414,0.0006846729,0.00024908857,0.00045858126,0.00082807126,0.0012411742,0.0006041943,0.0025087937],"category_scores_gemma":[0.001272867,0.00019768502,0.00022981195,0.0003331555,0.0005237592,0.0008440097,0.0012845839,0.0005613284,0.0003821835],"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":[0.00027888714,0.00013245376,0.00097500096,0.0001387697,0.000036039684,0.00022688785,0.000114162234,0.8605774,0.022040674,0.010431508,0.004452521,0.10059578],"study_design_scores_gemma":[0.000008370607,0.00002428052,0.00009959875,0.0000033472927,0.000002634305,0.000013283266,0.0000146404645,0.9973888,0.00085656904,0.0010401019,0.00054403144,0.0000044212165],"about_ca_topic_score_codex":0.004801748,"about_ca_topic_score_gemma":0.007529411,"teacher_disagreement_score":0.004801748,"about_ca_system_score_codex":0.00049273676,"about_ca_system_score_gemma":0.00067877566,"threshold_uncertainty_score":0.009547591},"labels":[],"label_agreement":null},{"id":"W4312389599","doi":"10.1109/tmc.2022.3223119","title":"QoE-Aware Decentralized Task Offloading and Resource Allocation for End-Edge-Cloud Systems: A Game-Theoretical Approach","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":137,"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":"Basic and Applied Basic Research Foundation of Guangdong Province; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Computer science; Cloud computing; Nash equilibrium; Server; Distributed computing; Computation offloading; Quality of experience; Mobile edge computing; Potential game; Mobile device; Game theory; Task (project management); Resource allocation; Enhanced Data Rates for GSM Evolution; Edge computing; Computer network; Mathematical optimization; Quality of service; Artificial intelligence","score_opus":0.013516009334974226,"score_gpt":0.23998565083244414,"score_spread":0.2264696414974699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312389599","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019941252,0.00028360417,0.97288245,0.00037698896,0.00006120959,0.00017337878,0.000051955878,0.00006708705,0.0061620986],"genre_scores_gemma":[0.91684407,0.00050829037,0.07849814,0.0001899116,0.00008351111,0.0002552006,0.000049729322,0.000044422348,0.0035267074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989177,0.00041785103,0.000032051423,0.00017142195,0.00022449209,0.00023654288],"domain_scores_gemma":[0.99872005,0.00082935643,0.00010523675,0.00005911133,0.00017917239,0.00010705916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013300224,0.001221766,0.0015324382,0.00057956285,0.00092309486,0.0016570361,0.001772007,0.0012248323,0.0020808785],"category_scores_gemma":[0.0028634013,0.0005264108,0.00065585604,0.0006862577,0.0014186294,0.0016950552,0.0014484705,0.0014033279,0.00018335224],"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":[0.00007226304,0.00009288019,0.00027997335,0.00007583741,0.000029196603,0.00013216455,0.00007546189,0.94151324,0.0021412445,0.04545144,0.0010727389,0.009063591],"study_design_scores_gemma":[0.00000606081,0.000014082105,0.00004173982,0.0000027191186,0.0000035426337,0.00001630395,0.000017595934,0.99341667,0.00011210484,0.006161373,0.00020380433,0.000004030188],"about_ca_topic_score_codex":0.0074161575,"about_ca_topic_score_gemma":0.00663654,"teacher_disagreement_score":0.0074161575,"about_ca_system_score_codex":0.0020894015,"about_ca_system_score_gemma":0.0025560071,"threshold_uncertainty_score":0.015159726},"labels":[],"label_agreement":null},{"id":"W4312399768","doi":"10.1109/tmc.2022.3232543","title":"Edge-Based Video Stream Generation for Multi-Party Mobile Augmented Reality","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Image and Video Quality Assessment","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":"Simon Fraser University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Augmented reality; Mobile edge computing; Mobile device; Rendering (computer graphics); Overlay; Distributed computing; Edge computing; Enhanced Data Rates for GSM Evolution; Quality of experience; Edge device; Mobile computing; Reinforcement learning; Computer network; Quality of service; Human–computer interaction; Cloud computing; Artificial intelligence; Operating system","score_opus":0.08053232138263793,"score_gpt":0.34977079695506996,"score_spread":0.269238475572432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312399768","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021323442,0.00024394371,0.97500706,0.00011951937,0.000055304707,0.000077099096,0.000069068745,0.0011187249,0.001985734],"genre_scores_gemma":[0.70552605,0.00029371385,0.29113218,0.00012799252,0.00006644181,0.000082498336,0.0001896748,0.00011637103,0.0024650758],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996455,0.00008231708,0.000016037318,0.00007573734,0.00013264206,0.00004774208],"domain_scores_gemma":[0.9996111,0.00013211333,0.00004130664,0.000067591856,0.00010630943,0.00004155208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047180473,0.00058388617,0.0004904839,0.00045326,0.00028008863,0.00060236617,0.0009641103,0.00051860814,0.0026325176],"category_scores_gemma":[0.0014835,0.00024234096,0.00034692883,0.00039842265,0.00022878965,0.00084371935,0.000791942,0.0009492321,0.00055127434],"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":[0.001055891,0.00043315467,0.0017369714,0.00015681704,0.000046051566,0.00028928547,0.00016385676,0.3005448,0.07943027,0.007917839,0.006760765,0.6014644],"study_design_scores_gemma":[0.000012105097,0.000044743887,0.0001714901,0.0000030126453,0.00000441267,0.000029852483,0.000009524788,0.99190605,0.0060147853,0.0010612651,0.0007363665,0.0000064092815],"about_ca_topic_score_codex":0.002017919,"about_ca_topic_score_gemma":0.0029885524,"teacher_disagreement_score":0.0026325176,"about_ca_system_score_codex":0.0004487667,"about_ca_system_score_gemma":0.00040625228,"threshold_uncertainty_score":0.0088067055},"labels":[],"label_agreement":null},{"id":"W4312689295","doi":"10.1109/tmc.2022.3230758","title":"BlockSense: Towards Trustworthy Mobile Crowdsensing via Proof-of-Data Blockchain","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Blockchain Technology Applications and Security","field":"Computer Science","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":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Blockchain; Computer science; Crowdsensing; Trustworthiness; Computer security; Mobile computing; Proof of concept; Computer network; Operating system","score_opus":0.020791260614942606,"score_gpt":0.26973512234979136,"score_spread":0.24894386173484875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312689295","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02924337,0.00013063023,0.9650617,0.00061471557,0.000069334004,0.0003581336,0.0001273597,0.0011517743,0.0032430266],"genre_scores_gemma":[0.85185635,0.00025446233,0.142303,0.00018914275,0.00005013873,0.0005134999,0.00023648413,0.00008950768,0.0045073465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966197,0.0011805552,0.00021061968,0.0005227406,0.0011019749,0.00036439186],"domain_scores_gemma":[0.99336314,0.0029969916,0.0006161878,0.0016560993,0.0008804855,0.0004872581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038670115,0.00071541005,0.00094884593,0.0007361879,0.0014520896,0.0017876822,0.002310274,0.0018148879,0.0039431024],"category_scores_gemma":[0.010113599,0.00058250816,0.0006807931,0.0008506922,0.0026176923,0.0038826559,0.0060246633,0.001770272,0.0009948409],"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":[0.00086240564,0.0002682584,0.0031112982,0.0004740972,0.00011696774,0.0012631,0.0011164265,0.63294894,0.030484932,0.20567663,0.0049303835,0.11874652],"study_design_scores_gemma":[0.00010699286,0.00011271627,0.00013156845,0.000025242503,0.00001126743,0.0000775196,0.00008523336,0.9260134,0.005941158,0.061936505,0.0055340095,0.000024456449],"about_ca_topic_score_codex":0.0038670758,"about_ca_topic_score_gemma":0.003147743,"teacher_disagreement_score":0.0039431024,"about_ca_system_score_codex":0.0010498465,"about_ca_system_score_gemma":0.0034703012,"threshold_uncertainty_score":0.02045095},"labels":[],"label_agreement":null},{"id":"W4312735650","doi":"10.1109/tmc.2022.3230370","title":"CROMOSim: A Deep Learning-Based Cross-Modality Inertial Measurement Simulator","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Human Pose and Action Recognition","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":"Western University; McMaster University","funders":"","keywords":"Computer science; Inertial measurement unit; Artificial intelligence; Wearable computer; Deep learning; Motion capture; Computer vision; Activity recognition; Modality (human–computer interaction); Pose; Simulation; Motion (physics); Embedded system","score_opus":0.024614681269913185,"score_gpt":0.27271634740766876,"score_spread":0.24810166613775558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312735650","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11628825,0.00074557506,0.84985226,0.00058017863,0.0005350849,0.00040982876,0.005655551,0.020540575,0.005392613],"genre_scores_gemma":[0.7050927,0.0004688489,0.26430348,0.00074260135,0.0000638504,0.00079246896,0.020140609,0.0009787817,0.0074165342],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969184,0.00007562934,0.000017134558,0.00009017347,0.00009215381,0.00003303809],"domain_scores_gemma":[0.999673,0.00011624975,0.00003236718,0.00006433749,0.00006890644,0.0000451529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006295246,0.0013081919,0.00061994116,0.00046501332,0.00020265878,0.00049357844,0.0025542367,0.00088144786,0.0032285615],"category_scores_gemma":[0.0022074266,0.0005163983,0.0008753542,0.00050132046,0.00045132227,0.00074898096,0.0016005505,0.0015469195,0.0010796968],"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":[0.00051368197,0.00036968195,0.003919379,0.0002469489,0.00031071494,0.00024094766,0.00007772497,0.84371454,0.010725811,0.0021095476,0.016343664,0.12142737],"study_design_scores_gemma":[0.000021970602,0.00006752832,0.0003598781,0.00000701269,0.00000727624,0.000031059757,0.000006319868,0.9952099,0.0021615413,0.0005849989,0.0015340316,0.000008563872],"about_ca_topic_score_codex":0.011373488,"about_ca_topic_score_gemma":0.016191185,"teacher_disagreement_score":0.011373488,"about_ca_system_score_codex":0.00062699325,"about_ca_system_score_gemma":0.0010431047,"threshold_uncertainty_score":0.022614598},"labels":[],"label_agreement":null},{"id":"W4312997030","doi":"10.1109/tmc.2022.3228870","title":"Joint Optimization of Mobility and Reliability-Guaranteed Air-to-Ground Communication for UAVs","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":20,"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":"National Postdoctoral Program for Innovative Talents; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Probabilistic logic; Reliability (semiconductor); Fading; Transmission (telecommunications); Energy consumption; Optimization problem; Scheduling (production processes); Channel (broadcasting); Mathematical optimization; Real-time computing; Computer network; Power (physics); Algorithm; Telecommunications","score_opus":0.010687536946377588,"score_gpt":0.22738759703343686,"score_spread":0.21670006008705928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312997030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12288418,0.0010490727,0.8692554,0.00054637896,0.00008394385,0.000055857796,0.00014741416,0.00029946637,0.0056781764],"genre_scores_gemma":[0.9733046,0.0002571324,0.024768025,0.000041703628,0.000021685237,0.00005662829,0.000076414406,0.00004853903,0.0014252347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994512,0.00017612672,0.00002010781,0.00010957514,0.000092647235,0.00015024986],"domain_scores_gemma":[0.9989114,0.0006609591,0.00016844276,0.00005117718,0.00012836234,0.000079672354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008613105,0.0013588093,0.0010951875,0.000490186,0.00044204423,0.00091735844,0.0008430459,0.00081043824,0.0013593956],"category_scores_gemma":[0.002529146,0.00052905065,0.0006668733,0.0005839029,0.00078501,0.0008226991,0.0010725872,0.00085335056,0.00022494122],"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":[0.000035227724,0.000011278631,0.00021699292,0.000023453567,0.000013331999,0.000040773004,0.000017072274,0.9926669,0.0008477859,0.0021991192,0.0002878884,0.003640238],"study_design_scores_gemma":[0.000004322703,0.000019571056,0.00008414248,0.0000021472276,0.000004242418,0.000008203753,0.00000781298,0.9987092,0.00016638047,0.00090384996,0.00008798921,0.0000021711528],"about_ca_topic_score_codex":0.007732318,"about_ca_topic_score_gemma":0.0043254746,"teacher_disagreement_score":0.007732318,"about_ca_system_score_codex":0.0010307963,"about_ca_system_score_gemma":0.0013436377,"threshold_uncertainty_score":0.0153746605},"labels":[],"label_agreement":null},{"id":"W4321483959","doi":"10.1109/tmc.2023.3246994","title":"Stochastic Resource Optimization for Wireless Powered Hybrid Coded Edge Computing Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"UAV Applications and Optimization","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":"University of Waterloo","funders":"","keywords":"Computer science; Server; Mobile edge computing; Computation; Distributed computing; Computation offloading; Wireless network; Edge computing; Enhanced Data Rates for GSM Evolution; Wireless; Optimization problem; Quality of service; Edge device; Minification; Computer network; Cloud computing; Algorithm; Artificial intelligence","score_opus":0.010181551506705553,"score_gpt":0.2274304397939131,"score_spread":0.21724888828720754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321483959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08020852,0.0006406778,0.90714115,0.00056995754,0.00008785121,0.000077088705,0.00020840528,0.00016976321,0.010896623],"genre_scores_gemma":[0.9575656,0.00031194498,0.0362905,0.00013597243,0.000024554396,0.00010894321,0.000112762296,0.00005453307,0.0053952183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995602,0.00014116235,0.000010751561,0.000073144016,0.00009091676,0.00012382674],"domain_scores_gemma":[0.9991597,0.0005751466,0.00008141909,0.000033791635,0.00010100728,0.00004895954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008275187,0.0010328748,0.0007228333,0.00028065106,0.0003589246,0.0011335231,0.0009455839,0.0007298083,0.0027150242],"category_scores_gemma":[0.0019504748,0.00036197892,0.00039242618,0.00052148965,0.00083211553,0.00081319956,0.00085307984,0.0008806478,0.00020007642],"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":[0.000037637048,0.0000117470945,0.00011826111,0.000018942865,0.0000072153634,0.000030766514,0.000009457582,0.98803526,0.0004396905,0.00876317,0.00030529522,0.0022225364],"study_design_scores_gemma":[0.0000020390548,0.0000048799902,0.000019324349,0.000001329757,0.0000010271077,0.000002626917,0.0000036540623,0.998212,0.00006952617,0.0016098804,0.00007254952,0.0000011417278],"about_ca_topic_score_codex":0.0051515647,"about_ca_topic_score_gemma":0.0044103498,"teacher_disagreement_score":0.0051515647,"about_ca_system_score_codex":0.0015125911,"about_ca_system_score_gemma":0.0010582488,"threshold_uncertainty_score":0.010974646},"labels":[],"label_agreement":null},{"id":"W4321608109","doi":"10.1109/tmc.2023.3248376","title":"Time-Varying Resource Graph Based Processing on the Way for Space-Terrestrial Integrated Vehicle Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Satellite Communication Systems","field":"Engineering","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":"St. Francis Xavier University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Network topology; Resource (disambiguation); Graph; Theoretical computer science; Distributed computing; Computer network","score_opus":0.029763882311624495,"score_gpt":0.253296682379178,"score_spread":0.22353280006755352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321608109","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006505704,0.0001481194,0.9896464,0.00020470872,0.00006315977,0.000049534883,0.00010963331,0.0008293345,0.0024434659],"genre_scores_gemma":[0.37530342,0.0008709641,0.61559385,0.00026874058,0.00007151835,0.00028790426,0.000784166,0.00024875984,0.0065706526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994068,0.00017722991,0.00004356169,0.00014553613,0.0001535436,0.00007324874],"domain_scores_gemma":[0.9995454,0.00016083583,0.000054236018,0.0001272378,0.00007245652,0.000039882165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007107066,0.000819963,0.00056021905,0.0006604601,0.00072109466,0.0016424084,0.0015443374,0.0006921016,0.002239867],"category_scores_gemma":[0.0013453049,0.00031344427,0.0010808742,0.0010449399,0.0007284533,0.0021619434,0.0011132616,0.0011627588,0.00040494118],"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":[0.00007994766,0.000041012718,0.0006596425,0.00008182905,0.000053584,0.00019693388,0.00014689052,0.76335514,0.004072683,0.16826877,0.003796594,0.05924696],"study_design_scores_gemma":[0.0000045673355,0.000011884549,0.000079070276,0.0000053638055,0.000013526451,0.000019983403,0.000017539001,0.97339123,0.0008682039,0.021080889,0.0044976794,0.000010033434],"about_ca_topic_score_codex":0.018290464,"about_ca_topic_score_gemma":0.022405155,"teacher_disagreement_score":0.018290464,"about_ca_system_score_codex":0.0015008893,"about_ca_system_score_gemma":0.0015086605,"threshold_uncertainty_score":0.036368012},"labels":[],"label_agreement":null},{"id":"W4327523253","doi":"10.1109/tmc.2023.3256404","title":"Decoupled Association With Rate Splitting Multiple Access in UAV-Assisted Cellular Networks Using Multi-Agent Deep Reinforcement Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"UAV Applications and Optimization","field":"Engineering","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":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Computer science; Telecommunications link; Reinforcement learning; Beamforming; Backhaul (telecommunications); Multicast; Markov decision process; Precoding; Transmitter power output; Computer network; Partially observable Markov decision process; Base station; Spectral efficiency; Power control; Transmitter; Cellular network; Markov process; Markov chain; Telecommunications; Artificial intelligence; Power (physics); MIMO; Markov model; Machine learning","score_opus":0.019811890814123476,"score_gpt":0.2500340366751445,"score_spread":0.23022214586102102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327523253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19460051,0.0006746511,0.8003835,0.00057973305,0.00007144629,0.00005865343,0.000044943106,0.00033053794,0.0032559952],"genre_scores_gemma":[0.9871969,0.000075030664,0.011679396,0.000079534526,0.000012562724,0.00003496078,0.000021709837,0.00000984695,0.00089006894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996153,0.00013388936,0.000016111122,0.00007191873,0.000066637,0.000096145064],"domain_scores_gemma":[0.9985266,0.0009545945,0.00018960505,0.00005159963,0.00019537448,0.000082243925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010561484,0.000707012,0.0010732189,0.00022113093,0.0003070336,0.0006987073,0.0008497184,0.0009082032,0.0006608583],"category_scores_gemma":[0.0026118252,0.00040803375,0.00035202995,0.00024949334,0.0010177774,0.00062244287,0.0008636461,0.0011888965,0.000092068396],"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":[0.00002370041,0.000024658677,0.00042954469,0.000010909775,0.000014020697,0.000030316029,0.000017322118,0.9941298,0.00026915397,0.0012473788,0.000098687866,0.0037045807],"study_design_scores_gemma":[0.0000023917416,0.000006239436,0.000020182622,6.670445e-7,0.0000010915618,0.0000011179632,0.000001583239,0.9996152,0.000032379296,0.0003023958,0.000016174516,6.79005e-7],"about_ca_topic_score_codex":0.012299698,"about_ca_topic_score_gemma":0.008882695,"teacher_disagreement_score":0.012299698,"about_ca_system_score_codex":0.00091538875,"about_ca_system_score_gemma":0.0010796794,"threshold_uncertainty_score":0.024456203},"labels":[],"label_agreement":null},{"id":"W4327808488","doi":"10.1109/tmc.2023.3258750","title":"Optimal Random Access Strategies for Trigger-Based Multiple-Packet Reception Channels","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Notation; Network packet; Channel (broadcasting); Computer science; Algorithm; Discrete mathematics; Mathematics; Arithmetic; Computer network","score_opus":0.04334691770578537,"score_gpt":0.31602089040250614,"score_spread":0.2726739726967208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327808488","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04944164,0.0005615794,0.94255847,0.0002503121,0.000059783317,0.00016760586,0.00009862789,0.00031930793,0.006542658],"genre_scores_gemma":[0.94200516,0.00049384835,0.053387687,0.0001620582,0.00007317574,0.00025369984,0.0000708256,0.00008834266,0.0034651055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797887,0.00070473005,0.00008118022,0.0003156735,0.0004305776,0.0004890371],"domain_scores_gemma":[0.99324787,0.00449435,0.0010109999,0.00032251643,0.0005907816,0.0003333351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019892135,0.0016030174,0.0010296077,0.0009574745,0.00055984134,0.0017762319,0.002413643,0.0011231262,0.0025732243],"category_scores_gemma":[0.009416894,0.0005952698,0.0006340137,0.0008978337,0.0013863029,0.00168054,0.0015374303,0.0011526045,0.00058802444],"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":[0.00045382185,0.00016399003,0.0007977749,0.00020268034,0.00010030601,0.00038808314,0.00023773508,0.8066922,0.015735906,0.1463043,0.0016839342,0.02723927],"study_design_scores_gemma":[0.000035945668,0.000109314744,0.000106803556,0.000014277189,0.000022537472,0.00008133559,0.000039052906,0.98264253,0.0016320635,0.014748339,0.0005463111,0.000021468602],"about_ca_topic_score_codex":0.0017668776,"about_ca_topic_score_gemma":0.0017589311,"teacher_disagreement_score":0.0025732243,"about_ca_system_score_codex":0.0014170929,"about_ca_system_score_gemma":0.0018636812,"threshold_uncertainty_score":0.010520101},"labels":[],"label_agreement":null},{"id":"W4328007429","doi":"10.1109/tmc.2023.3259007","title":"Tree Learning: Towards Promoting Coordination in Scalable Multi-Client Training Acceleration","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Science, Technology and Innovation Commission of Shenzhen Municipality","keywords":"Computer science; Scalability; Synchronization (alternating current); Distributed computing; Convergence (economics); Edge device; Speedup; Data synchronization; Enhanced Data Rates for GSM Evolution; Process (computing); Partition (number theory); Tree (set theory); Artificial intelligence; Machine learning; Parallel computing; Computer network; Cloud computing","score_opus":0.06824652339246039,"score_gpt":0.31188069329587326,"score_spread":0.24363416990341286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4328007429","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017855803,0.00028575805,0.9770504,0.00025551603,0.000060955237,0.000054365013,0.00006049876,0.0023935006,0.001983191],"genre_scores_gemma":[0.575757,0.0002972774,0.41748476,0.00038270958,0.00011349362,0.00029794537,0.00044916244,0.00036915764,0.0048484104],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988268,0.00028341342,0.000051240335,0.00025034937,0.0003840739,0.000204078],"domain_scores_gemma":[0.99759513,0.0010773388,0.00014947906,0.00046769693,0.000507265,0.0002029798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001789005,0.00090571225,0.0012990042,0.0006510727,0.0006894995,0.0010191732,0.0028481025,0.0013519716,0.0036957157],"category_scores_gemma":[0.006629453,0.0004393696,0.0005593664,0.0010559337,0.00066912686,0.0024619878,0.002243125,0.0018751589,0.0014236701],"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":[0.00046524286,0.00035703462,0.0023457773,0.00013341996,0.00008388187,0.0001531917,0.00022754958,0.6412125,0.006897119,0.019501595,0.013920002,0.31470272],"study_design_scores_gemma":[0.000009444977,0.000019428298,0.000052572646,0.0000023356317,0.0000030000117,0.000010868095,0.000009342742,0.9962457,0.00057554815,0.0026434232,0.00042617344,0.000002141817],"about_ca_topic_score_codex":0.0057919296,"about_ca_topic_score_gemma":0.007604322,"teacher_disagreement_score":0.0057919296,"about_ca_system_score_codex":0.0008559419,"about_ca_system_score_gemma":0.002333477,"threshold_uncertainty_score":0.012363434},"labels":[],"label_agreement":null},{"id":"W4361856134","doi":"10.1109/tmc.2023.3263229","title":"Pa-Count: Passenger Counting in Vehicles Using Wi-Fi Signals","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":66,"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":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Algorithm; Artificial intelligence","score_opus":0.01924642407147712,"score_gpt":0.251195030620437,"score_spread":0.2319486065489599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361856134","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13569455,0.0006439458,0.8325336,0.00031228745,0.00043521155,0.0002548218,0.0016470782,0.01662017,0.011858369],"genre_scores_gemma":[0.84279054,0.00048141385,0.14590628,0.00028262768,0.0002255609,0.00023912774,0.0020022132,0.00017914265,0.007893083],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995528,0.0000613318,0.000021314932,0.00012925922,0.00016048722,0.00007475901],"domain_scores_gemma":[0.9995565,0.00008421058,0.00008215024,0.000078783414,0.0001586783,0.00003963747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031940817,0.0014862099,0.0005522157,0.0011319525,0.000423848,0.00073487044,0.0013395988,0.000521683,0.0017702837],"category_scores_gemma":[0.0016315565,0.00022302668,0.00030216773,0.0008198567,0.00031449195,0.00091377913,0.00081207213,0.0006831484,0.0014478404],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089419534,0.0003630392,0.03248132,0.00037050183,0.00019590286,0.00066174055,0.00030712286,0.082634784,0.05363676,0.0077075046,0.019587658,0.8011596],"study_design_scores_gemma":[0.000040970688,0.0002975573,0.009524141,0.00003770065,0.00007149703,0.00064073416,0.00014961665,0.9257488,0.04679137,0.0032447672,0.013360161,0.00009269925],"about_ca_topic_score_codex":0.0056771953,"about_ca_topic_score_gemma":0.0062688864,"teacher_disagreement_score":0.0056771953,"about_ca_system_score_codex":0.0003959885,"about_ca_system_score_gemma":0.00069653534,"threshold_uncertainty_score":0.011288285},"labels":[],"label_agreement":null},{"id":"W4378965902","doi":"10.1109/tmc.2023.3268323","title":"Energy Efficient and Differentially Private Federated Learning via a Piggyback Approach","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Privacy-Preserving Technologies in Data","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":"National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Additive white Gaussian noise; Wireless; Efficient energy use; Mobile device; Artificial noise; Leverage (statistics); Differential privacy; Algorithm; Telecommunications; Channel (broadcasting); Artificial intelligence; Electrical engineering","score_opus":0.019575428839413548,"score_gpt":0.24560936744401513,"score_spread":0.22603393860460158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378965902","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01203425,0.000100235025,0.98650837,0.00016398856,0.00001935006,0.000029128245,0.000028989485,0.00020266615,0.00091292796],"genre_scores_gemma":[0.8643226,0.00015156707,0.13229765,0.00027869528,0.000039236515,0.00009965543,0.000072061775,0.000048309994,0.0026901774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813974,0.0007154184,0.0000850625,0.00037600176,0.00045555673,0.00022820942],"domain_scores_gemma":[0.9975647,0.0012810912,0.00020898474,0.0005690984,0.00027551467,0.00010064878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023051768,0.0007931507,0.0010871332,0.00048213342,0.00054551027,0.0011938268,0.001877301,0.0013944528,0.0014540871],"category_scores_gemma":[0.005638876,0.0003277349,0.0005500446,0.0008075763,0.0014812413,0.002488701,0.0023318555,0.0014300817,0.00033229525],"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":[0.0005356481,0.00023399382,0.0008867849,0.00012951408,0.00007503325,0.00026723318,0.0001705676,0.7505187,0.011919068,0.08924711,0.0021453926,0.14387101],"study_design_scores_gemma":[0.000016352675,0.00006369609,0.000057878926,0.000006699186,0.0000060772945,0.000066085304,0.000011269113,0.9776283,0.0025387146,0.019166663,0.00043003456,0.0000083915065],"about_ca_topic_score_codex":0.00060383655,"about_ca_topic_score_gemma":0.0006499583,"teacher_disagreement_score":0.0023051768,"about_ca_system_score_codex":0.0008968914,"about_ca_system_score_gemma":0.0009659423,"threshold_uncertainty_score":0.012191117},"labels":[],"label_agreement":null},{"id":"W4379116721","doi":"10.1109/tmc.2023.3282243","title":"Joint In-Orbit Computation and Communication for Minimizing Download Time From LEO Satellites","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Satellite Communication Systems","field":"Engineering","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":"Toronto Metropolitan University; Carleton University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Mathematical optimization; Computation; Convex optimization; Scheduling (production processes); Optimization problem; Online algorithm; Regular polygon; Algorithm; Mathematics","score_opus":0.029022304052284675,"score_gpt":0.25798230192456784,"score_spread":0.22895999787228316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379116721","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08010583,0.00021946765,0.91403157,0.00021168972,0.000033126,0.000060652434,0.000048745456,0.00031138075,0.004977547],"genre_scores_gemma":[0.8998622,0.00018710786,0.097655416,0.000046427467,0.000029486813,0.000119056014,0.00008322815,0.00007743798,0.0019396347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962354,0.00009705679,0.000016288628,0.00007965148,0.00009088785,0.00009242783],"domain_scores_gemma":[0.99965966,0.00013177546,0.000060396145,0.00004111344,0.000060021997,0.00004698599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037662388,0.000732228,0.0007495075,0.00028670972,0.0005693001,0.000683745,0.00070044975,0.0004352734,0.0015049565],"category_scores_gemma":[0.0012379563,0.00031685684,0.00033431078,0.0005351969,0.00047919372,0.0009408793,0.0009284613,0.00053236453,0.00022119505],"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":[0.0002146887,0.00010756186,0.001017406,0.00009164167,0.000026857404,0.0001005701,0.00010086517,0.93294305,0.0122666545,0.009153602,0.0017913723,0.04218575],"study_design_scores_gemma":[0.0000102870345,0.00003683918,0.00015128154,0.0000024867052,0.000005299192,0.00001612073,0.00001942759,0.99622077,0.0017941542,0.0013976957,0.0003424832,0.0000031642587],"about_ca_topic_score_codex":0.0024986095,"about_ca_topic_score_gemma":0.0036489207,"teacher_disagreement_score":0.0024986095,"about_ca_system_score_codex":0.00053848786,"about_ca_system_score_gemma":0.001334214,"threshold_uncertainty_score":0.005034566},"labels":[],"label_agreement":null},{"id":"W4381785874","doi":"10.1109/tmc.2023.3288392","title":"Federated Learning With Dynamic Epoch Adjustment and Collaborative Training in Mobile Edge Computing","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Privacy-Preserving Technologies in Data","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":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Server; Edge computing; Mobile edge computing; Reliability (semiconductor); Convergence (economics); Enhanced Data Rates for GSM Evolution; Distributed computing; Edge device; Wireless; Computer network; Artificial intelligence; Cloud computing; Operating system","score_opus":0.020398010748838936,"score_gpt":0.2812393761255259,"score_spread":0.26084136537668695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381785874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049231272,0.00030479504,0.94848454,0.00018554772,0.00004462847,0.000031499087,0.000027729271,0.0006576111,0.0010323711],"genre_scores_gemma":[0.9431007,0.00010035136,0.055708934,0.00011355857,0.000021325304,0.00004405695,0.000047286045,0.00002674851,0.00083714555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907035,0.000274598,0.000062181876,0.00025477598,0.00016265066,0.00017542622],"domain_scores_gemma":[0.998321,0.00071494404,0.00016579432,0.00042107544,0.00028441002,0.000092786286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017979197,0.0006428742,0.0010043321,0.0004292052,0.00071386615,0.00079811475,0.001673996,0.0010022927,0.0006895405],"category_scores_gemma":[0.0052098916,0.0003015487,0.00049792207,0.00061102194,0.00080920244,0.0019305731,0.0016470811,0.0012948698,0.00017136717],"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":[0.00020904581,0.00010814552,0.0014498767,0.000035072262,0.00004156494,0.0001089241,0.000106024774,0.9194681,0.0021077532,0.0066535855,0.00072705286,0.06898498],"study_design_scores_gemma":[0.000004868585,0.000024584862,0.00008131746,0.000001957729,0.0000038094395,0.00001480397,0.000006496968,0.9967237,0.00055487634,0.0024572841,0.00012294142,0.0000033400368],"about_ca_topic_score_codex":0.0041677025,"about_ca_topic_score_gemma":0.0032167824,"teacher_disagreement_score":0.0041677025,"about_ca_system_score_codex":0.0006908897,"about_ca_system_score_gemma":0.0010916279,"threshold_uncertainty_score":0.009508371},"labels":[],"label_agreement":null},{"id":"W4385287558","doi":"10.1109/tmc.2023.3298641","title":"UAV-Assisted Wireless Cooperative Communication and Coded Caching: A Multiagent Two-Timescale DRL Approach","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":29,"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":"National Science Foundation of Sri Lanka; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Reinforcement learning; Aggregate (composite); Wireless; Computer network; Wireless network; Base station; Cluster analysis; Cache; Transmission (telecommunications); Scalability; Distributed computing; Artificial intelligence; Telecommunications; Database","score_opus":0.027949167263826815,"score_gpt":0.2689508654677831,"score_spread":0.24100169820395625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385287558","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029414985,0.00049123866,0.9657754,0.00043062068,0.000064019354,0.000051171253,0.000049331346,0.0002865356,0.0034368322],"genre_scores_gemma":[0.9407456,0.00018845714,0.055209536,0.0002630542,0.00004694632,0.000098362754,0.00007852527,0.000038131395,0.0033314284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999501,0.00013247346,0.000021947326,0.00014514163,0.0000982321,0.00010133891],"domain_scores_gemma":[0.9989932,0.00048021408,0.00014543138,0.00007501331,0.00021517361,0.00009099891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000910406,0.00080161786,0.0009522956,0.00033558137,0.0004581526,0.0008134018,0.002058858,0.0011471534,0.0015139463],"category_scores_gemma":[0.0022244216,0.00040974436,0.0004898758,0.00040889825,0.0007508744,0.0013487848,0.001300034,0.0010658675,0.00023006363],"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":[0.00005554869,0.00004698037,0.00088879775,0.000057384026,0.000035447363,0.0001319798,0.00008347488,0.9617701,0.0014611724,0.007811323,0.0009289371,0.026728703],"study_design_scores_gemma":[0.00000440184,0.000012684694,0.000035615976,0.0000017865347,0.000004191821,0.000009446416,0.0000069408356,0.9986902,0.00010320568,0.0009986825,0.00013065693,0.000002216542],"about_ca_topic_score_codex":0.011491193,"about_ca_topic_score_gemma":0.009041227,"teacher_disagreement_score":0.011491193,"about_ca_system_score_codex":0.001191533,"about_ca_system_score_gemma":0.0011311771,"threshold_uncertainty_score":0.022848606},"labels":[],"label_agreement":null},{"id":"W4385444829","doi":"10.1109/tmc.2023.3300311","title":"Joint Energy-Efficiency Communication Optimization and Perimeter Traffic Flow Control for Multi-Region LTE-V2V Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Cell Transmission Model; Controller (irrigation); Efficient energy use; Traffic flow (computer networking); Optimization problem; Power control; Real-time computing; Computer network; Power (physics); Traffic congestion; Engineering; Algorithm","score_opus":0.018963120222466007,"score_gpt":0.22983600134549964,"score_spread":0.21087288112303362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385444829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019862749,0.00014949776,0.97827107,0.00006892704,0.000016551294,0.000015860795,0.000019373654,0.00012792161,0.0014680488],"genre_scores_gemma":[0.96050733,0.00012766501,0.038163513,0.000035479876,0.000021412567,0.000046100446,0.000047093523,0.00003352619,0.0010179178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996569,0.0000882549,0.000011124384,0.00008774792,0.00008868469,0.000067319415],"domain_scores_gemma":[0.99966884,0.0001470235,0.00006840697,0.000025211784,0.0000668236,0.000023734634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070857466,0.00072621653,0.00075105723,0.00038620108,0.0003756269,0.00076166796,0.0008201829,0.000514151,0.0005545444],"category_scores_gemma":[0.0009817516,0.00029045588,0.00047324944,0.00043181426,0.0005532024,0.00083974,0.0008237314,0.0005275795,0.00007191559],"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":[0.000019447963,0.000009957621,0.00018897877,0.000012475334,0.000008179324,0.000012919972,0.000017386916,0.9843374,0.001203756,0.0034494093,0.00020652368,0.0105335405],"study_design_scores_gemma":[0.00000192636,0.0000084154,0.000047814952,8.3902245e-7,0.0000020925283,0.0000027257486,0.0000034762024,0.9986615,0.0002148625,0.00094155746,0.00011323311,0.0000013866508],"about_ca_topic_score_codex":0.005732779,"about_ca_topic_score_gemma":0.0047793505,"teacher_disagreement_score":0.005732779,"about_ca_system_score_codex":0.0010340667,"about_ca_system_score_gemma":0.00103999,"threshold_uncertainty_score":0.011398792},"labels":[],"label_agreement":null},{"id":"W4385627407","doi":"10.1109/tmc.2023.3303017","title":"DetFed: Dynamic Resource Scheduling for Deterministic Federated Learning Over Time-Sensitive Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":140,"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; University of Windsor; Memorial University of Newfoundland","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Jitter; Reinforcement learning; Scheduling (production processes); Network packet; Distributed computing; Markov decision process; Queueing theory; Transmission delay; Packet loss; Server; Computer network; Real-time computing; Markov process; Artificial intelligence; Mathematical optimization","score_opus":0.011562342655380685,"score_gpt":0.2615333743360205,"score_spread":0.24997103168063978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385627407","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019302843,0.0002497273,0.97797626,0.00020341875,0.00006527722,0.000048246016,0.00005178427,0.0007167623,0.0013856881],"genre_scores_gemma":[0.89828813,0.00013927426,0.09927641,0.00021348003,0.000041193307,0.00012394186,0.00010641523,0.000068202586,0.0017428637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932325,0.00014077078,0.000037734582,0.00019874766,0.00015150134,0.00014790363],"domain_scores_gemma":[0.9989158,0.0004944894,0.00014309297,0.00013557154,0.00020578521,0.00010529763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014958946,0.0008644838,0.0008669804,0.00031428452,0.000526954,0.00080285553,0.0018512574,0.0009276934,0.0017828887],"category_scores_gemma":[0.0030126355,0.00030367234,0.00043772,0.00034532612,0.00078913325,0.0011852453,0.0014086559,0.0012863061,0.000249698],"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":[0.0000899554,0.00006810178,0.000573919,0.000037939317,0.00002631719,0.00006172876,0.0000317907,0.9441877,0.0014389976,0.006366529,0.00097380765,0.046143238],"study_design_scores_gemma":[0.0000045079755,0.000011220485,0.000028123712,0.0000016988045,0.0000022075048,0.0000051500133,0.0000023034568,0.99812835,0.00023840905,0.001435642,0.00014069006,0.0000018100345],"about_ca_topic_score_codex":0.0055582128,"about_ca_topic_score_gemma":0.0064238952,"teacher_disagreement_score":0.0055582128,"about_ca_system_score_codex":0.001190071,"about_ca_system_score_gemma":0.0020017084,"threshold_uncertainty_score":0.011051714},"labels":[],"label_agreement":null},{"id":"W4385901200","doi":"10.1109/tmc.2023.3301577","title":"Wireless and Service Allocation for Mobile Computation Offloading With Task Deadlines","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","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":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Mobile edge computing; Wireless; Computation offloading; Distributed computing; Task (project management); Quality of service; Mobile device; Mobile computing; Computation; Server; Computer network; Enhanced Data Rates for GSM Evolution; Edge computing; Algorithm; Telecommunications; Operating system","score_opus":0.019401823504249644,"score_gpt":0.2692003969797334,"score_spread":0.24979857347548376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385901200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036864128,0.0011533857,0.9534342,0.00035134054,0.0001536913,0.000096670716,0.00008279329,0.0001884682,0.007675351],"genre_scores_gemma":[0.9065351,0.00083929213,0.08564311,0.00013507252,0.000103580984,0.00018058058,0.00009207457,0.00009836421,0.006372907],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949276,0.00015009656,0.00002011534,0.00008928848,0.00012432421,0.00012341756],"domain_scores_gemma":[0.99956197,0.00025476504,0.000054537435,0.0000316772,0.00006076784,0.000036185298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064582925,0.0008187996,0.0007912129,0.00033641388,0.00052307243,0.0009642826,0.0008358155,0.00060454744,0.0025679816],"category_scores_gemma":[0.0018514197,0.00028160523,0.00034512533,0.0007804221,0.00047994097,0.0010759179,0.00081488094,0.00080059795,0.00044984344],"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":[0.00021490008,0.0001330274,0.00038378648,0.00021247017,0.00002954703,0.00017293911,0.000095915144,0.85485446,0.0074696983,0.042638615,0.0031941028,0.090600595],"study_design_scores_gemma":[0.000007835918,0.000035835423,0.00010239657,0.0000068866702,0.0000035058433,0.00003082212,0.000025984295,0.9899548,0.0007147737,0.0075272,0.0015843111,0.0000055957644],"about_ca_topic_score_codex":0.0028365056,"about_ca_topic_score_gemma":0.0035746354,"teacher_disagreement_score":0.0028365056,"about_ca_system_score_codex":0.000994884,"about_ca_system_score_gemma":0.0010791405,"threshold_uncertainty_score":0.008590758},"labels":[],"label_agreement":null},{"id":"W4386081024","doi":"10.1109/tmc.2023.3327097","title":"ILCAS: Imitation Learning-Based Configuration- Adaptive Streaming for Live Video Analytics With Cross-Camera Collaboration","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Image and Video Quality Assessment","field":"Computer Science","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":"Simon Fraser University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Analytics; Imitation; Multimedia; Real-time computing; Computer vision; Human–computer interaction; Artificial intelligence; Data science","score_opus":0.03273965689453764,"score_gpt":0.3320054092933656,"score_spread":0.29926575239882797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386081024","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038298227,0.00026782445,0.94763273,0.00023560619,0.00011013485,0.0002387256,0.0001047521,0.008826207,0.004285828],"genre_scores_gemma":[0.7385838,0.00014777739,0.25489464,0.00029066103,0.00007456824,0.00030208373,0.00037947198,0.00025737306,0.005069627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99954706,0.0000769725,0.000026886535,0.0001393032,0.00014329363,0.00006638913],"domain_scores_gemma":[0.99914026,0.00028545273,0.00010277738,0.00017407259,0.00016284223,0.00013461693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007696519,0.0009239268,0.00080339314,0.00035091466,0.0004098848,0.0005949127,0.003006326,0.00083788123,0.0026823578],"category_scores_gemma":[0.0027860631,0.00031032367,0.00037499794,0.00030215908,0.0007390998,0.0014501433,0.0017551398,0.0014194747,0.0007486965],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005160358,0.0004495762,0.0028134203,0.00011960931,0.0001156495,0.00048214183,0.00032019307,0.5092708,0.024663365,0.00973895,0.011174691,0.44033557],"study_design_scores_gemma":[0.000019655794,0.000061738116,0.00014846123,0.0000036792055,0.000005840419,0.000037996448,0.0000132698,0.9941757,0.0027268026,0.0016453398,0.0011537379,0.00000773752],"about_ca_topic_score_codex":0.0048284153,"about_ca_topic_score_gemma":0.005363729,"teacher_disagreement_score":0.0048284153,"about_ca_system_score_codex":0.0007953717,"about_ca_system_score_gemma":0.0010489563,"threshold_uncertainty_score":0.009600639},"labels":[],"label_agreement":null},{"id":"W4386280917","doi":"10.1109/tmc.2023.3309633","title":"RingSFL: An Adaptive Split Federated Learning Towards Taming Client Heterogeneity","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Privacy-Preserving Technologies in Data","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 Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Convergence (economics); Scheme (mathematics); Distributed computing; Data modeling; Raw data; Information privacy; Benchmark (surveying); Computer network; Computer security; Database","score_opus":0.048144002707414105,"score_gpt":0.3056492883687118,"score_spread":0.2575052856612977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386280917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028766,0.00016086097,0.9686687,0.0001789837,0.0000315903,0.000047618072,0.000042203694,0.001500052,0.000603983],"genre_scores_gemma":[0.8496545,0.00009100225,0.14710483,0.00047863915,0.000043748383,0.00011810128,0.00021441864,0.00011687305,0.0021778962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99734396,0.0009496073,0.0001338347,0.0006884927,0.0005503547,0.00033370708],"domain_scores_gemma":[0.99480706,0.0019627542,0.00037610633,0.0018849486,0.0007031337,0.00026603835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041076816,0.00078302086,0.0012319261,0.0006078548,0.0008477034,0.0010791915,0.003330804,0.001550841,0.0012545658],"category_scores_gemma":[0.00956602,0.00041688315,0.0006917976,0.00068263727,0.0015466219,0.0036945464,0.0040409053,0.0018632626,0.00047125522],"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":[0.0010419172,0.00044986964,0.003917073,0.00010150462,0.00017041402,0.00022965831,0.00030886728,0.6410261,0.010652334,0.015929373,0.0044857026,0.32168722],"study_design_scores_gemma":[0.00002375993,0.00007780402,0.00011380309,0.0000043759273,0.000008651579,0.00004384735,0.000017784456,0.9917389,0.0020030052,0.005565671,0.00039377037,0.0000086680475],"about_ca_topic_score_codex":0.0022828216,"about_ca_topic_score_gemma":0.0025559561,"teacher_disagreement_score":0.0041076816,"about_ca_system_score_codex":0.00095155835,"about_ca_system_score_gemma":0.0016885888,"threshold_uncertainty_score":0.021723747},"labels":[],"label_agreement":null},{"id":"W4386766554","doi":"10.1109/tmc.2023.3315961","title":"Multi-Agent Deep Reinforcement Learning to Enable Dynamic TDD in a Multi-Cell Environment","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Distributed Control Multi-Agent Systems","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":"Université du Québec à Trois-Rivières","funders":"","keywords":"Reinforcement learning; Computer science; Distributed computing; Human–computer interaction; Artificial intelligence","score_opus":0.019648982804701794,"score_gpt":0.2545781797765407,"score_spread":0.2349291969718389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386766554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043494686,0.00025896216,0.9519776,0.00029426845,0.00006606536,0.000032870856,0.000036419788,0.0004570607,0.0033820367],"genre_scores_gemma":[0.9414587,0.00008465941,0.056120183,0.0001555626,0.000020591846,0.00005467305,0.000044729193,0.000024916446,0.002035934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997539,0.00006354445,0.000010853667,0.000059414033,0.000045873574,0.000066475426],"domain_scores_gemma":[0.99942374,0.00028174944,0.00008328974,0.0000378211,0.00011597784,0.000057459645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058794214,0.0005604001,0.00059535797,0.00016679459,0.00025615975,0.0005082236,0.00086153625,0.0006507582,0.0013157467],"category_scores_gemma":[0.0014425067,0.00024249511,0.0003369496,0.00018480854,0.0004972258,0.00053155655,0.00070820126,0.000990675,0.00018331484],"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":[0.000034923796,0.00003764283,0.00047272624,0.00002308608,0.000016045828,0.000044390905,0.000021100293,0.9775138,0.0012175376,0.0026544523,0.0004889083,0.017475424],"study_design_scores_gemma":[0.0000040077734,0.000012469833,0.000026949765,0.0000012627409,0.0000018720782,0.0000038628964,0.0000019708607,0.9991497,0.00014194928,0.0005475376,0.00010736692,0.0000011633713],"about_ca_topic_score_codex":0.007079646,"about_ca_topic_score_gemma":0.0065481924,"teacher_disagreement_score":0.007079646,"about_ca_system_score_codex":0.0006605724,"about_ca_system_score_gemma":0.0010590012,"threshold_uncertainty_score":0.014076889},"labels":[],"label_agreement":null},{"id":"W4387068050","doi":"10.1109/tmc.2023.3319544","title":"A Blockchain-Based Distributed and Intelligent Clustering-Enabled Authentication Protocol for UAV Swarms","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":38,"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; Université du Québec à Montréal","funders":"Innovation for Defence Excellence and Security","keywords":"Computer science; Authentication (law); Cluster analysis; Single point of failure; Authentication protocol; Computer network; Wireless; Protocol (science); Distributed computing; Computer security; Artificial intelligence","score_opus":0.019198814239219996,"score_gpt":0.27694972150468833,"score_spread":0.25775090726546834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387068050","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08005595,0.00050289213,0.9077979,0.00060376996,0.0001755976,0.0005703381,0.00025114196,0.0021931026,0.0078493245],"genre_scores_gemma":[0.93622065,0.00027868163,0.058119614,0.00008222468,0.000038604292,0.00032499785,0.00028587427,0.000049162605,0.004600199],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988732,0.00024150134,0.00011716494,0.00021291616,0.00036648122,0.00018864784],"domain_scores_gemma":[0.9981997,0.00047927833,0.00024898376,0.0005250616,0.00038272998,0.00016430115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010873164,0.00047814182,0.00069651206,0.0005744442,0.0014639519,0.0012031817,0.0015615181,0.0010602545,0.0028903766],"category_scores_gemma":[0.002584802,0.0002913595,0.0004340672,0.0007524553,0.0010520879,0.0023721894,0.0024393802,0.0009210368,0.0006727339],"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":[0.0011994262,0.00045899674,0.0030971053,0.00053879945,0.00011444116,0.0014355376,0.001100495,0.47908524,0.068478666,0.2572983,0.010616025,0.17657699],"study_design_scores_gemma":[0.00011563653,0.00018590943,0.00025303676,0.000028150722,0.00002929538,0.00018759676,0.000050625167,0.9525139,0.012613728,0.025013361,0.008970245,0.000038520207],"about_ca_topic_score_codex":0.0035651403,"about_ca_topic_score_gemma":0.002921627,"teacher_disagreement_score":0.0035651403,"about_ca_system_score_codex":0.0012612958,"about_ca_system_score_gemma":0.0023224575,"threshold_uncertainty_score":0.009669304},"labels":[],"label_agreement":null},{"id":"W4387068346","doi":"10.1109/tmc.2023.3319545","title":"DQ-Based Random Access NOMA for Massive Critical IoT Scenarios in 5G Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Wireless Communication Technologies","field":"Engineering","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":"Ericsson (Canada); Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Noma; Aloha; Base station; Low latency (capital markets); Benchmark (surveying); Distributed computing; Node (physics); Key (lock); Transmission (telecommunications); Throughput; Wireless; Telecommunications link; Telecommunications; Computer security","score_opus":0.02614667819666722,"score_gpt":0.3121826186513627,"score_spread":0.2860359404546955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387068346","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048329752,0.0008564553,0.94747424,0.00028912438,0.00011379711,0.000110046094,0.000052124073,0.0001756805,0.002598923],"genre_scores_gemma":[0.92148924,0.0007012743,0.07625431,0.00018134149,0.00009199314,0.0001046358,0.000051767118,0.000026400803,0.0010990583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99884653,0.00057325314,0.000037662416,0.00014547078,0.00020285064,0.0001941836],"domain_scores_gemma":[0.9977679,0.0013464811,0.00024602044,0.00017357919,0.000361459,0.000104541425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002403196,0.0007724094,0.0007023926,0.0005562646,0.00093695073,0.0007161809,0.0010950832,0.00057087396,0.0010808654],"category_scores_gemma":[0.0042264853,0.00026503034,0.0005014495,0.00063653756,0.0010447407,0.000997771,0.0010821641,0.0007580944,0.00020646656],"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":[0.00023254316,0.00013040533,0.0010891851,0.00020963691,0.000044729855,0.00038814894,0.00021278475,0.8885311,0.011429317,0.05449112,0.0017295623,0.041511495],"study_design_scores_gemma":[0.000012520808,0.00012259849,0.00013563815,0.0000065726485,0.000009834707,0.0000645362,0.000037743022,0.9925774,0.0006820458,0.0057996595,0.0005383392,0.000013207367],"about_ca_topic_score_codex":0.0036672433,"about_ca_topic_score_gemma":0.0057712654,"teacher_disagreement_score":0.0036672433,"about_ca_system_score_codex":0.0011367486,"about_ca_system_score_gemma":0.0014577737,"threshold_uncertainty_score":0.012709498},"labels":[],"label_agreement":null},{"id":"W4387303184","doi":"10.1109/tmc.2023.3321701","title":"Online Incentive Mechanisms for Socially-Aware and Socially-Unaware Mobile Crowdsensing","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Crowdsensing and Crowdsourcing","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Innovation for Defence Excellence and Security","keywords":"Computer science; Incentive; Incentive compatibility; Reverse auction; Rationality; Mobile device; Exploit; Computer security; Selection (genetic algorithm); Bidding; Artificial intelligence; World Wide Web; Microeconomics","score_opus":0.019873434698961558,"score_gpt":0.2798639538124632,"score_spread":0.25999051911350163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387303184","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03887644,0.00032794746,0.95398504,0.00054961344,0.000121006386,0.0002575366,0.00009626212,0.0003191616,0.0054669776],"genre_scores_gemma":[0.88676995,0.00022642557,0.108881034,0.00017854877,0.00007151502,0.00031419474,0.000080847174,0.00004074178,0.0034366406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968829,0.0012603563,0.00017166584,0.0006129753,0.0006608425,0.000411172],"domain_scores_gemma":[0.99481875,0.0028243395,0.0007640603,0.0005225566,0.0005800266,0.0004902841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004216715,0.0011595826,0.0013190334,0.0008421005,0.0011562201,0.0014833749,0.0031778566,0.0016013416,0.0028278036],"category_scores_gemma":[0.00890682,0.00055272545,0.0009648035,0.00071109616,0.0011453507,0.0025707432,0.0026830276,0.0016118529,0.00030607553],"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":[0.00053899334,0.00059945666,0.0023907218,0.000441549,0.00016354336,0.00072539167,0.0005230062,0.56820655,0.0113861775,0.31257418,0.004880597,0.0975698],"study_design_scores_gemma":[0.00007741351,0.00008878398,0.00020528458,0.00001927664,0.000020886395,0.00010118449,0.000051836745,0.94377583,0.0008607914,0.05207693,0.0026996853,0.000022115955],"about_ca_topic_score_codex":0.0014183755,"about_ca_topic_score_gemma":0.0014770974,"teacher_disagreement_score":0.004216715,"about_ca_system_score_codex":0.0014948501,"about_ca_system_score_gemma":0.0025239643,"threshold_uncertainty_score":0.022300363},"labels":[],"label_agreement":null},{"id":"W4387441444","doi":"10.1109/tmc.2023.3298935","title":"A Novel Federated Learning-Based Smart Power and 3D Trajectory Control for Fairness Optimization in Secure UAV-Assisted MEC Services","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"UAV Applications and Optimization","field":"Engineering","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":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Ministère de la Défense Nationale; Innovation for Defence Excellence and Security","keywords":"Computer science; Reinforcement learning; Artificial intelligence; Machine learning","score_opus":0.007048894619185787,"score_gpt":0.21621561681018997,"score_spread":0.20916672219100418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387441444","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018424362,0.00016927674,0.97776765,0.00022070418,0.000064643245,0.000040641142,0.00003718045,0.00041452897,0.0028609456],"genre_scores_gemma":[0.92054176,0.00011464827,0.075640745,0.00018140554,0.000046701345,0.00006830284,0.000058787704,0.000048264515,0.0032993744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994311,0.00008140409,0.000023231114,0.00018025392,0.00013336417,0.00015067401],"domain_scores_gemma":[0.9994387,0.0001934567,0.000083341845,0.00006164001,0.00014488008,0.0000779361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009639574,0.00075692136,0.0009683144,0.0004144044,0.0007812697,0.0010150949,0.001958297,0.001010467,0.0021495568],"category_scores_gemma":[0.0017435098,0.00034261847,0.00057878694,0.00050087675,0.0008797991,0.0012186018,0.0015634462,0.0010077673,0.0002710241],"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":[0.00012977514,0.000074806776,0.0005752318,0.000030373627,0.000023846627,0.00007871485,0.000055226803,0.9464937,0.0031228897,0.008072355,0.0012530915,0.040089857],"study_design_scores_gemma":[0.0000041202525,0.000009513639,0.000020990004,0.0000012224709,0.0000017821486,0.000004210305,0.0000023117084,0.99885356,0.00023220228,0.00076967094,0.00009867081,0.0000017483856],"about_ca_topic_score_codex":0.01229427,"about_ca_topic_score_gemma":0.01112088,"teacher_disagreement_score":0.01229427,"about_ca_system_score_codex":0.0016466257,"about_ca_system_score_gemma":0.001981323,"threshold_uncertainty_score":0.024445415},"labels":[],"label_agreement":null},{"id":"W4387490197","doi":"10.1109/tmc.2023.3323280","title":"Real-Time Contactless Eye Blink Detection Using UWB Radar","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":44,"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":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Radar; Computer vision; Artificial intelligence; Real-time computing; Telecommunications","score_opus":0.01640704340066011,"score_gpt":0.2541898789256774,"score_spread":0.2377828355250173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387490197","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44922048,0.0021617694,0.54164803,0.00023223551,0.00016992528,0.00010709502,0.00026523723,0.0027803078,0.003414942],"genre_scores_gemma":[0.9038512,0.000750316,0.09307417,0.00024828056,0.00007481021,0.000055925524,0.00019277079,0.0000711975,0.0016812686],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999686,0.0000749486,0.000016426819,0.00007448387,0.00012461263,0.000023497681],"domain_scores_gemma":[0.99949014,0.00016875846,0.00010558007,0.0000640874,0.00014515003,0.000026244039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029122844,0.00039870915,0.00042057966,0.00048265533,0.0000966261,0.00034430803,0.00043430828,0.0005257016,0.00061258173],"category_scores_gemma":[0.0010430814,0.00014066804,0.00018452734,0.00027809804,0.00014795468,0.00057326275,0.00031009418,0.00032445256,0.00032140224],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005218191,0.00017833128,0.0063513312,0.00044320265,0.00007774225,0.00032342767,0.0002210971,0.0034302403,0.74970275,0.0005131972,0.0017564597,0.23648034],"study_design_scores_gemma":[0.00015612751,0.0015686421,0.033132754,0.00008402287,0.00019533184,0.0034359838,0.00022200715,0.29334992,0.6584242,0.0012195678,0.008082192,0.00012921683],"about_ca_topic_score_codex":0.00023959183,"about_ca_topic_score_gemma":0.00035316308,"teacher_disagreement_score":0.00061258173,"about_ca_system_score_codex":0.00011071987,"about_ca_system_score_gemma":0.000113093876,"threshold_uncertainty_score":0.002049327},"labels":[],"label_agreement":null},{"id":"W4387717444","doi":"10.1109/tmc.2023.3325301","title":"Bayesian Meta-Learning for Adaptive Traffic Prediction in Wireless Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Probabilistic logic; Mean squared error; Data mining; Inference; Machine learning; Artificial intelligence; Wireless; Wireless network; Baseline (sea); Statistics","score_opus":0.019122635876919786,"score_gpt":0.23543061419045966,"score_spread":0.21630797831353987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387717444","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021346198,0.00097264553,0.97536963,0.0005021219,0.00007325215,0.000033121185,0.00015401747,0.0005836142,0.0009653608],"genre_scores_gemma":[0.8244692,0.00086799875,0.1700584,0.00055726443,0.00022932736,0.0002471812,0.000864553,0.00017223968,0.0025338547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919075,0.00030138288,0.00005160648,0.00020800008,0.000146114,0.00010210914],"domain_scores_gemma":[0.9971615,0.0020370346,0.00023293134,0.00012410368,0.000348681,0.000095701835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024335796,0.0016035249,0.002086836,0.0015530998,0.0005764686,0.0012279026,0.0025551198,0.0019024089,0.0011995421],"category_scores_gemma":[0.0062405965,0.0011399769,0.0015550095,0.0014772218,0.00078813965,0.002067967,0.0011044256,0.0026583008,0.00036965485],"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":[0.000048004047,0.00005071863,0.00083753176,0.00003735009,0.00007754095,0.000021426584,0.000024486942,0.97130334,0.00030148268,0.002443567,0.000634445,0.024220066],"study_design_scores_gemma":[0.00000260673,0.00000605649,0.000042613443,0.0000030900412,0.0000044863377,0.000002025426,0.000002002576,0.9983557,0.000055243694,0.0014682623,0.000055805936,0.0000020298387],"about_ca_topic_score_codex":0.014563269,"about_ca_topic_score_gemma":0.012935339,"teacher_disagreement_score":0.014563269,"about_ca_system_score_codex":0.0015895247,"about_ca_system_score_gemma":0.0016324462,"threshold_uncertainty_score":0.02895701},"labels":[],"label_agreement":null},{"id":"W4387789555","doi":"10.1109/tmc.2023.3325826","title":"Combining IMU With Acoustics for Head Motion Tracking Leveraging Wireless Earphone","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","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":"Simon Fraser University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Inertial measurement unit; Wireless; Tracking (education); Head (geology); Match moving; Motion (physics); Acoustics; Computer vision; Telecommunications","score_opus":0.021588765239083436,"score_gpt":0.24942493380143993,"score_spread":0.2278361685623565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387789555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0698851,0.001455485,0.9194301,0.0002816959,0.00041600797,0.00013205012,0.00027702464,0.0036552015,0.004467426],"genre_scores_gemma":[0.69321454,0.0014606498,0.29702377,0.00059657544,0.00032997847,0.0002133854,0.00042001036,0.0001371863,0.0066038626],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996025,0.00007695931,0.000026023714,0.00010186937,0.00015222156,0.00004056035],"domain_scores_gemma":[0.999655,0.00007587804,0.00005048689,0.00005615154,0.0001441987,0.000018234396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036198722,0.0008224851,0.000592947,0.00067065854,0.00022325368,0.00054021284,0.0005920068,0.00065919635,0.0013750559],"category_scores_gemma":[0.0014413203,0.0002809825,0.00028838695,0.00068349356,0.00021090155,0.0008177236,0.0009009066,0.00033329654,0.0013373692],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047654405,0.0001266102,0.0091046365,0.00042083513,0.00016142317,0.00037358867,0.00030614086,0.013848084,0.16222179,0.0016401613,0.00567724,0.80564296],"study_design_scores_gemma":[0.00012063922,0.0015002516,0.024979446,0.00016224172,0.00051345775,0.0021527517,0.00032473865,0.638331,0.2836337,0.0033801922,0.04468635,0.0002152268],"about_ca_topic_score_codex":0.001423321,"about_ca_topic_score_gemma":0.0024157257,"teacher_disagreement_score":0.001423321,"about_ca_system_score_codex":0.00016325535,"about_ca_system_score_gemma":0.00030952,"threshold_uncertainty_score":0.0045999885},"labels":[],"label_agreement":null},{"id":"W4387790169","doi":"10.1109/tmc.2023.3325334","title":"Accelerating and Securing Blockchain-Enabled Distributed Machine Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","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":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Science, Technology and Innovation Commission of Shenzhen Municipality; Public Safety Canada; Western Canada Research Grid; Compute Canada","keywords":"Computer science; Blockchain; Latency (audio); Server; Proof-of-work system; Distributed computing; Artificial intelligence; Theoretical computer science; Computer network; Computer security","score_opus":0.014232909913778208,"score_gpt":0.24125340069763987,"score_spread":0.22702049078386166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387790169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2012895,0.0004900478,0.78801066,0.0012389711,0.00019017467,0.0004236721,0.00017121117,0.0040703523,0.004115404],"genre_scores_gemma":[0.9518033,0.00012809035,0.046267252,0.00009622626,0.000030073974,0.0001475453,0.00013643275,0.000057291883,0.0013338438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9963021,0.0011375397,0.00023170127,0.0005192194,0.0012418248,0.0005675399],"domain_scores_gemma":[0.9878237,0.0043617687,0.001177722,0.004738032,0.0013384685,0.00056035287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039896187,0.00070399424,0.0010332011,0.00068323634,0.0013978867,0.0013218883,0.0020232594,0.0011622261,0.0015645273],"category_scores_gemma":[0.012931186,0.000474476,0.00044122475,0.0008900354,0.0013433094,0.0045378027,0.0044565406,0.0017834626,0.00055113906],"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":[0.0011123612,0.000440377,0.006241706,0.0002494907,0.00012259855,0.0007423044,0.0008691371,0.61753523,0.038230922,0.08540807,0.006670687,0.24237704],"study_design_scores_gemma":[0.00004385183,0.00006705105,0.00013635877,0.0000072180706,0.000006393703,0.000045432094,0.00002849365,0.9741657,0.007437282,0.016860852,0.0011914015,0.000009984789],"about_ca_topic_score_codex":0.0018830264,"about_ca_topic_score_gemma":0.0019296648,"teacher_disagreement_score":0.0039896187,"about_ca_system_score_codex":0.0010464729,"about_ca_system_score_gemma":0.0030339283,"threshold_uncertainty_score":0.021099389},"labels":[],"label_agreement":null},{"id":"W4389317812","doi":"10.1109/tmc.2023.3338602","title":"A Repeated Auction Model for Load-Aware Dynamic Resource Allocation in Multi-Access Edge Computing","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","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":"University of Manitoba","funders":"","keywords":"Computer science; Bidding; Server; Resource allocation; Computation offloading; Distributed computing; Resource management (computing); Edge computing; Mobile edge computing; Computer network; Nash equilibrium; Service provider; Service (business); Cloud computing; Mathematical optimization; Operating system; Microeconomics","score_opus":0.05189364837230872,"score_gpt":0.32698734434799676,"score_spread":0.27509369597568806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389317812","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036750305,0.0005751146,0.95249766,0.00046562677,0.00013344675,0.00015332288,0.00013884075,0.00017476191,0.009110912],"genre_scores_gemma":[0.92211944,0.0004755002,0.06653923,0.00016227867,0.00006713021,0.00021814834,0.00010294324,0.000076649754,0.010238639],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986765,0.0005108224,0.000054436143,0.00022696225,0.0002286721,0.00030259916],"domain_scores_gemma":[0.99853206,0.0008562267,0.00017342014,0.00007818843,0.0001973098,0.00016268663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017689764,0.0013149111,0.0019024559,0.0006209633,0.0007058827,0.00273421,0.003305208,0.0020811034,0.0055705192],"category_scores_gemma":[0.004020767,0.00076046336,0.0012399944,0.0009873972,0.001276783,0.002331943,0.0011860512,0.0019332774,0.000653448],"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":[0.000089944384,0.00008164957,0.0003247024,0.0000799089,0.000048135313,0.00030494377,0.000061135026,0.9382308,0.0014263402,0.052248795,0.0012878171,0.0058158203],"study_design_scores_gemma":[0.000010914965,0.000012842269,0.000032122316,0.0000022403499,0.0000054168017,0.000019666995,0.000007952385,0.99471563,0.000051131734,0.0049619963,0.00017464528,0.000005390504],"about_ca_topic_score_codex":0.0069354917,"about_ca_topic_score_gemma":0.0051531047,"teacher_disagreement_score":0.0069354917,"about_ca_system_score_codex":0.0018798403,"about_ca_system_score_gemma":0.0018611416,"threshold_uncertainty_score":0.018635273},"labels":[],"label_agreement":null},{"id":"W4389543055","doi":"10.1109/tmc.2023.3341082","title":"Age of Processing-Aware Offloading Decision for Autonomous Vehicles in 5G Open RAN Environment","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Age of Information Optimization","field":"Computer Science","cited_by":7,"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":"Mitacs","keywords":"Computer science; Cloud computing; Computation offloading; Edge computing; Distributed computing; Computation; Latency (audio); Embedded system; Enhanced Data Rates for GSM Evolution; Real-time computing; Artificial intelligence; Operating system","score_opus":0.024624974739677556,"score_gpt":0.28214784803396364,"score_spread":0.2575228732942861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389543055","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23790467,0.00039244004,0.7530335,0.0007407873,0.00010370139,0.00010915167,0.00009579659,0.00036132403,0.0072586224],"genre_scores_gemma":[0.979202,0.00006916035,0.019533226,0.000048210848,0.000021112477,0.00002506442,0.00003755209,0.000020934358,0.0010426271],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994031,0.000099053665,0.000019843445,0.00014580942,0.00009608543,0.00023592118],"domain_scores_gemma":[0.999238,0.00033517534,0.00012023305,0.00004475167,0.00014260935,0.000119345874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006775289,0.00073669804,0.000691977,0.00033025348,0.0007753925,0.0011743685,0.00083779654,0.00060746074,0.0011940007],"category_scores_gemma":[0.00156573,0.0003586792,0.00028206687,0.00037174483,0.00053339405,0.0011139389,0.0008243647,0.00070987875,0.00013800817],"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":[0.00030084932,0.00010800673,0.0021033809,0.000059645507,0.000022759586,0.00017768623,0.00013010741,0.93783504,0.0058997744,0.007949376,0.001816982,0.04359642],"study_design_scores_gemma":[0.0000040081673,0.000030598552,0.0001951386,0.0000018998246,0.000003995321,0.0000135777,0.00004781101,0.9966174,0.00066116476,0.0021559312,0.00026455458,0.000003986643],"about_ca_topic_score_codex":0.0058271154,"about_ca_topic_score_gemma":0.00817104,"teacher_disagreement_score":0.0058271154,"about_ca_system_score_codex":0.0009558458,"about_ca_system_score_gemma":0.0017633598,"threshold_uncertainty_score":0.011586428},"labels":[],"label_agreement":null},{"id":"W4389610117","doi":"10.1109/tmc.2023.3341810","title":"End-to-End Resource Slicing for Coexistence of eMBB and URLLC Services in 5G-Advanced/6G Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Software-Defined Networks and 5G","field":"Computer Science","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":"University of Manitoba","funders":"Business Finland; Oulun Yliopisto; Academy of Finland","keywords":"Computer science; C-RAN; Resource allocation; Latency (audio); Radio access network; Optimization problem; Computer network; Distributed computing; Mathematical optimization; Algorithm; Base station; Telecommunications; Mathematics","score_opus":0.015328514790672139,"score_gpt":0.2619358727123419,"score_spread":0.2466073579216698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389610117","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14047468,0.0018339229,0.8511866,0.0005301583,0.00011372115,0.00008946717,0.0000995959,0.00025712408,0.005414726],"genre_scores_gemma":[0.92984587,0.00076052966,0.06775962,0.00011638501,0.000039129507,0.00005303439,0.000103559,0.00004719024,0.001274703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934655,0.00023585383,0.00002388593,0.00010706687,0.00011536154,0.00017127555],"domain_scores_gemma":[0.9988457,0.0006928372,0.00014346496,0.000053012984,0.00013896961,0.0001260491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012303635,0.0011076138,0.0011554213,0.00034474884,0.000667311,0.0008965843,0.0008268731,0.0006959587,0.0018702451],"category_scores_gemma":[0.0020827993,0.00035704992,0.0005532217,0.00063139055,0.0006986664,0.0014323164,0.0010928265,0.0009091036,0.00015300668],"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":[0.000101189806,0.00004598201,0.00041480066,0.00005852252,0.00001852318,0.00008089837,0.000032944263,0.97505796,0.0025834932,0.00612129,0.0006670298,0.014817393],"study_design_scores_gemma":[0.0000046903424,0.000022401673,0.000072235045,0.000003143591,0.0000052881137,0.000016258244,0.000018409619,0.997775,0.00052055856,0.0013972023,0.00016138668,0.0000034203963],"about_ca_topic_score_codex":0.007935707,"about_ca_topic_score_gemma":0.005792399,"teacher_disagreement_score":0.007935707,"about_ca_system_score_codex":0.0011816634,"about_ca_system_score_gemma":0.0016298604,"threshold_uncertainty_score":0.015779018},"labels":[],"label_agreement":null},{"id":"W4389610120","doi":"10.1109/tmc.2023.3341809","title":"Deployment Cost-Aware UAV and BS Collaboration in Cell-Free Integrated Aerial-Terrestrial Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale","keywords":"Computer science; MIMO; Software deployment; Base station; Cluster analysis; Real-time computing; Computer network; Distributed computing; Non-line-of-sight propagation; Telecommunications link; Wireless; Telecommunications; Channel (broadcasting); Artificial intelligence","score_opus":0.009129844519161774,"score_gpt":0.2350227940320617,"score_spread":0.22589294951289993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389610120","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14735577,0.00050535763,0.84604174,0.00019987233,0.00003556576,0.00005160318,0.00007376695,0.00017770081,0.005558621],"genre_scores_gemma":[0.96550983,0.0001487796,0.03313583,0.000037887687,0.000009434616,0.000028754162,0.000041872823,0.0000134574375,0.0010741234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995561,0.00015660748,0.000009718097,0.00007492792,0.000098953446,0.0001036768],"domain_scores_gemma":[0.9994223,0.00028799183,0.000094100986,0.000043461943,0.000087404725,0.00006471513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056556484,0.00063066644,0.000507349,0.00027808954,0.00036486544,0.00058251596,0.0009054875,0.0005188267,0.0007376848],"category_scores_gemma":[0.0015167191,0.00028344768,0.00025996848,0.0005587802,0.0004299886,0.0007796884,0.0010009872,0.00038210955,0.0001251308],"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":[0.000041177645,0.000016116586,0.00047816604,0.000018994688,0.000011823148,0.0000719898,0.000029784513,0.9846563,0.0013742489,0.003678714,0.00034105155,0.00928157],"study_design_scores_gemma":[0.0000037547468,0.000024740786,0.00013298316,0.0000018051846,0.0000043667415,0.00002512795,0.000020441723,0.9980033,0.00040164712,0.0011960462,0.00018336857,0.0000024699564],"about_ca_topic_score_codex":0.005573436,"about_ca_topic_score_gemma":0.007766644,"teacher_disagreement_score":0.005573436,"about_ca_system_score_codex":0.0009305505,"about_ca_system_score_gemma":0.00082862144,"threshold_uncertainty_score":0.011081934},"labels":[],"label_agreement":null},{"id":"W4389610124","doi":"10.1109/tmc.2023.3340925","title":"SlpRoF: Improving the Temporal Coverage and Robustness of RF-Based Vital Sign Monitoring During Sleep","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Computer science; Torso; Sleep (system call); Robustness (evolution); Heart rate; Vital signs; Real-time computing; Simulation; Speech recognition; Medicine; Blood pressure; Anesthesia","score_opus":0.010690342174390407,"score_gpt":0.21828000584569396,"score_spread":0.20758966367130355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389610124","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22738265,0.0016732253,0.76216066,0.0002482171,0.00017201797,0.00008425349,0.00038163853,0.004391644,0.0035057897],"genre_scores_gemma":[0.88400173,0.0005067607,0.11239918,0.0003594428,0.00012048866,0.00008357602,0.00056074315,0.00014710832,0.0018210196],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995394,0.000091582406,0.000028292416,0.000115491246,0.00018077926,0.000044603406],"domain_scores_gemma":[0.99935824,0.00024857526,0.00008634037,0.00008913039,0.00019120278,0.000026478634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006034531,0.00049529347,0.00038344032,0.0005338839,0.00014247274,0.00035095998,0.000418101,0.0005163414,0.00085340545],"category_scores_gemma":[0.0025383907,0.0001504698,0.00023354053,0.00026477652,0.00015660276,0.00062884245,0.00064372516,0.00033839114,0.00061170774],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010276638,0.00014812441,0.012065571,0.00043487473,0.000103402774,0.00040138754,0.00028343237,0.016135763,0.44354802,0.00090740755,0.00430823,0.5206361],"study_design_scores_gemma":[0.00013066492,0.0018273889,0.05280835,0.000098710116,0.0001671485,0.0042725443,0.00020472861,0.65832675,0.26431832,0.0016883299,0.016025659,0.00013143079],"about_ca_topic_score_codex":0.00045094965,"about_ca_topic_score_gemma":0.000656253,"teacher_disagreement_score":0.00085340545,"about_ca_system_score_codex":0.00009903726,"about_ca_system_score_gemma":0.000160019,"threshold_uncertainty_score":0.0031914115},"labels":[],"label_agreement":null},{"id":"W4389887582","doi":"10.1109/tmc.2023.3343715","title":"Distributionally Robust Cost Minimized Edge Semantic Intelligence in the Sustainable Metaverse","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Info-communications Media Development Authority; National Research Foundation Singapore","keywords":"Computer science; Metaverse; Reservation; Matching (statistics); Computer network; Virtual reality; Human–computer interaction","score_opus":0.02857787603887938,"score_gpt":0.2718848308018686,"score_spread":0.24330695476298922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389887582","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032126114,0.00057949946,0.95743173,0.0006712785,0.00006940583,0.000055404224,0.00013721205,0.00015265869,0.008776703],"genre_scores_gemma":[0.93926775,0.00041031273,0.05394452,0.00024207159,0.000050118444,0.00014097158,0.00015160318,0.00008141088,0.005711106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888664,0.0003998927,0.000044037035,0.00026425146,0.00022093799,0.00018424338],"domain_scores_gemma":[0.998262,0.0010581082,0.0002021192,0.00010806071,0.00024801362,0.000121767465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020511984,0.0013974387,0.0017475896,0.00058727287,0.0005591228,0.0022650235,0.0016763007,0.0019403988,0.0029604859],"category_scores_gemma":[0.0039878157,0.0006530567,0.0009150983,0.0007880788,0.0017833785,0.0023257225,0.0024724996,0.0015721276,0.00034311562],"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":[0.00009777583,0.00003465553,0.0002373999,0.000081381855,0.000045509678,0.00014294965,0.000039844745,0.9485866,0.0011434713,0.040707212,0.0009189207,0.007964284],"study_design_scores_gemma":[0.000009963452,0.000031877877,0.00006061312,0.0000071865825,0.000007124484,0.000018571021,0.000015431506,0.9828036,0.00016806561,0.016533004,0.00033659316,0.000008021342],"about_ca_topic_score_codex":0.0030893984,"about_ca_topic_score_gemma":0.0017029918,"teacher_disagreement_score":0.0030893984,"about_ca_system_score_codex":0.0016300287,"about_ca_system_score_gemma":0.0013273149,"threshold_uncertainty_score":0.011826694},"labels":[],"label_agreement":null},{"id":"W4389924147","doi":"10.1109/tmc.2023.3343709","title":"Accuracy-Aware Cooperative Sensing and Computing for Connected Autonomous Vehicles","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Distributed computing; Computer network; Human–computer interaction","score_opus":0.025946546468648767,"score_gpt":0.2828374104328161,"score_spread":0.2568908639641674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389924147","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13562624,0.00033649852,0.8599232,0.00022765828,0.000049158596,0.000051366766,0.000031172633,0.00047094488,0.0032837489],"genre_scores_gemma":[0.9702589,0.000061899016,0.028898284,0.00003561847,0.000011721777,0.00002437429,0.000025324134,0.000012108742,0.00067182735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999466,0.00007558753,0.00001730459,0.0001365894,0.00019589876,0.000108698005],"domain_scores_gemma":[0.9995284,0.00014946668,0.00006730697,0.00008488971,0.0001263216,0.000043655244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047158933,0.0005290257,0.0004696867,0.0004119341,0.0007039852,0.0007746384,0.0014947278,0.00050697435,0.00047441045],"category_scores_gemma":[0.0013190806,0.00024815486,0.00030878876,0.0006210802,0.0006449042,0.00129458,0.0012901605,0.00051644014,0.00012262301],"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":[0.00019317967,0.000121778,0.0022222942,0.00007191366,0.000057906942,0.00020571401,0.0003156835,0.8656909,0.025533793,0.013132616,0.001456265,0.090998046],"study_design_scores_gemma":[0.00000336861,0.00002951681,0.00021318441,0.0000015652112,0.0000053418757,0.000018309882,0.000033157852,0.9945286,0.0014404729,0.003284657,0.00043761253,0.0000040873533],"about_ca_topic_score_codex":0.0064397682,"about_ca_topic_score_gemma":0.00873508,"teacher_disagreement_score":0.0064397682,"about_ca_system_score_codex":0.00083287625,"about_ca_system_score_gemma":0.00096245646,"threshold_uncertainty_score":0.012804568},"labels":[],"label_agreement":null},{"id":"W4390204169","doi":"10.1109/tmc.2023.3345898","title":"ShuttleBus: Dense Packet Assembling With QUIC Stream Multiplexing for Massive IoT","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Calgary","funders":"National Postdoctoral Program for Innovative Talents; China Postdoctoral Science Foundation; Special Project for Research and Development in Key areas of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Computer network; Network packet; Multiplexing; Latency (audio); Cluster analysis; Overhead (engineering); Packet forwarding; Data stream mining; Distributed computing; Telecommunications","score_opus":0.029146836093488837,"score_gpt":0.28241630203361096,"score_spread":0.2532694659401221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390204169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10429767,0.0005526941,0.8894794,0.00020097898,0.00015114198,0.00031882667,0.00008551607,0.0019447848,0.0029689954],"genre_scores_gemma":[0.77802724,0.00027065168,0.2197034,0.00017591447,0.00006517451,0.00015699217,0.00020724724,0.00008658206,0.0013068541],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995752,0.000086849876,0.000028168775,0.000072305986,0.00014281007,0.00009471515],"domain_scores_gemma":[0.9993316,0.00019716445,0.00010017913,0.00016892604,0.00011683936,0.00008531252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082390016,0.0006853104,0.00055290695,0.00086837227,0.0008263001,0.00078845856,0.0012727118,0.00039960415,0.0009837965],"category_scores_gemma":[0.0014564783,0.00022419417,0.0003380431,0.0007817749,0.0006364839,0.0018505711,0.0013187772,0.00066474883,0.0002035624],"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":[0.001027695,0.00054235087,0.007583083,0.0003390357,0.00014262092,0.0009771779,0.0007805607,0.24136215,0.15108456,0.052049745,0.007825318,0.5362857],"study_design_scores_gemma":[0.000027434227,0.00039788845,0.0008818732,0.000015763004,0.00003051073,0.00027051082,0.00012742738,0.9555146,0.029460393,0.0074467864,0.0057898615,0.000036969566],"about_ca_topic_score_codex":0.0018848663,"about_ca_topic_score_gemma":0.0021418252,"teacher_disagreement_score":0.0018848663,"about_ca_system_score_codex":0.0008234627,"about_ca_system_score_gemma":0.0007453677,"threshold_uncertainty_score":0.00597471},"labels":[],"label_agreement":null},{"id":"W4390421908","doi":"10.1109/tmc.2023.3348136","title":"Privacy-Preserving Location-Based Advertising via Longitudinal Geo-Indistinguishability","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","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":"University of Waterloo","funders":"National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Obfuscation; Computer science; Inference; Differential privacy; Computer security; Data mining; Artificial intelligence","score_opus":0.0295127119607585,"score_gpt":0.2908800245725671,"score_spread":0.2613673126118086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390421908","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10528066,0.00045376635,0.88587517,0.00091511075,0.000061893246,0.00009915938,0.00024546677,0.0023732728,0.0046955594],"genre_scores_gemma":[0.9605951,0.00015273862,0.037047155,0.00018411655,0.000039866827,0.00006926049,0.00013877,0.000051055405,0.0017220005],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99570847,0.0014869517,0.00025879222,0.00067382725,0.0012697714,0.0006021798],"domain_scores_gemma":[0.9872469,0.0038632315,0.0014376832,0.0064084264,0.0007018144,0.00034204454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034487825,0.0006851569,0.0011667946,0.0006392271,0.0012231632,0.002224282,0.0015265035,0.0014049187,0.0014884121],"category_scores_gemma":[0.01232661,0.00052278064,0.0009030238,0.0011348834,0.002008749,0.0057944776,0.0055142897,0.0024113664,0.00070048944],"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":[0.0034502018,0.00049050356,0.011318767,0.0003075168,0.00018538536,0.0014064746,0.0015325679,0.29370737,0.053295877,0.3248648,0.008065362,0.3013752],"study_design_scores_gemma":[0.000094944604,0.00024057014,0.0010026128,0.000026839365,0.00005542592,0.00075754104,0.00012120736,0.85426736,0.03278365,0.10386796,0.0067066047,0.000075264106],"about_ca_topic_score_codex":0.000828578,"about_ca_topic_score_gemma":0.000592204,"teacher_disagreement_score":0.0034487825,"about_ca_system_score_codex":0.000945292,"about_ca_system_score_gemma":0.0011770322,"threshold_uncertainty_score":0.01823914},"labels":[],"label_agreement":null},{"id":"W4390534264","doi":"10.1109/tmc.2023.3347580","title":"Cooperative Deep Reinforcement Learning Enabled Power Allocation for Packet Duplication URLLC in Multi-Connectivity Vehicular Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":60,"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; Toronto Metropolitan University","funders":"Natural Science Foundation of Jiangsu Province for Distinguished Young Scholars; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Retransmission; Network packet; Computer network; Reinforcement learning; Scheduling (production processes); Telecommunications link; Distributed computing; Artificial intelligence","score_opus":0.015167811872945696,"score_gpt":0.2680953196626805,"score_spread":0.25292750778973483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390534264","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0975117,0.00021862917,0.8991996,0.0002172549,0.000033581728,0.000036128367,0.000015728934,0.0002835079,0.002483802],"genre_scores_gemma":[0.9893444,0.00003749677,0.00981889,0.00004743509,0.000005878014,0.000022795988,0.000009842515,0.000008315186,0.0007049773],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996699,0.000090131434,0.000012806672,0.00007145476,0.00006815452,0.00008762387],"domain_scores_gemma":[0.9994019,0.00029369345,0.00009467074,0.000035392437,0.00011968552,0.00005453942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079645816,0.00050609343,0.0005622003,0.00021065013,0.00027557375,0.0004477098,0.0009388546,0.00058010913,0.0006996165],"category_scores_gemma":[0.0017889377,0.00024164299,0.0002392852,0.00021043375,0.0007165,0.00058319396,0.00084459776,0.0007369129,0.000106135565],"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":[0.00008409601,0.000047313617,0.0006781342,0.000026443646,0.000018715637,0.00007942015,0.00005163376,0.96915126,0.0024385736,0.0030358948,0.00042507966,0.023963416],"study_design_scores_gemma":[0.00000404013,0.000019544294,0.00003348046,9.75802e-7,0.0000023437042,0.0000060371153,0.000003226588,0.99905986,0.00028078008,0.00053671416,0.00005179619,0.0000013036048],"about_ca_topic_score_codex":0.004012606,"about_ca_topic_score_gemma":0.0032766338,"teacher_disagreement_score":0.004012606,"about_ca_system_score_codex":0.000659862,"about_ca_system_score_gemma":0.00088562095,"threshold_uncertainty_score":0.007978499},"labels":[],"label_agreement":null},{"id":"W4390659127","doi":"10.1109/tmc.2024.3350885","title":"UAV Swarm-Enabled Collaborative Secure Relay Communications With Time-Domain Colluding Eavesdropper","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"UAV Applications and Optimization","field":"Engineering","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 British Columbia","funders":"National Natural Science Foundation of China","keywords":"Computer science; Relay; Swarm behaviour; Beamforming; Computer network; Optimization problem; Base station; Energy consumption; Terminal (telecommunication); Real-time computing; Telecommunications; Algorithm; Artificial intelligence; Electrical engineering","score_opus":0.0058059094570404235,"score_gpt":0.22284385407659657,"score_spread":0.21703794461955614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390659127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.065010145,0.00036931716,0.93173957,0.00011104977,0.000036071426,0.00002557691,0.00002546942,0.00015735155,0.0025254933],"genre_scores_gemma":[0.965689,0.00019393611,0.032830555,0.000034622026,0.000012681005,0.000033092194,0.000023847419,0.00000946612,0.0011728483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997056,0.00009744373,0.000013609257,0.00006987686,0.0000658091,0.000047718047],"domain_scores_gemma":[0.99969935,0.00013443957,0.000058533013,0.000035967587,0.000050918523,0.000020825615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040523853,0.0007762541,0.0005932359,0.00021902066,0.0002840339,0.00048617777,0.0005362815,0.00050149515,0.0006758979],"category_scores_gemma":[0.0007667102,0.00018447982,0.0004239264,0.0002822046,0.00042024217,0.00065888616,0.0007487729,0.0004499652,0.00016920266],"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":[0.00016840213,0.00003211964,0.0006983063,0.00010959274,0.00007411789,0.00036500112,0.00014113126,0.93838406,0.018317843,0.00961197,0.0007490326,0.031348515],"study_design_scores_gemma":[0.000009840467,0.00008710829,0.000095676856,0.000002806462,0.000014732097,0.000050512248,0.000024703804,0.9960232,0.0019711854,0.0013775898,0.00033798732,0.0000047252283],"about_ca_topic_score_codex":0.0014834484,"about_ca_topic_score_gemma":0.0012004732,"teacher_disagreement_score":0.0014834484,"about_ca_system_score_codex":0.0003002709,"about_ca_system_score_gemma":0.00036577915,"threshold_uncertainty_score":0.0029495955},"labels":[],"label_agreement":null},{"id":"W4390659130","doi":"10.1109/tmc.2024.3350886","title":"Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks With Edge and Fog Computing for Post-Disaster Rescue","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":171,"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 Natural Science Foundation of China","keywords":"Computer science; Edge computing; Mobile edge computing; Resource allocation; Task (project management); Resource management (computing); Distributed computing; Enhanced Data Rates for GSM Evolution; Software deployment; Mobile device; Real-time computing; Computer network; Artificial intelligence; Operating system","score_opus":0.00898458552482317,"score_gpt":0.21574714763041822,"score_spread":0.20676256210559504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390659130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19254449,0.00091234874,0.7988264,0.00034347505,0.00011730479,0.00011164345,0.00007033621,0.00031342581,0.006760549],"genre_scores_gemma":[0.9737289,0.00013235335,0.025019893,0.00006394681,0.00001568609,0.000035902598,0.000031883017,0.000013324679,0.000958164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996728,0.000066461085,0.000012181444,0.00006548902,0.000051677678,0.00013139745],"domain_scores_gemma":[0.9997838,0.00008921514,0.00002905909,0.00002334496,0.000039590865,0.000034996076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003560279,0.00064337684,0.0005882162,0.00025797443,0.00059402426,0.00056987203,0.0008050918,0.00043656613,0.0006921455],"category_scores_gemma":[0.00062724185,0.0001873946,0.00030178681,0.00035975515,0.00037324065,0.0007707271,0.00075254636,0.0004522611,0.00009426106],"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":[0.00024363273,0.0001229061,0.0011350805,0.00008225936,0.00004089128,0.0002818943,0.000105915926,0.9172216,0.009413622,0.0070068073,0.0020195316,0.062325865],"study_design_scores_gemma":[0.000004306323,0.000028327053,0.00016102822,0.0000024960787,0.0000058891005,0.000022307455,0.00002733427,0.9973569,0.0007248514,0.001401611,0.0002616974,0.0000032201854],"about_ca_topic_score_codex":0.0072088833,"about_ca_topic_score_gemma":0.010191493,"teacher_disagreement_score":0.0072088833,"about_ca_system_score_codex":0.00060556154,"about_ca_system_score_gemma":0.000953266,"threshold_uncertainty_score":0.014333844},"labels":[],"label_agreement":null},{"id":"W4391128425","doi":"10.1109/tmc.2024.3357499","title":"CoralDB: A Collaborative Database for Data Sharing Based on Permissioned Blockchain","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Blockchain Technology Applications and Security","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":"Key Research and Development Program of Hunan Province of China; Hunan Provincial Innovation Foundation for Postgraduate; National Natural Science Foundation of China","keywords":"Blockchain; Computer science; Data sharing; USable; Database; Distributed database; Database transaction; Leverage (statistics); Usability; Data access; Block (permutation group theory); Distributed computing; Computer security; World Wide Web; Operating system","score_opus":0.0376172104951786,"score_gpt":0.317061174145097,"score_spread":0.2794439636499184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391128425","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039154693,0.0018018477,0.9111692,0.00075020763,0.0004529049,0.0011358596,0.0024151485,0.02985751,0.013262666],"genre_scores_gemma":[0.5363923,0.0019242018,0.4320358,0.00050478865,0.00022700094,0.0012877109,0.008630706,0.0011884101,0.017809086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971878,0.0004907899,0.00035632146,0.00047219466,0.0012147316,0.0002781446],"domain_scores_gemma":[0.9954732,0.0006180623,0.00024739935,0.0024650062,0.00060731167,0.0005888897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003049032,0.0006414714,0.0011848661,0.001516114,0.0017986593,0.003799587,0.003918183,0.0012258833,0.0075851553],"category_scores_gemma":[0.0055132466,0.0007669872,0.00068931415,0.0025355034,0.0011466421,0.005775751,0.0068850424,0.0016580344,0.0023949267],"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":[0.0044365153,0.0009710994,0.010417706,0.0016721728,0.0005490575,0.0018382698,0.0016277853,0.06624657,0.054892298,0.22859134,0.09432317,0.53443396],"study_design_scores_gemma":[0.0012652382,0.0007920743,0.0020640406,0.00014542026,0.00027292105,0.0012896004,0.00037114145,0.5673572,0.047346484,0.09954188,0.2792779,0.00027613394],"about_ca_topic_score_codex":0.0068137795,"about_ca_topic_score_gemma":0.004970002,"teacher_disagreement_score":0.0075851553,"about_ca_system_score_codex":0.0009573761,"about_ca_system_score_gemma":0.0042709955,"threshold_uncertainty_score":0.02537489},"labels":[],"label_agreement":null},{"id":"W4391853792","doi":"10.1109/tmc.2024.3366340","title":"PPRP: Preserving Location Privacy for Range-Based Positioning in Mobile Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"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; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Hybrid positioning system; Mobile computing; Location-based service; Information privacy; Computer security; Mobile telephony; Privacy protection; Range (aeronautics); Computer network; Internet privacy; Mobile radio; Positioning system","score_opus":0.009627036629012042,"score_gpt":0.2444950658575567,"score_spread":0.23486802922854466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391853792","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006065843,0.0002221927,0.9907532,0.00023243706,0.00005120047,0.00007210982,0.00005247397,0.00045156147,0.0020990807],"genre_scores_gemma":[0.6733377,0.00091772317,0.31882498,0.00045204043,0.00020448232,0.00041354788,0.00026261708,0.000119863456,0.0054670847],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9964855,0.0009815833,0.00019144735,0.0005969081,0.0013870014,0.00035761108],"domain_scores_gemma":[0.99701285,0.00079809444,0.00031358862,0.0015112433,0.00028851992,0.00007572476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019879378,0.00066647393,0.0009514713,0.00058636424,0.001102683,0.0014716197,0.0024300653,0.0014033493,0.0022184965],"category_scores_gemma":[0.0050507095,0.00042923487,0.0010190998,0.0010820543,0.0019139698,0.004700237,0.0045146225,0.0022305346,0.0011706158],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045970868,0.0001319461,0.0012969332,0.0004969425,0.00014286271,0.0008379661,0.00070412667,0.15736303,0.054522622,0.5947811,0.0058154613,0.18344733],"study_design_scores_gemma":[0.00012480754,0.0005494723,0.0005289643,0.00007374405,0.000101128724,0.0016221647,0.0001861394,0.7438101,0.049197517,0.17414692,0.029537747,0.000121220066],"about_ca_topic_score_codex":0.0006039396,"about_ca_topic_score_gemma":0.00038372978,"teacher_disagreement_score":0.0024300653,"about_ca_system_score_codex":0.00065085065,"about_ca_system_score_gemma":0.0012515961,"threshold_uncertainty_score":0.010513306},"labels":[],"label_agreement":null},{"id":"W4391853798","doi":"10.1109/tmc.2024.3365951","title":"CPPer-FL: Clustered Parallel Training for Efficient Personalized Federated Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Privacy-Preserving Technologies in Data","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":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Training (meteorology); Computer architecture; Multimedia; Distributed computing; Computer network; Artificial intelligence","score_opus":0.04878786099221449,"score_gpt":0.3069871642057065,"score_spread":0.258199303213492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391853798","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011497224,0.00010772507,0.98297286,0.0001261038,0.000034460136,0.00007394027,0.0000553898,0.003834786,0.0012975283],"genre_scores_gemma":[0.5451008,0.00011707341,0.4481615,0.0004500496,0.000051173058,0.0003066301,0.00068676256,0.000355784,0.004770213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99888223,0.00021559747,0.000059238835,0.00036287805,0.00028453683,0.00019556619],"domain_scores_gemma":[0.99865437,0.0003172645,0.00009748089,0.00053150504,0.00030560244,0.00009379286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014553255,0.0009717057,0.0011176692,0.0007223441,0.00089502527,0.0009047088,0.003040278,0.0013012263,0.003369031],"category_scores_gemma":[0.0036579447,0.0004508084,0.0006593465,0.0008087676,0.0006607821,0.002479823,0.0020997839,0.0017424388,0.0010894461],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004003568,0.0004397659,0.0021064614,0.00011696977,0.00009360393,0.00015710578,0.0001570348,0.4878222,0.007215057,0.007962801,0.008617128,0.48491162],"study_design_scores_gemma":[0.000016924902,0.000039828537,0.00015456964,0.0000034264333,0.0000064056885,0.00004827621,0.000017224695,0.99381024,0.0018151174,0.0032518413,0.00082967273,0.0000065011855],"about_ca_topic_score_codex":0.006309932,"about_ca_topic_score_gemma":0.007130659,"teacher_disagreement_score":0.006309932,"about_ca_system_score_codex":0.0010183734,"about_ca_system_score_gemma":0.0017464493,"threshold_uncertainty_score":0.01254642},"labels":[],"label_agreement":null},{"id":"W4392309314","doi":"10.1109/tmc.2024.3371772","title":"Filling the Missing: Exploring Generative AI for Enhanced Federated Learning Over Heterogeneous Mobile Edge Devices","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":58,"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":"Basic and Applied Basic Research Foundation of Guangdong Province; Info-communications Media Development Authority; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Research Foundation Singapore","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Mobile device; Generative grammar; Generative model; Mobile computing; Computer network; Distributed computing; Multimedia; Artificial intelligence; World Wide Web","score_opus":0.05206983829696601,"score_gpt":0.30882655792391595,"score_spread":0.25675671962694996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392309314","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04186929,0.00016512764,0.95616513,0.00023830208,0.000023157692,0.00002461123,0.000034969307,0.0004335168,0.0010457758],"genre_scores_gemma":[0.9185371,0.00009025335,0.07966392,0.00024409247,0.00002860426,0.000073069874,0.00010428277,0.000055408043,0.0012033029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923253,0.00028616053,0.00003554378,0.00019415398,0.0001348752,0.00011683658],"domain_scores_gemma":[0.99768186,0.0014385981,0.00012758424,0.00040534476,0.00023473632,0.00011179026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017693966,0.00066567725,0.0009346495,0.0003763843,0.000416939,0.000988184,0.0016476741,0.0009944381,0.0010715072],"category_scores_gemma":[0.0054537966,0.00035299783,0.00054064865,0.0005063236,0.0011284803,0.0020662653,0.0024784293,0.0014927164,0.0002686193],"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":[0.00015429087,0.00011531677,0.0013633074,0.00004843241,0.00003889717,0.00011226774,0.0001159626,0.92460334,0.002860284,0.0075290813,0.00080669014,0.06225212],"study_design_scores_gemma":[0.0000036343235,0.00001806301,0.000059808288,0.0000022646273,0.0000025208699,0.00001170895,0.000008597839,0.99647,0.00044679252,0.002878467,0.000096002994,0.0000021897729],"about_ca_topic_score_codex":0.0015395817,"about_ca_topic_score_gemma":0.0014617195,"teacher_disagreement_score":0.0017693966,"about_ca_system_score_codex":0.00058582687,"about_ca_system_score_gemma":0.0006612562,"threshold_uncertainty_score":0.009357572},"labels":[],"label_agreement":null},{"id":"W4392449633","doi":"10.1109/tmc.2024.3373529","title":"Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution Using Radar-Aided Dynamic Blockage Recognition","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Millimeter-Wave Propagation and Modeling","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":"Carleton University","funders":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Radar; Real-time computing; Scalability; Reliability (semiconductor); Computer network; Telecommunications","score_opus":0.02152382095383333,"score_gpt":0.26692371894976047,"score_spread":0.24539989799592715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392449633","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044328693,0.00050078647,0.95225513,0.00021340516,0.000059507227,0.00003710336,0.00008057356,0.0014630667,0.0010616992],"genre_scores_gemma":[0.90264827,0.00025075112,0.0954217,0.000112996095,0.000057553636,0.000045991826,0.00020571142,0.000048045094,0.0012090707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994235,0.00013732522,0.000029762368,0.00014477152,0.00016989547,0.00009473528],"domain_scores_gemma":[0.99854314,0.0004965752,0.0002387589,0.0003169077,0.0003413784,0.000063257634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010143371,0.00079316506,0.0006668656,0.0005466239,0.00031583352,0.00064402807,0.0014036562,0.0007081689,0.0005853363],"category_scores_gemma":[0.0032703297,0.00024007935,0.00035817106,0.00037576354,0.00037133924,0.0015064018,0.0011958133,0.0007843818,0.00031243823],"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":[0.00033620992,0.0001514342,0.005884537,0.00012663641,0.00007272624,0.0002452683,0.00017735462,0.5378693,0.021764262,0.0037172483,0.002771297,0.4268837],"study_design_scores_gemma":[0.0000065851455,0.00007150873,0.0007089638,0.000007456101,0.000014431521,0.00007367025,0.000031737258,0.99038357,0.005241889,0.0026093663,0.0008432738,0.0000076089837],"about_ca_topic_score_codex":0.0026622177,"about_ca_topic_score_gemma":0.0024599172,"teacher_disagreement_score":0.0026622177,"about_ca_system_score_codex":0.00048345444,"about_ca_system_score_gemma":0.00053393096,"threshold_uncertainty_score":0.005364418},"labels":[],"label_agreement":null},{"id":"W4392796562","doi":"10.1109/tmc.2024.3377226","title":"A Unified Framework for Guiding Generative AI With Wireless Perception in Resource Constrained Mobile Edge Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":73,"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":"Guangdong Provincial Pearl River Talents Program; Info-communications Media Development Authority; National Natural Science Foundation of China; Ministry of Education - Singapore; National Research Foundation Singapore","keywords":"Computer science; Wireless; Enhanced Data Rates for GSM Evolution; Wireless network; Resource (disambiguation); Generative grammar; Mobile computing; Perception; Computer network; Mobile telephony; Distributed computing; Mobile radio; Human–computer interaction; Telecommunications; Artificial intelligence","score_opus":0.02564544004550459,"score_gpt":0.2788105216645401,"score_spread":0.2531650816190355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392796562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063315365,0.00006872964,0.99247557,0.000085030275,0.000008300176,0.000020807758,0.000011953803,0.00022254608,0.0007755689],"genre_scores_gemma":[0.57213163,0.00021350439,0.4252374,0.0001583013,0.00004266245,0.00016957954,0.00007982018,0.000120144905,0.0018470403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946886,0.00013809711,0.00002558348,0.00014763682,0.00013711171,0.00008271186],"domain_scores_gemma":[0.99933594,0.00033836896,0.00008172209,0.000069978174,0.00010635246,0.000067602254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009204188,0.0006949003,0.0007324723,0.00066027977,0.0006358284,0.0011491736,0.0020644777,0.00096122886,0.0012436629],"category_scores_gemma":[0.0026535098,0.0004997265,0.0006367265,0.000556966,0.0012415177,0.0019213689,0.0018387135,0.0011733678,0.0002620064],"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":[0.00006236153,0.000058961115,0.0010204979,0.00004581809,0.000029673307,0.00010768048,0.0002876635,0.8661676,0.0057899863,0.044633962,0.0009932112,0.08080257],"study_design_scores_gemma":[0.000002935204,0.000010983732,0.0000465683,0.000001628588,0.0000025011689,0.0000067766587,0.000011100865,0.9958752,0.0002939897,0.00348852,0.0002559352,0.0000037985126],"about_ca_topic_score_codex":0.0091709485,"about_ca_topic_score_gemma":0.008361778,"teacher_disagreement_score":0.0091709485,"about_ca_system_score_codex":0.0009919518,"about_ca_system_score_gemma":0.0013424405,"threshold_uncertainty_score":0.018235147},"labels":[],"label_agreement":null},{"id":"W4394896937","doi":"10.1109/tmc.2024.3390208","title":"ProSecutor: Protecting Mobile AIGC Services on Two-Layer Blockchain via Reputation and Contract Theoretic Approaches","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Blockchain Technology Applications and Security","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 Waterloo","funders":"","keywords":"Blockchain; Computer science; Computer security; Reputation; Layer (electronics); Reputation management; Computer network; Law; Chemistry","score_opus":0.014244770123394618,"score_gpt":0.2520721446569496,"score_spread":0.23782737453355499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394896937","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064088814,0.00022963148,0.9169004,0.0014764927,0.000057127585,0.00037465277,0.00014853677,0.0010780244,0.015646402],"genre_scores_gemma":[0.9103764,0.00016444131,0.08124821,0.00015734663,0.000038811653,0.00021545532,0.0001140629,0.00006595062,0.0076192087],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99397707,0.0023534612,0.00028307395,0.00067521684,0.0019372056,0.00077396864],"domain_scores_gemma":[0.9892306,0.0038124304,0.0009522753,0.0037832307,0.0016265436,0.0005949695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066438345,0.00060236704,0.0007711874,0.0010845487,0.0013531401,0.0028748917,0.001922696,0.0019696483,0.003494582],"category_scores_gemma":[0.016667228,0.00040645525,0.000590874,0.0009032381,0.00261514,0.006261298,0.005090292,0.0017340381,0.00086919847],"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":[0.00039965071,0.00017562776,0.0030216612,0.00019562483,0.00006587253,0.00073318335,0.0009599267,0.25030357,0.012453579,0.6122636,0.0038627607,0.115565024],"study_design_scores_gemma":[0.00005387589,0.00008879237,0.00025263443,0.000040752217,0.000019439784,0.00014711884,0.000101403966,0.82800317,0.005792793,0.15968281,0.005778031,0.000039131864],"about_ca_topic_score_codex":0.0043743732,"about_ca_topic_score_gemma":0.0034955833,"teacher_disagreement_score":0.0066438345,"about_ca_system_score_codex":0.002425753,"about_ca_system_score_gemma":0.004155938,"threshold_uncertainty_score":0.035136342},"labels":[],"label_agreement":null},{"id":"W4396594881","doi":"10.1109/tmc.2024.3395388","title":"Enabling Efficient and Distributed Access Control for Pervasive Edge Computing Services","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Access Control and Trust","field":"Social Sciences","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":"Queen's University; University of Waterloo","funders":"","keywords":"Computer science; Ubiquitous computing; Distributed computing; Edge computing; Computer network; Access control; Context-aware pervasive systems; Mobile computing; Enhanced Data Rates for GSM Evolution; Computer security; Telecommunications; Operating system","score_opus":0.018638181771750698,"score_gpt":0.32096647719704297,"score_spread":0.30232829542529227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396594881","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014647104,0.00038724355,0.9801348,0.00022027154,0.000060525825,0.0001257583,0.000025333002,0.0009375724,0.0034614108],"genre_scores_gemma":[0.78373075,0.0005331447,0.21219915,0.00025501716,0.0001303611,0.00014216018,0.00010296915,0.00007398364,0.0028324472],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978765,0.00037364967,0.00011801804,0.0003546824,0.00087417266,0.00040299993],"domain_scores_gemma":[0.9982962,0.00044375312,0.0001714418,0.0005648229,0.00038068742,0.00014305522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013596448,0.0005609099,0.00063844223,0.0006926216,0.0009945731,0.0017726296,0.0016170795,0.00077997934,0.0014765813],"category_scores_gemma":[0.0025366687,0.00024775602,0.0005029387,0.00055003434,0.0013913084,0.0036444576,0.0029953183,0.001813969,0.0004194205],"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":[0.00042260322,0.0004669118,0.0024279198,0.00036936483,0.00008979118,0.00067355565,0.00053358276,0.12029283,0.06711172,0.52125996,0.007169659,0.2791822],"study_design_scores_gemma":[0.000060410726,0.00015321237,0.0006019323,0.000035514444,0.000035637127,0.00045633255,0.0001573824,0.8901399,0.017468758,0.06163888,0.029192308,0.0000597567],"about_ca_topic_score_codex":0.0023680471,"about_ca_topic_score_gemma":0.002004325,"teacher_disagreement_score":0.0023680471,"about_ca_system_score_codex":0.0007832443,"about_ca_system_score_gemma":0.0013669073,"threshold_uncertainty_score":0.007190585},"labels":[],"label_agreement":null},{"id":"W4396680684","doi":"10.1109/tmc.2024.3397164","title":"Mobility-Aware Congestion Control for Multipath QUIC in Integrated Terrestrial Satellite Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Satellite Communication Systems","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 Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Computer science; Computer network; Multipath propagation; Satellite; Network congestion; Telecommunications","score_opus":0.020411419310946154,"score_gpt":0.26773080366651014,"score_spread":0.247319384355564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396680684","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13749805,0.0008586104,0.85705245,0.0003429893,0.00010658237,0.00011932583,0.00007527236,0.0011278065,0.0028188936],"genre_scores_gemma":[0.96706223,0.00016815816,0.03189103,0.000049164388,0.000029001762,0.000042531883,0.000037739886,0.000023898532,0.0006963591],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945575,0.00011700251,0.000029860648,0.00013435118,0.00013472507,0.00012826815],"domain_scores_gemma":[0.998857,0.0004951578,0.00016646479,0.00010042323,0.0002771635,0.00010375102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001059091,0.0005955728,0.00060341455,0.0006683793,0.0008720742,0.0008815314,0.0011899372,0.00031761476,0.00075059215],"category_scores_gemma":[0.0033154623,0.00029819575,0.00024958732,0.00059190945,0.0006599683,0.00091019744,0.0010227589,0.0006173901,0.000086805056],"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":[0.00023278173,0.000078701334,0.003048355,0.000058947164,0.000050036877,0.00011665452,0.00018564415,0.88557225,0.010776425,0.011433158,0.0018296175,0.0866175],"study_design_scores_gemma":[0.000006268268,0.000026870377,0.00018979149,0.0000027029303,0.000007938627,0.000011078145,0.00001211702,0.99784565,0.00068430224,0.0009881445,0.00021952025,0.000005668738],"about_ca_topic_score_codex":0.017115286,"about_ca_topic_score_gemma":0.0196701,"teacher_disagreement_score":0.017115286,"about_ca_system_score_codex":0.0015950432,"about_ca_system_score_gemma":0.0018235398,"threshold_uncertainty_score":0.03403133},"labels":[],"label_agreement":null},{"id":"W4396680687","doi":"10.1109/tmc.2024.3396793","title":"Covert Communication in Large-Scale Multi-Tier LEO Satellite Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cryptographic Implementations and Security","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 Manitoba; Ericsson (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Communications satellite; Computer network; Covert; Satellite; Scale (ratio); Telecommunications; Distributed computing; Geography","score_opus":0.016606250999084554,"score_gpt":0.2944146018835499,"score_spread":0.2778083508844653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396680687","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17895938,0.0008129452,0.8095513,0.0006040135,0.00009240974,0.00010068725,0.00015992209,0.0002505187,0.009468783],"genre_scores_gemma":[0.98480785,0.00022659423,0.013272614,0.0000690021,0.000014483723,0.000045567864,0.000050707866,0.000020661872,0.0014925456],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928975,0.00024035554,0.000015740901,0.00010935096,0.00012773454,0.00021708579],"domain_scores_gemma":[0.99683905,0.0020846708,0.0005046044,0.00013488863,0.00022344971,0.00021344567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011509275,0.0011169603,0.0009102803,0.0005541299,0.000761114,0.0013471979,0.0015483968,0.0012590183,0.0024361925],"category_scores_gemma":[0.0047939476,0.00043662294,0.0006861564,0.0006113936,0.0015483946,0.0020247311,0.0017327104,0.0010763576,0.00022227198],"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":[0.000033878365,0.000011268017,0.00041954056,0.00002447303,0.000012971375,0.00016383875,0.000026658312,0.98679024,0.0006354921,0.009622347,0.0002789379,0.0019803373],"study_design_scores_gemma":[0.0000032258015,0.00002008764,0.00007501898,0.0000027616986,0.0000029059288,0.000022561371,0.000013597901,0.99613273,0.00012997916,0.0034773168,0.000116760726,0.0000030058227],"about_ca_topic_score_codex":0.0056136847,"about_ca_topic_score_gemma":0.0051151486,"teacher_disagreement_score":0.0056136847,"about_ca_system_score_codex":0.0016722343,"about_ca_system_score_gemma":0.0007810609,"threshold_uncertainty_score":0.012133002},"labels":[],"label_agreement":null},{"id":"W4399110650","doi":"10.1109/tmc.2024.3406607","title":"Dynamic Human Digital Twin Deployment at the Edge for Task Execution: A Two-Timescale Accuracy-Aware Online Optimization","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":111,"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; Concordia University","funders":"National Natural Science Foundation of China; National Research Foundation Singapore","keywords":"Computer science; Software deployment; Task (project management); Enhanced Data Rates for GSM Evolution; Distributed computing; Real-time computing; Operating system; Artificial intelligence","score_opus":0.014809786531655145,"score_gpt":0.2728077714048264,"score_spread":0.25799798487317127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399110650","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03438893,0.00026178025,0.9616171,0.00023847006,0.000056538844,0.000062008956,0.00003882091,0.00025887208,0.0030774309],"genre_scores_gemma":[0.9111308,0.00018572623,0.08625268,0.00010392962,0.000042171472,0.000108388536,0.00007415985,0.00007029628,0.0020318383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949896,0.000115431074,0.000022526703,0.00014204766,0.000112706155,0.00010825922],"domain_scores_gemma":[0.9993298,0.00030979243,0.00008841369,0.00007357667,0.00009374412,0.00010473234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008239379,0.0010876594,0.0010417581,0.00028889993,0.00043889237,0.0010697647,0.001084747,0.0008229869,0.0019929174],"category_scores_gemma":[0.0025182934,0.00039196556,0.00041423476,0.0003877862,0.00059327175,0.0012630852,0.0014057772,0.0010177429,0.00027668866],"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":[0.00009537681,0.00005504027,0.00060838216,0.000051243325,0.000017388036,0.00006757091,0.000045539044,0.9695644,0.0020861765,0.004251607,0.0007794889,0.02237784],"study_design_scores_gemma":[0.0000049015425,0.000024751995,0.00007060453,0.0000019624345,0.0000032521348,0.000009492097,0.000009450358,0.9987288,0.00020358835,0.0007668352,0.00017415694,0.0000021682326],"about_ca_topic_score_codex":0.0041058464,"about_ca_topic_score_gemma":0.003096925,"teacher_disagreement_score":0.0041058464,"about_ca_system_score_codex":0.0005635193,"about_ca_system_score_gemma":0.0016647744,"threshold_uncertainty_score":0.008163869},"labels":[],"label_agreement":null},{"id":"W4399311126","doi":"10.1109/tmc.2024.3408425","title":"Hypergraph-Aided Task-Resource Matching for Maximizing Value of Task Completion in Collaborative IoT Systems","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Task (project management); Hypergraph; Matching (statistics); Resource (disambiguation); Distributed computing; Resource allocation; Value (mathematics); Theoretical computer science; Computer network; Machine learning","score_opus":0.012957326192174986,"score_gpt":0.25840187178312074,"score_spread":0.24544454559094575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399311126","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02849806,0.00029337266,0.9668392,0.00023595737,0.000036755067,0.00010120507,0.00007371685,0.0002781623,0.0036435165],"genre_scores_gemma":[0.85291654,0.00038693,0.14123781,0.00018437176,0.000039785904,0.00023249257,0.00016765417,0.00013206422,0.004702382],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998941,0.0003776055,0.00004278433,0.00024498606,0.00019869985,0.00019497226],"domain_scores_gemma":[0.9985537,0.0007869662,0.00015457817,0.0001322007,0.00018433521,0.00018809606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014190124,0.0010985212,0.0012238247,0.0011427619,0.0008925841,0.001491185,0.0020583116,0.0013866937,0.003745787],"category_scores_gemma":[0.004577943,0.00053284626,0.0007126779,0.0016689252,0.0009635271,0.0033044852,0.001963122,0.0013129564,0.00046386864],"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":[0.00011610616,0.00009906417,0.00062504713,0.0000882693,0.000044450502,0.00009240658,0.00012708985,0.9209543,0.0019347606,0.027230645,0.0018471204,0.04684077],"study_design_scores_gemma":[0.0000072228113,0.000022234844,0.00010255974,0.000004279211,0.0000074529835,0.000015973197,0.000022049064,0.9855619,0.00033975768,0.013476725,0.00043346442,0.0000064437486],"about_ca_topic_score_codex":0.008403961,"about_ca_topic_score_gemma":0.00671345,"teacher_disagreement_score":0.008403961,"about_ca_system_score_codex":0.002032477,"about_ca_system_score_gemma":0.0027499946,"threshold_uncertainty_score":0.016710103},"labels":[],"label_agreement":null},{"id":"W4399526294","doi":"10.1109/tmc.2024.3412751","title":"Bridge the Present and Future: A Cross-Layer Matching Game in Dynamic Cloud-Aided Mobile Edge Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Opportunistic and Delay-Tolerant Networks","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":"Western University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Aeronautical Science Foundation of China; National Natural Science Foundation of China; Natural Science Foundation of Xiamen City","keywords":"Computer science; Cloud computing; Bridge (graph theory); Enhanced Data Rates for GSM Evolution; Matching (statistics); Layer (electronics); Mobile telephony; Distributed computing; Computer network; Telecommunications; Mobile radio; Operating system","score_opus":0.015787920957551466,"score_gpt":0.2856436364600143,"score_spread":0.2698557155024628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399526294","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28780198,0.000528582,0.5172411,0.014933338,0.0005272448,0.0009659755,0.00053966,0.0014384727,0.1760237],"genre_scores_gemma":[0.9029099,0.00024122086,0.076948024,0.0011044946,0.00005838709,0.000219694,0.00024561892,0.00013537846,0.018137446],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982343,0.00083692063,0.0000743228,0.00024703136,0.00022582844,0.00038157153],"domain_scores_gemma":[0.99628097,0.0017259335,0.00024281544,0.0003162112,0.00021055892,0.0012235574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003846092,0.00078275596,0.00072025985,0.0005084717,0.0028396402,0.0039854166,0.0023420292,0.0044591795,0.014172186],"category_scores_gemma":[0.008262368,0.0004403405,0.00080316275,0.000486801,0.001478418,0.009320323,0.0053462246,0.0026608051,0.0015055392],"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":[0.003153776,0.0009982196,0.005652614,0.00034174416,0.00022505515,0.002283954,0.003216462,0.16423143,0.0124225635,0.6363035,0.038094882,0.1330759],"study_design_scores_gemma":[0.00018366949,0.00043882176,0.0007705587,0.000072678624,0.00006886338,0.00036382757,0.001983797,0.68696135,0.002513895,0.27779505,0.028775433,0.00007198784],"about_ca_topic_score_codex":0.0036846166,"about_ca_topic_score_gemma":0.004041836,"teacher_disagreement_score":0.014172186,"about_ca_system_score_codex":0.0016762089,"about_ca_system_score_gemma":0.0019950336,"threshold_uncertainty_score":0.047410727},"labels":[],"label_agreement":null},{"id":"W4399618835","doi":"10.1109/tmc.2024.3412810","title":"An Efficient Resource Allocation Scheme With Uncertain Network Status in Edge Computing-Enabled Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","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":"Carleton University","funders":"Natural Science Foundation of Chongqing; National Natural Science Foundation of China","keywords":"Computer science; Resource allocation; Scheme (mathematics); Computer network; Distributed computing; Resource management (computing); Enhanced Data Rates for GSM Evolution; Edge computing; Mobile edge computing; Server; Telecommunications","score_opus":0.011408793552076264,"score_gpt":0.2532576635229399,"score_spread":0.2418488699708636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399618835","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018673483,0.00019591731,0.97965264,0.000111586356,0.000027799017,0.000038176073,0.000018719045,0.00007644683,0.0012051667],"genre_scores_gemma":[0.92867714,0.00028917153,0.0696024,0.00007849167,0.00004269203,0.000090235466,0.000036208698,0.000021804271,0.0011617532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990308,0.00027547867,0.000059947983,0.0002447792,0.00020188712,0.00018717362],"domain_scores_gemma":[0.99920005,0.00041139536,0.00012801198,0.00007649224,0.00011518787,0.00006890604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013071034,0.00081867556,0.0011462665,0.0003962621,0.0008830089,0.0012102592,0.0016463577,0.0008933945,0.0010716582],"category_scores_gemma":[0.0028175039,0.0004009719,0.0004261719,0.00085217156,0.00076454255,0.0021610034,0.0017130524,0.0009952518,0.00017305107],"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":[0.00022353602,0.00006183595,0.0005584652,0.00009530461,0.000035704517,0.0002618545,0.00019969534,0.89015424,0.010344306,0.042922143,0.0014139629,0.05372888],"study_design_scores_gemma":[0.0000053542876,0.000020605574,0.000050867693,0.000003316867,0.0000060095936,0.000029708635,0.000017012273,0.9937535,0.00068238267,0.005202113,0.00022205555,0.0000070396827],"about_ca_topic_score_codex":0.0021739977,"about_ca_topic_score_gemma":0.0021052202,"teacher_disagreement_score":0.0021739977,"about_ca_system_score_codex":0.0008502675,"about_ca_system_score_gemma":0.0010798855,"threshold_uncertainty_score":0.0069127083},"labels":[],"label_agreement":null},{"id":"W4400314708","doi":"10.1109/tmc.2024.3423399","title":"A Hierarchical Incentive Mechanism for Federated Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":20,"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":"Natural Science Foundation of Beijing Municipality","keywords":"Computer science; Incentive; Mechanism (biology); Computer network; Distributed computing","score_opus":0.024439552802130414,"score_gpt":0.28901657292238775,"score_spread":0.26457702012025736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400314708","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031443484,0.00018454251,0.95887446,0.0008617426,0.0000875999,0.00022750502,0.00020427638,0.00072195946,0.007394313],"genre_scores_gemma":[0.8522197,0.00018803417,0.13862973,0.0003070415,0.00008084311,0.00036893575,0.00016045957,0.000055398425,0.0079898825],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99508566,0.0019621905,0.00030433445,0.0009720459,0.0008932355,0.0007824177],"domain_scores_gemma":[0.9939231,0.0026032142,0.00074710906,0.00126142,0.0007468226,0.00071823015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00681548,0.00081186864,0.0011642677,0.000988482,0.0012053233,0.0021624384,0.004020895,0.0022556484,0.0057323067],"category_scores_gemma":[0.011634228,0.0005864274,0.0011498607,0.00117482,0.0014431698,0.005044182,0.0030650876,0.0017085823,0.0008397627],"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":[0.00044604932,0.00036768708,0.0013576227,0.00018154745,0.000074414325,0.00034940132,0.00039718952,0.29513785,0.002867016,0.62053955,0.0054038023,0.072877854],"study_design_scores_gemma":[0.00007745277,0.00007819003,0.00020786625,0.000024029265,0.000014434143,0.00009474312,0.00004305731,0.8074788,0.00077059487,0.18713059,0.004050575,0.000029714147],"about_ca_topic_score_codex":0.0027169345,"about_ca_topic_score_gemma":0.001832915,"teacher_disagreement_score":0.00681548,"about_ca_system_score_codex":0.0022948792,"about_ca_system_score_gemma":0.0033996822,"threshold_uncertainty_score":0.03604412},"labels":[],"label_agreement":null},{"id":"W4400447774","doi":"10.1109/tmc.2024.3415661","title":"Large Language Models (LLMs) Inference Offloading and Resource Allocation in Cloud-Edge Computing: An Active Inference Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cloud Data Security Solutions","field":"Computer Science","cited_by":72,"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 Natural Science Foundation of China","keywords":"Computer science; Inference; Cloud computing; Resource allocation; Enhanced Data Rates for GSM Evolution; Distributed computing; Artificial intelligence; Computer network","score_opus":0.02912644320797005,"score_gpt":0.2995773100000774,"score_spread":0.2704508667921074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400447774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022321776,0.0003892259,0.97333694,0.00064626615,0.00006365099,0.0000684715,0.00012730614,0.0020025193,0.0010438372],"genre_scores_gemma":[0.7638154,0.00030891565,0.23074527,0.0008849464,0.00015687171,0.00018888859,0.00044851605,0.0003501294,0.0031010904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885845,0.00039033085,0.00005848668,0.00030328849,0.00019144043,0.00019801116],"domain_scores_gemma":[0.9970204,0.00211807,0.00016980535,0.00022523633,0.0003259478,0.00014054272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018107857,0.001098043,0.0015106011,0.00073167623,0.0007280337,0.0013971904,0.0025411812,0.0010757186,0.0018141944],"category_scores_gemma":[0.0052073,0.0008405064,0.0010269328,0.00078247336,0.00086001604,0.0026930138,0.0016089082,0.0027473555,0.0004200027],"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":[0.00042468985,0.00032642865,0.001984208,0.00016342902,0.00013083716,0.0002752728,0.00028348793,0.76613295,0.0060543823,0.0123867635,0.00572704,0.20611058],"study_design_scores_gemma":[0.000006875492,0.0000054995076,0.000035921395,0.0000018003484,0.000004799913,0.000004907286,0.000006549238,0.9972831,0.00035946124,0.0021510446,0.00013708991,0.0000029266494],"about_ca_topic_score_codex":0.019434744,"about_ca_topic_score_gemma":0.02684699,"teacher_disagreement_score":0.019434744,"about_ca_system_score_codex":0.0012156027,"about_ca_system_score_gemma":0.0028032947,"threshold_uncertainty_score":0.03864324},"labels":[],"label_agreement":null},{"id":"W4400487859","doi":"10.1109/tmc.2024.3426046","title":"De-Anonymizing Avatars in Virtual Reality: Attacks and Countermeasures","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Malware Detection Techniques","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 Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Virtual reality; Computer security; Human–computer interaction","score_opus":0.018249200687069537,"score_gpt":0.2963100311414671,"score_spread":0.27806083045439756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400487859","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25338364,0.004745894,0.7119121,0.0027753823,0.0009007237,0.000617245,0.0007985214,0.0106739355,0.014192594],"genre_scores_gemma":[0.91187304,0.0007965167,0.08260007,0.00056118646,0.00014074917,0.00016433431,0.00061308884,0.00019148421,0.0030595795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99364835,0.0025636142,0.0004255657,0.00086745055,0.0020642728,0.00043077048],"domain_scores_gemma":[0.9911896,0.002330731,0.0010040336,0.0046712924,0.0005884943,0.00021582938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023857052,0.001110277,0.00096384395,0.0007845392,0.0010385469,0.0013154247,0.0011969125,0.0012657673,0.0011763569],"category_scores_gemma":[0.012758034,0.00037168278,0.0007631828,0.000676779,0.0011841948,0.0033911788,0.0034637742,0.002022649,0.0008440124],"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":[0.0023099775,0.00073595665,0.016696455,0.0011080904,0.00061727746,0.002530029,0.004074785,0.07622556,0.13386846,0.086167626,0.028835088,0.6468307],"study_design_scores_gemma":[0.00017460373,0.0010104153,0.008003378,0.00041481617,0.00028099067,0.0073885466,0.0018132271,0.6400902,0.23280427,0.03730236,0.070473485,0.00024362656],"about_ca_topic_score_codex":0.0006256152,"about_ca_topic_score_gemma":0.00061413617,"teacher_disagreement_score":0.0023857052,"about_ca_system_score_codex":0.00043590058,"about_ca_system_score_gemma":0.00043033,"threshold_uncertainty_score":0.012616992},"labels":[],"label_agreement":null},{"id":"W4401357717","doi":"10.1109/tmc.2024.3439099","title":"A Binary Structured Sparsity Approach for Multi-Anchor Direct Localization","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Structural Health Monitoring Techniques","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":"University of Toronto","funders":"University of Toronto","keywords":"Computer science; Binary number; Theoretical computer science; Algorithm; Arithmetic; Mathematics","score_opus":0.03448263951078525,"score_gpt":0.3037689634810982,"score_spread":0.269286323970313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401357717","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011452755,0.00004800578,0.9979527,0.00008703483,0.000014890869,0.000009662174,0.00002479485,0.00005274536,0.0006649331],"genre_scores_gemma":[0.2593371,0.000595845,0.7333621,0.00036989927,0.00016683561,0.00020410154,0.00036926693,0.000110476234,0.005484388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99924684,0.00027195492,0.000030761636,0.00012056664,0.00027717702,0.000052739015],"domain_scores_gemma":[0.99876654,0.0006374954,0.00013705836,0.000172823,0.00022908564,0.0000569931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010010464,0.00057838217,0.000682205,0.0006101112,0.0002957695,0.000835657,0.0011135484,0.00093202613,0.0027868277],"category_scores_gemma":[0.003706196,0.00036887365,0.00048232553,0.0008999067,0.00072307995,0.0015783897,0.0017476436,0.0016578072,0.0008002101],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022917826,0.000121379904,0.0011186985,0.0003221922,0.00006245433,0.00018436965,0.00020868878,0.5390477,0.021482738,0.14341696,0.0070432704,0.2867624],"study_design_scores_gemma":[0.000009724763,0.000041458567,0.00009102137,0.0000133074345,0.000005593934,0.000062721214,0.000013784003,0.98167866,0.0017086156,0.014565454,0.0018001344,0.000009603688],"about_ca_topic_score_codex":0.0011965482,"about_ca_topic_score_gemma":0.0014165917,"teacher_disagreement_score":0.0027868277,"about_ca_system_score_codex":0.00040940198,"about_ca_system_score_gemma":0.0007805266,"threshold_uncertainty_score":0.009322822},"labels":[],"label_agreement":null},{"id":"W4401567242","doi":"10.1109/tmc.2024.3442909","title":"Task Offloading and Trajectory Optimization for Secure Communications in Dynamic User Multi-UAV MEC Systems","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":46,"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 Science Foundation of Hunan Province; Natural Sciences and Engineering Research Council of Canada; Universidade de Macau; National Natural Science Foundation of China","keywords":"Computer science; Mobile edge computing; Markov decision process; Base station; Resource allocation; Robustness (evolution); Optimization problem; Trajectory optimization; Bidding; Distributed computing; Software deployment; Trajectory; Computer network; Markov process; Server","score_opus":0.012366648009519383,"score_gpt":0.2584067837113384,"score_spread":0.246040135701819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401567242","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06264158,0.0005868511,0.9317197,0.00031513203,0.000047031102,0.0000384375,0.000059828162,0.00015757796,0.0044338037],"genre_scores_gemma":[0.96936965,0.00027209072,0.02805066,0.000051222283,0.000016117252,0.00006421553,0.000066677436,0.000023403973,0.00208603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960095,0.00011634163,0.000014860034,0.00007587161,0.00008904193,0.00010295193],"domain_scores_gemma":[0.99961025,0.00018996638,0.00006260436,0.000031632117,0.00007058877,0.000035101864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000424772,0.00072319154,0.00079558854,0.000243791,0.0005369912,0.0007169771,0.0006338971,0.0005691989,0.0012633492],"category_scores_gemma":[0.0010943833,0.00031506392,0.000382608,0.00048486202,0.00047937286,0.00075785065,0.0008554795,0.00069768156,0.00018750507],"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":[0.00004672022,0.00002069957,0.0003822549,0.000031659427,0.000012509151,0.000082607025,0.000037869988,0.9805751,0.001620298,0.00515462,0.000513541,0.011522173],"study_design_scores_gemma":[0.000002614322,0.00001194621,0.000050888044,0.0000011831893,0.0000015750159,0.000008987286,0.0000074932777,0.99882954,0.0001559555,0.0007924758,0.00013566716,0.0000017300309],"about_ca_topic_score_codex":0.008275918,"about_ca_topic_score_gemma":0.0053692157,"teacher_disagreement_score":0.008275918,"about_ca_system_score_codex":0.00080001156,"about_ca_system_score_gemma":0.0010713143,"threshold_uncertainty_score":0.016455531},"labels":[],"label_agreement":null},{"id":"W4401878764","doi":"10.1109/tmc.2024.3449645","title":"Cross-Modal Generative Semantic Communications for Mobile AIGC: Joint Semantic Encoding and Prompt Engineering","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Robotics and Automated Systems","field":"Engineering","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":"Info-communications Media Development Authority; National Natural Science Foundation of China; Singapore University of Technology and Design; Ministry of Education - Singapore; Ministry of Education, India; National Science Foundation","keywords":"Computer science; Joint (building); Encoding (memory); Modal; Semantic integration; Generative grammar; Semantic computing; Artificial intelligence; Semantic Web","score_opus":0.021769592912191292,"score_gpt":0.27314620341828094,"score_spread":0.25137661050608967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401878764","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01783421,0.00011490799,0.97342765,0.0002876553,0.0000461388,0.000056806166,0.00014172932,0.0027154053,0.0053755236],"genre_scores_gemma":[0.58443594,0.00027253802,0.4079529,0.00027754784,0.00006552153,0.00012644332,0.0006555356,0.0005958562,0.0056177797],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99926776,0.00028214828,0.00003548928,0.00017014786,0.00016427175,0.000080125574],"domain_scores_gemma":[0.99833137,0.000828455,0.000101306374,0.000415532,0.00024177512,0.0000814926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011094497,0.00076667586,0.00037988977,0.00086272164,0.00057947036,0.0014821127,0.0011593987,0.0012085411,0.0042962823],"category_scores_gemma":[0.0045594494,0.00034053126,0.00075629045,0.0008939417,0.001250889,0.0033734466,0.0024115958,0.0013932602,0.001030621],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052221015,0.00029166613,0.00217812,0.0003171752,0.00006165972,0.0009992586,0.0031067024,0.0990887,0.045530394,0.36341563,0.009306891,0.4751816],"study_design_scores_gemma":[0.000025630416,0.000070552676,0.0005800625,0.000032758548,0.00004479899,0.00032605935,0.00043455698,0.8204355,0.021537801,0.14181927,0.014642157,0.000050871546],"about_ca_topic_score_codex":0.0057817893,"about_ca_topic_score_gemma":0.005818362,"teacher_disagreement_score":0.0057817893,"about_ca_system_score_codex":0.0010302062,"about_ca_system_score_gemma":0.0013623098,"threshold_uncertainty_score":0.014372468},"labels":[],"label_agreement":null},{"id":"W4402039818","doi":"10.1109/tmc.2024.3452510","title":"Privacy-Preserving Gaze-Assisted Immersive Video Streaming","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Wireless Communication Technologies","field":"Engineering","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":"McGill University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province","keywords":"Computer science; Gaze; Video streaming; Multimedia; Human–computer interaction; Computer vision; Computer network","score_opus":0.015226939448324942,"score_gpt":0.2603802102320453,"score_spread":0.24515327078372035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402039818","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09482487,0.0015264559,0.8967229,0.00034476558,0.00008915213,0.0001286585,0.00031646746,0.0030245625,0.003022201],"genre_scores_gemma":[0.9205015,0.0007659605,0.075679965,0.00016854586,0.00013442915,0.00008027315,0.00028683228,0.000110575034,0.0022719954],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924195,0.00017067879,0.00003825861,0.00016783552,0.00026930377,0.00011204411],"domain_scores_gemma":[0.99877244,0.00041505284,0.00015750325,0.00029547804,0.00026956384,0.0000898724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007692233,0.0008882682,0.000729341,0.00045592524,0.00042625968,0.0007463934,0.0011754452,0.00055037445,0.0014255613],"category_scores_gemma":[0.004057339,0.00027225868,0.00050867867,0.00038951976,0.00043292376,0.001668833,0.0016600941,0.0010707031,0.00032922297],"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":[0.0016978176,0.00043385377,0.0066148876,0.00038764245,0.00026506485,0.001192233,0.0012533651,0.12046058,0.2128418,0.016320797,0.009450616,0.6290814],"study_design_scores_gemma":[0.000052723964,0.00022673821,0.0028098433,0.00003125857,0.00007686787,0.0006815597,0.0001487364,0.935753,0.048295807,0.008384971,0.0034813718,0.000057186313],"about_ca_topic_score_codex":0.003802543,"about_ca_topic_score_gemma":0.0035296115,"teacher_disagreement_score":0.003802543,"about_ca_system_score_codex":0.0004923703,"about_ca_system_score_gemma":0.0008015388,"threshold_uncertainty_score":0.0075607896},"labels":[],"label_agreement":null},{"id":"W4402568879","doi":"10.1109/tmc.2024.3462721","title":"FD MU-MIMO Systems: Performance Analysis in the Presence of Imperfect CSI and Non-Ideal Transceivers","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","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":"Lakehead University; Confederation College; Western University","funders":"","keywords":"Computer science; Imperfect; Transceiver; MIMO; Ideal (ethics); Telecommunications; Electronic engineering; Wireless; Beamforming; Engineering","score_opus":0.006216130444227058,"score_gpt":0.22407471296612957,"score_spread":0.21785858252190252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402568879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10608166,0.004011586,0.8641911,0.0005980219,0.00013501168,0.000061411825,0.00028496783,0.00037016193,0.024266068],"genre_scores_gemma":[0.9799954,0.0016610175,0.015724648,0.00008628252,0.0000915001,0.00003576886,0.00007583309,0.000022635682,0.0023068434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904007,0.00025526248,0.000030030506,0.000111072775,0.0003542316,0.00020932319],"domain_scores_gemma":[0.997519,0.0014256735,0.00031579146,0.00019555089,0.0004823734,0.00006148548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013548196,0.0010764042,0.0007785351,0.0005894551,0.0005186657,0.0013157887,0.00070199074,0.000983024,0.0013183276],"category_scores_gemma":[0.0046801027,0.00038908646,0.00038518186,0.0007727116,0.0012510022,0.0009429955,0.0009491077,0.0009184132,0.00038397385],"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":[0.00009003416,0.000024126202,0.0010462824,0.00015077455,0.000043417436,0.00031174367,0.000112235204,0.95483994,0.0046935617,0.029193174,0.00064847973,0.008846284],"study_design_scores_gemma":[0.0000038536323,0.000044789176,0.0006117213,0.00001718754,0.00001374079,0.00011808539,0.000042751177,0.9944699,0.0009518218,0.0033738655,0.00033948122,0.000012779178],"about_ca_topic_score_codex":0.005868565,"about_ca_topic_score_gemma":0.0039323075,"teacher_disagreement_score":0.005868565,"about_ca_system_score_codex":0.0013382388,"about_ca_system_score_gemma":0.00091464247,"threshold_uncertainty_score":0.011668801},"labels":[],"label_agreement":null},{"id":"W4402570247","doi":"10.1109/tmc.2024.3461708","title":"LI2: A New Learning-Based Approach to Timely Monitoring of Points-of-Interest With UAV","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Target Tracking and Data Fusion in Sensor Networks","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 Victoria","funders":"Southeast University; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Point of interest; Human–computer interaction; Real-time computing; Artificial intelligence","score_opus":0.03987953474150036,"score_gpt":0.2704630004616543,"score_spread":0.23058346572015392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402570247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011482252,0.00049201277,0.9831314,0.00024291321,0.00009603697,0.0000990705,0.00018660146,0.0020725569,0.002197164],"genre_scores_gemma":[0.34134227,0.0004390534,0.64687556,0.0005582909,0.00016372278,0.00041603163,0.0011961594,0.00030023188,0.008708605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995722,0.00006470608,0.000020242724,0.00016134944,0.00012230857,0.000059237027],"domain_scores_gemma":[0.99951124,0.00017557932,0.00006995088,0.000058522615,0.0001334049,0.000051227842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005956792,0.0012108191,0.0011640032,0.0008878079,0.00046307896,0.0009007749,0.002897161,0.0012407765,0.0016276404],"category_scores_gemma":[0.0018428654,0.0005831181,0.00071010576,0.0010583738,0.000542201,0.0013856284,0.0016180351,0.0015287873,0.00064955954],"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":[0.00018827553,0.00017378316,0.0026007174,0.00013774518,0.000112933165,0.0001524395,0.00009309273,0.6722007,0.0070489664,0.0047744047,0.008196693,0.30432034],"study_design_scores_gemma":[0.000010017536,0.000043021882,0.00011405814,0.000004635382,0.000006111241,0.00002097567,0.000008422878,0.9967608,0.00074823305,0.0011599541,0.001118847,0.000004871102],"about_ca_topic_score_codex":0.010258876,"about_ca_topic_score_gemma":0.012542214,"teacher_disagreement_score":0.010258876,"about_ca_system_score_codex":0.0009940009,"about_ca_system_score_gemma":0.0013740394,"threshold_uncertainty_score":0.020398319},"labels":[],"label_agreement":null},{"id":"W4402626816","doi":"10.1109/tmc.2024.3464512","title":"Knowledge-Aware Parameter Coaching for Communication-Efficient Personalized Federated Learning in Mobile Edge Computing","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Recommender Systems and Techniques","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 Victoria","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Coaching; Mobile computing; Edge computing; Mobile edge computing; Mobile telephony; Enhanced Data Rates for GSM Evolution; Multimedia; Mobile device; Computer network; Distributed computing; World Wide Web; Artificial intelligence; Mobile radio","score_opus":0.030426088110508648,"score_gpt":0.3143957564740178,"score_spread":0.2839696683635091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402626816","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03273561,0.00034949236,0.96400684,0.00020094286,0.000031165397,0.000039661274,0.00005875111,0.0012724214,0.0013051714],"genre_scores_gemma":[0.85068923,0.00023013882,0.14657201,0.00024593284,0.000039171926,0.00008753456,0.00019868005,0.00011515706,0.0018222764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990582,0.0002659329,0.000055313678,0.00027752353,0.00020446233,0.00013858473],"domain_scores_gemma":[0.9983359,0.0006395448,0.00013360938,0.0005263238,0.00027231322,0.00009236037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013624097,0.001028063,0.0013154191,0.0006390577,0.0008101658,0.0012513335,0.0020572532,0.0013145205,0.0013094896],"category_scores_gemma":[0.0045290897,0.00046148093,0.00061919657,0.00093516655,0.00066481443,0.003230689,0.002006458,0.001864651,0.0005626764],"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":[0.00031320876,0.0003775519,0.0032441295,0.00011289385,0.0001205069,0.0002217605,0.0003361966,0.6956251,0.007449855,0.0083926525,0.004006061,0.2798],"study_design_scores_gemma":[0.000008202294,0.000027785632,0.00018279064,0.0000052946075,0.0000112836315,0.000042950112,0.00003014621,0.99342346,0.0014968377,0.004362773,0.0003987165,0.00000977279],"about_ca_topic_score_codex":0.0051547107,"about_ca_topic_score_gemma":0.0067859236,"teacher_disagreement_score":0.0051547107,"about_ca_system_score_codex":0.0008040361,"about_ca_system_score_gemma":0.0010579092,"threshold_uncertainty_score":0.010249376},"labels":[],"label_agreement":null},{"id":"W4402809523","doi":"10.1109/tmc.2024.3465591","title":"Multi-User Task Offloading in UAV-Assisted LEO Satellite Edge Computing: A Game-Theoretic Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","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":"University of Waterloo","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Computer science; Task (project management); Edge computing; Mobile edge computing; Enhanced Data Rates for GSM Evolution; Game theory; Distributed computing; Human–computer interaction; Server; Computer network; Artificial intelligence; Systems engineering","score_opus":0.02398179475272112,"score_gpt":0.2700767323897098,"score_spread":0.2460949376369887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402809523","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06382058,0.00038214127,0.9205848,0.00059632404,0.00009307666,0.0002829333,0.00009422215,0.000085636486,0.01406015],"genre_scores_gemma":[0.92682624,0.00039344284,0.06811755,0.000178515,0.000040765197,0.00022407458,0.00004749401,0.000033239237,0.0041386154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921596,0.00032058137,0.000027445356,0.00011352126,0.00013151664,0.00019095873],"domain_scores_gemma":[0.99905556,0.0006183386,0.00008277114,0.00003727832,0.00009128457,0.000114859045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096612127,0.0010760372,0.0009956677,0.00050090376,0.00089525385,0.0013149368,0.0013108036,0.0010485387,0.0019332267],"category_scores_gemma":[0.0021339932,0.0003903874,0.0006080333,0.0005869516,0.0011663246,0.001556679,0.001479749,0.0011216541,0.00015098737],"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":[0.00011929213,0.00009290525,0.0006455351,0.000087535474,0.000040863906,0.0002428197,0.00013826374,0.9370075,0.0026923139,0.045240857,0.0014168331,0.012275282],"study_design_scores_gemma":[0.00000788166,0.000026733891,0.00007400122,0.0000043160508,0.0000058248106,0.00003069947,0.000044066564,0.99249506,0.00021253111,0.006672217,0.00042105714,0.0000055964697],"about_ca_topic_score_codex":0.006167241,"about_ca_topic_score_gemma":0.00743854,"teacher_disagreement_score":0.006167241,"about_ca_system_score_codex":0.0014358993,"about_ca_system_score_gemma":0.0015306947,"threshold_uncertainty_score":0.012262642},"labels":[],"label_agreement":null},{"id":"W4402978230","doi":"10.1109/tmc.2024.3470993","title":"Long-Term or Temporary? Hybrid Worker Recruitment for Mobile Crowd Sensing and Computing","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Crowdsensing and Crowdsourcing","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":"Aeronautical Science Foundation of China; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Term (time); Mobile computing; Crowd sourcing; Mobile telephony; Computer security; Computer network; World Wide Web; Mobile radio","score_opus":0.04354741402426556,"score_gpt":0.30814538540501735,"score_spread":0.2645979713807518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402978230","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048531786,0.00035768937,0.9426005,0.0013943457,0.00006949556,0.00024908283,0.000089970825,0.00042563034,0.006281408],"genre_scores_gemma":[0.6514858,0.00023239486,0.34098864,0.00036328862,0.00007337855,0.00043179645,0.0001317715,0.00007949237,0.006213397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998357,0.0007187795,0.00004796106,0.0003528995,0.00023516348,0.00028818406],"domain_scores_gemma":[0.9979323,0.0013003956,0.00021144605,0.00021752069,0.00011806652,0.00022031029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028996153,0.00072318985,0.0010540537,0.00040211133,0.0012703345,0.0015259084,0.0022263234,0.0015962321,0.0034100264],"category_scores_gemma":[0.004386695,0.00040731597,0.0007403966,0.0005414181,0.0010876645,0.0023202023,0.00269647,0.001198888,0.00049732014],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008608995,0.00062053226,0.004625695,0.00040548557,0.00009646515,0.00053352077,0.001107259,0.5576702,0.009328828,0.11848635,0.009984157,0.29628062],"study_design_scores_gemma":[0.000040077266,0.00017086664,0.0005098013,0.000030269422,0.000014057686,0.0001500853,0.0004198119,0.94345886,0.0019099083,0.047587633,0.0056807376,0.00002789308],"about_ca_topic_score_codex":0.0018552975,"about_ca_topic_score_gemma":0.0030114916,"teacher_disagreement_score":0.0034100264,"about_ca_system_score_codex":0.0010322379,"about_ca_system_score_gemma":0.0019101333,"threshold_uncertainty_score":0.015334845},"labels":[],"label_agreement":null},{"id":"W4402978271","doi":"10.1109/tmc.2024.3470831","title":"Joint Optimization of Data Acquisition and Trajectory Planning for UAV-Assisted Wireless Powered Internet of Things","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Joint (building); Wireless; Trajectory; Internet of Things; The Internet; Computer network; Real-time computing; Telecommunications; Computer security; World Wide Web; Engineering","score_opus":0.027750972139213015,"score_gpt":0.2645786008264033,"score_spread":0.23682762868719026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402978271","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074407555,0.00047045836,0.9197335,0.00060455134,0.000078958634,0.00009819638,0.00010807156,0.0003452849,0.004153305],"genre_scores_gemma":[0.95794123,0.0001247946,0.039911818,0.000078785604,0.000016191236,0.000094362025,0.00010681754,0.000033835506,0.0016921305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969864,0.00007950446,0.000013189583,0.00007718745,0.000059855476,0.00007159459],"domain_scores_gemma":[0.99922574,0.0004515274,0.000111723464,0.000034306704,0.00011075491,0.00006589227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068704435,0.00079373136,0.00090178224,0.0003877383,0.0003847358,0.0006050674,0.00061338535,0.0008408277,0.0014608706],"category_scores_gemma":[0.0019486303,0.0004986295,0.00046713007,0.0004560935,0.00065962336,0.00062893773,0.00086280506,0.000888338,0.00015721095],"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":[0.000026697018,0.000016431048,0.000284244,0.000016609853,0.000009069121,0.00002778681,0.0000114601025,0.99177575,0.0003288426,0.0011732437,0.00024832948,0.0060816286],"study_design_scores_gemma":[0.0000029807181,0.000008906278,0.00004953378,0.0000011407254,0.0000013965816,0.0000028749828,0.0000032203902,0.9993837,0.00007188514,0.0004193349,0.000053878204,0.0000011425459],"about_ca_topic_score_codex":0.013625193,"about_ca_topic_score_gemma":0.00999392,"teacher_disagreement_score":0.013625193,"about_ca_system_score_codex":0.0008810288,"about_ca_system_score_gemma":0.0018007477,"threshold_uncertainty_score":0.027091801},"labels":[],"label_agreement":null},{"id":"W4403052626","doi":"10.1109/tmc.2024.3472643","title":"Secure Localization for Underwater Wireless Sensor Networks via AUV Cooperative Beamforming With Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","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":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Computer science; Reinforcement learning; Beamforming; Underwater; Wireless sensor network; Wireless; Computer network; Acoustic sensor; Underwater acoustic communication; Computer security; Telecommunications; Artificial intelligence; Acoustics; Oceanography; Geology","score_opus":0.009653512937284581,"score_gpt":0.22327743163194522,"score_spread":0.21362391869466063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403052626","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01999685,0.00013020365,0.97845685,0.00014994155,0.000021311691,0.000015647023,0.0000070544543,0.00018027474,0.0010420332],"genre_scores_gemma":[0.94903976,0.00014630958,0.049415354,0.000075646,0.000016584227,0.000070395596,0.000020213001,0.000016841052,0.0011989299],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967563,0.00009705185,0.000014485051,0.000067579385,0.00010385428,0.000041459327],"domain_scores_gemma":[0.9996413,0.00016207897,0.000071585506,0.000030133926,0.00007230876,0.000022469489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006138471,0.0005798034,0.0005047671,0.00020490917,0.00031466858,0.0003618159,0.000557526,0.00053447555,0.00056891766],"category_scores_gemma":[0.0012074134,0.00025294503,0.00029402695,0.00022679371,0.00082573853,0.0006939175,0.0010690688,0.00077201164,0.00015947247],"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":[0.0000422526,0.000020738387,0.00046197648,0.000026524396,0.000014126175,0.000047118563,0.000048211623,0.9591969,0.004789279,0.0051257834,0.00033379276,0.02989329],"study_design_scores_gemma":[0.0000034199204,0.000016773904,0.000023849097,0.0000012285734,0.0000014882164,0.000004179833,0.000003673251,0.9984024,0.00037805177,0.0010717413,0.0000914607,0.0000017383605],"about_ca_topic_score_codex":0.003967245,"about_ca_topic_score_gemma":0.0025669304,"teacher_disagreement_score":0.003967245,"about_ca_system_score_codex":0.000494521,"about_ca_system_score_gemma":0.00072906166,"threshold_uncertainty_score":0.007888317},"labels":[],"label_agreement":null},{"id":"W4403420284","doi":"10.1109/tmc.2024.3481276","title":"Deep Reinforcement Learning-Based Joint Caching and Routing in AI-Driven Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Caching and Content Delivery","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 British Columbia","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; National Research Foundation Singapore","keywords":"Computer science; Reinforcement learning; Joint (building); Routing (electronic design automation); Computer network; Artificial intelligence; Distributed computing","score_opus":0.012709366229248538,"score_gpt":0.23611230977656303,"score_spread":0.2234029435473145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403420284","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13159381,0.0007628561,0.86216325,0.00074103737,0.00008717671,0.00006264819,0.000085346044,0.00077198166,0.003731972],"genre_scores_gemma":[0.9718777,0.000138431,0.026160162,0.00016262697,0.000022601127,0.000053593456,0.000059135127,0.000023507417,0.0015021915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996153,0.00011849831,0.000018622579,0.00008512702,0.00006572162,0.000096708594],"domain_scores_gemma":[0.99850357,0.00097218633,0.00015315591,0.000062190586,0.00020796015,0.00010096531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010925423,0.00062512886,0.0011545578,0.00032939127,0.00039979807,0.0006215924,0.0014584007,0.0010258578,0.0011203848],"category_scores_gemma":[0.0027875816,0.0004132113,0.00033993102,0.0004441157,0.0008897057,0.000958842,0.0006977328,0.0011367021,0.0001340317],"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":[0.000044098946,0.000034705947,0.00035866193,0.00001768348,0.000014614807,0.000022404105,0.000016820899,0.9866348,0.00034019194,0.0024003396,0.00031061014,0.009805048],"study_design_scores_gemma":[0.000003465133,0.0000060602606,0.000021219104,6.8014094e-7,0.0000017137496,0.0000016886839,0.0000010592789,0.9991498,0.000051820272,0.0007329393,0.000028686565,8.362829e-7],"about_ca_topic_score_codex":0.019469028,"about_ca_topic_score_gemma":0.014434422,"teacher_disagreement_score":0.019469028,"about_ca_system_score_codex":0.0015858223,"about_ca_system_score_gemma":0.001677364,"threshold_uncertainty_score":0.03871143},"labels":[],"label_agreement":null},{"id":"W4403826584","doi":"10.1109/tmc.2024.3487175","title":"MANSY: Generalizing Neural Adaptive Immersive Video Streaming With Ensemble and Representation Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Image and Video Quality Assessment","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":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Multimedia; Video streaming; Representation (politics); Streaming data; Artificial intelligence; Human–computer interaction; Computer network; Data mining","score_opus":0.026361263789720566,"score_gpt":0.2990375361200079,"score_spread":0.2726762723302873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403826584","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032561384,0.00017670591,0.96475786,0.00011257559,0.00003585424,0.000042911415,0.000065253,0.0013410515,0.00090647663],"genre_scores_gemma":[0.7630088,0.00029663878,0.2318261,0.00024074098,0.000085975655,0.00018297935,0.0004748654,0.00012999355,0.0037539375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976414,0.00004456457,0.000014594382,0.000071503215,0.00007122825,0.000033973174],"domain_scores_gemma":[0.9995421,0.00018082942,0.000043761098,0.0000763148,0.00013045638,0.000026579555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089605263,0.0007554085,0.00071220635,0.00035399347,0.00020162744,0.0003463624,0.001408957,0.00062188576,0.0010386315],"category_scores_gemma":[0.0018868499,0.00031816846,0.00066448504,0.0003795032,0.00031501407,0.0011141022,0.001042323,0.0013003844,0.00027585108],"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":[0.00007629591,0.00011132944,0.00076086103,0.000026762522,0.00007108381,0.000042096934,0.000045003126,0.81521094,0.005782342,0.0026353577,0.001394011,0.17384398],"study_design_scores_gemma":[0.0000015923607,0.000013344197,0.00003559104,6.834923e-7,0.000002181191,0.0000027692292,9.4621953e-7,0.9991757,0.000336922,0.00035393608,0.000075114425,0.0000010928741],"about_ca_topic_score_codex":0.0096690105,"about_ca_topic_score_gemma":0.008928663,"teacher_disagreement_score":0.0096690105,"about_ca_system_score_codex":0.0005346521,"about_ca_system_score_gemma":0.00063800946,"threshold_uncertainty_score":0.019225478},"labels":[],"label_agreement":null},{"id":"W4403826586","doi":"10.1109/tmc.2024.3486689","title":"Defending Data Poisoning Attacks in DP-Based Crowdsensing: A Game-Theoretic Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Crowdsensing; Computer security; Game theory; Computer network","score_opus":0.03899618105861772,"score_gpt":0.2929222057781125,"score_spread":0.25392602471949477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403826586","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010817853,0.00010529799,0.9847585,0.00070648675,0.000029652296,0.00012783219,0.00003546655,0.0000775484,0.003341368],"genre_scores_gemma":[0.82496923,0.0002519583,0.16880228,0.00056624966,0.00007741203,0.00041754206,0.00006289263,0.000042212843,0.004810302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956541,0.002064396,0.000170035,0.00072638225,0.00091627333,0.00046869164],"domain_scores_gemma":[0.9937429,0.0043929867,0.00055177003,0.00043347024,0.00052052253,0.0003582757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004519464,0.0013676045,0.0016236154,0.0011922187,0.001106361,0.002169347,0.003219834,0.0029123959,0.0014581286],"category_scores_gemma":[0.009718852,0.0006871828,0.0015096248,0.0007780441,0.0032842641,0.0029208506,0.0037380154,0.0025032084,0.00022472812],"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":[0.00012100784,0.000094572504,0.00067436136,0.00011597304,0.00012435281,0.00019840941,0.0002375848,0.8167042,0.0038643398,0.15927821,0.0011041141,0.017482907],"study_design_scores_gemma":[0.0000149936905,0.000040084647,0.000059254613,0.0000071973445,0.0000112725875,0.000033967986,0.000027533833,0.9602174,0.0004729259,0.038482998,0.0006190775,0.000013243701],"about_ca_topic_score_codex":0.0028597058,"about_ca_topic_score_gemma":0.0021482264,"teacher_disagreement_score":0.004519464,"about_ca_system_score_codex":0.0028304574,"about_ca_system_score_gemma":0.002763532,"threshold_uncertainty_score":0.023901463},"labels":[],"label_agreement":null},{"id":"W4404787749","doi":"10.1109/tmc.2024.3507051","title":"QoS-Driven Contextual MAB for MPQUIC Supporting Video Streaming in Mobile Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Quality of service; Computer network; Video streaming; Mobile computing; Mobile QoS; Multimedia; Service provider; Service (business)","score_opus":0.018668472399198333,"score_gpt":0.2909358073951642,"score_spread":0.2722673349959659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404787749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036878217,0.00028264412,0.9570976,0.00017307275,0.00006536654,0.00006813244,0.000021926477,0.0007231585,0.004689892],"genre_scores_gemma":[0.78596956,0.00028055022,0.2099897,0.00020354478,0.00008038787,0.000118557924,0.0000591385,0.00007944246,0.0032191984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996989,0.0000864166,0.0000127370195,0.000054169694,0.00008465646,0.00006314516],"domain_scores_gemma":[0.99955255,0.00018925274,0.00005174943,0.000056794288,0.000103085185,0.000046569137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046273455,0.00035807412,0.00040318817,0.000184556,0.0004652286,0.00068972626,0.0006823068,0.0004886213,0.0013301464],"category_scores_gemma":[0.0013753388,0.00016043028,0.00022608165,0.00015597463,0.00044275413,0.0005021361,0.0007397696,0.0007199243,0.0005198098],"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":[0.00038561667,0.0003428977,0.0015240951,0.00014877215,0.000037621732,0.00046793596,0.00032687184,0.5314793,0.17169532,0.077015065,0.00454613,0.21203038],"study_design_scores_gemma":[0.0000062448507,0.000058329413,0.00009330298,0.0000038551275,0.000004703317,0.000028448394,0.000014177305,0.9911908,0.005190477,0.0020092288,0.0013947481,0.000005729078],"about_ca_topic_score_codex":0.0024104272,"about_ca_topic_score_gemma":0.0032259766,"teacher_disagreement_score":0.0024104272,"about_ca_system_score_codex":0.00050330436,"about_ca_system_score_gemma":0.0007699912,"threshold_uncertainty_score":0.00479275},"labels":[],"label_agreement":null},{"id":"W4404788100","doi":"10.1109/tmc.2024.3507035","title":"HearLoc: Locating Unknown Sound Sources in 3D With a Small-Sized Microphone Array","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Speech and Audio Processing","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 Victoria","funders":"National Natural Science Foundation of China","keywords":"Computer science; Microphone array; Sound (geography); Microphone; Speech recognition; Acoustics; Telecommunications; Sound pressure","score_opus":0.014054655585145327,"score_gpt":0.24058046981448242,"score_spread":0.2265258142293371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404788100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006287864,0.00018084567,0.98750174,0.000105513915,0.00008901837,0.00004598374,0.00014736902,0.0039339913,0.0017076179],"genre_scores_gemma":[0.1278357,0.00035760613,0.8658141,0.00033849818,0.00011163625,0.00023138891,0.0006202472,0.0003348324,0.0043561007],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99952054,0.00009309688,0.000019384588,0.00011508523,0.00021964812,0.000032260803],"domain_scores_gemma":[0.99957365,0.00015391705,0.000049401846,0.00008005393,0.00010264571,0.00004029962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004605831,0.0010871803,0.00072760996,0.0006086212,0.0003132525,0.0008702948,0.0014041548,0.0010949554,0.00508415],"category_scores_gemma":[0.0016993118,0.00045842543,0.00056883227,0.0006069885,0.00053955754,0.0013147077,0.0023689903,0.0008157013,0.0032800105],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075663393,0.00013712759,0.0022614114,0.0006697635,0.00014754313,0.0010132511,0.00074049784,0.08339012,0.23918515,0.00941559,0.016751127,0.6455317],"study_design_scores_gemma":[0.00015791286,0.0004642393,0.001714361,0.000066019056,0.00006798241,0.0014196952,0.00027030872,0.8774738,0.07782982,0.0063863415,0.034007493,0.00014212246],"about_ca_topic_score_codex":0.0009942263,"about_ca_topic_score_gemma":0.0020231022,"teacher_disagreement_score":0.00508415,"about_ca_system_score_codex":0.00025541204,"about_ca_system_score_gemma":0.0006374412,"threshold_uncertainty_score":0.017008185},"labels":[],"label_agreement":null},{"id":"W4404809472","doi":"10.1109/tmc.2024.3508260","title":"Incentive Mechanism Design for Cross-Device Federated Learning: A Reinforcement Auction Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Blockchain Technology Applications and Security","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":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Reinforcement learning; Incentive; Mechanism design; Mechanism (biology); Double auction; Incentive compatibility; Computer security; Common value auction; Artificial intelligence; Microeconomics","score_opus":0.02566485237195155,"score_gpt":0.28531135585152273,"score_spread":0.2596465034795712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404809472","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008955774,0.00016455968,0.98874205,0.00020941149,0.000037439826,0.00010572657,0.00002367808,0.0001340323,0.001627363],"genre_scores_gemma":[0.86582404,0.00024705732,0.13036786,0.00020319414,0.000055754732,0.00028559752,0.000045473196,0.00004123107,0.0029298267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99716586,0.0012723627,0.00013416666,0.000514583,0.0004792292,0.00043381666],"domain_scores_gemma":[0.9951231,0.00277585,0.0006065625,0.00042594498,0.0006646375,0.00040384967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052845585,0.0011554104,0.0019235655,0.00071943976,0.0006183243,0.0019166624,0.0031126149,0.0020130973,0.0035787362],"category_scores_gemma":[0.008554088,0.0005967069,0.0008482958,0.00071313715,0.00147957,0.002288131,0.002068783,0.0026107498,0.00043214328],"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":[0.00025256784,0.00027214797,0.00080449285,0.00020852762,0.00010755898,0.0003697,0.00015894398,0.85612804,0.0028811053,0.09351881,0.0013579358,0.0439402],"study_design_scores_gemma":[0.000036430964,0.000059373506,0.000047965204,0.0000112123,0.00001040091,0.000050072613,0.000014566584,0.97950244,0.00034541916,0.01942147,0.0004910876,0.000009439219],"about_ca_topic_score_codex":0.0013492088,"about_ca_topic_score_gemma":0.0010340352,"teacher_disagreement_score":0.0052845585,"about_ca_system_score_codex":0.0012936951,"about_ca_system_score_gemma":0.0023255455,"threshold_uncertainty_score":0.027947783},"labels":[],"label_agreement":null},{"id":"W4405178998","doi":"10.1109/tmc.2024.3514173","title":"Service Function Chain Deployment With VNF-Dependent Software Migration in Multi-Domain Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","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":"York University","funders":"Info-communications Media Development Authority; National Natural Science Foundation of China; Ministry of Education - Singapore; National Research Foundation Singapore","keywords":"Computer science; Software deployment; Computer network; Service (business); Function (biology); Domain (mathematical analysis); Distributed computing; Software engineering; Business","score_opus":0.015382851257527015,"score_gpt":0.23651939773821665,"score_spread":0.22113654648068964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405178998","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13671261,0.00075430557,0.8578464,0.0005673755,0.00011162227,0.00023703292,0.00007548218,0.00057182036,0.0031233134],"genre_scores_gemma":[0.7763547,0.00024764927,0.22173141,0.00012208763,0.000022537768,0.00013551826,0.00013058889,0.000044855486,0.0012106305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992563,0.00026040283,0.000037767804,0.00015454952,0.00010541563,0.00018550629],"domain_scores_gemma":[0.9989712,0.00042681763,0.00018699847,0.0001106009,0.00011721645,0.00018719313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010800965,0.0010636167,0.00087691785,0.0006658743,0.0007652683,0.0008275463,0.0014807661,0.0011224368,0.0009565714],"category_scores_gemma":[0.0021268204,0.00043676095,0.0005393999,0.00067033933,0.00061096693,0.0009575137,0.0012543298,0.00069378136,0.00018048656],"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":[0.00007424374,0.0000658677,0.0010154572,0.00006718893,0.000020764614,0.000116149655,0.00006362733,0.94713926,0.0026490504,0.0041198814,0.00096902344,0.043699477],"study_design_scores_gemma":[0.000009156871,0.000037054608,0.0001162263,0.00000574274,0.000004850847,0.000032238764,0.000034760087,0.9975535,0.00047908525,0.0013506225,0.00037298317,0.0000038276867],"about_ca_topic_score_codex":0.0052096895,"about_ca_topic_score_gemma":0.0050489176,"teacher_disagreement_score":0.0052096895,"about_ca_system_score_codex":0.0010741947,"about_ca_system_score_gemma":0.001390591,"threshold_uncertainty_score":0.010358751},"labels":[],"label_agreement":null},{"id":"W4405179107","doi":"10.1109/tmc.2024.3514214","title":"Joint Encoding and Enhancement for Low-Light Video Analytics in Mobile Edge Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Image and Video Quality Assessment","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 Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Joint (building); Analytics; Encoding (memory); Enhanced Data Rates for GSM Evolution; Mobile telephony; Computer network; Telecommunications; Artificial intelligence; Mobile radio; Data science","score_opus":0.027309099170652048,"score_gpt":0.30751356645251854,"score_spread":0.2802044672818665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405179107","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0321602,0.00033781776,0.96382076,0.00018333479,0.000040242136,0.00011866201,0.000051095747,0.0015191431,0.0017687326],"genre_scores_gemma":[0.69538605,0.00028012696,0.30033305,0.00023639975,0.00005912874,0.00009902126,0.0001377258,0.00012269498,0.0033458231],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99941385,0.00011642107,0.000026719397,0.00013457664,0.000186565,0.0001218724],"domain_scores_gemma":[0.99906427,0.00036906108,0.00010803715,0.0001429361,0.0002544134,0.00006129316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085134414,0.00086745643,0.00059892336,0.00038980128,0.00034805702,0.00086782844,0.0011056662,0.0005648015,0.0016904232],"category_scores_gemma":[0.0024049685,0.00031542216,0.00030473355,0.0003045137,0.00047821723,0.0018905579,0.0012959107,0.0011351693,0.0005924674],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018776604,0.000631865,0.0044977358,0.00026874087,0.0001016764,0.0007325394,0.0003569996,0.2509135,0.20577587,0.02095278,0.005628065,0.50826263],"study_design_scores_gemma":[0.000023634038,0.00020189186,0.0003664376,0.000011952899,0.000016329157,0.00015313798,0.000032781958,0.9547491,0.03912292,0.0033494134,0.0019542463,0.000018067283],"about_ca_topic_score_codex":0.001362686,"about_ca_topic_score_gemma":0.0018765205,"teacher_disagreement_score":0.0016904232,"about_ca_system_score_codex":0.0004930459,"about_ca_system_score_gemma":0.0005713345,"threshold_uncertainty_score":0.0056549907},"labels":[],"label_agreement":null},{"id":"W4405784836","doi":"10.1109/tmc.2024.3521934","title":"Reliability-Optimal UAV-Assisted Mobile Edge Computing: Joint Resource Allocation, Data Transmission Scheduling and Motion Control","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Scheduling (production processes); Mobile edge computing; Reliability (semiconductor); Distributed computing; Resource allocation; Mobile computing; Dynamic priority scheduling; Data transmission; Computer network; Real-time computing; Server; Quality of service; Mathematical optimization","score_opus":0.02743418996179658,"score_gpt":0.2722467677498975,"score_spread":0.2448125777881009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405784836","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035007905,0.00034698148,0.96185285,0.00018575769,0.000034892117,0.00003218468,0.000032092757,0.00012971947,0.0023776067],"genre_scores_gemma":[0.92859524,0.00027504444,0.06915121,0.000045225563,0.000033515513,0.00006969449,0.000048773996,0.000041277304,0.0017398958],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970514,0.00008670193,0.000010525323,0.00006619453,0.00006736627,0.00006413494],"domain_scores_gemma":[0.99962807,0.0001773723,0.00007881138,0.000028418133,0.000056432564,0.000030872903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052592234,0.0007275635,0.00064016075,0.00024349762,0.0002872112,0.0006056184,0.0006883269,0.0004899428,0.000732938],"category_scores_gemma":[0.0014351939,0.00035327993,0.00035599867,0.0004488653,0.0005563534,0.00060858467,0.00085064024,0.0006192402,0.00014100567],"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":[0.000031882126,0.000014690155,0.00019195046,0.000023782499,0.000008295618,0.00003272655,0.00002098612,0.9834184,0.0012471303,0.0055136387,0.0003565023,0.009140051],"study_design_scores_gemma":[0.000002107118,0.000010402365,0.000030568335,0.0000012662466,0.0000016335039,0.0000055702,0.0000034159395,0.9988135,0.00019264054,0.00084389764,0.0000935772,0.0000012950924],"about_ca_topic_score_codex":0.004902427,"about_ca_topic_score_gemma":0.0032093616,"teacher_disagreement_score":0.004902427,"about_ca_system_score_codex":0.0006125733,"about_ca_system_score_gemma":0.0011585652,"threshold_uncertainty_score":0.009747803},"labels":[],"label_agreement":null},{"id":"W4405812078","doi":"10.1109/tmc.2024.3522207","title":"Resource Allocation for the Uplink of a Multi-User Massive MIMO System","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Telecommunications link; MIMO; Computer network; Resource allocation; Multi-user MIMO; Channel (broadcasting)","score_opus":0.01311476158237268,"score_gpt":0.24814977630535676,"score_spread":0.23503501472298408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405812078","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.093470484,0.00044268137,0.89854705,0.00047675267,0.000053026753,0.000074114105,0.00008301455,0.00028398572,0.006568896],"genre_scores_gemma":[0.9339477,0.0002191818,0.06323236,0.000090449495,0.000056764453,0.0000748431,0.00003694991,0.000029891375,0.002311831],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961144,0.00016293146,0.00001026839,0.00005046438,0.000077933735,0.00008697239],"domain_scores_gemma":[0.9993741,0.00043207922,0.000051361887,0.000044848945,0.000058544058,0.000039172563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006932896,0.00063569634,0.0006995315,0.0002011654,0.00050673616,0.00083163165,0.0005611152,0.0006217871,0.002598957],"category_scores_gemma":[0.0016268844,0.00020933189,0.00028279985,0.00039523141,0.000611755,0.00065158954,0.00065526937,0.000595537,0.0002986481],"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":[0.00006788296,0.00003442908,0.00035268825,0.000054113738,0.000015299725,0.00009445005,0.000034492474,0.9790692,0.003729412,0.0067567998,0.0004945788,0.009296716],"study_design_scores_gemma":[0.0000042654956,0.000016747761,0.000068554255,0.0000014514957,0.0000028051518,0.00000997952,0.000010565382,0.9979888,0.0004655039,0.0013293951,0.000100067904,0.0000019353506],"about_ca_topic_score_codex":0.0028900963,"about_ca_topic_score_gemma":0.0032680084,"teacher_disagreement_score":0.0028900963,"about_ca_system_score_codex":0.0009335508,"about_ca_system_score_gemma":0.00082911464,"threshold_uncertainty_score":0.00869441},"labels":[],"label_agreement":null},{"id":"W4406028367","doi":"10.1109/tmc.2025.3525477","title":"Multi-Task Semantic Communication With Graph Attention-Based Feature Correlation Extraction","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Graph Neural Networks","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 Manitoba","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Computer science; Correlation; Feature extraction; Graph; Task (project management); Artificial intelligence; Feature (linguistics); Semantic feature; Natural language processing; Pattern recognition (psychology); Theoretical computer science","score_opus":0.009291780807133998,"score_gpt":0.2653748857324918,"score_spread":0.2560831049253578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406028367","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043354936,0.001058816,0.94326985,0.0005473698,0.00013737443,0.0001427753,0.0006468978,0.0067082485,0.0041337046],"genre_scores_gemma":[0.7380581,0.0006204888,0.24776487,0.00042741493,0.00016107298,0.00028066867,0.002898508,0.00044277756,0.009346144],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936014,0.0001402674,0.000030501727,0.00024122882,0.00013547386,0.00009235766],"domain_scores_gemma":[0.99929416,0.00027245248,0.00007708877,0.0001666166,0.00014222755,0.000047490386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078552554,0.001592374,0.00093322847,0.0010003843,0.00042681553,0.0007555611,0.0019394766,0.0009955794,0.002241541],"category_scores_gemma":[0.0026262354,0.0003574094,0.0011740274,0.0013870919,0.0005195319,0.0025776387,0.0015212934,0.001500212,0.0009712319],"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":[0.000552439,0.0005000788,0.0030269213,0.0003407065,0.0002000584,0.000358797,0.00032097934,0.220733,0.023124205,0.017401604,0.019754179,0.71368694],"study_design_scores_gemma":[0.000024895828,0.000081393504,0.0006745455,0.000011092748,0.000047362082,0.000062647465,0.000031292453,0.9740859,0.0062373625,0.015646081,0.0030792335,0.000018156634],"about_ca_topic_score_codex":0.007359849,"about_ca_topic_score_gemma":0.009121968,"teacher_disagreement_score":0.007359849,"about_ca_system_score_codex":0.0010170286,"about_ca_system_score_gemma":0.0014453736,"threshold_uncertainty_score":0.014634073},"labels":[],"label_agreement":null},{"id":"W4406110370","doi":"10.1109/tmc.2025.3526573","title":"FastTuner: Fast Resolution and Model Tuning for Multi-Object Tracking in Edge Video Analytics","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Video Surveillance and Tracking Methods","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":"McMaster University","funders":"China Scholarship Council","keywords":"Computer science; Analytics; Video tracking; Enhanced Data Rates for GSM Evolution; Tracking (education); Object (grammar); Computer vision; Artificial intelligence; Data mining","score_opus":0.05133420868989664,"score_gpt":0.3437616184360394,"score_spread":0.2924274097461428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406110370","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02858255,0.0005879021,0.9537627,0.00016237018,0.00008918032,0.0000993479,0.00014831111,0.01517761,0.0013900283],"genre_scores_gemma":[0.48594254,0.00041101806,0.5086015,0.000391432,0.000062964726,0.00021705923,0.00088452076,0.0014315182,0.0020574904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994318,0.00007721727,0.000023272552,0.00020457157,0.00018109627,0.00008203556],"domain_scores_gemma":[0.9988305,0.0005034449,0.00010646414,0.00029171217,0.00017770077,0.000090267786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013036552,0.00142151,0.0009768277,0.0007580897,0.00060805475,0.0011898776,0.002310881,0.001151902,0.002124284],"category_scores_gemma":[0.006132001,0.0006657928,0.00057723944,0.0006507505,0.00054963294,0.0024300108,0.0019599725,0.0021511775,0.0012294088],"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":[0.0004881906,0.0004531034,0.004646835,0.00020657052,0.00013221105,0.00023101771,0.00029241125,0.45655358,0.03679041,0.0042048274,0.013113883,0.48288697],"study_design_scores_gemma":[0.000013785778,0.000030264959,0.00026520694,0.000006413771,0.000005238513,0.000034038745,0.000015766645,0.9932827,0.0043401746,0.0011593977,0.00083624787,0.000010770247],"about_ca_topic_score_codex":0.0072649443,"about_ca_topic_score_gemma":0.009256356,"teacher_disagreement_score":0.0072649443,"about_ca_system_score_codex":0.00070001493,"about_ca_system_score_gemma":0.0012613359,"threshold_uncertainty_score":0.014445305},"labels":[],"label_agreement":null},{"id":"W4406457489","doi":"10.1109/tmc.2025.3530486","title":"Intelligent End-to-End Deterministic Scheduling Across Converged Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; End-to-end principle; Scheduling (production processes); Computer network; Distributed computing; Processor scheduling; Mathematical optimization","score_opus":0.017273020686504433,"score_gpt":0.29384679014102344,"score_spread":0.276573769454519,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406457489","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10581103,0.00022261152,0.8901189,0.00021478422,0.000044008637,0.00005665714,0.000053048614,0.0010459669,0.00243302],"genre_scores_gemma":[0.9510456,0.00008621972,0.04764924,0.000066523084,0.000010752359,0.00004318415,0.00006427452,0.000040712162,0.0009934752],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926955,0.00017448237,0.000035600224,0.00018916435,0.00015967335,0.00017161762],"domain_scores_gemma":[0.99916065,0.00030765598,0.00012651258,0.0001460064,0.00015320408,0.00010607584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001007382,0.0006249886,0.0005941619,0.000251063,0.0005677403,0.00069346593,0.0011949202,0.00044252526,0.001035874],"category_scores_gemma":[0.0022632054,0.00028559906,0.00032412205,0.000243499,0.00060353766,0.0010245172,0.0010194067,0.00090521405,0.0001755503],"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":[0.00015308012,0.00005456705,0.001041475,0.000027080665,0.000016620312,0.000060052953,0.000062748215,0.95253897,0.0044302363,0.004665322,0.0007003574,0.036249485],"study_design_scores_gemma":[0.000004686921,0.000013209085,0.00006792276,0.0000011931564,0.0000024942951,0.0000059500544,0.000007107388,0.99770063,0.0006068804,0.001441864,0.00014604992,0.0000020173136],"about_ca_topic_score_codex":0.0065852376,"about_ca_topic_score_gemma":0.0063866773,"teacher_disagreement_score":0.0065852376,"about_ca_system_score_codex":0.0010989619,"about_ca_system_score_gemma":0.0017550163,"threshold_uncertainty_score":0.013093829},"labels":[],"label_agreement":null},{"id":"W4406890314","doi":"10.1109/tmc.2025.3535567","title":"MWRS: A MAB-Based Worker Recruitment Scheme With Tripartite Stackelberg Game for Reliable Mobile Crowdsensing","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Evacuation and Crowd Dynamics","field":"Engineering","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 Alberta","funders":"National Natural Science Foundation of China","keywords":"Stackelberg competition; Crowdsensing; Computer science; Scheme (mathematics); Computer network; Computer security; Mathematics","score_opus":0.020245198762132318,"score_gpt":0.2787440308393798,"score_spread":0.2584988320772475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406890314","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020097755,0.00022986745,0.9757171,0.00043280912,0.00009657715,0.0002260511,0.000080944796,0.00040041108,0.002718452],"genre_scores_gemma":[0.8699547,0.00023234631,0.12351019,0.00049843953,0.00009050946,0.00056534895,0.00013877434,0.00005629075,0.004953379],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99698895,0.0013027792,0.00012648584,0.00055925996,0.00053756457,0.00048499423],"domain_scores_gemma":[0.9962992,0.0019757939,0.0004238256,0.00030922078,0.00045232795,0.00053970295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030838533,0.0015069413,0.0017958687,0.0006938524,0.0011892867,0.0012828363,0.0038046387,0.0022742406,0.0032599152],"category_scores_gemma":[0.008049836,0.0005468704,0.0010891779,0.0006510563,0.0015630316,0.0019007988,0.003595435,0.0018515068,0.0007638315],"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":[0.0008098393,0.00036038674,0.0024114654,0.00033278868,0.00016540229,0.00066687225,0.00077553804,0.8029025,0.013264824,0.059194617,0.006296691,0.11281915],"study_design_scores_gemma":[0.000031563406,0.000118711236,0.00015351026,0.00001147509,0.000016700982,0.00007200612,0.00004198194,0.9860985,0.0007019695,0.011735855,0.0009992142,0.000018536228],"about_ca_topic_score_codex":0.0037544398,"about_ca_topic_score_gemma":0.0036646521,"teacher_disagreement_score":0.0038046387,"about_ca_system_score_codex":0.0012602737,"about_ca_system_score_gemma":0.0024799388,"threshold_uncertainty_score":0.016309202},"labels":[],"label_agreement":null},{"id":"W4407354563","doi":"10.1109/tmc.2025.3541191","title":"Optimizing Federated Semantic Learning in Distributed AIGC-Enabled Human Digital Twins: A Multi-Criteria and Multi-Shard User Selection Framework","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Education - Singapore","keywords":"Computer science; Selection (genetic algorithm); Distributed learning; Distributed computing; Human–computer interaction; Artificial intelligence","score_opus":0.02530336937577078,"score_gpt":0.3048579214552302,"score_spread":0.27955455207945945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407354563","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029930688,0.00013001043,0.968352,0.00012917188,0.000021192473,0.00003429481,0.000026793805,0.00021666494,0.0011591621],"genre_scores_gemma":[0.91408515,0.00010322971,0.0827946,0.000106360305,0.000029960018,0.000076053824,0.00006470907,0.000044781387,0.0026952552],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888855,0.00039476124,0.000045849167,0.0002506998,0.00023296344,0.00018720086],"domain_scores_gemma":[0.99848217,0.00078534556,0.0001577766,0.00016911144,0.00027576808,0.00012975081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017719436,0.0007876116,0.0012477285,0.00046993393,0.00056399347,0.0011368705,0.0015514356,0.0012142747,0.0012375772],"category_scores_gemma":[0.0031068334,0.00042033195,0.00047824098,0.00073669036,0.0012129331,0.0016547793,0.0018279891,0.0009713082,0.00025043567],"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":[0.00018352951,0.00011709762,0.0008826534,0.00005020974,0.00004627241,0.00017831431,0.00010469769,0.9280269,0.0034689386,0.013255291,0.000833058,0.052853063],"study_design_scores_gemma":[0.0000049783966,0.000025777152,0.000047965827,0.00000140454,0.000004327526,0.000016890644,0.0000091650345,0.9968549,0.0005261305,0.0023881088,0.00011662795,0.0000036611239],"about_ca_topic_score_codex":0.0028050747,"about_ca_topic_score_gemma":0.0025691227,"teacher_disagreement_score":0.0028050747,"about_ca_system_score_codex":0.00097191636,"about_ca_system_score_gemma":0.0014327235,"threshold_uncertainty_score":0.009371042},"labels":[],"label_agreement":null},{"id":"W4407449107","doi":"10.1109/tmc.2025.3541575","title":"3D Cooperative Positioning via RIS and Sidelink Communications With Zero Access Points","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","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 Toronto","funders":"","keywords":"Computer science; Computer network; Telecommunications","score_opus":0.010232029976379552,"score_gpt":0.2616984447516208,"score_spread":0.25146641477524123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407449107","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.073117554,0.00014781173,0.9223662,0.000098750286,0.000029164567,0.000019924806,0.000029223453,0.00045721803,0.0037340971],"genre_scores_gemma":[0.8691999,0.0001183588,0.12746525,0.00007696101,0.000025665086,0.00006279416,0.00006106107,0.000019578576,0.0029704184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999196,0.00019726709,0.000030235737,0.00017087322,0.0003099129,0.00009574467],"domain_scores_gemma":[0.9992306,0.00025383866,0.00016363007,0.00021495746,0.000101889964,0.000035118996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042261241,0.000636143,0.00060282595,0.00043300717,0.00028518602,0.0007186093,0.00088204263,0.0008199715,0.00097445317],"category_scores_gemma":[0.0012529352,0.00027386838,0.00045645467,0.0005926235,0.00069213327,0.0009019305,0.0019923304,0.0005635486,0.0006493992],"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":[0.0005624374,0.00013983848,0.009076957,0.00023866913,0.00012583537,0.00085393636,0.0007373712,0.370018,0.20908657,0.03896379,0.0016090175,0.36858752],"study_design_scores_gemma":[0.00003827323,0.00056291145,0.0020531334,0.000021943199,0.000043021246,0.000646478,0.00015593549,0.9112656,0.07157943,0.0076265964,0.005957364,0.00004929598],"about_ca_topic_score_codex":0.0004524269,"about_ca_topic_score_gemma":0.00060826313,"teacher_disagreement_score":0.00097445317,"about_ca_system_score_codex":0.00026837536,"about_ca_system_score_gemma":0.0003228453,"threshold_uncertainty_score":0.0032598376},"labels":[],"label_agreement":null},{"id":"W4407900133","doi":"10.1109/tmc.2025.3545437","title":"MoCo: Urban User Mobile Contact Detection Based on Cellular Signaling Trace","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Context-Aware Activity Recognition Systems","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; University of Calgary","funders":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Computer science; TRACE (psycholinguistics); Mobile computing; Cellular network; Computer network; Human–computer interaction","score_opus":0.012879257999351773,"score_gpt":0.24567063704613445,"score_spread":0.23279137904678268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407900133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31877282,0.00033081035,0.65911305,0.00028688175,0.00008478243,0.0004422554,0.0027097166,0.008212645,0.010047105],"genre_scores_gemma":[0.8817685,0.0001818519,0.10932535,0.00011208699,0.000047613536,0.0002418933,0.0030942755,0.000101630096,0.00512674],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957687,0.000058379122,0.000017131753,0.000101782374,0.00017604088,0.00006983069],"domain_scores_gemma":[0.99949217,0.00007758438,0.00008213345,0.00010057491,0.0001830328,0.00006458143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022881823,0.0007722442,0.00057610526,0.0017513002,0.0003683722,0.0006182324,0.0008452986,0.00067721005,0.0012638926],"category_scores_gemma":[0.00129943,0.00016714468,0.00023215167,0.0013493154,0.00023042563,0.0008208315,0.0010437508,0.00038351747,0.00097452226],"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":[0.000992157,0.0006583989,0.105903685,0.00037689344,0.00018194197,0.0013224668,0.00057000737,0.073164366,0.10157155,0.010612332,0.018936444,0.6857097],"study_design_scores_gemma":[0.000028004484,0.00020347825,0.0336585,0.00002293013,0.000031567408,0.0007235827,0.00026764342,0.9227872,0.030115524,0.0025632838,0.009551943,0.000046346333],"about_ca_topic_score_codex":0.0054871584,"about_ca_topic_score_gemma":0.009773169,"teacher_disagreement_score":0.0054871584,"about_ca_system_score_codex":0.00041635655,"about_ca_system_score_gemma":0.0005438448,"threshold_uncertainty_score":0.010910392},"labels":[],"label_agreement":null},{"id":"W4407937491","doi":"10.1109/tmc.2025.3545444","title":"Vehicle-Assisted Service Caching for Task Offloading in Vehicular Edge Computing","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","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":"Simon Fraser University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Edge computing; Task (project management); Mobile edge computing; Computer network; Service (business); Mobile computing; Server; Enhanced Data Rates for GSM Evolution; Distributed computing; Cloud computing; Operating system; Telecommunications","score_opus":0.01663381487815505,"score_gpt":0.2727074205078433,"score_spread":0.25607360562968823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407937491","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29977724,0.0016525197,0.683148,0.00045017837,0.00020051411,0.00023050592,0.0002179626,0.0024106102,0.011912494],"genre_scores_gemma":[0.964447,0.00025554796,0.032452103,0.00007458028,0.000022733426,0.000050462866,0.00015972076,0.000043515858,0.0024941608],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951124,0.00008165375,0.000024740151,0.000095015865,0.00008383449,0.00020339548],"domain_scores_gemma":[0.9994616,0.00011498487,0.000035027893,0.00014746112,0.00015334233,0.000087643384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035565434,0.0006069755,0.0007858059,0.0005321923,0.0011911382,0.0011021931,0.0016434188,0.0006095893,0.0013174684],"category_scores_gemma":[0.000969183,0.000274728,0.00033177642,0.0009945276,0.00045100143,0.0014716947,0.00095705094,0.0003789548,0.00029655648],"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":[0.0011417792,0.00033523506,0.0068010073,0.00027135725,0.00012415208,0.0006773298,0.00047499625,0.6954293,0.049287282,0.0552144,0.013337562,0.17690559],"study_design_scores_gemma":[0.000009181267,0.000039155417,0.00028457135,0.0000049291493,0.000016756654,0.000040239775,0.000077003235,0.99081707,0.0040565496,0.0026191713,0.0020222622,0.000013087884],"about_ca_topic_score_codex":0.018213186,"about_ca_topic_score_gemma":0.028428242,"teacher_disagreement_score":0.018213186,"about_ca_system_score_codex":0.0013886304,"about_ca_system_score_gemma":0.0021531824,"threshold_uncertainty_score":0.03621435},"labels":[],"label_agreement":null},{"id":"W4408145244","doi":"10.1109/tmc.2025.3547946","title":"Multi-Agent Moth-Flame Reinforcement Learning Based Broadcast Beam Optimization","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Photonic 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":"Carleton University","funders":"","keywords":"Reinforcement learning; Computer science; Reinforcement; Beam (structure); Artificial intelligence; Materials science; Composite material; Structural engineering; Engineering","score_opus":0.013381880439858171,"score_gpt":0.25794490934524483,"score_spread":0.24456302890538667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408145244","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05714104,0.00035952235,0.93728155,0.00032021303,0.00007988129,0.000081556296,0.000061953586,0.00050087617,0.0041734492],"genre_scores_gemma":[0.90327907,0.00014445675,0.09311778,0.00019345904,0.000030165702,0.00017187872,0.00009087101,0.00004475147,0.002927538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997199,0.000077141405,0.000012821282,0.000052293595,0.00007888435,0.00005880452],"domain_scores_gemma":[0.9987676,0.00078176707,0.00012153624,0.000039016177,0.00022282102,0.000067323446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000998234,0.0009805696,0.0011548509,0.0004811452,0.00041189406,0.0006114379,0.001332932,0.0011369615,0.0017107876],"category_scores_gemma":[0.0022236893,0.0004707914,0.00066648854,0.0003260445,0.00074492145,0.0005513261,0.00092691986,0.0010713685,0.00018177375],"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":[0.000031382086,0.00001729146,0.00038335696,0.000019681736,0.000014704027,0.000027025457,0.000015743271,0.9908146,0.00043337277,0.0011052612,0.00021680335,0.006920672],"study_design_scores_gemma":[0.000005501747,0.000008703995,0.00001880506,0.0000011065466,0.0000017289232,0.000002266326,0.0000016665626,0.999658,0.00006895721,0.00018855027,0.000043628854,0.0000011884347],"about_ca_topic_score_codex":0.012764696,"about_ca_topic_score_gemma":0.007768651,"teacher_disagreement_score":0.012764696,"about_ca_system_score_codex":0.0008163631,"about_ca_system_score_gemma":0.0011892986,"threshold_uncertainty_score":0.02538079},"labels":[],"label_agreement":null},{"id":"W4408423544","doi":"10.1109/tmc.2025.3551537","title":"A Joint Secure Mechanism of Multi-Task Learning for a UAV Team Under FDI Attacks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Adversarial Robustness in Machine Learning","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 Toronto","funders":"National Natural Science Foundation of China","keywords":"Computer science; Joint (building); Task (project management); Mechanism (biology); Computer security; Human–computer interaction; Engineering","score_opus":0.020074393173923146,"score_gpt":0.29529807487008,"score_spread":0.27522368169615685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408423544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027273793,0.00009503154,0.97026104,0.00021948147,0.00003701808,0.000046830504,0.000022158387,0.00054876995,0.0014957769],"genre_scores_gemma":[0.93572897,0.00007114631,0.061270006,0.00012455549,0.00002702308,0.0001116193,0.000042560856,0.000035005363,0.0025891762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916816,0.00014139633,0.000044706525,0.0002472507,0.00020039783,0.00019825058],"domain_scores_gemma":[0.99898714,0.00022708092,0.00019639081,0.0002264814,0.00024028895,0.00012258464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015434221,0.00094186905,0.0007799634,0.0003949286,0.00062808086,0.00083125336,0.0017751474,0.0014124641,0.0019280482],"category_scores_gemma":[0.0026719589,0.00035773308,0.0006475143,0.00025807924,0.0012785126,0.0017506708,0.0028674216,0.0013687636,0.0005017225],"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":[0.0003332231,0.00012971384,0.0019067827,0.000088202505,0.00006460484,0.00025201862,0.00020938038,0.86411774,0.017038804,0.021945857,0.0017311565,0.09218241],"study_design_scores_gemma":[0.0000095268715,0.000060330432,0.00010241746,0.0000035334476,0.0000071636473,0.000021375974,0.000009069804,0.9942427,0.0017952427,0.003511743,0.00023062508,0.000006186845],"about_ca_topic_score_codex":0.0019891888,"about_ca_topic_score_gemma":0.0017623863,"teacher_disagreement_score":0.0019891888,"about_ca_system_score_codex":0.0007677437,"about_ca_system_score_gemma":0.0015327921,"threshold_uncertainty_score":0.0081624985},"labels":[],"label_agreement":null},{"id":"W4408951699","doi":"10.1109/tmc.2025.3555640","title":"A Digital Twin-Based Intelligent Network Architecture for Underwater Acoustic Sensor Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Architecture; Underwater acoustic communication; Underwater; Acoustic sensor; Wireless sensor network; Computer network; Computer architecture; Acoustics","score_opus":0.011479957096929502,"score_gpt":0.23202118813567377,"score_spread":0.22054123103874426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408951699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045847066,0.00026010134,0.9478002,0.00022403029,0.00009009639,0.00005778544,0.00007928355,0.00068916543,0.004952189],"genre_scores_gemma":[0.7577483,0.00034596273,0.23675501,0.00016928032,0.000045855286,0.00013338498,0.00025983248,0.000051638366,0.0044907127],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967575,0.000049169234,0.000029913142,0.000116145566,0.00008742589,0.00004156658],"domain_scores_gemma":[0.9997125,0.000049001676,0.000039542534,0.00006578784,0.000092734495,0.00004035068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003829724,0.00042357272,0.0003431171,0.0005269628,0.0006500011,0.0008036168,0.0012508454,0.00039510295,0.0014263999],"category_scores_gemma":[0.0007845141,0.00019804554,0.00039308757,0.00048158638,0.0006155575,0.0019321928,0.001451529,0.00062132796,0.00027239605],"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":[0.000333035,0.00012156011,0.002655303,0.00017619028,0.00008897072,0.00025919444,0.00042249166,0.657313,0.0434242,0.058260985,0.00371201,0.2332331],"study_design_scores_gemma":[0.000010235283,0.00008601396,0.00020956733,0.000007691525,0.000027212453,0.00007342346,0.000037512982,0.98274153,0.004451034,0.008642933,0.003696015,0.000016945005],"about_ca_topic_score_codex":0.004113487,"about_ca_topic_score_gemma":0.00524837,"teacher_disagreement_score":0.004113487,"about_ca_system_score_codex":0.00080446526,"about_ca_system_score_gemma":0.00090832904,"threshold_uncertainty_score":0.008179128},"labels":[],"label_agreement":null},{"id":"W4409014175","doi":"10.1109/tmc.2025.3556143","title":"Edge Intelligence Enhanced Monte Carlo Tree Search for Virtually Coupled Train Set Optimal Control","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Beijing Jiaotong University; Natural Science Foundation of Beijing Municipality; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Monte Carlo tree search; Monte Carlo method; Set (abstract data type); Tree (set theory); Algorithm; Mathematics; Statistics","score_opus":0.011992438618770775,"score_gpt":0.25474177258446307,"score_spread":0.2427493339656923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409014175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037666835,0.00016602791,0.9572521,0.00017265751,0.000032043503,0.00003559087,0.000022618598,0.00017556925,0.004476617],"genre_scores_gemma":[0.94785064,0.00006528493,0.05047454,0.000092588554,0.000015058669,0.00007515812,0.00003931322,0.0000285446,0.0013588497],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967396,0.000107561755,0.00001243288,0.000051503623,0.00009754069,0.00005707742],"domain_scores_gemma":[0.99919134,0.00048986054,0.00008898676,0.000041228508,0.00012473903,0.00006385328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072818017,0.0004996603,0.0007567075,0.00035953606,0.00038057234,0.0006120886,0.0008571175,0.00074781914,0.0015720573],"category_scores_gemma":[0.0023072504,0.00028687844,0.00035583158,0.00036544836,0.00079124566,0.0005622952,0.00090736593,0.00078657496,0.00014223723],"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":[0.000020088173,0.000010965856,0.00020953068,0.000008649832,0.0000071523377,0.000014604035,0.0000122693755,0.98875964,0.00025753918,0.005241341,0.00016534414,0.005292905],"study_design_scores_gemma":[0.0000019636418,0.0000053147114,0.00001606181,6.8928927e-7,9.831331e-7,0.0000013925406,9.405173e-7,0.9991079,0.00003527801,0.00076759205,0.00006117566,7.502241e-7],"about_ca_topic_score_codex":0.0073304945,"about_ca_topic_score_gemma":0.0056094695,"teacher_disagreement_score":0.0073304945,"about_ca_system_score_codex":0.0007909284,"about_ca_system_score_gemma":0.001249492,"threshold_uncertainty_score":0.01457566},"labels":[],"label_agreement":null},{"id":"W4409076546","doi":"10.1109/tmc.2025.3555322","title":"Joint Adaptation for Mobile 360-Degree Video Streaming and Enhancement","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Image and Video Quality Assessment","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":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Joint (building); Adaptation (eye); Video streaming; Degree (music); Mobile telephony; Multimedia; Real-time computing; Computer network; Mobile radio","score_opus":0.04516044204518072,"score_gpt":0.3184599806217179,"score_spread":0.27329953857653716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409076546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027390286,0.00019931622,0.9704926,0.000057307836,0.000025727999,0.000028208804,0.000019010553,0.00026229295,0.0015253006],"genre_scores_gemma":[0.8722774,0.00035500282,0.12523808,0.00006769231,0.000046936875,0.000046792247,0.00006124233,0.000051217292,0.0018556124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997421,0.000050191808,0.000012335979,0.00007296377,0.00009092099,0.000031472737],"domain_scores_gemma":[0.9996865,0.0001156811,0.000037527156,0.00005035956,0.00008374163,0.000026229302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040198272,0.00051258097,0.00044125883,0.00020118708,0.00018000466,0.00039789814,0.0005569389,0.00029942358,0.00093094865],"category_scores_gemma":[0.0011765447,0.00018385556,0.00038754227,0.00025815502,0.00035380558,0.0006351667,0.0006357612,0.00063666917,0.00026381368],"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":[0.00032718407,0.00012365561,0.0016362777,0.00012379781,0.00006328173,0.00028513547,0.00020252587,0.59605515,0.14419845,0.010699207,0.0014249191,0.24486046],"study_design_scores_gemma":[0.0000039806455,0.0000513775,0.000227432,0.0000032319056,0.0000069838593,0.000059790345,0.000015407342,0.9910711,0.006653291,0.0012513563,0.0006491159,0.0000069804496],"about_ca_topic_score_codex":0.0015958871,"about_ca_topic_score_gemma":0.0015811597,"teacher_disagreement_score":0.0015958871,"about_ca_system_score_codex":0.00030484772,"about_ca_system_score_gemma":0.00033603614,"threshold_uncertainty_score":0.003173232},"labels":[],"label_agreement":null},{"id":"W4409233275","doi":"10.1109/tmc.2025.3558406","title":"FedLFP: Communication-Efficient Personalized Federated Learning on Non-IID Data in Mobile Edge Computing Environments","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Privacy-Preserving Technologies in Data","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":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Mobile computing; Edge computing; Mobile telephony; Mobile edge computing; Enhanced Data Rates for GSM Evolution; Computer network; Distributed computing; Server; Mobile radio; Artificial intelligence","score_opus":0.027574735301641767,"score_gpt":0.29929109086068134,"score_spread":0.27171635555903956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409233275","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032659642,0.0002999994,0.9621131,0.00030210213,0.00005839306,0.00008883455,0.00019292522,0.0034613907,0.0008236961],"genre_scores_gemma":[0.6839864,0.00018069064,0.31145865,0.0005232867,0.000063806845,0.00025539793,0.0011436283,0.00025761506,0.0021303988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983051,0.00049810816,0.000109260516,0.0005239343,0.00035135943,0.00021227234],"domain_scores_gemma":[0.99580437,0.0016799887,0.00021146837,0.0014677973,0.0006465176,0.0001899403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028213528,0.0011804962,0.0016131095,0.00084618665,0.0011298723,0.0015352863,0.0036392305,0.002040841,0.0013390394],"category_scores_gemma":[0.0106344195,0.0005487122,0.00090191327,0.0012233986,0.001137877,0.0048569194,0.003969686,0.0023179948,0.00069846533],"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":[0.0006845863,0.00045866676,0.003298671,0.00017021551,0.00013294227,0.00033949956,0.0003052442,0.59307325,0.006843256,0.007817741,0.007899407,0.37897646],"study_design_scores_gemma":[0.000025031151,0.000060433937,0.00016594667,0.0000064579403,0.000007114406,0.0000645084,0.000041531595,0.98885787,0.0019043778,0.008295288,0.00056179846,0.000009603509],"about_ca_topic_score_codex":0.0040804357,"about_ca_topic_score_gemma":0.004472155,"teacher_disagreement_score":0.0040804357,"about_ca_system_score_codex":0.0010793046,"about_ca_system_score_gemma":0.0016145787,"threshold_uncertainty_score":0.01492095},"labels":[],"label_agreement":null},{"id":"W4409257386","doi":"10.1109/tmc.2025.3558793","title":"Plugging and Breathing on the Air: A Practical Defense System for Deep Learning-Based Wireless Semantic Communications","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","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":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China; National Research Foundation","keywords":"Computer science; Wireless; Artificial intelligence; Computer security; Multimedia; Telecommunications","score_opus":0.017428509419552717,"score_gpt":0.27211242333557556,"score_spread":0.25468391391602285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409257386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015890736,0.0001684263,0.9760448,0.00046898305,0.00007321155,0.000043594708,0.000043645377,0.0045464113,0.0027202792],"genre_scores_gemma":[0.7532143,0.00029616608,0.23576806,0.0009449661,0.00007210644,0.00013813582,0.00023048394,0.00023830998,0.009097467],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996495,0.000088950765,0.000017711754,0.000065737964,0.00011743562,0.000060654933],"domain_scores_gemma":[0.9995876,0.00013030664,0.000046999183,0.00011282155,0.00008585098,0.000036553087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008871919,0.00075541105,0.00046329395,0.0003976725,0.00040964066,0.0006560026,0.00118797,0.0010411955,0.0019620846],"category_scores_gemma":[0.0014057725,0.00026351504,0.00034801802,0.00022507852,0.0010495511,0.0019158178,0.0019827008,0.0013942403,0.0007107469],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082048326,0.0002815624,0.0021448631,0.00022512791,0.00014991325,0.0005073515,0.0004278395,0.22076377,0.07099796,0.06598031,0.018708505,0.6189924],"study_design_scores_gemma":[0.000026769047,0.00016765538,0.00026466558,0.000023033026,0.00002903769,0.00013980574,0.00004733499,0.93736535,0.03378452,0.017436046,0.010689377,0.000026496846],"about_ca_topic_score_codex":0.0012545371,"about_ca_topic_score_gemma":0.0017880277,"teacher_disagreement_score":0.0019620846,"about_ca_system_score_codex":0.0006050227,"about_ca_system_score_gemma":0.00073518284,"threshold_uncertainty_score":0.0065638423},"labels":[],"label_agreement":null},{"id":"W4409327552","doi":"10.1109/tmc.2025.3559676","title":"Multi-Task Reinforcement Learning-Based Multiple Access for Dynamic Wireless Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Reinforcement learning; Task (project management); Wireless; Wireless network; Computer network; Distributed computing; Artificial intelligence; Telecommunications","score_opus":0.01090824097278315,"score_gpt":0.2607953289238036,"score_spread":0.24988708795102044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409327552","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01908271,0.00035580105,0.9782534,0.000185597,0.00006914041,0.000045948735,0.000016839846,0.0003128509,0.0016777728],"genre_scores_gemma":[0.94434965,0.00021685792,0.053442042,0.00013323444,0.000062034684,0.00012772967,0.00004167283,0.0000308772,0.0015958231],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993647,0.00024319404,0.000030759365,0.00015308785,0.0001189645,0.000089344416],"domain_scores_gemma":[0.9986381,0.0007875838,0.00018676362,0.00009237617,0.00021354333,0.00008163932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016460199,0.0008215072,0.0009061845,0.00032671753,0.00043940332,0.0005111541,0.0012444011,0.0006135815,0.0010508642],"category_scores_gemma":[0.0027504524,0.00024485862,0.00038356046,0.0004037917,0.0006988245,0.0007401144,0.0008280591,0.0013413421,0.00018137522],"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":[0.000088682296,0.000090862515,0.00046114472,0.000055757446,0.000043756005,0.00006308062,0.000056644574,0.95417655,0.0020296755,0.005990456,0.0008424903,0.036100887],"study_design_scores_gemma":[0.0000070482242,0.000024555222,0.000036623172,0.0000011584922,0.0000035098356,0.0000068666577,0.0000023739317,0.9982173,0.00016958122,0.0013973558,0.00013097763,0.0000026344974],"about_ca_topic_score_codex":0.004582185,"about_ca_topic_score_gemma":0.0041874484,"teacher_disagreement_score":0.004582185,"about_ca_system_score_codex":0.000704357,"about_ca_system_score_gemma":0.00091597496,"threshold_uncertainty_score":0.009111047},"labels":[],"label_agreement":null},{"id":"W4409882680","doi":"10.1109/tmc.2025.3564843","title":"WiCG: In-Body Cardiac Motion Sensing Based on a Mix-Medium Wi-Fi Fresnel Zone Model","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Non-Invasive Vital Sign Monitoring","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":"McMaster University","funders":"European Commission","keywords":"Computer science; Fresnel zone; Computer graphics (images); Optics; Physics","score_opus":0.00866874640406717,"score_gpt":0.22921922985279142,"score_spread":0.22055048344872424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409882680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019922081,0.0007060738,0.97564054,0.00013600165,0.000048409293,0.0000727389,0.00014166569,0.0004791442,0.0028533565],"genre_scores_gemma":[0.734422,0.002133521,0.25365415,0.0002833376,0.000090393434,0.00035226607,0.0005085666,0.00013785205,0.008417913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997181,0.00006540261,0.000007919148,0.000058815465,0.00012367364,0.000026070522],"domain_scores_gemma":[0.99984944,0.00006764105,0.000023286653,0.000016569356,0.000033425564,0.000009723582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027125803,0.0008563502,0.00035338098,0.0004975674,0.00018101775,0.0004967876,0.0011657826,0.00089425256,0.001073785],"category_scores_gemma":[0.0006046875,0.0002128021,0.0005346386,0.00043812074,0.00042110076,0.00078318163,0.00054014236,0.00054969394,0.00055736216],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043145876,0.00020706869,0.004221827,0.00034974262,0.00013963481,0.00044015568,0.0002480657,0.60585433,0.19873083,0.032575898,0.003977107,0.15282398],"study_design_scores_gemma":[0.000006994329,0.00004867785,0.00043047665,0.000007136035,0.000012078272,0.000106735686,0.000007192121,0.99221236,0.004469722,0.0011441271,0.001539465,0.0000149792195],"about_ca_topic_score_codex":0.004069952,"about_ca_topic_score_gemma":0.0033199496,"teacher_disagreement_score":0.004069952,"about_ca_system_score_codex":0.0004628261,"about_ca_system_score_gemma":0.00038963376,"threshold_uncertainty_score":0.008092523},"labels":[],"label_agreement":null},{"id":"W4409916910","doi":"10.1109/tmc.2025.3563345","title":"BAT: A Versatile Bipartite Attention-Based Approach for Comprehensive Truth Inference in Mobile Crowdsourcing","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Crowdsensing and Crowdsourcing","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":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Crowdsourcing; Computer science; Inference; Bipartite graph; Artificial intelligence; Ground truth; Mobile computing; World Wide Web; Theoretical computer science; Computer network; Graph","score_opus":0.022646144104512447,"score_gpt":0.2863331307574352,"score_spread":0.2636869866529228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409916910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005033736,0.0002241143,0.99134904,0.0003828203,0.0000547761,0.000098061064,0.0002754359,0.0011371253,0.001444883],"genre_scores_gemma":[0.5298967,0.0005166886,0.45576936,0.0010368496,0.0003710731,0.0004919292,0.0021132445,0.00057582604,0.009228373],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99709153,0.0011449258,0.00010920286,0.00087644305,0.00055291865,0.00022485877],"domain_scores_gemma":[0.9941399,0.0035751336,0.00040805695,0.0006222434,0.00091902877,0.00033562715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003903312,0.0014278452,0.0017790393,0.0029540674,0.0013089161,0.0018244479,0.004976647,0.0029469025,0.00545914],"category_scores_gemma":[0.015796404,0.0010011237,0.001617643,0.0025408778,0.0017924776,0.0038859304,0.0047956198,0.0030312827,0.0015668028],"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":[0.0006053232,0.00031577682,0.0034059887,0.00044172633,0.00025695327,0.0003366043,0.0011613806,0.54004616,0.0054320367,0.0825869,0.011629193,0.35378185],"study_design_scores_gemma":[0.000016280856,0.000025524523,0.00024009113,0.000021286469,0.000018655628,0.0000274009,0.000043514017,0.9435124,0.00052451825,0.054235164,0.0013190123,0.000016158425],"about_ca_topic_score_codex":0.030943457,"about_ca_topic_score_gemma":0.02945882,"teacher_disagreement_score":0.030943457,"about_ca_system_score_codex":0.0024616416,"about_ca_system_score_gemma":0.0029212767,"threshold_uncertainty_score":0.061526656},"labels":[],"label_agreement":null},{"id":"W4410086631","doi":"10.1109/tmc.2025.3567179","title":"Mixture-of-Experts as Continual Knowledge Adapter for Mobile Vision Understanding","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Image Retrieval and Classification 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":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Adapter (computing); Mobile computing; Mobile telephony; Human–computer interaction; Multimedia; Computer security; Telecommunications; Mobile radio; Operating system","score_opus":0.0254552294492189,"score_gpt":0.3292681171957355,"score_spread":0.3038128877465166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410086631","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03094428,0.0002962007,0.96503884,0.00016318742,0.00003854124,0.000055746572,0.000043347623,0.0018395283,0.001580281],"genre_scores_gemma":[0.6402057,0.0003210637,0.35459962,0.0003453785,0.000057285783,0.00012615517,0.00025473913,0.00019118823,0.0038987913],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942786,0.00008267408,0.000027356413,0.00016673697,0.00019738589,0.00009793672],"domain_scores_gemma":[0.9993037,0.00019462351,0.000067205845,0.0001995966,0.00016924347,0.000065557615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012517864,0.0008338995,0.0007443371,0.00089524966,0.00040980495,0.0009151067,0.0021304118,0.001654165,0.0017050665],"category_scores_gemma":[0.0030780376,0.00057208125,0.0009583155,0.0006442957,0.0007349023,0.003013756,0.0028040467,0.0018714078,0.00076850166],"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":[0.0005217367,0.00028263705,0.0034799303,0.0001497376,0.00019001025,0.00051852025,0.00065331877,0.3056789,0.047849696,0.015189405,0.004881361,0.6206047],"study_design_scores_gemma":[0.000008156017,0.000049518952,0.00041717177,0.0000109135,0.000019650428,0.00012689056,0.00004595483,0.9815252,0.010167057,0.0055613383,0.0020507015,0.000017350738],"about_ca_topic_score_codex":0.003550373,"about_ca_topic_score_gemma":0.003673823,"teacher_disagreement_score":0.003550373,"about_ca_system_score_codex":0.00068791077,"about_ca_system_score_gemma":0.0007602199,"threshold_uncertainty_score":0.007059455},"labels":[],"label_agreement":null},{"id":"W4410294515","doi":"10.1109/tmc.2025.3569547","title":"TraCemop: Toward Federated Learning With Traceable Contribution Evaluation and Model Ownership Protection","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Key Research and Development Projects of Shaanxi Province; National Natural Science Foundation of China","keywords":"Computer science; Computer security; Federated learning; Distributed computing","score_opus":0.03738398925649043,"score_gpt":0.2903370247470432,"score_spread":0.25295303549055276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410294515","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015494441,0.00022590497,0.97871417,0.00047905312,0.000030247002,0.00014305404,0.000121413934,0.0040106666,0.0007810511],"genre_scores_gemma":[0.54667544,0.00016741366,0.4491653,0.00048217134,0.000059033813,0.00029243805,0.0008606289,0.0003808608,0.001916807],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9882693,0.0049153594,0.0006629147,0.0020818885,0.0033859713,0.00068450085],"domain_scores_gemma":[0.9798008,0.0054555736,0.0013930802,0.010120648,0.0024425304,0.00078728254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016053556,0.0015234519,0.0017767712,0.0020513593,0.0013075593,0.0033174404,0.0054589976,0.002587914,0.0017009889],"category_scores_gemma":[0.034816895,0.0007373495,0.0015993759,0.0017965665,0.0024010318,0.008863133,0.011523374,0.004281832,0.00061589817],"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":[0.00097433815,0.0011088984,0.012326498,0.00031127615,0.00037637178,0.0005456464,0.00079324935,0.3377131,0.006667762,0.047659233,0.008612476,0.58291113],"study_design_scores_gemma":[0.000038535098,0.00008680623,0.0003528374,0.000027623622,0.000025189016,0.00009679697,0.000063223684,0.94636333,0.004000546,0.04707863,0.0018464117,0.000020038968],"about_ca_topic_score_codex":0.0031051135,"about_ca_topic_score_gemma":0.0035366963,"teacher_disagreement_score":0.016053556,"about_ca_system_score_codex":0.0022253129,"about_ca_system_score_gemma":0.00459846,"threshold_uncertainty_score":0.08490032},"labels":[],"label_agreement":null},{"id":"W4410294847","doi":"10.1109/tmc.2025.3569407","title":"Efficient Model Training in Edge Networks With Hierarchical Split Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Machine Learning and ELM","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 Waterloo","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Training (meteorology); Enhanced Data Rates for GSM Evolution; Artificial intelligence; Machine learning","score_opus":0.01048774381927405,"score_gpt":0.2536253834284963,"score_spread":0.24313763960922222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410294847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03598565,0.0003756367,0.9589715,0.00025884376,0.000041225965,0.00007267664,0.00011415946,0.0025652822,0.0016149988],"genre_scores_gemma":[0.692235,0.00024438766,0.30013168,0.00055380055,0.000080595484,0.00019482274,0.0012594974,0.00029001082,0.0050101755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991697,0.0002360657,0.000040787963,0.00024311482,0.00017123902,0.00013914441],"domain_scores_gemma":[0.9985655,0.00061899173,0.000105007035,0.00034228156,0.00025220553,0.00011600618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012675081,0.0013815003,0.0014314981,0.0008349399,0.00065634784,0.00086792273,0.003113522,0.0017921551,0.004086478],"category_scores_gemma":[0.0038642038,0.00077933865,0.0008214708,0.0008180322,0.00080198154,0.0035530357,0.00263323,0.002549514,0.0013399796],"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":[0.0003870633,0.00019919999,0.0020449713,0.00008471208,0.00009611796,0.00012189143,0.0001226351,0.7300258,0.0038743117,0.006413807,0.004041671,0.25258777],"study_design_scores_gemma":[0.000004428854,0.000014465839,0.000044297012,0.0000013106221,0.0000033919562,0.000005761581,0.000004351957,0.9980489,0.00038103398,0.0013592277,0.00013108896,0.000001828963],"about_ca_topic_score_codex":0.007080427,"about_ca_topic_score_gemma":0.009747121,"teacher_disagreement_score":0.007080427,"about_ca_system_score_codex":0.00079818745,"about_ca_system_score_gemma":0.001142855,"threshold_uncertainty_score":0.014078438},"labels":[],"label_agreement":null},{"id":"W4410428100","doi":"10.1109/tmc.2025.3571186","title":"Optimizing Mobile-Friendly Viewport Prediction for Live 360-Degree Video Streaming","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Image and Video Quality Assessment","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":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Viewport; Computer science; Degree (music); Video streaming; Mobile telephony; Multimedia; Mobile computing; Mobile radio; Computer network; Computer graphics (images)","score_opus":0.027683589066853267,"score_gpt":0.3148080385263113,"score_spread":0.28712444945945803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410428100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07602831,0.00047703207,0.9194655,0.0001590217,0.00006360355,0.000047324287,0.00014693715,0.0015803891,0.0020319032],"genre_scores_gemma":[0.90531754,0.00021891837,0.09290231,0.00008143852,0.00004033806,0.000043854336,0.000238629,0.0000912625,0.0010656848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987006,0.000014573814,0.0000058137884,0.000042850144,0.00004465805,0.000021915515],"domain_scores_gemma":[0.9997696,0.00007166423,0.000025165751,0.000029039727,0.00007984506,0.000024653973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000263152,0.00068642356,0.0005026703,0.00026521992,0.00019180987,0.0004108417,0.00072766613,0.00036615,0.0012211105],"category_scores_gemma":[0.0012493863,0.00023192355,0.00027980306,0.00025268638,0.00022106602,0.0008632212,0.00042860844,0.0007949391,0.00033770953],"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":[0.00041719322,0.00018958356,0.0045472793,0.00012636089,0.00005893085,0.0002543619,0.00015278591,0.54160166,0.0687944,0.0035302797,0.004481915,0.37584522],"study_design_scores_gemma":[0.000004379186,0.000022247954,0.00029392898,0.000002995656,0.0000043177984,0.000021558131,0.000007948405,0.9958917,0.0029608614,0.0005423967,0.00024389956,0.0000037439202],"about_ca_topic_score_codex":0.00594565,"about_ca_topic_score_gemma":0.006409497,"teacher_disagreement_score":0.00594565,"about_ca_system_score_codex":0.00043608656,"about_ca_system_score_gemma":0.00057979405,"threshold_uncertainty_score":0.011822104},"labels":[],"label_agreement":null},{"id":"W4410428120","doi":"10.1109/tmc.2025.3570851","title":"Multi-Variate Time Series Prediction of Traffic and Users for Dynamic RRH-BBU Mapping in C-RAN","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","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 Waterloo","funders":"Key Research and Development Program of Hunan Province of China; Natural Science Foundation of Hainan Province; National Natural Science Foundation of China","keywords":"Computer science; Series (stratigraphy); Computer network; Time series; Random variate; Data mining; Machine learning; Mathematics","score_opus":0.022833299374764835,"score_gpt":0.26437327307352865,"score_spread":0.24153997369876382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410428120","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29320183,0.00044207607,0.702733,0.00092373503,0.00009887898,0.000060397415,0.0004216361,0.0008456241,0.0012728149],"genre_scores_gemma":[0.9633942,0.00019725227,0.034953002,0.00007691431,0.0000478349,0.000052878167,0.00053784664,0.000035663295,0.0007043641],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957436,0.00011430354,0.000026812808,0.00012968801,0.00009207472,0.00006266353],"domain_scores_gemma":[0.99820197,0.0011072509,0.00024802517,0.000112219066,0.00023460237,0.00009593828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014229009,0.00093583105,0.0008433963,0.0006694474,0.0005188054,0.0006354825,0.0011820439,0.00084275275,0.0008221775],"category_scores_gemma":[0.004119126,0.0005100495,0.00064961496,0.0007781735,0.00046218047,0.0008798359,0.0006396424,0.0018676405,0.00022592909],"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":[0.000060157738,0.00005767344,0.004657987,0.000018815004,0.000024975468,0.000042161966,0.00002291587,0.97866803,0.00078031933,0.0018421868,0.00048447508,0.01334024],"study_design_scores_gemma":[5.224801e-7,0.0000020737077,0.00013637697,5.1143405e-7,9.155236e-7,0.0000012305661,0.0000013588907,0.99964607,0.000047324425,0.0001463537,0.000016075212,0.0000011348773],"about_ca_topic_score_codex":0.025123335,"about_ca_topic_score_gemma":0.018806402,"teacher_disagreement_score":0.025123335,"about_ca_system_score_codex":0.0008110432,"about_ca_system_score_gemma":0.0011695966,"threshold_uncertainty_score":0.049954176},"labels":[],"label_agreement":null},{"id":"W4410771506","doi":"10.1109/tmc.2025.3574065","title":"Characterizing and Scheduling of Diffusion Process for Text-to-Image Generation in Edge Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Computing and Algorithms","field":"Social Sciences","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 Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Scheduling (production processes); Process (computing); Artificial intelligence; Distributed computing; Mathematical optimization","score_opus":0.020854987264623946,"score_gpt":0.3310176194665385,"score_spread":0.3101626322019146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410771506","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29471368,0.00016274727,0.70032465,0.00025198475,0.000045487614,0.00018695419,0.00014661586,0.0020107026,0.0021571575],"genre_scores_gemma":[0.9232567,0.00007790348,0.07487788,0.000061279956,0.000016692962,0.000068998525,0.00016990033,0.000102002385,0.0013685853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994211,0.00013183226,0.000037372352,0.0001647922,0.00013218577,0.0001127421],"domain_scores_gemma":[0.9981597,0.0008328835,0.00016016027,0.00024249643,0.00041195037,0.00019279188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009973339,0.00046826227,0.00053611794,0.00046842473,0.00089238386,0.0009925748,0.0010602353,0.0006984916,0.0016086119],"category_scores_gemma":[0.004150865,0.00028103447,0.00031795134,0.00050516805,0.00040001346,0.0013309092,0.000786951,0.00079751346,0.0004972893],"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":[0.0011725269,0.000503695,0.012856514,0.00012052965,0.00005088121,0.00040447083,0.00053642987,0.7187628,0.077000335,0.018979762,0.0042091976,0.16540287],"study_design_scores_gemma":[0.0000058578903,0.000021079226,0.00026845429,9.33352e-7,0.0000033012316,0.000020695168,0.000019172567,0.99362034,0.004531943,0.0012473823,0.00025597675,0.000004893284],"about_ca_topic_score_codex":0.009655468,"about_ca_topic_score_gemma":0.008943203,"teacher_disagreement_score":0.009655468,"about_ca_system_score_codex":0.0016070221,"about_ca_system_score_gemma":0.001390493,"threshold_uncertainty_score":0.019198537},"labels":[],"label_agreement":null},{"id":"W4410808519","doi":"10.1109/tmc.2025.3574740","title":"Toward Deterministic Satellite-Terrestrial Integrated Networks via Resource Adaptation and Differentiated Scheduling","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Satellite Communication Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Computer science; Scheduling (production processes); Distributed computing; Satellite; Adaptation (eye); Communications satellite; Computer network","score_opus":0.028640813480337618,"score_gpt":0.2497313255970823,"score_spread":0.2210905121167447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410808519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038237978,0.00020085198,0.958104,0.0002102437,0.00004072908,0.00004074291,0.000030790576,0.00027324673,0.002861353],"genre_scores_gemma":[0.84320587,0.00020507327,0.1542006,0.0001715356,0.00003534076,0.00006861532,0.00007309191,0.000036706053,0.0020032446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951696,0.00012904595,0.000022304146,0.00010939722,0.00012416832,0.00009818665],"domain_scores_gemma":[0.9995141,0.00015754251,0.000100813355,0.00007454141,0.00009616284,0.00005680218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081074145,0.0006434044,0.0004111712,0.00029134811,0.0004483096,0.0006472434,0.001164449,0.0005140442,0.00070032256],"category_scores_gemma":[0.0014154635,0.00028909583,0.00032343064,0.00059065217,0.00057877204,0.0008657784,0.0009719446,0.0010151308,0.00018191195],"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":[0.00006986266,0.000042339467,0.00047179047,0.000022931094,0.000022863487,0.000047779376,0.000047119713,0.946735,0.004128768,0.014651338,0.0008848721,0.032875333],"study_design_scores_gemma":[0.0000054708853,0.000016406671,0.00005014579,0.0000013989612,0.000004246625,0.0000085635,0.000006314227,0.9957877,0.00058269384,0.0031077818,0.00042632906,0.000002926539],"about_ca_topic_score_codex":0.0068082865,"about_ca_topic_score_gemma":0.009151146,"teacher_disagreement_score":0.0068082865,"about_ca_system_score_codex":0.0012081008,"about_ca_system_score_gemma":0.0018058848,"threshold_uncertainty_score":0.013537288},"labels":[],"label_agreement":null},{"id":"W4411550741","doi":"10.1109/tmc.2025.3582284","title":"Video Conferencing With Predictive Generation and Collaborative Computation Across Mobile Headsets","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Innovative Human-Technology Interaction","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":"Simon Fraser University; McGill University","funders":"","keywords":"Computer science; Teleconference; Videoconferencing; Multimedia; Mobile telephony; Mobile computing; Computer network; Computation; Mobile device; Human–computer interaction; Mobile radio; World Wide Web; Algorithm","score_opus":0.01329185020259089,"score_gpt":0.3030047322856835,"score_spread":0.2897128820830926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411550741","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08724794,0.00015930134,0.90173334,0.00019933797,0.000079747464,0.00021556842,0.00005866928,0.0026852125,0.0076208576],"genre_scores_gemma":[0.8721012,0.00010462867,0.12337345,0.000113489856,0.00006384189,0.0001668236,0.000087125314,0.000104505605,0.003884988],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994204,0.00016828017,0.000025991942,0.00010684976,0.00020557936,0.00007288661],"domain_scores_gemma":[0.9994941,0.00021522463,0.000036392623,0.00011171148,0.0000843405,0.000058244386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046598975,0.00063531235,0.0004967933,0.00041830374,0.0004781163,0.0008967048,0.001330045,0.0006227315,0.003351188],"category_scores_gemma":[0.0016327307,0.00024732033,0.0004039143,0.0002731419,0.0004692238,0.0008383344,0.0021855098,0.00063096674,0.00052431517],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001314332,0.0007551267,0.0026813904,0.00023980958,0.00015849191,0.0017092885,0.00152443,0.20678508,0.3572108,0.032786462,0.0071270433,0.38770768],"study_design_scores_gemma":[0.00010209726,0.00036438133,0.0007816413,0.000015533855,0.000038902417,0.00047568034,0.00013442477,0.93850434,0.04613108,0.0074689267,0.0059194304,0.00006353265],"about_ca_topic_score_codex":0.0016958645,"about_ca_topic_score_gemma":0.0013396868,"teacher_disagreement_score":0.003351188,"about_ca_system_score_codex":0.0003122652,"about_ca_system_score_gemma":0.00037800154,"threshold_uncertainty_score":0.011210799},"labels":[],"label_agreement":null},{"id":"W4411599731","doi":"10.1109/tmc.2025.3582864","title":"Embodied AI-Enhanced Vehicular Networks: An Integrated Vision Language Models and Reinforcement Learning Method","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Transportation and Mobility Innovations","field":"Engineering","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":"University of Waterloo","funders":"Chiang Mai University; Ministry of Science and ICT, South Korea; Ministry of Education - Singapore; National Research Foundation","keywords":"Embodied cognition; Computer science; Reinforcement learning; Human–computer interaction; Artificial intelligence; Cognitive science; Psychology","score_opus":0.008551245628869705,"score_gpt":0.28536783881716715,"score_spread":0.27681659318829743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411599731","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023642821,0.00022874457,0.9732592,0.00020268757,0.000036787533,0.000026071097,0.000021160631,0.00016543997,0.0024171155],"genre_scores_gemma":[0.93575704,0.00016170541,0.061098147,0.000114632094,0.000025753077,0.00007224227,0.000035031764,0.000032053278,0.0027034644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996458,0.00012045466,0.000013690659,0.00007399082,0.00008710952,0.000058873273],"domain_scores_gemma":[0.99920195,0.000476004,0.00009591888,0.00004291206,0.00013335203,0.0000498797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073804654,0.0006097235,0.00061975035,0.00033758543,0.0002634088,0.0007790004,0.0012330842,0.00073589326,0.0009997283],"category_scores_gemma":[0.0025279906,0.00033095223,0.0004081725,0.00033159967,0.00076822995,0.0010779907,0.0010882885,0.0010406228,0.00018656292],"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":[0.000028989793,0.000022566013,0.00023120118,0.000024637971,0.000015922436,0.000046186862,0.00005392633,0.97553885,0.0013059299,0.007543496,0.00025429632,0.014934071],"study_design_scores_gemma":[0.0000024260091,0.000010647527,0.000018454757,0.0000013849227,0.000002230492,0.0000043730925,0.0000036388053,0.9980166,0.00016923083,0.0016659516,0.00010303353,0.0000021056678],"about_ca_topic_score_codex":0.0071511026,"about_ca_topic_score_gemma":0.004120087,"teacher_disagreement_score":0.0071511026,"about_ca_system_score_codex":0.00090327725,"about_ca_system_score_gemma":0.001009085,"threshold_uncertainty_score":0.014218926},"labels":[],"label_agreement":null},{"id":"W4412081587","doi":"10.1109/tmc.2025.3586441","title":"Fed$n$nP: Federated Unlearning With Multiple Client Set Partitions","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Data Compression Techniques","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 Guelph","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions","keywords":"Computer science; Set (abstract data type); Theoretical computer science; Distributed computing; Computer network; Programming language","score_opus":0.014784199580807612,"score_gpt":0.2867042894395307,"score_spread":0.27192008985872307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412081587","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045248408,0.0001885342,0.94558626,0.0004423415,0.00007168547,0.00017305228,0.00019643139,0.0059863334,0.0021068896],"genre_scores_gemma":[0.59738135,0.00008268789,0.39682317,0.0005372743,0.000053686454,0.00029605263,0.0008712378,0.00038166804,0.003572887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973911,0.0008621543,0.00015051667,0.0006537121,0.00060433266,0.00033808616],"domain_scores_gemma":[0.9937651,0.0023795583,0.0003018358,0.002313109,0.00095900625,0.00028136442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034008585,0.0010663671,0.0014932525,0.00069627346,0.0014729481,0.0016937959,0.0036181065,0.0016334171,0.0028028856],"category_scores_gemma":[0.012114735,0.00054835767,0.00086112507,0.0011437045,0.0013083689,0.004987359,0.0038270713,0.0023980218,0.0008829235],"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":[0.0009220556,0.00055133435,0.0040705665,0.000092675844,0.000108215514,0.00025395857,0.0002729321,0.55459005,0.004106454,0.017760938,0.010944949,0.4063259],"study_design_scores_gemma":[0.000018536392,0.000034705627,0.000105111794,0.000003971556,0.000005538128,0.00003792294,0.000021996246,0.9868063,0.0025289834,0.009770635,0.0006591434,0.0000072205808],"about_ca_topic_score_codex":0.006525318,"about_ca_topic_score_gemma":0.008010698,"teacher_disagreement_score":0.006525318,"about_ca_system_score_codex":0.0017922566,"about_ca_system_score_gemma":0.0025391963,"threshold_uncertainty_score":0.017985702},"labels":[],"label_agreement":null},{"id":"W4412536710","doi":"10.1109/tmc.2025.3591016","title":"An Underwater Secure Localization Scheme Based on Physical Layer Cryptographic Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Underwater Vehicles and 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":"University of Ottawa","funders":"Tianjin Science and Technology Program; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Computer science; Cryptography; Physical layer; Scheme (mathematics); Computer network; Cryptographic primitive; Layer (electronics); Cryptographic protocol; Computer security; Telecommunications; Wireless; Mathematics","score_opus":0.010047616246897097,"score_gpt":0.2513022671787013,"score_spread":0.2412546509318042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412536710","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02533206,0.00022951691,0.97138286,0.00020689161,0.00006388814,0.00006191501,0.000029617613,0.00043142168,0.0022618326],"genre_scores_gemma":[0.88120747,0.00032160207,0.11439151,0.00013848352,0.000039737006,0.000095993906,0.00006870498,0.00002533244,0.0037111698],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941385,0.00010803601,0.00004777346,0.0001156982,0.00023025731,0.00008434419],"domain_scores_gemma":[0.9994524,0.00010447892,0.00014778695,0.00015232808,0.000110634544,0.000032407257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046265585,0.00044332288,0.0005236005,0.0004525233,0.0006567871,0.0005554299,0.0011172418,0.0007534139,0.0013156672],"category_scores_gemma":[0.0010443671,0.00020370045,0.00041282782,0.00054581166,0.000672332,0.0019294582,0.0021485556,0.0007993615,0.00054999476],"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":[0.0007922416,0.00019900479,0.0020425757,0.00041025248,0.00013991098,0.00078686874,0.0005134803,0.27687213,0.1832176,0.15736595,0.0045279767,0.3731321],"study_design_scores_gemma":[0.00007479386,0.00029988788,0.00028334928,0.000023931536,0.00004304256,0.00047576142,0.000062101164,0.9452173,0.035058513,0.0132964915,0.0051123425,0.000052500895],"about_ca_topic_score_codex":0.00053373154,"about_ca_topic_score_gemma":0.0004910914,"teacher_disagreement_score":0.0013156672,"about_ca_system_score_codex":0.00056632404,"about_ca_system_score_gemma":0.0008474075,"threshold_uncertainty_score":0.004401326},"labels":[],"label_agreement":null},{"id":"W4412567070","doi":"10.1109/tmc.2025.3591822","title":"An Overlapping Coalition Game Approach for Collaborative Block Mining and Edge Task Offloading in MEC-Assisted Blockchain Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":2,"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":"Basic and Applied Basic Research Foundation of Guangdong Province; National Key Research and Development Program of China; Ministry of Education, India; Queen's University; Nanyang Technological University; Info-communications Media Development Authority; National Research Foundation Singapore; Natural Science Foundation of Guangdong Province; National Research Foundation; Queen's University Belfast","keywords":"Computer science; Blockchain; Block (permutation group theory); Mobile edge computing; Task (project management); Enhanced Data Rates for GSM Evolution; Edge computing; Computer network; Game theory; Computer security; Distributed computing; Server; Artificial intelligence","score_opus":0.012603391807090983,"score_gpt":0.2607983203611138,"score_spread":0.2481949285540228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412567070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026543707,0.00022646473,0.96186507,0.0003964457,0.00006706611,0.00020506461,0.00017787545,0.00009446091,0.010423873],"genre_scores_gemma":[0.88024515,0.00049488316,0.105996214,0.00018378136,0.000060899798,0.00048594616,0.00022241766,0.000061127655,0.012249603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982533,0.00069326337,0.00007333778,0.00033652026,0.0003153787,0.00032823265],"domain_scores_gemma":[0.9968862,0.0020123897,0.00030273528,0.00016278739,0.00033406165,0.00030185425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001950813,0.0012191737,0.001700622,0.0007712627,0.0011626009,0.0019856687,0.0026891402,0.0020350232,0.0060169734],"category_scores_gemma":[0.0054961694,0.00065345643,0.0011975904,0.0009838509,0.0018283126,0.002939872,0.0024954088,0.0019248988,0.00055941864],"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":[0.00012910149,0.000080397745,0.0008419936,0.000121617835,0.00006608717,0.0005437764,0.00023700918,0.82780504,0.0018635053,0.15382819,0.0016459264,0.012837437],"study_design_scores_gemma":[0.0000147212895,0.00001933437,0.00005348236,0.000007217365,0.000007916496,0.000032592496,0.000034305704,0.97505933,0.00014247431,0.023955781,0.0006636222,0.000009228254],"about_ca_topic_score_codex":0.008774852,"about_ca_topic_score_gemma":0.010813332,"teacher_disagreement_score":0.008774852,"about_ca_system_score_codex":0.001935797,"about_ca_system_score_gemma":0.0026969626,"threshold_uncertainty_score":0.020128787},"labels":[],"label_agreement":null},{"id":"W4412718920","doi":"10.1109/tmc.2025.3588474","title":"Minimizing Age of Semantic Information for Analytics-Oriented Video Streaming Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Age of Information Optimization","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 Victoria","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Analytics; Video streaming; Multimedia; World Wide Web; Computer network; Data science","score_opus":0.009948387836885798,"score_gpt":0.24869344685170364,"score_spread":0.23874505901481785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412718920","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08531256,0.0020512997,0.9070877,0.00058894773,0.00013755557,0.00017759639,0.0002607599,0.002003942,0.0023796787],"genre_scores_gemma":[0.7776272,0.00096085184,0.21894896,0.00020891352,0.00019919804,0.00012193559,0.00054273143,0.00022609498,0.0011640076],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987435,0.00029241035,0.00010637997,0.00023832629,0.00050310977,0.0001162078],"domain_scores_gemma":[0.995216,0.002525504,0.000572953,0.0006011561,0.00086595945,0.00021846287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024882937,0.0013357685,0.0011674373,0.0010409203,0.0005269304,0.0013269035,0.0012140567,0.0007894484,0.0008940844],"category_scores_gemma":[0.01385377,0.00033897345,0.0003504702,0.001113489,0.0007029149,0.0039016826,0.001280482,0.0016481425,0.00029963034],"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":[0.00085929525,0.00033616117,0.005589233,0.00030346023,0.000102251,0.00018724173,0.000291883,0.44559512,0.03126986,0.018322473,0.005763259,0.49137974],"study_design_scores_gemma":[0.000011153381,0.00016835154,0.00071597594,0.000017117622,0.000022590219,0.000065359985,0.00004538238,0.9818793,0.009140501,0.0065317336,0.0013894407,0.000013109399],"about_ca_topic_score_codex":0.0024335715,"about_ca_topic_score_gemma":0.0026529613,"teacher_disagreement_score":0.0024882937,"about_ca_system_score_codex":0.0013135456,"about_ca_system_score_gemma":0.002013542,"threshold_uncertainty_score":0.013159573},"labels":[],"label_agreement":null},{"id":"W4412939447","doi":"10.1109/tmc.2025.3594358","title":"An Enhanced Dual-Currency VCG Auction Mechanism for Resource Allocation in IoV: A Value of Information Perspective","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Dual (grammatical number); Mechanism (biology); Perspective (graphical); Value (mathematics); Mechanism design; Vickrey–Clarke–Groves auction; Resource allocation; Currency; Computer network; Vickrey auction; Common value auction; Distributed computing; Auction theory; Microeconomics; Artificial intelligence","score_opus":0.006563893447016584,"score_gpt":0.2661177403370514,"score_spread":0.2595538468900348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412939447","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02763106,0.00027207026,0.967504,0.00030450523,0.00006352432,0.000118482836,0.000023527973,0.00011789583,0.0039648423],"genre_scores_gemma":[0.93052876,0.00017344255,0.066775605,0.00013366759,0.000032989705,0.000104724655,0.000023952802,0.000018252385,0.0022087304],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985455,0.00062805775,0.000057221125,0.00021556282,0.00035980676,0.00019382744],"domain_scores_gemma":[0.9983369,0.0007823255,0.00025809335,0.0001444377,0.00032109942,0.00015708289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002546299,0.00068636896,0.0010253657,0.0006156699,0.0004361489,0.0014808176,0.0026636792,0.0013325518,0.0016602555],"category_scores_gemma":[0.004974078,0.0003185992,0.000497195,0.0007483752,0.001323173,0.001742394,0.001332586,0.0012028539,0.00019482484],"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":[0.00024557253,0.00019013889,0.00083899207,0.00015299144,0.00007664531,0.00032011853,0.00014142487,0.8058179,0.0057399347,0.12770218,0.0015452899,0.057228893],"study_design_scores_gemma":[0.00002595709,0.000054919623,0.000051891144,0.000006896032,0.000007763029,0.00004938989,0.00001101677,0.9862266,0.00038855942,0.012689704,0.0004767058,0.000010480244],"about_ca_topic_score_codex":0.001983001,"about_ca_topic_score_gemma":0.0015063839,"teacher_disagreement_score":0.0026636792,"about_ca_system_score_codex":0.0012392008,"about_ca_system_score_gemma":0.0017828565,"threshold_uncertainty_score":0.013466239},"labels":[],"label_agreement":null},{"id":"W4413212349","doi":"10.1109/tmc.2025.3597713","title":"Energy-Efficient and Real-Time Sensing for Federated Continual Learning via Sample-Driven Control","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Trường Đại học Bách Khoa Hà Nội","keywords":"Computer science; Sample (material); Efficient energy use; Control (management); Energy (signal processing); Real-time computing; Artificial intelligence; Electrical engineering","score_opus":0.003918115053836531,"score_gpt":0.21132426555711756,"score_spread":0.20740615050328104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413212349","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026698612,0.000098988996,0.97095597,0.00023314063,0.00003127689,0.000035543377,0.000018035884,0.00029945842,0.0016289769],"genre_scores_gemma":[0.93698305,0.000071597475,0.061465573,0.0001143024,0.000026952182,0.00008289057,0.000029896746,0.00002824341,0.0011974574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933237,0.00016663456,0.00003166959,0.00019419912,0.00018729849,0.00008777859],"domain_scores_gemma":[0.9981311,0.0010997596,0.00026190426,0.00014900841,0.00025249354,0.00010572125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021157004,0.00074667734,0.00078152545,0.00031846858,0.00043339055,0.00112332,0.0016179727,0.0009273403,0.0010850953],"category_scores_gemma":[0.004523295,0.000337653,0.00044240695,0.00034804922,0.0015591632,0.0012196405,0.0014769082,0.0015950652,0.00015086001],"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":[0.00010576735,0.000071181115,0.00057986163,0.000047538102,0.000025764759,0.000054017997,0.00007138085,0.96230954,0.0028406612,0.009027469,0.0004446303,0.024422228],"study_design_scores_gemma":[0.0000031325285,0.00001183447,0.000025983189,0.0000016755597,0.0000013273691,0.0000028268018,0.0000022283757,0.9986094,0.00027586476,0.000996791,0.00006713603,0.0000017245437],"about_ca_topic_score_codex":0.004085376,"about_ca_topic_score_gemma":0.003641126,"teacher_disagreement_score":0.004085376,"about_ca_system_score_codex":0.0011229181,"about_ca_system_score_gemma":0.0012438957,"threshold_uncertainty_score":0.011188984},"labels":[],"label_agreement":null},{"id":"W4413267640","doi":"10.1109/tmc.2025.3593263","title":"Latency Minimization for Movable Relay-Aided D2D-MEC Communication Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Antenna Design and 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":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Relay; Computer network; Latency (audio); Minification; Telecommunications","score_opus":0.00948214675521368,"score_gpt":0.230651726873177,"score_spread":0.22116958011796334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413267640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028447239,0.0009837036,0.9630356,0.00030284142,0.00005856846,0.00004444424,0.00012515366,0.00021094168,0.0067915698],"genre_scores_gemma":[0.9528954,0.0007682595,0.041170288,0.00010025131,0.000051591567,0.00009542483,0.000117763186,0.000054154087,0.0047468566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995521,0.00011360266,0.0000217269,0.00009292852,0.00012226553,0.0000973852],"domain_scores_gemma":[0.99948883,0.00029851057,0.00006344345,0.000030731666,0.000086768945,0.00003175349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041771895,0.0013018659,0.0006736763,0.00028445665,0.00045083143,0.00095160044,0.00094232644,0.0007053374,0.0027680881],"category_scores_gemma":[0.0012211127,0.00031761263,0.00035493998,0.00058809563,0.0004806675,0.00084830343,0.0009916065,0.00059508934,0.0004742477],"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":[0.000089788475,0.000026999476,0.0003319667,0.00011649072,0.000021997652,0.00014020114,0.000058278674,0.9612875,0.0062266537,0.008523953,0.0012264708,0.021949673],"study_design_scores_gemma":[0.000005962133,0.000048577745,0.000084735235,0.000005586587,0.000008073962,0.000046986595,0.000020915217,0.9959489,0.0011839061,0.001998865,0.0006409891,0.000006418157],"about_ca_topic_score_codex":0.003047599,"about_ca_topic_score_gemma":0.002883549,"teacher_disagreement_score":0.003047599,"about_ca_system_score_codex":0.0010612857,"about_ca_system_score_gemma":0.0008652449,"threshold_uncertainty_score":0.009260237},"labels":[],"label_agreement":null},{"id":"W4413318797","doi":"10.1109/tmc.2025.3599917","title":"RipeTrack: Assessing Fruit Ripeness and Remaining Lifetime Using Smartphones","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Horticultural and Viticultural Research","field":"Agricultural and Biological Sciences","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ripeness; Computer science","score_opus":0.03647660004323944,"score_gpt":0.3164903274517235,"score_spread":0.28001372740848407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413318797","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89244074,0.0026768367,0.06650685,0.00030635894,0.00018621392,0.00049384194,0.015084085,0.012722658,0.00958251],"genre_scores_gemma":[0.94198084,0.0007451751,0.04699986,0.00024585234,0.00006121014,0.00021468791,0.005609353,0.00011364092,0.004029378],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997712,0.000019707057,0.000020478032,0.00008353415,0.00007926176,0.000025873822],"domain_scores_gemma":[0.99946314,0.00014530109,0.000121837504,0.000052737254,0.00016936932,0.000047462192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023140029,0.00091747905,0.0006418683,0.0012780153,0.00014431585,0.00047807116,0.0005362631,0.00074438786,0.0018993203],"category_scores_gemma":[0.0009911758,0.00016244696,0.00035767714,0.00053213345,0.00008994217,0.0006177739,0.0004947027,0.00024459237,0.0011794475],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030862286,0.0006704325,0.2640847,0.002377325,0.0006109946,0.0015683813,0.00048443134,0.015416305,0.16571118,0.0007794506,0.023392541,0.5218181],"study_design_scores_gemma":[0.00013764016,0.001896561,0.50407684,0.00021654034,0.0004247779,0.0026357884,0.0009471398,0.3755057,0.09781342,0.0012182812,0.014848198,0.00027913498],"about_ca_topic_score_codex":0.003265749,"about_ca_topic_score_gemma":0.006555465,"teacher_disagreement_score":0.003265749,"about_ca_system_score_codex":0.00021058669,"about_ca_system_score_gemma":0.00013423979,"threshold_uncertainty_score":0.006493449},"labels":[],"label_agreement":null},{"id":"W4413318829","doi":"10.1109/tmc.2025.3599838","title":"A Novel Secure Split Federated Semantic Learning Framework and its Optimization for Digital Twin Network Evolution","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Privacy-Preserving Technologies in Data","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":"Concordia University; University of the Fraser Valley","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Distributed computing; Theoretical computer science; Computer network","score_opus":0.014943278642971245,"score_gpt":0.26461885085755343,"score_spread":0.2496755722145822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413318829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068107597,0.000104158404,0.9916135,0.0001030981,0.000014580561,0.000025413385,0.00003181688,0.00013802314,0.0011587065],"genre_scores_gemma":[0.7852895,0.00024338537,0.21049368,0.00012804865,0.000031670937,0.00014362668,0.00017853061,0.000091568465,0.003399961],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891233,0.00029213337,0.000050496168,0.00025396,0.00031132772,0.00017975636],"domain_scores_gemma":[0.99895203,0.00043731404,0.00013835728,0.0001380989,0.00024255752,0.00009163296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020786605,0.0007892373,0.0009358463,0.00063828833,0.0005993624,0.0013718063,0.0016309525,0.0011548041,0.0019095277],"category_scores_gemma":[0.0037208956,0.00030128084,0.00065661996,0.0007535722,0.0012443833,0.0022770565,0.002237059,0.0013274115,0.00026485402],"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":[0.000059187114,0.000028228422,0.00034289734,0.00003628177,0.000018613682,0.000054508775,0.000039740597,0.93950397,0.0009642333,0.030352628,0.0007335594,0.027866295],"study_design_scores_gemma":[0.0000026482167,0.000009987406,0.00001982414,0.0000019046943,0.0000023287469,0.0000095895275,0.0000048988322,0.99338186,0.00016842726,0.0062016235,0.00019478852,0.0000020713233],"about_ca_topic_score_codex":0.004255763,"about_ca_topic_score_gemma":0.0030083577,"teacher_disagreement_score":0.004255763,"about_ca_system_score_codex":0.0016913031,"about_ca_system_score_gemma":0.0022345283,"threshold_uncertainty_score":0.012271285},"labels":[],"label_agreement":null},{"id":"W4413318886","doi":"10.1109/tmc.2025.3599885","title":"Distributed Resource Allocation and Coordinated Scheduling for End-Edge-Cloud Collaborative Computing","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Distributed and Parallel Computing Systems","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 Victoria","funders":"Key Research and Development Program of Zhejiang Province; National Natural Science Foundation of China","keywords":"Computer science; Cloud computing; Distributed computing; Scheduling (production processes); Resource allocation; Edge computing; Computer network; Operating system","score_opus":0.011005535577206257,"score_gpt":0.26607694674805243,"score_spread":0.2550714111708462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413318886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0134614445,0.00013606375,0.98375374,0.00017741387,0.00003708183,0.00004669232,0.000035887442,0.00016101285,0.0021906812],"genre_scores_gemma":[0.74824107,0.00027551022,0.24714825,0.000113485636,0.00007596559,0.00022559865,0.00013610252,0.000097267715,0.0036866642],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895835,0.00034016068,0.000041417097,0.00026012413,0.0002143714,0.00018551096],"domain_scores_gemma":[0.9993931,0.00027195297,0.000069704285,0.00008399039,0.00010277556,0.000078452074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011015738,0.0006929791,0.0009035458,0.00032668735,0.00077074656,0.0011913724,0.0014068339,0.00073132664,0.0014613911],"category_scores_gemma":[0.0019795897,0.00039088322,0.0005459108,0.0006701937,0.00062923034,0.0010516304,0.0012940032,0.0009119935,0.0002594],"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":[0.00007817456,0.000055156794,0.0002720005,0.000039957984,0.00002123282,0.00006758712,0.000052179388,0.9612007,0.0019956846,0.017634384,0.0013655429,0.017217452],"study_design_scores_gemma":[0.0000056952686,0.000008243576,0.000050313538,0.0000010778533,0.0000030236413,0.000005118539,0.000009619821,0.9955568,0.00021224002,0.0037632212,0.0003824166,0.0000022677148],"about_ca_topic_score_codex":0.009076958,"about_ca_topic_score_gemma":0.010972299,"teacher_disagreement_score":0.009076958,"about_ca_system_score_codex":0.0016322263,"about_ca_system_score_gemma":0.002233117,"threshold_uncertainty_score":0.018048227},"labels":[],"label_agreement":null},{"id":"W4413343973","doi":"10.1109/tmc.2025.3600434","title":"SC-GIR: Goal-Oriented Semantic Communication via Invariant Representation Learning for Image Transmission","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Data Compression Techniques","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; Invariant (physics); Theoretical computer science; Representation (politics); Artificial intelligence; Natural language processing; Multimedia; Mathematics","score_opus":0.012042501656240072,"score_gpt":0.31176892062399836,"score_spread":0.2997264189677583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413343973","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008687392,0.00016278136,0.9885712,0.00018837121,0.000031706077,0.000046848,0.000082834136,0.00078806246,0.0014408776],"genre_scores_gemma":[0.5472778,0.0004815957,0.44471127,0.0005622107,0.00016104818,0.00030749087,0.0009698139,0.0003182271,0.005210525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991359,0.0002526094,0.00003935079,0.00017247915,0.0002896678,0.00011006509],"domain_scores_gemma":[0.9987777,0.00047756397,0.00016087132,0.0003321219,0.00017580437,0.00007595248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012272607,0.0008753768,0.0007869337,0.00073283113,0.00040637876,0.0008822083,0.0016437859,0.0010656457,0.0023084152],"category_scores_gemma":[0.0041182423,0.0002597039,0.0006714211,0.0010519505,0.0012015685,0.0023944038,0.0021770338,0.0021751255,0.00087370357],"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":[0.00040746454,0.00041563043,0.0011199035,0.00025114344,0.00010184,0.00027099924,0.00037153708,0.31234497,0.0350793,0.080722645,0.012228968,0.55668557],"study_design_scores_gemma":[0.000020472784,0.00011808226,0.0001738344,0.000012850564,0.000013195427,0.00007741158,0.00003562103,0.9561136,0.009171688,0.03242611,0.0018194978,0.000017596041],"about_ca_topic_score_codex":0.0014483097,"about_ca_topic_score_gemma":0.001543412,"teacher_disagreement_score":0.0023084152,"about_ca_system_score_codex":0.000646702,"about_ca_system_score_gemma":0.0012950622,"threshold_uncertainty_score":0.0077224374},"labels":[],"label_agreement":null},{"id":"W4413677374","doi":"10.1109/tmc.2025.3602911","title":"PPBR: Privacy-Preserving and Byzantine-Robust Edge-Assisted Hierarchical Federated Learning in Mobile Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Computer network; Enhanced Data Rates for GSM Evolution; Information privacy; Mobile computing; Distributed computing; Computer security; Artificial intelligence","score_opus":0.019793392581460522,"score_gpt":0.27110543312694985,"score_spread":0.25131204054548933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413677374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024345387,0.00022177753,0.9737655,0.00018399983,0.00002724181,0.000032267624,0.000069168666,0.0007303062,0.00062435464],"genre_scores_gemma":[0.88638383,0.00017969284,0.111103736,0.00029420186,0.00003479965,0.00007678624,0.00025256982,0.00005957042,0.0016148991],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793553,0.00072249805,0.00010469369,0.00046380673,0.000473653,0.00029982105],"domain_scores_gemma":[0.9971527,0.0010865958,0.00029411764,0.001014446,0.00031718647,0.00013499067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024453134,0.0007844768,0.0010499177,0.0004609164,0.00074869336,0.0009695168,0.002069552,0.0011307029,0.00082761847],"category_scores_gemma":[0.0068849903,0.00034400207,0.0007179918,0.0007320142,0.00096456823,0.0029013874,0.003432221,0.001651412,0.00030001166],"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":[0.000552954,0.00020599784,0.00279955,0.00010339333,0.0000982244,0.00027124016,0.00025152834,0.7511868,0.0069940314,0.03067043,0.0034783166,0.2033876],"study_design_scores_gemma":[0.000008459795,0.00004422604,0.00009614691,0.000003695532,0.0000048484894,0.00004605184,0.000013063907,0.9891405,0.0012355809,0.009098494,0.0003027315,0.0000062185163],"about_ca_topic_score_codex":0.0020197625,"about_ca_topic_score_gemma":0.0018598081,"teacher_disagreement_score":0.0024453134,"about_ca_system_score_codex":0.0008298161,"about_ca_system_score_gemma":0.0013552868,"threshold_uncertainty_score":0.012932181},"labels":[],"label_agreement":null},{"id":"W4413925784","doi":"10.1109/tmc.2025.3604722","title":"Uplink Resource Allocation for RSMA-Aided Digital Twin-Assisted User-Centric Cell-Free Massive MIMO Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","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":"Université Laval; University of Calgary; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Telecommunications link; Resource allocation; MIMO; Computer network; Distributed computing","score_opus":0.008187402815690583,"score_gpt":0.2244636609554674,"score_spread":0.21627625813977683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413925784","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.079345904,0.00041155817,0.91526884,0.00017001435,0.000036205012,0.000034290573,0.000040654326,0.00013172066,0.0045608003],"genre_scores_gemma":[0.9630015,0.00017895098,0.035470586,0.00005389454,0.000024919951,0.00003738217,0.000024553849,0.000012346298,0.0011958276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995772,0.00016838763,0.000011851126,0.0000686601,0.00009545073,0.00007851578],"domain_scores_gemma":[0.9996146,0.00020512985,0.00005487882,0.000035987145,0.000059680937,0.000029580178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005484544,0.0006020459,0.00059873244,0.00021018629,0.00028461454,0.0007436373,0.00064413296,0.00039708323,0.0008171947],"category_scores_gemma":[0.001037055,0.00026890245,0.00023708762,0.00047858641,0.0005118911,0.00067460473,0.00078141486,0.00040014202,0.00019074313],"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":[0.00011220245,0.000048934387,0.00046358767,0.000055694192,0.000036208807,0.00013708461,0.00004880566,0.95307136,0.009453726,0.010034167,0.00057658996,0.025961606],"study_design_scores_gemma":[0.0000050012304,0.000039916486,0.00007886268,0.0000019390588,0.0000052399296,0.000021543869,0.00001117338,0.9974201,0.0009719801,0.0012404185,0.00019987693,0.000003983755],"about_ca_topic_score_codex":0.001072576,"about_ca_topic_score_gemma":0.0014708184,"teacher_disagreement_score":0.001072576,"about_ca_system_score_codex":0.00049467606,"about_ca_system_score_gemma":0.0005630385,"threshold_uncertainty_score":0.0035891533},"labels":[],"label_agreement":null},{"id":"W4414079705","doi":"10.1109/tmc.2025.3606847","title":"MaestroBot: Generalized Gesture-Driven Hierarchical Coordination for Robotic Formations","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Robot Manipulation and Learning","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":"Impact","funders":"National Natural Science Foundation of China","keywords":"Scalability; Adaptability; Wearable computer; Wireless; Testbed; Swarm behaviour; Robot; Domain (mathematical analysis); Gesture","score_opus":0.014953940781619835,"score_gpt":0.26316172608730876,"score_spread":0.24820778530568893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414079705","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020453833,0.00018549134,0.9710627,0.00007909619,0.000054370048,0.00007447099,0.000093999486,0.0040532313,0.0039427853],"genre_scores_gemma":[0.67270666,0.00014402268,0.3220502,0.00010774576,0.000019374058,0.00021935425,0.0002382927,0.00022194309,0.0042923135],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998104,0.000026087428,0.000009668149,0.00005032369,0.000077641875,0.000025943875],"domain_scores_gemma":[0.99981683,0.000045302826,0.000026568712,0.000055364973,0.000025723028,0.00003024506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002224136,0.00047867652,0.00041854425,0.00021157648,0.00034905088,0.00033144385,0.0010531559,0.00036717838,0.0017809635],"category_scores_gemma":[0.00073916744,0.00019527509,0.00024288223,0.00019303289,0.0005422263,0.00052131596,0.001381203,0.000567851,0.00043090424],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021771355,0.000093207454,0.0015021146,0.0001897941,0.000048088343,0.00028558768,0.00026515286,0.64688414,0.06678718,0.019915314,0.006112164,0.25769955],"study_design_scores_gemma":[0.000022862765,0.00008411435,0.00032936368,0.000009012812,0.0000050464996,0.00006195901,0.000026496293,0.9801308,0.009226079,0.0047102007,0.005381188,0.000012956472],"about_ca_topic_score_codex":0.0032657865,"about_ca_topic_score_gemma":0.0061333925,"teacher_disagreement_score":0.0032657865,"about_ca_system_score_codex":0.00029038696,"about_ca_system_score_gemma":0.000663729,"threshold_uncertainty_score":0.0064935684},"labels":[],"label_agreement":null},{"id":"W4414079816","doi":"10.1109/tmc.2025.3607138","title":"Mobility Resilient Vehicular Federated Learning: Enhancing Training Efficiency in Dynamic Environments","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","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":"University of Victoria","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Scheduling (production processes); Federated learning; Latency (audio); Key (lock); Vehicular ad hoc network; Robustness (evolution); Training (meteorology); Spectral efficiency; Cloud computing","score_opus":0.01451585427400681,"score_gpt":0.26887630076241426,"score_spread":0.25436044648840744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414079816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11396894,0.00029593942,0.8820459,0.00028692707,0.000064239444,0.000047688376,0.000045624496,0.00092604593,0.0023185804],"genre_scores_gemma":[0.98018503,0.000059588237,0.019026866,0.00006575462,0.000011052822,0.00002182858,0.000037255457,0.000015985306,0.00057668245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994649,0.00012457784,0.000026705147,0.00012567578,0.00010846521,0.00014966329],"domain_scores_gemma":[0.99903345,0.00032778448,0.00011918587,0.00021563873,0.00020200359,0.00010196024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001101557,0.0005292834,0.00058696896,0.00040293863,0.00057995017,0.00065492536,0.0014307833,0.0005906955,0.00057919585],"category_scores_gemma":[0.0029916808,0.00015944146,0.0002475473,0.00041428968,0.00057461177,0.001290638,0.0014578694,0.00068450364,0.00017446114],"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":[0.00027498577,0.00015706936,0.0028006716,0.000054284672,0.000030641484,0.00014023084,0.00014338557,0.84293,0.009551921,0.006803047,0.0014229842,0.13569067],"study_design_scores_gemma":[0.000007053103,0.000047063022,0.00017946924,0.000003551888,0.0000041694548,0.000031244235,0.000024092333,0.9952987,0.0018394289,0.002162038,0.00039819308,0.0000051651036],"about_ca_topic_score_codex":0.0034206393,"about_ca_topic_score_gemma":0.0032159996,"teacher_disagreement_score":0.0034206393,"about_ca_system_score_codex":0.0006839015,"about_ca_system_score_gemma":0.0011093186,"threshold_uncertainty_score":0.006801486},"labels":[],"label_agreement":null},{"id":"W4414165923","doi":"10.1109/tmc.2025.3607599","title":"Reliable Intelligent Reflecting Surface-Assisted Mobile Edge Computing Systems: A Physical Layer Security and Encryption Design","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Wireless Communication Technologies","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":"École de Technologie Supérieure","funders":"","keywords":"Encryption; Physical layer; Mobile edge computing; Wireless; Energy consumption; Secure transmission; Wireless network; Mobile computing; Cryptography","score_opus":0.03259891439398486,"score_gpt":0.3060555179537226,"score_spread":0.2734566035597377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414165923","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031889465,0.00040616398,0.96169746,0.00031835513,0.00003884904,0.00006577918,0.000026510335,0.00015866522,0.005398712],"genre_scores_gemma":[0.83401304,0.0005345479,0.16186298,0.00013076181,0.000032242773,0.00010867189,0.000037360023,0.00003007771,0.0032504152],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963033,0.00009372154,0.000018191324,0.00006437917,0.0001513643,0.000041941898],"domain_scores_gemma":[0.99974865,0.000060362934,0.0000553969,0.00004561116,0.00007380962,0.00001612689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000282997,0.0005048643,0.00036170642,0.00020920418,0.00023335779,0.0007895492,0.00076674967,0.0006438546,0.0012604287],"category_scores_gemma":[0.00055887,0.0002257973,0.00043503405,0.0002749523,0.0004979031,0.0009011694,0.0006884056,0.0006098264,0.00042931465],"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":[0.00028213637,0.000116807794,0.0018048387,0.00028995113,0.00008496132,0.0004639596,0.00021677786,0.6052413,0.21291472,0.053756114,0.0024359105,0.12239255],"study_design_scores_gemma":[0.000009478222,0.00014870572,0.00015781648,0.000008388403,0.000012447855,0.00013541438,0.00002059221,0.9836352,0.011928393,0.002010548,0.0019224342,0.000010629358],"about_ca_topic_score_codex":0.00039164713,"about_ca_topic_score_gemma":0.0005026567,"teacher_disagreement_score":0.0012604287,"about_ca_system_score_codex":0.0004533011,"about_ca_system_score_gemma":0.00036958136,"threshold_uncertainty_score":0.004216552},"labels":[],"label_agreement":null},{"id":"W4414229223","doi":"10.1109/tmc.2025.3610915","title":"Accelerating Stable Matching Between Workers and Spatial-Temporal Tasks for Dynamic MCS: A Stagewise Service Trading Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Transportation and Mobility Innovations","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":"Western University","funders":"Aeronautical Science Foundation of China; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Matching (statistics); Futures contract; Key (lock); Service (business); Task (project management); Incentive; Software deployment; Path (computing); Pareto principle","score_opus":0.020129966484849562,"score_gpt":0.2632609947721499,"score_spread":0.24313102828730038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414229223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023232935,0.00010132058,0.97456914,0.00020187204,0.000029753757,0.00008012596,0.00004379976,0.0001784143,0.0015626538],"genre_scores_gemma":[0.85932404,0.00011412671,0.13717455,0.00015607353,0.000040661133,0.00017477757,0.000070434515,0.000052873085,0.0028925284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986174,0.0004095974,0.00006343482,0.00033893777,0.00027549546,0.00029509963],"domain_scores_gemma":[0.9973086,0.0014477452,0.00029744554,0.0003293807,0.00027926333,0.0003374842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024463232,0.0007873703,0.0011478438,0.00050508115,0.00084205205,0.0010522908,0.0029813559,0.0014717656,0.0035449956],"category_scores_gemma":[0.006047347,0.00052201084,0.0007883264,0.0006412697,0.00115892,0.001945004,0.0029600707,0.0013945702,0.0004192922],"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":[0.00037431603,0.0002453446,0.0023099044,0.000156711,0.00006407011,0.00022292633,0.0003580363,0.82972944,0.009945255,0.07022876,0.0019537662,0.08441137],"study_design_scores_gemma":[0.000013843156,0.000051989744,0.00011596603,0.0000037226168,0.000006724056,0.000029648272,0.00002489794,0.9823729,0.0006265652,0.016181767,0.00056452584,0.000007522109],"about_ca_topic_score_codex":0.003929186,"about_ca_topic_score_gemma":0.0034407992,"teacher_disagreement_score":0.003929186,"about_ca_system_score_codex":0.0010920537,"about_ca_system_score_gemma":0.002235076,"threshold_uncertainty_score":0.012937546},"labels":[],"label_agreement":null},{"id":"W4414348312","doi":"10.1109/tmc.2025.3612221","title":"Energy-Efficient Multi-UAV Navigation for Cooperative Data Sensing and Transmission","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Transmission (telecommunications); Data transmission; Signal processing; Signal-to-noise ratio (imaging); Key (lock)","score_opus":0.016771140461484393,"score_gpt":0.2689798549207823,"score_spread":0.25220871445929793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414348312","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05411899,0.00073635625,0.9390047,0.00020548666,0.000087089466,0.00002301428,0.000049373266,0.00018609905,0.005588917],"genre_scores_gemma":[0.90083104,0.00026519372,0.095447615,0.00005078717,0.000023691702,0.000032235992,0.000056223027,0.0000244425,0.0032687178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976414,0.00004706975,0.000010358131,0.000050128907,0.00008866866,0.000039666502],"domain_scores_gemma":[0.9997936,0.000081396036,0.000023471228,0.000035844,0.00005357667,0.0000120191135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019531866,0.0004510341,0.0004913974,0.0003130459,0.0004988998,0.0005205555,0.0005726039,0.0005076777,0.0011455924],"category_scores_gemma":[0.00071649044,0.00022061456,0.00026712424,0.0004618871,0.00022431584,0.00083708676,0.00090467255,0.00034591247,0.00032004798],"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":[0.0004962867,0.00017439654,0.0020219157,0.00015600279,0.00009533731,0.00036067615,0.00021948859,0.5588975,0.13198571,0.028627397,0.0035077874,0.27345753],"study_design_scores_gemma":[0.000006733555,0.00006178335,0.00030686287,0.000006227853,0.000008102443,0.00009211656,0.00003619991,0.9868652,0.008417188,0.0030564338,0.0011360139,0.0000071011536],"about_ca_topic_score_codex":0.0016177616,"about_ca_topic_score_gemma":0.0044186553,"teacher_disagreement_score":0.0016177616,"about_ca_system_score_codex":0.00029121953,"about_ca_system_score_gemma":0.00045521805,"threshold_uncertainty_score":0.0038323998},"labels":[],"label_agreement":null},{"id":"W4414404758","doi":"10.1109/tmc.2025.3612469","title":"Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-Play Neural Network Toolbox Enabling Edge Deployment","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","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":"Carleton University; University of British Columbia","funders":"Beijing Union University; National Natural Science Foundation of China","keywords":"Object detection; Enhanced Data Rates for GSM Evolution; Convolutional neural network; Edge device; Adaptation (eye); Toolbox; Object (grammar); Edge detection","score_opus":0.01918597781620886,"score_gpt":0.27286429295063575,"score_spread":0.2536783151344269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414404758","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008245175,0.000068902315,0.9328745,0.00009792469,0.00006517234,0.000082667786,0.00026307872,0.053438503,0.0048640827],"genre_scores_gemma":[0.2154,0.00022290113,0.7522309,0.00054265413,0.00003994073,0.00042022215,0.0022200036,0.0073305136,0.021592796],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997093,0.00003646064,0.0000143297275,0.00008613964,0.000105586296,0.000048131253],"domain_scores_gemma":[0.9995995,0.000103193604,0.000027185779,0.000105218576,0.00010391224,0.000061021594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063512585,0.0010976054,0.00037768527,0.0006598727,0.00021806525,0.00076551514,0.0031219681,0.0008826904,0.015484037],"category_scores_gemma":[0.0021258774,0.00054164807,0.00054871745,0.00032093498,0.00043274782,0.0015547882,0.0018503212,0.0015731443,0.0054071615],"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":[0.0012827887,0.0005052731,0.002697333,0.0003193493,0.00014187414,0.00088844023,0.0002571379,0.17157109,0.07573505,0.031366847,0.07343248,0.6418023],"study_design_scores_gemma":[0.000047406476,0.000044269902,0.00026550368,0.000014143008,0.000011417129,0.00010803167,0.000009763018,0.9454097,0.03722564,0.0034210861,0.013418923,0.000024025096],"about_ca_topic_score_codex":0.0031692989,"about_ca_topic_score_gemma":0.004520275,"teacher_disagreement_score":0.015484037,"about_ca_system_score_codex":0.0008387311,"about_ca_system_score_gemma":0.00073671946,"threshold_uncertainty_score":0.051799238},"labels":[],"label_agreement":null},{"id":"W4414871320","doi":"10.1109/tmc.2025.3618147","title":"User-Centric Communication Service Provision for Edge-Assisted Mobile Augmented Reality","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Augmented Reality Applications","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":"Carleton University; University of Waterloo","funders":"","keywords":"Upload; Frame (networking); Robustness (evolution); Cellular network; Service (business); Data as a service; Mobile telephony; Server; Data modeling","score_opus":0.022746937659097386,"score_gpt":0.30973428383872753,"score_spread":0.2869873461796301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414871320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048139814,0.00016863679,0.9463648,0.00019336535,0.00006785833,0.00009253321,0.00006903688,0.001893493,0.0030104648],"genre_scores_gemma":[0.89332163,0.00013592535,0.104286715,0.00013750963,0.000044140288,0.00007498762,0.00017861939,0.00010243397,0.0017180355],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99895763,0.0002653716,0.00006413314,0.00019137164,0.00028731875,0.00023409471],"domain_scores_gemma":[0.9983833,0.00026410667,0.00014710409,0.0006054486,0.0004687092,0.00013133853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011431893,0.0007946857,0.00056465855,0.00068705645,0.00073026423,0.0014863472,0.0014688832,0.0007151343,0.0018555252],"category_scores_gemma":[0.0025958526,0.00027958545,0.00043608368,0.00076079165,0.0006890909,0.002511492,0.0021269144,0.0011251379,0.00062815956],"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":[0.0008528163,0.00055668096,0.00791294,0.00027343538,0.00013676287,0.00070710457,0.0009481661,0.286649,0.11372343,0.08347991,0.012400194,0.49235958],"study_design_scores_gemma":[0.000012523563,0.00014592857,0.000545878,0.00000850159,0.000018154446,0.0002156485,0.00010458247,0.97492945,0.012370184,0.0051553473,0.0064667347,0.000027059694],"about_ca_topic_score_codex":0.0036892127,"about_ca_topic_score_gemma":0.0042946916,"teacher_disagreement_score":0.0036892127,"about_ca_system_score_codex":0.00087425276,"about_ca_system_score_gemma":0.0013181493,"threshold_uncertainty_score":0.0073354244},"labels":[],"label_agreement":null},{"id":"W4415123913","doi":"10.1109/tmc.2025.3620352","title":"Distributed and Controllable Mobile Text-to-Image Generation With User Preference Guarantee","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Video Analysis and Summarization","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 Waterloo","funders":"","keywords":"Mobile edge computing; Adaptability; Reinforcement learning; Transmission (telecommunications); Mobile device; Enhanced Data Rates for GSM Evolution; Image quality; Resource allocation","score_opus":0.011616211442236677,"score_gpt":0.23733211896698778,"score_spread":0.2257159075247511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415123913","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0682998,0.00012633484,0.9268198,0.000111646936,0.000021924141,0.00011823562,0.00003784743,0.0012583319,0.0032060072],"genre_scores_gemma":[0.8806418,0.000094245355,0.114648476,0.000087517445,0.000035986417,0.000113026705,0.00008328545,0.00016380206,0.0041319034],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991387,0.00020242679,0.000043227992,0.00024132078,0.00028396,0.000090323025],"domain_scores_gemma":[0.9982126,0.0007558426,0.00016744643,0.00041930028,0.00033055575,0.000114256945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081058237,0.0007578127,0.0005690685,0.00019206645,0.00038271782,0.0007668922,0.0013097841,0.00071895245,0.003101324],"category_scores_gemma":[0.0033316717,0.00030903317,0.00037143767,0.00024848228,0.0004945872,0.0016479281,0.0010734265,0.00075811066,0.00082870707],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015418647,0.0005446616,0.0027851388,0.0003058709,0.00008463196,0.0010785647,0.00068913493,0.22766557,0.4519361,0.024235,0.0030946843,0.28603876],"study_design_scores_gemma":[0.000070040864,0.0002168682,0.000837587,0.0000061564438,0.000023366834,0.0002631807,0.00007762509,0.92726463,0.06339711,0.0054787914,0.0023332187,0.000031445375],"about_ca_topic_score_codex":0.0010170484,"about_ca_topic_score_gemma":0.0010630985,"teacher_disagreement_score":0.003101324,"about_ca_system_score_codex":0.00042953715,"about_ca_system_score_gemma":0.00035232506,"threshold_uncertainty_score":0.010374963},"labels":[],"label_agreement":null},{"id":"W4415482248","doi":"10.1109/tmc.2025.3624561","title":"Toward Seamless Hierarchical Federated Learning Under Intermittent Client Participation: A Stagewise Decision-Making Methodology","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Mobile Computing","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":"Western University","funders":"","keywords":"Federated learning; Plan (archaeology); Backhaul (telecommunications); Edge device; Enhanced Data Rates for GSM Evolution; Focus (optics); Energy consumption","score_opus":0.08849740813167134,"score_gpt":0.38136382737250824,"score_spread":0.2928664192408369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415482248","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013479177,0.00016025835,0.98407733,0.00037608334,0.000018708575,0.00015034463,0.000056248515,0.00026282528,0.0014190632],"genre_scores_gemma":[0.60303885,0.00018849265,0.39356497,0.0003853951,0.00006293713,0.00047628355,0.00026472297,0.000105334926,0.0019130512],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99640584,0.0014905952,0.0001653324,0.00073149404,0.00057018985,0.00063660793],"domain_scores_gemma":[0.9930099,0.004420038,0.0005607475,0.00051899353,0.0009478388,0.0005425443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005943455,0.0015495555,0.0018779543,0.0009596161,0.00096116884,0.0021574558,0.0041676867,0.0022270447,0.0036849717],"category_scores_gemma":[0.008997402,0.0009782652,0.0014238193,0.0011321721,0.0014711105,0.0025816776,0.0035673263,0.0028677913,0.00048220326],"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":[0.00024953016,0.0002819703,0.00198118,0.00018764258,0.000120305376,0.00020483044,0.00029544797,0.9002247,0.0018055747,0.022612587,0.0013476107,0.07068862],"study_design_scores_gemma":[0.000012402151,0.000052281524,0.00005480865,0.000010327292,0.000012157412,0.00001575962,0.00002526222,0.98915136,0.00048111915,0.009910482,0.00026770477,0.0000062505483],"about_ca_topic_score_codex":0.003920387,"about_ca_topic_score_gemma":0.0039295107,"teacher_disagreement_score":0.005943455,"about_ca_system_score_codex":0.001469205,"about_ca_system_score_gemma":0.004038304,"threshold_uncertainty_score":0.03143239},"labels":[],"label_agreement":null},{"id":"W4415593467","doi":"10.1109/tmc.2025.3625263","title":"A QoE-Driven Personalized Incentive Mechanism Design for AIGC Services in Resource-Constrained Edge Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":0,"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":"National Key Research and Development Program of China; Natural Science Foundation of Guangdong Province; Queen's University; National Natural Science Foundation of China; Queen's University Belfast","keywords":"Server; Mobile edge computing; Enhanced Data Rates for GSM Evolution; Service (business); Service provider; Key (lock); Incentive; Edge device; Resource allocation; Mobile telephony","score_opus":0.015474510073235338,"score_gpt":0.2570238871812326,"score_spread":0.24154937710799726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415593467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035035588,0.0001314439,0.9610335,0.000428628,0.000062019455,0.00025866367,0.00006400384,0.00028781564,0.0026982229],"genre_scores_gemma":[0.87684363,0.00014081417,0.11973361,0.00020344529,0.00005104968,0.00032101583,0.000056382654,0.00003981843,0.002610164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775654,0.00081915676,0.000120934215,0.0005212078,0.000381105,0.00040108588],"domain_scores_gemma":[0.99720925,0.0012879793,0.00035791792,0.00020353563,0.0006217988,0.00031947962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003236483,0.00070090545,0.0012819567,0.0006423028,0.0008988831,0.0017183798,0.0033467617,0.001788856,0.0030088297],"category_scores_gemma":[0.006935609,0.00060315756,0.00054131047,0.0008404748,0.00080190477,0.0022040368,0.0015791609,0.0014357748,0.00036204865],"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":[0.00046773473,0.00044020053,0.0013092373,0.0001832542,0.00008516823,0.00031219193,0.00027748913,0.83061665,0.011297466,0.07667379,0.0035780545,0.07475876],"study_design_scores_gemma":[0.00003138592,0.00006172917,0.00008444462,0.000005307341,0.000010484261,0.00004695204,0.000020581701,0.9930709,0.0006051584,0.005456217,0.0005962261,0.000010698806],"about_ca_topic_score_codex":0.0038214761,"about_ca_topic_score_gemma":0.0028648297,"teacher_disagreement_score":0.0038214761,"about_ca_system_score_codex":0.0019050973,"about_ca_system_score_gemma":0.0026527971,"threshold_uncertainty_score":0.017116368},"labels":[],"label_agreement":null},{"id":"W4415748005","doi":"10.1109/tmc.2025.3627066","title":"5Guard: Isolation-Aware End-to-End Slicing of 5G Networks","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Mobile Computing","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 Waterloo","funders":"Ministère de la Défense Nationale; Innovation for Defence Excellence and Security","keywords":"Slicing; Isolation (microbiology); Program slicing; Resource (disambiguation); Shared resource; Temporal isolation among virtual machines; Key (lock)","score_opus":0.011189251273626327,"score_gpt":0.2594960459611886,"score_spread":0.24830679468756225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415748005","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035029877,0.0007325583,0.95718056,0.00023011897,0.00008334468,0.0001258449,0.00016945251,0.0017629613,0.0046853223],"genre_scores_gemma":[0.72600067,0.00039607543,0.27068892,0.00016893932,0.000044835902,0.000081376624,0.00036811765,0.0002483721,0.0020026448],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993573,0.00019350792,0.000026734884,0.00012119814,0.00015261004,0.00014870366],"domain_scores_gemma":[0.9993218,0.00029056927,0.00008936853,0.00011222803,0.00009925296,0.000086841035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011905171,0.0013746414,0.000820952,0.0004112059,0.00058971415,0.0010710045,0.0012927153,0.00068641896,0.002622229],"category_scores_gemma":[0.0021593815,0.00042249035,0.0005907535,0.00040532605,0.00068202463,0.0013613532,0.0014343604,0.0011051849,0.00033896088],"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":[0.0001388938,0.00003537449,0.00074648036,0.000058104048,0.00003121654,0.00008402021,0.00006363511,0.9408761,0.004725457,0.006021,0.002077561,0.045142185],"study_design_scores_gemma":[0.000008546643,0.00003562375,0.00009497194,0.0000066286143,0.0000071147756,0.000023768744,0.000021406817,0.99476236,0.0013046705,0.002928993,0.00080097624,0.0000049463792],"about_ca_topic_score_codex":0.006819219,"about_ca_topic_score_gemma":0.009573608,"teacher_disagreement_score":0.006819219,"about_ca_system_score_codex":0.0013489915,"about_ca_system_score_gemma":0.0017458725,"threshold_uncertainty_score":0.013559043},"labels":[],"label_agreement":null},{"id":"W4416368409","doi":"10.1109/tmc.2025.3634587","title":"Graph Neural Network-Based Multicast Routing for On-Demand Streaming Services in 6G Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Multicast; Protocol Independent Multicast; Multipath routing; Xcast; Geographic routing; Static routing; Distance Vector Multicast Routing Protocol; Routing (electronic design automation); Dynamic Source Routing; Reinforcement learning","score_opus":0.010614371773958593,"score_gpt":0.25505384241323914,"score_spread":0.24443947063928054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416368409","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049529213,0.00064896746,0.94232833,0.00085653045,0.00010307341,0.00005788808,0.00014016754,0.001146787,0.0051889974],"genre_scores_gemma":[0.8406167,0.00057043147,0.15355815,0.00025798756,0.000084779145,0.0000970562,0.0003425304,0.00011474983,0.004357558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998598,0.00004148698,0.0000055795776,0.000034378896,0.000032255295,0.00002644109],"domain_scores_gemma":[0.9997745,0.00011546632,0.000028874234,0.000018161667,0.000050440325,0.000012601953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037330206,0.0005683375,0.00046852138,0.00054884795,0.00038092112,0.00046918285,0.0011019323,0.0008331351,0.0019348669],"category_scores_gemma":[0.0013141325,0.00025049562,0.0004174433,0.00048168804,0.00036901576,0.0008995394,0.0005531083,0.0009509442,0.00024251915],"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":[0.000045339257,0.000031115753,0.00035387266,0.000033290766,0.00001795539,0.000040652318,0.000025135327,0.9254169,0.0016276846,0.00683912,0.002082324,0.0634867],"study_design_scores_gemma":[0.000001131002,0.0000039841802,0.00003259414,0.0000010629078,0.0000018851894,0.0000034351615,0.0000022520326,0.99793565,0.00013856427,0.0017643273,0.00011405859,0.0000010237346],"about_ca_topic_score_codex":0.015589764,"about_ca_topic_score_gemma":0.019974789,"teacher_disagreement_score":0.015589764,"about_ca_system_score_codex":0.0014597893,"about_ca_system_score_gemma":0.0008268546,"threshold_uncertainty_score":0.030998051},"labels":[],"label_agreement":null},{"id":"W4416582538","doi":"10.1109/tmc.2025.3636717","title":"Joint Trajectory Planning and Task Offloading in UAV-Assisted Inspection Networks: A Transformer-Based Approach","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Beijing Nova Program; National Natural Science Foundation of China","keywords":"Trajectory; Task (project management); Benchmark (surveying); Process (computing); Task analysis; Optimization problem; Computational complexity theory; Joint (building); Trajectory optimization","score_opus":0.013349009724199334,"score_gpt":0.23516970203766574,"score_spread":0.2218206923134664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416582538","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012910087,0.00009760106,0.9843264,0.000095250165,0.000018141818,0.000050377843,0.000035224923,0.00023931735,0.0022276898],"genre_scores_gemma":[0.7269862,0.00028186405,0.26736766,0.00008813906,0.000045298893,0.00020523928,0.00024968362,0.00016115207,0.004614705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955636,0.00010380525,0.000021154297,0.0000927071,0.00010413136,0.00012172181],"domain_scores_gemma":[0.99950254,0.0002369151,0.00006406142,0.00005117881,0.00008689294,0.000058344114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059913227,0.0008118315,0.00097331987,0.0005316348,0.00052177627,0.00079343416,0.0016809282,0.00080747553,0.0023493036],"category_scores_gemma":[0.0012567617,0.0004905627,0.00074754376,0.0006654169,0.0006224384,0.0010533008,0.0011723606,0.00072047993,0.00035212474],"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":[0.000082183324,0.000051303166,0.00034069436,0.000052636566,0.000020479887,0.00008248389,0.00008233599,0.9504168,0.0026583748,0.0070506083,0.00073314755,0.038429018],"study_design_scores_gemma":[0.000005370985,0.000015249221,0.000043735043,0.00000230964,0.0000038336957,0.000008987034,0.000019116342,0.997758,0.00037553834,0.0015054096,0.00026031755,0.0000022447687],"about_ca_topic_score_codex":0.010997108,"about_ca_topic_score_gemma":0.01009486,"teacher_disagreement_score":0.010997108,"about_ca_system_score_codex":0.0010304992,"about_ca_system_score_gemma":0.0016528332,"threshold_uncertainty_score":0.021866202},"labels":[],"label_agreement":null},{"id":"W4416582540","doi":"10.1109/tmc.2025.3636225","title":"A Radical Heavy-Ball Method for Gradient Acceleration in Communication-Efficient Mobile Federated Learning","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Dalian Science and Technology Innovation Fund; Natural Science Foundation of Liaoning Province","keywords":"Stochastic gradient descent; Convergence (economics); Gradient descent; Acceleration; Momentum (technical analysis); Process (computing)","score_opus":0.032082071630689026,"score_gpt":0.3350631530160988,"score_spread":0.30298108138540975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416582540","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007712559,0.00018489912,0.9901329,0.00014937033,0.00006897279,0.000035512312,0.000019241885,0.0006291089,0.0010673973],"genre_scores_gemma":[0.52218395,0.00029889354,0.47041813,0.0003527776,0.00011078822,0.0002616383,0.00019603805,0.00033475316,0.0058430056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992822,0.00021324989,0.000040668492,0.00014219778,0.00021786106,0.0001038853],"domain_scores_gemma":[0.9989575,0.00039907373,0.0000858974,0.00015047297,0.00031549225,0.00009166301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014607017,0.0010746778,0.0013812082,0.0005999597,0.0006931921,0.0008999171,0.0018451799,0.0010785465,0.0022530712],"category_scores_gemma":[0.0044231024,0.00047544396,0.0006199329,0.0005883034,0.0009157625,0.0012889084,0.0017477068,0.0016415368,0.0007882693],"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":[0.00021369533,0.00009780858,0.0011489874,0.00011436105,0.00005349281,0.00014619903,0.00015857608,0.802612,0.0040673283,0.02431753,0.0041247047,0.16294535],"study_design_scores_gemma":[0.0000092060145,0.000020614862,0.000031320444,0.0000037065035,0.0000023396651,0.000011848713,0.00000535593,0.99681705,0.00038268752,0.0022457552,0.00046648722,0.0000037097284],"about_ca_topic_score_codex":0.006336573,"about_ca_topic_score_gemma":0.0048768753,"teacher_disagreement_score":0.006336573,"about_ca_system_score_codex":0.00080219976,"about_ca_system_score_gemma":0.0018621783,"threshold_uncertainty_score":0.012599349},"labels":[],"label_agreement":null},{"id":"W4416706962","doi":"10.1109/tmc.2025.3637283","title":"PEP-Policer: Eliminating the On-Off Traffic Pattern in PEP Over Satellite Networks","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Network Traffic and Congestion Control","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Goodput; Geostationary orbit; Limiting; Satellite; Network congestion; Communications satellite","score_opus":0.008342108884710606,"score_gpt":0.25108116581208373,"score_spread":0.24273905692737313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416706962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40516952,0.0007992305,0.5589512,0.00045085693,0.00027435293,0.00046526582,0.00028596073,0.026462669,0.0071409983],"genre_scores_gemma":[0.9575667,0.00012958296,0.04029165,0.00016771184,0.000044821212,0.00008127489,0.00018642105,0.00018097035,0.0013507684],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992943,0.00021869928,0.000045031487,0.0001306744,0.00017680768,0.00013455503],"domain_scores_gemma":[0.9988072,0.00029419994,0.00018030725,0.00039306478,0.0002058979,0.000119424854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001207402,0.00061168795,0.0005341674,0.00054725626,0.00049948954,0.00072501786,0.0012178476,0.00042464148,0.001348054],"category_scores_gemma":[0.003004483,0.00022392905,0.00031176518,0.00027977853,0.0007396709,0.00095111906,0.0012025458,0.0009106151,0.00044927152],"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":[0.0023403938,0.00081306766,0.015852638,0.00053803716,0.00013709492,0.0014622712,0.00084624393,0.21524045,0.23172362,0.015521861,0.013116261,0.502408],"study_design_scores_gemma":[0.00016456384,0.00089078763,0.004783642,0.000047198897,0.00006743447,0.0006198752,0.000114225535,0.8478054,0.12708028,0.0049822913,0.01337262,0.00007161213],"about_ca_topic_score_codex":0.0013782234,"about_ca_topic_score_gemma":0.0009895816,"teacher_disagreement_score":0.0013782234,"about_ca_system_score_codex":0.00037185167,"about_ca_system_score_gemma":0.00072368723,"threshold_uncertainty_score":0.006385386},"labels":[],"label_agreement":null},{"id":"W4417131162","doi":"10.1109/tmc.2025.3640955","title":"Leveraging Generative Artificial Intelligence for Uplink Feedback-Free Transmission in 6G FD-RAN","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Telecommunications link; Channel state information; Transmission (telecommunications); Autoencoder; Overhead (engineering); Spectral efficiency; Wireless; Reinforcement learning; Artificial noise","score_opus":0.023564673328594424,"score_gpt":0.2812870237724589,"score_spread":0.2577223504438645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417131162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04232031,0.00021048111,0.9546886,0.0001797945,0.000021114889,0.000013981361,0.0000218354,0.00025167232,0.0022923178],"genre_scores_gemma":[0.9501128,0.00012889641,0.048253167,0.00010382385,0.00001612328,0.000029359948,0.000036203146,0.0000396189,0.001279934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997594,0.000085457024,0.000008216452,0.000047800782,0.00006481504,0.00003430104],"domain_scores_gemma":[0.99944824,0.0003457101,0.000062891486,0.00004948929,0.00006942763,0.000024140647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064905826,0.00045527788,0.0004654335,0.00019073405,0.00022148395,0.0005269887,0.00068463775,0.0006066085,0.0006432925],"category_scores_gemma":[0.0017149355,0.0003525433,0.00042260677,0.0001935818,0.0008546257,0.00060558406,0.00077147706,0.00092912867,0.00013271933],"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":[0.000016063941,0.000013771992,0.0003350142,0.000013753197,0.000014472592,0.000033792763,0.000030112424,0.98064464,0.0021572728,0.0062040878,0.00016028846,0.010376742],"study_design_scores_gemma":[9.113415e-7,0.0000047077506,0.00002802864,8.975625e-7,0.0000015841899,0.0000046628365,0.000001327125,0.9988029,0.00019899721,0.00089873513,0.000055686614,0.0000014762444],"about_ca_topic_score_codex":0.0028570816,"about_ca_topic_score_gemma":0.0033730697,"teacher_disagreement_score":0.0028570816,"about_ca_system_score_codex":0.00055164803,"about_ca_system_score_gemma":0.00047387733,"threshold_uncertainty_score":0.0056809187},"labels":[],"label_agreement":null},{"id":"W4417251850","doi":"10.1109/tmc.2025.3642535","title":"Noisy Multi-Label Aggregation With Self-Supervised Graph Transformer in Mobile Crowdsourcing","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Crowdsensing and Crowdsourcing","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":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Crowdsourcing; Transformer; Graph; Aggregate (composite); Knowledge graph; Noisy data; Labeled data; Data modeling","score_opus":0.013464037910435895,"score_gpt":0.25566481478042274,"score_spread":0.24220077686998684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417251850","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04413704,0.00038463005,0.9505385,0.00056623237,0.000112808615,0.00017041473,0.00029411912,0.0021684968,0.0016277038],"genre_scores_gemma":[0.749845,0.000223711,0.24353392,0.00046930608,0.00020276748,0.00023598844,0.0009392062,0.0002632832,0.0042868215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821293,0.00061471044,0.00005310171,0.000639187,0.00032914814,0.00015097062],"domain_scores_gemma":[0.9964684,0.0017769025,0.0004329332,0.00068083225,0.00045344315,0.0001874664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00303349,0.0013508989,0.001742973,0.0016917259,0.0011156922,0.001390301,0.003290356,0.0018980135,0.0010111522],"category_scores_gemma":[0.007184571,0.00070999545,0.001195273,0.0017166898,0.0015858473,0.0030421996,0.0028325743,0.0020589707,0.00054287893],"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":[0.00045173385,0.00027688988,0.0031462389,0.00020453274,0.00016395109,0.00028325454,0.00066530803,0.75347316,0.0063402336,0.012614316,0.0056288745,0.2167514],"study_design_scores_gemma":[0.0000107777205,0.000015540638,0.00023263932,0.000004873876,0.000007898613,0.000015769072,0.000032936827,0.9894058,0.0010454158,0.008611154,0.0006079342,0.000009219896],"about_ca_topic_score_codex":0.015544363,"about_ca_topic_score_gemma":0.018436175,"teacher_disagreement_score":0.015544363,"about_ca_system_score_codex":0.002089309,"about_ca_system_score_gemma":0.0016508838,"threshold_uncertainty_score":0.03090775},"labels":[],"label_agreement":null},{"id":"W4417251858","doi":"10.1109/tmc.2025.3642569","title":"Locally Differentially Private Truth Discovery Over Data Streams","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Differential privacy; Ground truth; Upload; Reliability (semiconductor); Noise (video); Synthetic data; Data stream mining; Information privacy; Data stream","score_opus":0.018917808418519925,"score_gpt":0.27331478683510346,"score_spread":0.25439697841658354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417251858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02063918,0.000280815,0.97403234,0.0014306869,0.000078641424,0.00021183226,0.00060252065,0.0010574264,0.0016665554],"genre_scores_gemma":[0.80355585,0.000355705,0.18875153,0.0010295906,0.0003539417,0.0005100475,0.0013931654,0.00018468275,0.0038654283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9770413,0.0078087146,0.0018060022,0.0063854363,0.0053304103,0.0016282016],"domain_scores_gemma":[0.9284383,0.04147605,0.0051918747,0.018760635,0.004379477,0.0017536778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016536565,0.0013972265,0.0033714029,0.0015640232,0.0019023247,0.005864988,0.0062124366,0.0034310303,0.0032430412],"category_scores_gemma":[0.072063304,0.0011049907,0.0019346615,0.0034739145,0.0036273147,0.012546893,0.011370684,0.0047189808,0.001151002],"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":[0.0053187246,0.00053949136,0.008065772,0.0008147495,0.00046883154,0.0017061285,0.0029750883,0.3440134,0.02193327,0.30658558,0.010895844,0.2966832],"study_design_scores_gemma":[0.00013852277,0.0001643487,0.000516489,0.000037179645,0.00006963476,0.00033647157,0.00023481122,0.7189485,0.011140686,0.26443425,0.00392166,0.00005744389],"about_ca_topic_score_codex":0.0012281378,"about_ca_topic_score_gemma":0.0011806148,"teacher_disagreement_score":0.016536565,"about_ca_system_score_codex":0.00291057,"about_ca_system_score_gemma":0.0044401786,"threshold_uncertainty_score":0.087454736},"labels":[],"label_agreement":null},{"id":"W6903324742","doi":"10.1109/tmc.2025.3554568","title":"Cloud-Edge-End Collaborative Computing-Enabled Intelligent Sharding Blockchain for Industrial IoT Based on PPO Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","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":"Carleton University","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Scalability; Reliability (semiconductor); Throughput; Markov decision process; Hash function; Industrial Internet; Ant colony optimization algorithms; Interoperability; Computational complexity theory","score_opus":0.0292694094569313,"score_gpt":0.2779552570227571,"score_spread":0.2486858475658258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6903324742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055693198,0.0003447988,0.93306845,0.00029964987,0.0000793993,0.000125023,0.00007443752,0.00029485876,0.0100202095],"genre_scores_gemma":[0.95788556,0.00020891814,0.03868578,0.000073350806,0.000020566042,0.00007313176,0.00006068794,0.000016596778,0.0029754457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994318,0.00009474203,0.000026393223,0.00014558794,0.00017155465,0.00012988226],"domain_scores_gemma":[0.99959534,0.00011771978,0.000053370746,0.000070101356,0.00009952006,0.00006392993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004851567,0.00036255945,0.00071701675,0.00027512954,0.00091491267,0.00091504084,0.0010765187,0.000568002,0.0021688836],"category_scores_gemma":[0.00079041405,0.00020299644,0.0004099489,0.0006008644,0.00061993144,0.0011611118,0.001357117,0.0005965664,0.00024698855],"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":[0.00025641816,0.00011983217,0.0011857866,0.00012784127,0.000043507665,0.0005978971,0.0001854431,0.8594604,0.010800196,0.060655124,0.0019610967,0.06460646],"study_design_scores_gemma":[0.000010492228,0.000029556964,0.00006974426,0.0000038506796,0.0000064478513,0.000041790783,0.000017176437,0.99037594,0.0010209726,0.007742157,0.0006761268,0.0000058357346],"about_ca_topic_score_codex":0.0049546505,"about_ca_topic_score_gemma":0.0055303476,"teacher_disagreement_score":0.0049546505,"about_ca_system_score_codex":0.0007507281,"about_ca_system_score_gemma":0.0017700192,"threshold_uncertainty_score":0.009851575},"labels":[],"label_agreement":null},{"id":"W6903379985","doi":"10.1109/tmc.2025.3587702","title":"A Generalizable Prompt-Based Prototypical Framework for CSI-Based Few-Shot and Cross-Domain Activity Recognition","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","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":"Simon Fraser University","funders":"National Natural Science Foundation of China; China Institute of Communications","keywords":"Activity recognition; Generalization; Representation (politics); Class (philosophy); Variety (cybernetics); Euclidean distance; Convolutional neural network; Facial recognition system; Pattern recognition (psychology)","score_opus":0.021991917191092673,"score_gpt":0.29232596668507826,"score_spread":0.2703340494939856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6903379985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055103567,0.00023403195,0.9913641,0.00007781334,0.000039785642,0.00008721232,0.00017399907,0.001809878,0.0007028831],"genre_scores_gemma":[0.39960426,0.00056442135,0.592228,0.00051036244,0.00014327496,0.00052231544,0.0015468051,0.00029530204,0.004585198],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991135,0.0001444091,0.000041785464,0.0004223705,0.00018539083,0.00009243001],"domain_scores_gemma":[0.99919814,0.00020525628,0.000088994326,0.0002042343,0.00022200673,0.00008133678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000989319,0.0011322334,0.0011327649,0.0010007767,0.0004330123,0.0008889497,0.0025965453,0.0011029746,0.002478268],"category_scores_gemma":[0.0028851384,0.00043946508,0.00087266933,0.0011182037,0.0008965471,0.0024649855,0.0019682746,0.0014327195,0.0010374577],"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":[0.0006031757,0.00043514575,0.0031070241,0.00033628085,0.000114720984,0.0005013971,0.00040557157,0.20648193,0.039059706,0.024775084,0.0093514165,0.71482867],"study_design_scores_gemma":[0.000019537294,0.00012439232,0.0007288054,0.000011842941,0.00001630327,0.00021036205,0.000048559992,0.9781869,0.0047059967,0.012846269,0.0030755186,0.000025532445],"about_ca_topic_score_codex":0.0044034584,"about_ca_topic_score_gemma":0.0051773987,"teacher_disagreement_score":0.0044034584,"about_ca_system_score_codex":0.00075114815,"about_ca_system_score_gemma":0.0014120303,"threshold_uncertainty_score":0.008755684},"labels":[],"label_agreement":null},{"id":"W7076052587","doi":"10.1109/tmc.2025.3599384","title":"Two-Tier Submodel Partition Framework for Enhancing UAV Swarm Robustness in Forest Fire Detection","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Geochemistry and Geologic Mapping","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":"York University","funders":"National Natural Science Foundation of China","keywords":"Robustness (evolution); Backup; Software deployment; Swarm behaviour; Adaptability; Upload","score_opus":0.013729224672915876,"score_gpt":0.26504813576543185,"score_spread":0.251318911092516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7076052587","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047486953,0.00032576662,0.9500626,0.00011373513,0.000031094936,0.000038262453,0.00006029227,0.000805196,0.0010761783],"genre_scores_gemma":[0.8974015,0.00019043735,0.10082767,0.00012469859,0.000032606345,0.00010149289,0.0002231689,0.00011060796,0.0009878597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995602,0.000118930475,0.000019418201,0.00012042883,0.000093994946,0.00008695581],"domain_scores_gemma":[0.9992834,0.00031173046,0.000110542016,0.0001035349,0.00011790389,0.000072866045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089947815,0.0011577163,0.00087929156,0.00052017876,0.0004625893,0.00072628504,0.0013100947,0.0007917299,0.0009134606],"category_scores_gemma":[0.002195136,0.00043805077,0.00089431944,0.00029210685,0.0006424326,0.0012152988,0.0014131333,0.0008530083,0.00019352125],"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":[0.000086842265,0.000042144307,0.0011916531,0.000044131066,0.000038376893,0.000083308405,0.000080128986,0.9634389,0.0051806457,0.0020513614,0.00042276608,0.027339723],"study_design_scores_gemma":[0.0000037767843,0.000023768633,0.000115474904,0.000002049325,0.000006290485,0.000008360707,0.000010472216,0.99825495,0.00052153185,0.0009024482,0.00014758499,0.0000031284346],"about_ca_topic_score_codex":0.009274175,"about_ca_topic_score_gemma":0.007471063,"teacher_disagreement_score":0.009274175,"about_ca_system_score_codex":0.0006100438,"about_ca_system_score_gemma":0.00096252206,"threshold_uncertainty_score":0.018440425},"labels":[],"label_agreement":null},{"id":"W7093301899","doi":"10.1109/tmc.2025.3623636","title":"Digital Twin-Assisted Space-Air-Ground Integrated Multi-Access Edge Computing for Low-Altitude Economy: An Online Decentralized Optimization Approach","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Lyapunov optimization; Quality of service; Optimization problem; Resource allocation; Benchmark (surveying); Edge computing; Mobile edge computing; Latency (audio); Low latency (capital markets)","score_opus":0.04652105700738119,"score_gpt":0.31146235445495984,"score_spread":0.26494129744757866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7093301899","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020358028,0.00030868477,0.96743906,0.00079168245,0.00006288476,0.0000664504,0.00006895063,0.00022361703,0.010680703],"genre_scores_gemma":[0.7760426,0.00040606764,0.21580385,0.00025057,0.00008679344,0.00014295714,0.00013742654,0.00008567941,0.0070441943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999608,0.00013167268,0.000012931365,0.00007356685,0.0000959026,0.000077942386],"domain_scores_gemma":[0.9994553,0.00027479604,0.000048405534,0.000058611437,0.000102102276,0.000060781756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081815425,0.00047308143,0.00071809854,0.00034023903,0.00053862005,0.0013299381,0.0012795031,0.00077556656,0.0029974424],"category_scores_gemma":[0.0014628237,0.00027199896,0.0003697535,0.000547245,0.00060499704,0.0014661414,0.0011452193,0.0008257897,0.00029419994],"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":[0.00009717818,0.00006660227,0.0003527686,0.00006246461,0.00003116653,0.000096335076,0.000044359225,0.9042969,0.0015612162,0.055177137,0.0038297363,0.034384098],"study_design_scores_gemma":[0.00000504048,0.00000643988,0.000022759075,0.0000013832745,0.0000021779715,0.000006583832,0.0000068811605,0.99318874,0.0001361425,0.0061660307,0.00045614401,0.0000017361056],"about_ca_topic_score_codex":0.003469658,"about_ca_topic_score_gemma":0.0051027443,"teacher_disagreement_score":0.003469658,"about_ca_system_score_codex":0.0010147155,"about_ca_system_score_gemma":0.0014909116,"threshold_uncertainty_score":0.010027409},"labels":[],"label_agreement":null},{"id":"W7108227258","doi":"10.1109/tmc.2025.3638816","title":"LearnRouter: A Reinforcement Learning-Based Routing for Opportunistic Mobile Networks Using Multi-Armed Bandit Approach","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Routing protocol; Routing (electronic design automation); Reinforcement learning; Transmission (telecommunications); Protocol (science); Adaptive routing; Dynamic Source Routing; Replication (statistics); Upper and lower bounds; Hierarchical routing","score_opus":0.04299475508212949,"score_gpt":0.2975966587367513,"score_spread":0.25460190365462176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7108227258","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022345372,0.00029935385,0.97352684,0.00019301673,0.000052291925,0.00007917947,0.000027779211,0.0012425309,0.0022337069],"genre_scores_gemma":[0.81088597,0.00026354758,0.18447046,0.00022385303,0.000045895205,0.00023983768,0.0000769682,0.00008793037,0.003705483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997614,0.0000800327,0.0000108412105,0.000043172757,0.000059323946,0.00004516301],"domain_scores_gemma":[0.99951255,0.00026125347,0.00008514831,0.000034842105,0.00006817213,0.000038021455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006865103,0.0004962225,0.0007252576,0.00035646206,0.00034360992,0.0004984059,0.0012928451,0.0006981076,0.0010149794],"category_scores_gemma":[0.001135642,0.00021774572,0.0003262628,0.00024380752,0.00050603796,0.0005865196,0.00076843647,0.00070214196,0.00020236624],"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":[0.000078256475,0.000107152395,0.0005895232,0.000044516284,0.000049538234,0.0000842996,0.00005616652,0.91858476,0.0019258959,0.0062456275,0.0015440978,0.070690095],"study_design_scores_gemma":[0.000008913422,0.00002907226,0.000035364388,0.0000019489464,0.0000036059535,0.000012726822,0.0000043421583,0.9984433,0.00022380354,0.0008462512,0.00038787123,0.0000027853598],"about_ca_topic_score_codex":0.0048050988,"about_ca_topic_score_gemma":0.004196561,"teacher_disagreement_score":0.0048050988,"about_ca_system_score_codex":0.00053575495,"about_ca_system_score_gemma":0.0010111791,"threshold_uncertainty_score":0.009554267},"labels":[],"label_agreement":null},{"id":"W7117134956","doi":"10.1109/tmc.2025.3647655","title":"KAFL-HD: Knowledge Alignment in Asynchronous Federated Learning With Heterogeneous Data","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Mobile Computing","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":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Federated learning; Asynchronous communication; Convergence (economics); Training (meteorology); Order (exchange); Quality (philosophy); Recommender system; Training set","score_opus":0.03051056994780319,"score_gpt":0.29884756921556765,"score_spread":0.26833699926776444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117134956","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026382098,0.00030851457,0.9694605,0.000230948,0.000056664183,0.00008039776,0.000102956685,0.0022019367,0.0011759632],"genre_scores_gemma":[0.78720856,0.00018862836,0.20891464,0.00043554264,0.00008897148,0.00020443962,0.00041817277,0.00012748988,0.0024136521],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979395,0.00048833695,0.00015672533,0.00070933416,0.00042532416,0.00028076948],"domain_scores_gemma":[0.9961797,0.0013577648,0.0003597425,0.0011174395,0.0007443592,0.00024107695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033886866,0.000943429,0.0014353278,0.00093314267,0.0009877551,0.0017521415,0.0031936807,0.0013774243,0.0017396822],"category_scores_gemma":[0.010206785,0.00039822084,0.0005999354,0.001259906,0.00092722545,0.004634887,0.0032683578,0.0018312641,0.000513017],"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":[0.00072688685,0.00059224287,0.0042668334,0.00025266787,0.00014833164,0.00023723277,0.0005007352,0.42211455,0.0053190147,0.014922369,0.0058334414,0.5450857],"study_design_scores_gemma":[0.0000416162,0.00006546879,0.000320895,0.000010378393,0.000017985642,0.0000592933,0.00004819941,0.9845255,0.0018812381,0.012087473,0.0009282377,0.000013646916],"about_ca_topic_score_codex":0.0061371354,"about_ca_topic_score_gemma":0.0052628512,"teacher_disagreement_score":0.0061371354,"about_ca_system_score_codex":0.00131667,"about_ca_system_score_gemma":0.0024625466,"threshold_uncertainty_score":0.017921329},"labels":[],"label_agreement":null},{"id":"W7117559511","doi":"10.1109/tmc.2025.3649382","title":"Cross-Sensory Transmission for 6G-Enabled Immersive Communication","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Network packet; Packet loss; Transmission (telecommunications); Data compression; Reliability (semiconductor); Encoding (memory); Data transmission; Coding (social sciences); Transmission delay","score_opus":0.01707638603877888,"score_gpt":0.3053885496599548,"score_spread":0.28831216362117595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117559511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0981245,0.0015710423,0.8887259,0.00039330934,0.00016188086,0.000077860546,0.00010118755,0.0010018997,0.009842514],"genre_scores_gemma":[0.8679608,0.0010560612,0.12561686,0.00030780447,0.00007837872,0.000075344455,0.00013164703,0.00011441532,0.0046586543],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976903,0.000050583938,0.00000854808,0.000031349267,0.00010647093,0.00003398202],"domain_scores_gemma":[0.9997106,0.00010081664,0.000042695454,0.000052037205,0.000071127644,0.000022643748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028144152,0.0005101375,0.00020694095,0.00033722888,0.00017519711,0.00047536872,0.0005398371,0.00042238567,0.0031425445],"category_scores_gemma":[0.0006482358,0.0001295,0.00022911622,0.00029631652,0.00036149097,0.0008400213,0.00080475933,0.0005626555,0.0005195383],"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":[0.0004775865,0.00018781165,0.0016458217,0.00055095984,0.000083450584,0.00085505884,0.00051048986,0.03761343,0.6053042,0.023882177,0.004183987,0.3247051],"study_design_scores_gemma":[0.00006961287,0.0014282815,0.0031771557,0.000249021,0.00014922582,0.0029649262,0.0004922407,0.48637778,0.44597512,0.016886229,0.04209441,0.00013601207],"about_ca_topic_score_codex":0.00039513665,"about_ca_topic_score_gemma":0.00071495376,"teacher_disagreement_score":0.0031425445,"about_ca_system_score_codex":0.00020415719,"about_ca_system_score_gemma":0.00018320492,"threshold_uncertainty_score":0.010512829},"labels":[],"label_agreement":null},{"id":"W7117720092","doi":"10.1109/tmc.2025.3649541","title":"Efficient Collision-Free Data Collection for Underwater Acoustic Sensor Networks: A Hierarchical DRL Approach","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Data collection; Leverage (statistics); Network packet; Trajectory; Underwater; Reinforcement learning; Wireless sensor network","score_opus":0.02787122702188533,"score_gpt":0.26818142534530653,"score_spread":0.2403101983234212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117720092","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010393048,0.00030280632,0.9857685,0.00039113732,0.00003418336,0.00009510621,0.000079433,0.0009327323,0.0020029764],"genre_scores_gemma":[0.4837806,0.0003496368,0.5093762,0.0006139572,0.00009818875,0.00035162404,0.0004833204,0.0002967373,0.004649725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984218,0.00037092218,0.00009150662,0.00038726119,0.0004702942,0.0002582391],"domain_scores_gemma":[0.9978243,0.00084884395,0.00030112776,0.0003396093,0.00048659823,0.00019948748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001698314,0.000858386,0.0013865457,0.0010817801,0.000980827,0.0011258122,0.004639487,0.0011351445,0.0023489092],"category_scores_gemma":[0.0038028536,0.00086641184,0.0010542499,0.0012459975,0.0009150499,0.0025485489,0.0036629029,0.0013862567,0.0007851252],"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":[0.0001477168,0.00021651239,0.0012557867,0.00017956545,0.000066234694,0.00015568976,0.00030052746,0.8277333,0.006503172,0.009383704,0.0040793926,0.14997844],"study_design_scores_gemma":[0.000010075324,0.000026276977,0.000055654626,0.0000027012572,0.0000055744476,0.000015879077,0.000022230757,0.9972947,0.0005334011,0.0015762774,0.00045104354,0.000006190084],"about_ca_topic_score_codex":0.008211705,"about_ca_topic_score_gemma":0.010456373,"teacher_disagreement_score":0.008211705,"about_ca_system_score_codex":0.0019115618,"about_ca_system_score_gemma":0.0029139775,"threshold_uncertainty_score":0.016327798},"labels":[],"label_agreement":null}]}