{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":91,"total_is_capped":false,"direct_labels_cover":1,"predictions_cover":91,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"7494d603d71a","filters":{"venue":"IEEE Transactions on Services Computing"}},"results":[{"id":"W2999645065","doi":"10.1109/tsc.2020.2966970","title":"An Energy-Efficient SDN Controller Architecture for IoT Networks With Blockchain-Based Security","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":333,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"National Science Foundation","keywords":"Computer science; Computer network; Routing protocol; Blockchain; Energy consumption; Efficient energy use; Distributed computing; Computer security; Routing (electronic design automation)","retraction":null,"screen_n_in":null,"score":{"opus":0.007143031788117552,"gpt":0.2147177771639377,"spread":0.2075747453758201,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002061182,0.0003118259,0.0003339041,0.0001441185,0.0007116729,0.0001266555,0.001141216,0.0002084421,0.000004060375],"category_scores_gemma":[8.845132e-7,0.0002770965,0.0001344677,0.0008362581,0.00007646116,0.00002233624,0.000007144173,0.0004652818,0.000002601198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003310586,"about_ca_system_score_gemma":0.00006466726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005529527,"about_ca_topic_score_gemma":0.0001912284,"domain_scores_codex":[0.9980375,0.0000923621,0.0003240918,0.0008314772,0.0002420998,0.0004725289],"domain_scores_gemma":[0.9985465,0.0002619821,0.000168776,0.0006270956,0.0001670414,0.0002285372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009205862,0.0002727867,0.000006301647,0.00003853469,0.00004244664,0.000002426021,0.000948872,0.9659918,0.0001756436,0.003548075,0.000005664529,0.02887537],"study_design_scores_gemma":[0.00134036,0.0004943793,0.00001024833,0.00003732077,0.0000303297,0.000007157429,0.00009381082,0.9930515,0.003537631,0.0005114845,0.0005693574,0.0003164481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08460496,0.00004714579,0.9096102,0.004219783,0.00009995652,0.0004753965,0.00001428659,0.000908746,0.00001958863],"genre_scores_gemma":[0.9427471,6.646914e-7,0.05117874,0.005844182,0.0001047361,0.00009218694,0.000003476343,0.00002743338,0.000001515954],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8584314,"threshold_uncertainty_score":0.9999681,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1974304004","doi":"10.1109/tsc.2013.2295611","title":"Panda: Public Auditing for Shared Data with Efficient User Revocation in the Cloud","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Cloud Data Security Solutions","field":"Computer Science","cited_by":321,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Revocation; Cloud computing; Audit; Data integrity; Computer security; Data sharing; Database; Operating system; Overhead (engineering)","retraction":null,"screen_n_in":null,"score":{"opus":0.04147783546398809,"gpt":0.2758063149882002,"spread":0.2343284795242122,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015506,0.0001884761,0.0001809818,0.0001808069,0.0007456737,0.0005558181,0.002706224,0.00006217499,0.000003900889],"category_scores_gemma":[0.00001973898,0.0001491859,0.00004676078,0.001026963,0.00003664532,0.0006348567,0.00003586033,0.000294543,0.00001911298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000673047,"about_ca_system_score_gemma":0.00006527986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001830716,"about_ca_topic_score_gemma":0.0020691,"domain_scores_codex":[0.997897,0.0002198465,0.0003669623,0.0006979213,0.0003885123,0.000429783],"domain_scores_gemma":[0.9968535,0.0009672815,0.0001868488,0.001783353,0.0001429906,0.00006600895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001214797,0.002593545,0.0007517039,0.001019909,0.0002234212,0.00001029493,0.03680352,0.6558773,0.0006623683,0.04453616,0.001155281,0.256245],"study_design_scores_gemma":[0.0006139661,0.00007769971,0.0007733529,0.0001477294,0.00001815902,0.00001782789,0.0004090509,0.9920893,0.00009723272,0.0001064697,0.00545371,0.0001955444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05087892,0.00001454198,0.9444596,0.003275208,0.0004762851,0.0005311592,0.00009101183,0.0001956158,0.00007761388],"genre_scores_gemma":[0.9570674,0.000001894699,0.04168478,0.0009377326,0.0001598164,0.00003556227,0.0000915219,0.00001602452,0.000005306104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9061884,"threshold_uncertainty_score":0.6083626,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2888896501","doi":"10.1109/tsc.2018.2867482","title":"A Double Deep Q-Learning Model for Energy-Efficient Edge Scheduling","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":156,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"St. Francis Xavier University","funders":"National Science Foundation of Sri Lanka","keywords":"Computer science; Sigmoid function; Edge device; Energy consumption; Frequency scaling; Deep learning; Efficient energy use; Scheduling (production processes); Energy (signal processing); Algorithm; Artificial intelligence; Mathematical optimization; Artificial neural network; Mathematics; Electrical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02310774106076888,"gpt":0.2503590141494647,"spread":0.2272512730886958,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003217276,0.0002177218,0.0002001177,0.0002124211,0.001130339,0.0002947709,0.0007096471,0.00008603856,0.000003347301],"category_scores_gemma":[0.000001083959,0.0002261806,0.000179639,0.0003952739,0.00003208553,0.0002354257,0.00001434241,0.0002470883,0.00003055949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006485645,"about_ca_system_score_gemma":0.00004202278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001474046,"about_ca_topic_score_gemma":0.0001423239,"domain_scores_codex":[0.9983525,0.00004402487,0.0003035329,0.0005803299,0.0002602085,0.0004594367],"domain_scores_gemma":[0.9989693,0.0001475204,0.0001245111,0.0004052543,0.0002361858,0.0001172221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004237184,0.00008689813,0.000002971469,0.00003019014,0.00002816388,9.657838e-7,0.002055896,0.933229,0.001424247,0.001390986,0.000001704958,0.06170658],"study_design_scores_gemma":[0.0007877836,0.0001241504,0.000002531084,0.0001060301,0.00002011449,0.000008870759,0.0001689409,0.9942657,0.004009377,0.0001388345,0.0001119815,0.0002556734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1184996,0.0000497725,0.8795035,0.0001039614,0.0009239729,0.0001239058,0.000001495497,0.0005065778,0.0002872621],"genre_scores_gemma":[0.9512227,0.000003910981,0.04778173,0.0005647295,0.0001883861,0.00001672858,0.000001480699,0.00002398363,0.0001963186],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8327231,"threshold_uncertainty_score":0.9223377,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2283608423","doi":"10.1109/tsc.2015.2497705","title":"Deep Computation Model for Unsupervised Feature Learning on Big Data","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":151,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"St. Francis Xavier University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Computer science; Deep learning; Artificial intelligence; Feature vector; Big data; Tensor (intrinsic definition); Data modeling; Feature (linguistics); Pattern recognition (psychology); Autoencoder; Computation; Feature learning; Machine learning; Data mining; Algorithm; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1697865211088342,"gpt":0.3558426370773456,"spread":0.1860561159685115,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003130082,0.0002193679,0.0002374432,0.0001483829,0.0004874653,0.0001127513,0.0004242036,0.0001186407,0.000004277739],"category_scores_gemma":[0.000007708683,0.0002218468,0.00008602383,0.000275813,0.00001915605,0.0001505345,0.000007093954,0.0003504943,0.00004245756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006594015,"about_ca_system_score_gemma":0.00003990635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001193332,"about_ca_topic_score_gemma":0.00008936493,"domain_scores_codex":[0.9986089,0.00007301666,0.0003055087,0.0004744219,0.0002835595,0.0002546355],"domain_scores_gemma":[0.9984612,0.0004992023,0.0001579576,0.0005167339,0.0002172882,0.0001475652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007005454,0.0002818372,0.000004342568,0.0001151279,0.00004722681,5.896964e-7,0.001834193,0.9532151,0.0002579592,0.0008181647,0.0003724872,0.04298297],"study_design_scores_gemma":[0.001086136,0.00008848603,0.000007944555,0.00009392881,0.00007331492,0.000006408893,0.0007511956,0.9905558,0.0004518152,0.006222681,0.0004363305,0.0002260248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03738101,0.00001053762,0.9601305,0.0007519748,0.0003161597,0.0004947248,0.00006320507,0.0004374112,0.0004144627],"genre_scores_gemma":[0.890043,0.000002322827,0.1088304,0.0005382014,0.0001549342,0.00002577963,0.0001450227,0.00005027417,0.0002101329],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.852662,"threshold_uncertainty_score":0.9046652,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1973467201","doi":"10.1109/tsc.2015.2402679","title":"An Integrated Semantic Web Service Discovery and Composition Framework","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Mountain Equipment Co-operative","keywords":"Scalability; Service discovery; Web service; Semantic Web; Dependency graph; Service composition; Flexibility (engineering); Graph","retraction":null,"screen_n_in":null,"score":{"opus":0.01358951972328085,"gpt":0.2526650459007549,"spread":0.2390755261774741,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003522988,0.0004224768,0.0003629152,0.0003479702,0.000484583,0.0008837797,0.00121976,0.0001950496,0.000004963569],"category_scores_gemma":[6.46216e-7,0.0003909186,0.00007794213,0.001487213,0.00003685258,0.001867202,0.00002755068,0.0006042088,0.00005966766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006400958,"about_ca_system_score_gemma":0.0001083549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009076525,"about_ca_topic_score_gemma":0.001231168,"domain_scores_codex":[0.9974036,0.0002617672,0.0004444986,0.0008667561,0.0005156354,0.0005077953],"domain_scores_gemma":[0.9979765,0.0002512868,0.0001839929,0.0009118315,0.0002796237,0.0003967786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000552971,0.002441875,0.001345438,0.001568744,0.0005364111,0.0001481244,0.1017611,0.6529952,0.02144843,0.00966479,0.00002625578,0.2075106],"study_design_scores_gemma":[0.0008131042,0.0003379335,0.0003523104,0.000560477,0.0000559008,0.0001035334,0.00252317,0.9868427,0.005929331,0.001535088,0.0003751209,0.0005713523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4196257,0.00005558308,0.578138,0.0007060288,0.0007264476,0.0001629999,0.0000106067,0.0004792295,0.00009537244],"genre_scores_gemma":[0.946169,0.00001241628,0.04744353,0.00616172,0.0001430274,0.000008948981,0.00001898877,0.00003536429,0.00000706942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5306945,"threshold_uncertainty_score":0.9998543,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2087492381","doi":"10.1109/tsc.2011.10","title":"Runtime Enforcement of Web Service Message Contracts with Data","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":107,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal; Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Stateful firewall; Web service; XML; Programming language; Overhead (engineering); Java applet; SOAP; Database transaction; Database; Algorithm; Computer network; Java; Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.03238225192993553,"gpt":0.2371036943777905,"spread":0.204721442447855,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004090834,0.0004112569,0.0004449535,0.0003086957,0.0003341532,0.0001154508,0.003687951,0.0001139259,0.000146864],"category_scores_gemma":[3.87463e-7,0.0003448045,0.00007861896,0.001297829,0.00004102157,0.001088217,0.00008197491,0.0003709505,0.00006266749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002579525,"about_ca_system_score_gemma":0.0001238543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002438893,"about_ca_topic_score_gemma":0.003787973,"domain_scores_codex":[0.9971527,0.0001153852,0.0006138053,0.0009103057,0.0006458246,0.0005619904],"domain_scores_gemma":[0.9966196,0.0002007707,0.0004120367,0.002273333,0.0002973156,0.000196894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002653711,0.00929819,0.002426642,0.007562303,0.005375369,0.0004832673,0.2363544,0.2501299,0.02672217,0.03593201,0.0001014734,0.4229606],"study_design_scores_gemma":[0.002729523,0.0006862103,0.00090833,0.0009399931,0.0002328458,0.00009123369,0.001960138,0.9294462,0.05938648,0.0003712875,0.00227647,0.0009713214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1321933,0.00007948908,0.855262,0.0002650893,0.0006591664,0.0004910055,0.00007062309,0.0005069447,0.01047233],"genre_scores_gemma":[0.95449,0.00001373158,0.04284792,0.002517201,0.00005125603,0.000007762626,0.00002159802,0.00003382761,0.00001671804],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8222967,"threshold_uncertainty_score":0.9999004,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2118448117","doi":"10.1109/tsc.2010.44","title":"An Adaptive and Intelligent SLA Negotiation System for Web Services","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":104,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Negotiation; Service-level agreement; Quality of service; Function (biology); Web service; Service level; Service (business); Service provider; Process management; World Wide Web; Computer network; Business; Marketing","retraction":null,"screen_n_in":null,"score":{"opus":0.01553146166447645,"gpt":0.2549581396855782,"spread":0.2394266780211017,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004385932,0.0002314199,0.0002280961,0.0002052419,0.0005663069,0.0003579384,0.000535755,0.0001385148,0.000003480902],"category_scores_gemma":[4.870028e-7,0.000224502,0.00008110073,0.0002671804,0.0000157791,0.0008470614,0.000006626909,0.0002191889,0.00002058715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004567681,"about_ca_system_score_gemma":0.00003057579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003229989,"about_ca_topic_score_gemma":0.001162704,"domain_scores_codex":[0.9983748,0.00009255405,0.0004092294,0.0005824686,0.0002521607,0.0002887639],"domain_scores_gemma":[0.9987592,0.0001741852,0.0002511284,0.0004797234,0.0001860648,0.0001496686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002751486,0.001076579,0.001286722,0.004784261,0.00039309,0.00001110796,0.04207557,0.1450648,0.3167482,0.02278613,0.00001465376,0.4654837],"study_design_scores_gemma":[0.0004235301,0.0001872722,0.0007599377,0.000167517,0.00002314927,0.00001822582,0.00111308,0.9810824,0.01579351,0.0000391301,0.0001604596,0.000231722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3622548,0.00001505652,0.6353389,0.00005057091,0.001607002,0.0003917273,0.00001086533,0.0002839593,0.00004708452],"genre_scores_gemma":[0.9716167,0.000003440584,0.02796921,0.0001457343,0.0001976626,0.00002892925,0.000005933271,0.00002124946,0.00001107567],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8360177,"threshold_uncertainty_score":0.9154927,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2023794939","doi":"10.1109/tsc.2013.20","title":"Constructing a Global Social Service Network for Better Quality of Web Service Discovery","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Web service; World Wide Web; Service (business); Service design; Service discovery; Service level objective; Social network (sociolinguistics); Service delivery framework; Business; Social media; Marketing","retraction":null,"screen_n_in":null,"score":{"opus":0.01909038331524103,"gpt":0.2718709132904562,"spread":0.2527805299752151,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005219775,0.0005276499,0.0007232287,0.0001440722,0.0008968639,0.0004983821,0.001896323,0.000243202,0.00002798736],"category_scores_gemma":[0.000001336392,0.0005198753,0.0003391138,0.002209252,0.00005391035,0.001435956,0.00006260513,0.0003906687,0.00005236504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001113267,"about_ca_system_score_gemma":0.0001403285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004688769,"about_ca_topic_score_gemma":0.00852395,"domain_scores_codex":[0.9961171,0.0002727256,0.00111278,0.0009453242,0.0006188136,0.0009333112],"domain_scores_gemma":[0.9969185,0.0006075138,0.0007134357,0.0007964461,0.0007886566,0.0001754635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007181847,0.001826374,0.01152828,0.01322648,0.002319145,0.00001120236,0.03079793,0.2256184,0.0149289,0.03572198,0.0003667623,0.6629363],"study_design_scores_gemma":[0.004092366,0.0002704694,0.009979119,0.0008305243,0.000191314,0.00005221566,0.006430253,0.9600723,0.006068488,0.009651971,0.0006103098,0.001750687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4482791,0.00002565316,0.5463853,0.002902923,0.0009842666,0.0005430168,0.00008947928,0.0002958686,0.0004943885],"genre_scores_gemma":[0.9195932,0.000001875029,0.06622761,0.01357917,0.0004712839,0.00005912899,0.00002241279,0.00003619724,0.000009154182],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7344539,"threshold_uncertainty_score":0.9997253,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3011380968","doi":"10.1109/tsc.2020.2980793","title":"A Survey on Web Service QoS Prediction Methods","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Web service; Collaborative filtering; Quality of service; The Internet; Field (mathematics); Service (business); World Wide Web; Data mining; Recommender system; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.0637809884499326,"gpt":0.3188678052974772,"spread":0.2550868168475446,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009011487,0.0002874075,0.0003334722,0.0001728841,0.0003738934,0.0002823414,0.001036721,0.0001370214,0.00001365459],"category_scores_gemma":[0.000003125416,0.0002787881,0.0001211585,0.001194733,0.00001007052,0.0004095184,0.00001618272,0.0004458181,0.000074103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005832173,"about_ca_system_score_gemma":0.00005126052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005964366,"about_ca_topic_score_gemma":0.000345105,"domain_scores_codex":[0.997426,0.0006744926,0.0004893611,0.0007206018,0.0003477281,0.0003417966],"domain_scores_gemma":[0.9983797,0.0004591452,0.0001880508,0.0005961075,0.0001695989,0.000207364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001610049,0.001079361,0.002260618,0.001300402,0.0005931483,0.00003800579,0.01738016,0.1638289,0.01374781,0.001093246,0.002903379,0.7956139],"study_design_scores_gemma":[0.0003369506,0.0002879787,0.002372233,0.0001363251,0.00001147485,0.000007474388,0.00006190375,0.9852936,0.008971165,0.00002693883,0.002233787,0.0002602132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009223324,0.00001983387,0.9845328,0.002059672,0.001574527,0.0003313945,0.00003873471,0.001474997,0.0007446884],"genre_scores_gemma":[0.9106316,0.000008432571,0.08284255,0.006256151,0.0001889919,0.00001610111,0.000005562154,0.00002864736,0.00002197718],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9016903,"threshold_uncertainty_score":0.9999664,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2976431391","doi":"10.1109/tsc.2019.2944360","title":"Improving the Schedulability of Real-Time Tasks Using Fog Computing","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Cloud computing; Scheduling (production processes); Fog computing; Response time; Execution time; Schedule; Computation; Notation; Distributed computing; Parallel computing; Embedded system; Algorithm; Operating system; Arithmetic","retraction":null,"screen_n_in":null,"score":{"opus":0.01452352159092712,"gpt":0.2472570099561804,"spread":0.2327334883652532,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001373642,0.0003835897,0.0005135646,0.0002380115,0.0008146105,0.0002806618,0.001723924,0.0001512635,0.00001024268],"category_scores_gemma":[0.000005315435,0.0003282048,0.0002839848,0.001133054,0.00008205593,0.000538344,0.0000803475,0.0005670909,0.00009430999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001370079,"about_ca_system_score_gemma":0.0001384805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009868963,"about_ca_topic_score_gemma":0.000008146326,"domain_scores_codex":[0.99674,0.0002883897,0.000850584,0.0008346459,0.0005645468,0.0007217759],"domain_scores_gemma":[0.9968989,0.000837508,0.0005719972,0.00130244,0.000272435,0.0001167304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000554633,0.0004464035,0.002142534,0.001053653,0.0002244354,0.000009069443,0.009917632,0.5820557,0.1770524,0.0003089365,0.00001536611,0.2267184],"study_design_scores_gemma":[0.0004297204,0.00009692118,0.0007892364,0.0002833864,0.00003069047,0.00002630025,0.000180313,0.9803031,0.01740363,0.00008812971,0.00003078385,0.0003378066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5043156,0.00001666973,0.4915372,0.00005268018,0.003310106,0.0002612365,6.489995e-7,0.0002338943,0.0002719506],"genre_scores_gemma":[0.9348334,0.000001690496,0.0644877,0.0001884596,0.0004252203,0.000001071109,0.000001113409,0.00003448872,0.00002687125],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4305178,"threshold_uncertainty_score":0.999917,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2082897135","doi":"10.1109/tsc.2015.2401833","title":"An Efficient Approach to Generating Location-Sensitive Recommendations in Ad-hoc Social Network Environments","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"St. Francis Xavier University","funders":"Seventh Framework Programme; National Natural Science Foundation of China","keywords":"Computer science; Recommender system; Bipartite graph; Social network (sociolinguistics); Information retrieval; Graph; Set (abstract data type); Similarity (geometry); Collaborative filtering; Data mining; World Wide Web; Artificial intelligence; Social media; Theoretical computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.03243849047183832,"gpt":0.2751093813230916,"spread":0.2426708908512533,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007842766,0.0002135664,0.0002344336,0.0002098016,0.0004913587,0.000237819,0.0005530874,0.00009328726,9.813323e-7],"category_scores_gemma":[8.873103e-7,0.0002310896,0.00005187277,0.0008620694,0.0000135139,0.0003120491,0.0000156175,0.0002503121,0.00002411076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002319885,"about_ca_system_score_gemma":0.00004552367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001441492,"about_ca_topic_score_gemma":0.0001601813,"domain_scores_codex":[0.9979517,0.0003160457,0.0004586256,0.0005844971,0.0002990284,0.0003901444],"domain_scores_gemma":[0.9991316,0.00006805472,0.0001463166,0.0004018235,0.00007476388,0.0001775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006772415,0.0003249683,0.00002002705,0.00001406359,0.00001519191,0.000001364121,0.0118491,0.9126108,0.0001264434,0.0003283994,0.00006768877,0.07463519],"study_design_scores_gemma":[0.0003069359,0.0001077521,0.0002404519,0.00007525604,0.000005270269,0.000007975773,0.00131293,0.9962399,0.0007029315,0.00005421802,0.0006879632,0.0002584111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03779207,0.00002113832,0.9600472,0.000428806,0.0006368881,0.0004105897,0.000003144368,0.0002419515,0.0004182135],"genre_scores_gemma":[0.7897285,0.00000196111,0.20938,0.0006942021,0.0001215946,0.00003663448,0.000007008393,0.00001708909,0.000013075],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7519364,"threshold_uncertainty_score":0.9423559,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2293605201","doi":"10.1109/tsc.2015.2480396","title":"Hybrid Optimization Algorithm for Large-Scale QoS-Aware Service Composition","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Mountain Equipment Co-operative","keywords":"Computer science; Quality of service; Service composition; Algorithm; Scale (ratio); Distributed computing; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.01408581322858381,"gpt":0.245835908621848,"spread":0.2317500953932642,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004456648,0.0004311274,0.0003839039,0.0003402619,0.0007460907,0.000394391,0.00130926,0.0001383443,0.00001087018],"category_scores_gemma":[3.78464e-7,0.0004445364,0.0001776998,0.0009879622,0.00001386175,0.0009530758,0.0000313228,0.0003242616,0.00006855621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001101502,"about_ca_system_score_gemma":0.00009576418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002306555,"about_ca_topic_score_gemma":0.0003137937,"domain_scores_codex":[0.9971443,0.0001455272,0.0005492285,0.000894238,0.0005754982,0.0006912387],"domain_scores_gemma":[0.9976423,0.0002114331,0.0002671595,0.0007857737,0.0007476109,0.000345666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004567016,0.0003902356,0.00000689228,0.0002033449,0.00007323996,0.000006823266,0.006650941,0.941843,0.0001458701,0.0001052556,0.00004677554,0.05048199],"study_design_scores_gemma":[0.001736945,0.0002183983,0.00001306918,0.0001781518,0.00005429815,0.00005451735,0.001213967,0.9871192,0.0074663,0.0002105382,0.001260094,0.0004745127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01051306,0.00005007318,0.9840093,0.001070773,0.00249133,0.0006574678,0.0001076163,0.0008951955,0.0002052115],"genre_scores_gemma":[0.5152846,0.000007505219,0.4761419,0.007782318,0.0004826257,0.00006051287,0.0001512854,0.0000656308,0.00002359173],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5078673,"threshold_uncertainty_score":0.9998006,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2037386159","doi":"10.1109/tsc.2012.1","title":"THEMIS: A Mutually Verifiable Billing System for the Cloud Computing Environment","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Cloud Data Security Solutions","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Cloud computing; Computer science; Public key infrastructure; Computer security; Verifiable secret sharing; Service provider; Public-key cryptography; Overhead (engineering); Service-level agreement; Computer network; Service (business); Encryption; Operating system; Business","retraction":null,"screen_n_in":null,"score":{"opus":0.02220452439406845,"gpt":0.2375801313520965,"spread":0.215375606958028,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001105578,0.0003207976,0.0002832584,0.0001156241,0.002075332,0.0003291615,0.001558161,0.0001121236,0.000008177547],"category_scores_gemma":[0.000003167583,0.0002664443,0.0002195886,0.0004481401,0.00005898056,0.0005330152,0.00004402586,0.0003748599,0.0001451466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002404016,"about_ca_system_score_gemma":0.00004478139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001854381,"about_ca_topic_score_gemma":0.00002390177,"domain_scores_codex":[0.9974761,0.0001204728,0.0005322239,0.0005556137,0.0004459906,0.0008696195],"domain_scores_gemma":[0.9971387,0.001216821,0.0002437868,0.001146146,0.00006698399,0.0001875884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004360891,0.0007580252,0.00009234843,0.0008330969,0.0004170723,0.000003834605,0.02387408,0.8917317,0.002647764,0.01108942,0.0001627525,0.06834625],"study_design_scores_gemma":[0.0004779749,0.00005432652,0.00005054285,0.0002084265,0.0000714753,0.00004743609,0.001995731,0.9851487,0.003925369,0.00002872494,0.007658483,0.0003328068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02520009,0.0003682432,0.9695975,0.0003552719,0.003060905,0.0006642563,0.00005412052,0.0005626741,0.0001369787],"genre_scores_gemma":[0.9505282,0.0000162929,0.04843408,0.0003629366,0.0005650544,0.00003331172,0.000007395975,0.00003462032,0.00001808437],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9253281,"threshold_uncertainty_score":0.9999788,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2078852715","doi":"10.1109/tsc.2015.2413111","title":"CCCloud: Context-Aware and Credible Cloud Service Selection Based on Subjective Assessment and Objective Assessment","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"University of Wollongong; University of Victoria","keywords":"Cloud computing; Computer science; Context (archaeology); Collusion; Credibility; Cloud testing; Data mining; Selection (genetic algorithm); Benchmark (surveying); Machine learning; Cloud computing security","retraction":null,"screen_n_in":null,"score":{"opus":0.02555015777364206,"gpt":0.2922483581638534,"spread":0.2666982003902113,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008610906,0.0004514899,0.0004269571,0.0003784134,0.0009235595,0.0005391347,0.0004569628,0.0001705772,0.000002324378],"category_scores_gemma":[0.000002633134,0.0004599978,0.00008037498,0.001044684,0.00004724906,0.0005760959,0.00003478344,0.000712322,0.00001223189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004468116,"about_ca_system_score_gemma":0.0003557438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007947439,"about_ca_topic_score_gemma":0.0002462614,"domain_scores_codex":[0.9970179,0.0003524028,0.0004368253,0.001007248,0.0006168126,0.0005688085],"domain_scores_gemma":[0.9979224,0.0005696005,0.0002487669,0.0004186307,0.0005096792,0.0003309582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005543592,0.002589236,0.01825481,0.001181739,0.0007383139,0.00008727687,0.03333922,0.508733,0.001094206,0.001870663,0.0006096441,0.4309475],"study_design_scores_gemma":[0.00177084,0.0007079825,0.008137412,0.000290786,0.00004340127,0.00004011744,0.001148133,0.9855406,0.00131469,0.0002945571,0.0002383952,0.0004731452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2464154,0.0000246497,0.7463134,0.0006018509,0.005031436,0.000389045,0.000001996693,0.0004087653,0.0008134877],"genre_scores_gemma":[0.9765982,0.000004247791,0.02074169,0.001989909,0.0005844062,0.00001923354,0.000004084463,0.00003588875,0.0000223385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7301828,"threshold_uncertainty_score":0.9997852,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4391020236","doi":"10.1109/tsc.2024.3355937","title":"Hierarchical Deep Reinforcement Learning for Joint Service Caching and Computation Offloading in Mobile Edge-Cloud Computing","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Chongqing Research Program of Basic Research and Frontier Technology; Chongqing University of Posts and Telecommunications","keywords":"Computer science; Computation offloading; Cloud computing; Reinforcement learning; Distributed computing; Edge computing; Latency (audio); Mobile edge computing; Mobile cloud computing; Enhanced Data Rates for GSM Evolution; Server; Edge device; Computer network; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.01995441115390935,"gpt":0.2733401587742251,"spread":0.2533857476203157,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001258428,0.0004541467,0.0004895843,0.0007446493,0.001090478,0.0009642487,0.000532581,0.0001708715,0.000001914059],"category_scores_gemma":[0.000006776819,0.0004918821,0.0001715086,0.001289277,0.00003552248,0.0006958421,0.00006518991,0.001053211,0.0000251251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002382627,"about_ca_system_score_gemma":0.00008828667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002357858,"about_ca_topic_score_gemma":0.00007477379,"domain_scores_codex":[0.9965321,0.0002064221,0.0009270333,0.001067315,0.0004163031,0.000850781],"domain_scores_gemma":[0.9980522,0.001133044,0.000187882,0.0002900137,0.0001465245,0.0001902975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001330754,0.00004375432,0.0000395544,0.0007460603,0.0000425828,0.0000142063,0.01080749,0.756003,0.0006004144,0.0001185767,0.000008182586,0.2315628],"study_design_scores_gemma":[0.0006812816,0.0002090723,0.0001579854,0.001343969,0.00002807566,0.00006435727,0.0004591488,0.9946972,0.00100214,0.0003140143,0.0005452224,0.0004975638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2222749,0.0003227873,0.769862,0.0002795906,0.005873099,0.0005646517,2.875046e-7,0.0006815761,0.0001411022],"genre_scores_gemma":[0.9671264,0.00001649477,0.03125198,0.0005219796,0.0009706323,0.00002309034,0.00001012795,0.00005743492,0.00002190368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7448515,"threshold_uncertainty_score":0.9997533,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2525723988","doi":"10.1109/tsc.2015.2444845","title":"Energy Efficient Scheduling and Management for Large-Scale Services Computing Systems","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Energy consumption; Efficient energy use; Distributed computing; Scheduling (production processes); Quality of service; Queue; Computer network; Mathematical optimization","retraction":null,"screen_n_in":null,"score":{"opus":0.01471659144002781,"gpt":0.2368543200152572,"spread":0.2221377285752293,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001044726,0.0004105447,0.0004161265,0.0003736983,0.000945438,0.0006997554,0.001049168,0.0001118632,5.581149e-7],"category_scores_gemma":[5.57668e-7,0.0004044285,0.0001458479,0.0006525294,0.00002594225,0.00006838371,0.0001081896,0.0002073798,0.0000135359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001282831,"about_ca_system_score_gemma":0.00002153174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002114037,"about_ca_topic_score_gemma":0.00006027197,"domain_scores_codex":[0.9968718,0.0001378915,0.0006079001,0.001010117,0.000583267,0.0007890072],"domain_scores_gemma":[0.998313,0.0002123509,0.0002802528,0.0006946882,0.0001934271,0.0003063442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001906417,0.0002020612,0.00002686466,0.0007887644,0.0001113632,0.000009439492,0.004013147,0.9581147,0.00001560578,0.002612341,0.00001249226,0.03407418],"study_design_scores_gemma":[0.001429099,0.000130826,0.00004523515,0.000659114,0.00005908185,0.00002583348,0.003845374,0.9900658,0.0001730079,0.00007635088,0.003058615,0.0004317051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.199702,0.0003342947,0.7964855,0.0002228761,0.001727881,0.0004231784,0.000004986227,0.0005938097,0.0005054815],"genre_scores_gemma":[0.9425094,0.00000948244,0.05634264,0.0007177195,0.0002203327,0.00002019459,0.000003357155,0.00003958405,0.0001373056],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7428074,"threshold_uncertainty_score":0.9998407,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4407782981","doi":"10.1109/tsc.2025.3544124","title":"Joint Trajectory Optimization and Resource Allocation in UAV-MEC Systems: A Lyapunov-Assisted DRL Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Computer science; Trajectory; Joint (building); Resource allocation; Trajectory optimization; Lyapunov optimization; Resource management (computing); Lyapunov function; Mathematical optimization; Distributed computing; Lyapunov equation; Lyapunov exponent; Computer network; Optimal control; Artificial intelligence; Engineering; Nonlinear system; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01864020649896743,"gpt":0.237268395229263,"spread":0.2186281887302956,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006177153,0.000231239,0.0003016931,0.0006088862,0.0003098883,0.0002839063,0.0004541626,0.0001399766,6.152499e-7],"category_scores_gemma":[0.000003240643,0.0002484701,0.00005199557,0.001227431,0.00003058156,0.0003294314,0.00001208691,0.000325872,0.000003338802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001609136,"about_ca_system_score_gemma":0.00007427312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002799334,"about_ca_topic_score_gemma":0.00001250912,"domain_scores_codex":[0.9979658,0.00027762,0.0005242887,0.000644419,0.0002735119,0.0003143933],"domain_scores_gemma":[0.9990112,0.0001956086,0.0001640902,0.0004734255,0.00008506261,0.00007058646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006643451,0.000128461,0.00004369926,0.0002478147,0.00002703244,0.00000437703,0.001695956,0.9836165,0.0002153368,0.0001580674,0.000005886954,0.01385028],"study_design_scores_gemma":[0.0005786409,0.00003722921,0.001458527,0.0005726313,0.0000190072,0.00003047926,0.0006055558,0.9961623,0.0002930238,0.00001066394,0.00002753764,0.0002043989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01080801,0.0001649868,0.9867635,0.0002035603,0.0005504135,0.0003964931,0.000002030788,0.0003735151,0.0007375321],"genre_scores_gemma":[0.7643088,0.000006528707,0.2353292,0.0002091934,0.00002739479,0.00002372209,0.000005587353,0.00001371916,0.00007588582],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7535008,"threshold_uncertainty_score":0.9999968,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2900957219","doi":"10.1109/tsc.2018.2881147","title":"Achieving Efficient and Privacy-Preserving Multi-Domain Big Data Deduplication in Cloud","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Cloud Data Security Solutions","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"China Scholarship Council; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Data deduplication; Computer science; Ciphertext; Encryption; Cloud computing; Brute-force attack; Plaintext; Cloud storage; Domain (mathematical analysis); Computer security; Computer network; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.05500071436828526,"gpt":0.2962869501954648,"spread":0.2412862358271795,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008484153,0.0002401258,0.0002253289,0.000341668,0.0007188132,0.0003359187,0.002664602,0.0001009545,0.000004431169],"category_scores_gemma":[0.0000146877,0.000267375,0.00003833194,0.001141478,0.00009164404,0.0005373461,0.0002958981,0.0003740925,0.00004916586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009855942,"about_ca_system_score_gemma":0.00005588275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007941825,"about_ca_topic_score_gemma":0.003271109,"domain_scores_codex":[0.9975058,0.0001789131,0.0004802192,0.001033086,0.0003173944,0.0004846271],"domain_scores_gemma":[0.9971808,0.0003090792,0.0001606573,0.002112578,0.00009720252,0.0001396771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000141955,0.004475446,0.006738057,0.001196366,0.0002885257,0.00005601466,0.08015012,0.1283022,0.03171347,0.0046375,0.0003269146,0.7419735],"study_design_scores_gemma":[0.0005898793,0.00004631369,0.004538484,0.0002356219,0.0000108815,0.00002405058,0.0002929131,0.992625,0.0006434036,0.00009326095,0.0006295005,0.0002707206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3764309,0.00005869657,0.6217468,0.0005426696,0.0007475802,0.0002227331,0.00002674849,0.0001853302,0.00003846196],"genre_scores_gemma":[0.9074249,0.00001110975,0.09192002,0.000381494,0.0002166464,0.000006978076,0.0000152102,0.00001906779,0.000004567923],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8643228,"threshold_uncertainty_score":0.9999778,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4210879831","doi":"10.1109/tsc.2022.3149847","title":"An Accurate and Privacy-Preserving Retrieval Scheme Over Outsourced Medical Images","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Mahalanobis distance; Image retrieval; Cloud computing; Encryption; Outsourcing; Scheme (mathematics); Information privacy; Private information retrieval; Content-based image retrieval; Image (mathematics); Information retrieval; Fuzzy logic; Security analysis; Data mining; Server; Computer security; Artificial intelligence; World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.01447848395916748,"gpt":0.3012466800315574,"spread":0.2867681960723899,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007540853,0.0002609616,0.0002712122,0.0002383836,0.001125663,0.0003168022,0.001957129,0.00008447826,0.0001387707],"category_scores_gemma":[0.00001343115,0.0002725745,0.00008829874,0.0008462801,0.00005404652,0.001284715,0.0001568103,0.0008028973,0.000005657971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007752541,"about_ca_system_score_gemma":0.00006747853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006985859,"about_ca_topic_score_gemma":0.000006879473,"domain_scores_codex":[0.9972681,0.0002491585,0.0004053675,0.0007431958,0.0008918291,0.0004423094],"domain_scores_gemma":[0.9983463,0.0002699547,0.0001752152,0.0008649433,0.0000888742,0.0002547571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007576953,0.002065712,0.001505679,0.000811746,0.0003785853,0.0006562319,0.01920895,0.05643551,0.121961,0.001498781,0.0003513129,0.7943687],"study_design_scores_gemma":[0.0007066281,0.0004083688,0.0006530529,0.00008864611,0.00001405273,0.0001097729,0.0003520873,0.9421763,0.05298029,0.0007020113,0.001344598,0.0004641281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1776532,0.00009756873,0.8204163,0.0004843677,0.0002878541,0.0001884026,0.000009321018,0.0007676748,0.00009531814],"genre_scores_gemma":[0.9461359,0.00004015187,0.05222617,0.001440982,0.00007135962,0.000008338862,0.000003065426,0.00002960426,0.0000444594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8857409,"threshold_uncertainty_score":0.9999726,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2625108112","doi":"10.1109/tsc.2017.2712773","title":"A Framework of Hypergraph-Based Data Placement Among Geo-Distributed Datacenters","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Hypergraph; Distributed computing; Replica; Partition (number theory); Set (abstract data type); Distributed database; Hash function; TRACE (psycholinguistics); Data mining; Big data; Scheme (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.039873908681169,"gpt":0.2837651615139468,"spread":0.2438912528327778,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003737999,0.0002109251,0.0002581362,0.0001474311,0.0008658827,0.0004381707,0.003997792,0.00008840891,0.000007168465],"category_scores_gemma":[0.000006400599,0.0002144944,0.0001114489,0.0001927522,0.00008296443,0.0008109483,0.00007261484,0.0003431916,0.00001147172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003215876,"about_ca_system_score_gemma":0.00004648881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008894835,"about_ca_topic_score_gemma":0.0002299227,"domain_scores_codex":[0.9982035,0.00007607814,0.0003641584,0.0006408547,0.0003937887,0.0003216004],"domain_scores_gemma":[0.9958115,0.0002066451,0.0003753466,0.003403586,0.00009318159,0.0001096979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003430444,0.002060869,0.01626054,0.0008723327,0.0008747398,0.00009531481,0.002008047,0.8556064,0.00414313,0.001051726,0.0003491677,0.1163347],"study_design_scores_gemma":[0.0005966024,0.00007217789,0.003257406,0.0005521842,0.00004098481,0.000002873105,0.0001173802,0.9933732,0.00160885,0.00004018719,0.0001134387,0.0002247576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2217365,0.00002121692,0.7766136,0.0002802827,0.0008494513,0.0001276959,0.0001774408,0.00014957,0.00004418355],"genre_scores_gemma":[0.987381,0.000007533513,0.01222124,0.0002805395,0.00003964369,0.000003111286,0.00004583355,0.0000127691,0.000008303828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7656445,"threshold_uncertainty_score":0.874683,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4397026309","doi":"10.1109/tsc.2024.3402169","title":"Task Decomposition and Hierarchical Scheduling for Collaborative Cloud-Edge-End Computing","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Distributed computing; Cloud computing; Scheduling (production processes); Granularity; Edge computing; Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.01403144146046864,"gpt":0.2831515233503029,"spread":0.2691200818898343,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007181832,0.000402282,0.0003912792,0.0004260459,0.00133902,0.001252127,0.000608639,0.0001697606,0.000001840329],"category_scores_gemma":[0.00000399036,0.000415571,0.0001731898,0.001159602,0.00008040951,0.0006456599,0.00003629781,0.0006108111,0.00002997295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009043434,"about_ca_system_score_gemma":0.0001316726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002750084,"about_ca_topic_score_gemma":0.00001035461,"domain_scores_codex":[0.9972106,0.0001578887,0.0005840402,0.001033899,0.0003384664,0.0006751243],"domain_scores_gemma":[0.997754,0.001335676,0.0001282803,0.0003649967,0.0002085387,0.0002085009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001035336,0.0002750287,0.00004835298,0.001574215,0.000380021,0.00006421641,0.02132496,0.1291949,0.006091394,0.007940773,0.0002279413,0.8327747],"study_design_scores_gemma":[0.0005258606,0.0002080464,0.00008041119,0.0007873334,0.00004563113,0.00007635338,0.0002145091,0.9909441,0.003382719,0.0009988846,0.002283674,0.0004524945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1484212,0.0004655467,0.8375196,0.0007170149,0.01133747,0.0004267476,0.000005734933,0.0008511744,0.0002555909],"genre_scores_gemma":[0.8549027,0.00001528071,0.1427985,0.000445061,0.001757996,0.0000097707,0.000008323545,0.0000427106,0.00001955824],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8617492,"threshold_uncertainty_score":0.9999611,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2321314104","doi":"10.1109/tsc.2015.2426185","title":"Trust and Reputation of Web Services Through QoS Correlation Lens","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Reputation; Quality of service; Web service; Service (business); Service provider; WS-Policy; Selection (genetic algorithm); Mobile QoS; World Wide Web; Data mining; Computer network; Web application security; Machine learning; Web development","retraction":null,"screen_n_in":null,"score":{"opus":0.01835333284390641,"gpt":0.2458715172939186,"spread":0.2275181844500122,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003281699,0.0002963154,0.0003279585,0.0002556616,0.0002922492,0.0001671128,0.0006815878,0.0001441907,0.000005531002],"category_scores_gemma":[7.849922e-7,0.0002863668,0.00008971874,0.0009200579,0.0000416567,0.001391538,0.00002449041,0.0002801685,0.00002697006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003992892,"about_ca_system_score_gemma":0.00007195296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007980458,"about_ca_topic_score_gemma":0.0006682175,"domain_scores_codex":[0.9978364,0.0001406504,0.0005555733,0.00061766,0.0005137672,0.0003359868],"domain_scores_gemma":[0.9984117,0.0002038781,0.000372088,0.000576247,0.0003024495,0.0001336432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002834401,0.0007650126,0.003704232,0.002193961,0.0003526136,0.00002680928,0.1524454,0.7091639,0.006678818,0.006885967,0.00002749847,0.1174724],"study_design_scores_gemma":[0.001234568,0.0002952393,0.000881838,0.0003538993,0.00006133428,0.00005126285,0.003278589,0.9836819,0.007534605,0.001239879,0.001018417,0.0003684792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4358373,0.0001509784,0.5608087,0.0003405806,0.001077442,0.000209632,0.000008599635,0.00028055,0.001286235],"genre_scores_gemma":[0.9749161,0.00003456083,0.02359177,0.001280372,0.0001164289,0.000006397289,0.000009640355,0.00002351031,0.0000212277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5390788,"threshold_uncertainty_score":0.9999589,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3022856335","doi":"10.1109/tsc.2020.2992303","title":"Achieving Practical Symmetric Searchable Encryption With Search Pattern Privacy Over Cloud","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Natural Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Computer science; Encryption; Pseudorandom function family; Cloud computing; Symmetric-key algorithm; Identifier; Overhead (engineering); Scheme (mathematics); Bloom filter; Theoretical computer science; Pseudorandom generator; Security analysis; Cryptography; Computer security; Computer network; Public-key cryptography; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.03250516878725188,"gpt":0.2827424459918655,"spread":0.2502372772046136,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004246954,0.0002622224,0.0002594335,0.0003024041,0.0005766058,0.0005057887,0.0008985041,0.00009714888,0.00004284592],"category_scores_gemma":[0.000004375285,0.0002410908,0.00011865,0.002396389,0.00004872778,0.001254887,0.00004323718,0.0008354199,0.00009924698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000479851,"about_ca_system_score_gemma":0.00008536835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002500697,"about_ca_topic_score_gemma":0.00004190058,"domain_scores_codex":[0.9973414,0.0002302104,0.0003221514,0.0007868922,0.000764076,0.0005552631],"domain_scores_gemma":[0.9984043,0.0004320147,0.0001113586,0.0005991713,0.0001250182,0.0003280863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007824586,0.003102223,0.01563923,0.003050233,0.0008510875,0.0005151405,0.05672992,0.1301293,0.007304709,0.02028951,0.0002715555,0.7613347],"study_design_scores_gemma":[0.001014919,0.0006488778,0.004572054,0.000190532,0.00003970769,0.0000579065,0.000445225,0.9884185,0.003233902,0.00009353925,0.0007887185,0.0004961277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2702067,0.00002206924,0.7273841,0.001512591,0.0002046229,0.0002114094,0.00001141409,0.0003296542,0.0001174258],"genre_scores_gemma":[0.9555641,0.00002087484,0.04259758,0.001604583,0.000177909,0.00000533461,0.000006106322,0.00002213062,0.000001329054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8582892,"threshold_uncertainty_score":0.9831396,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392909546","doi":"10.1109/tsc.2024.3376202","title":"PBScaler: A Bottleneck-Aware Autoscaling Framework for Microservice-Based Applications","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Tech University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Bottleneck; Distributed computing; Embedded system","retraction":null,"screen_n_in":null,"score":{"opus":0.01251944357142602,"gpt":0.2799768292064115,"spread":0.2674573856349855,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004365816,0.0002915935,0.0002780567,0.0002413268,0.0007237281,0.0005458965,0.0009638122,0.0002224247,0.00001080776],"category_scores_gemma":[0.000001333174,0.0002713897,0.0002974207,0.00107389,0.00003426498,0.0003949041,0.000007219161,0.0004069135,0.0001444764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001196882,"about_ca_system_score_gemma":0.0001462229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005065311,"about_ca_topic_score_gemma":0.00003138283,"domain_scores_codex":[0.9978397,0.00005613035,0.0004998966,0.0008392237,0.0002960103,0.0004690528],"domain_scores_gemma":[0.9978074,0.0009459998,0.00009495283,0.0008546301,0.0001640446,0.000133008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005430574,0.0006137883,0.0002394155,0.009589357,0.0002537078,0.000009673909,0.004420613,0.4064354,0.001827772,0.006388622,0.00007340071,0.5700939],"study_design_scores_gemma":[0.0002448395,0.00009200932,0.00007361405,0.001013096,0.00003650348,0.00001009234,0.0001001742,0.9848043,0.006826987,0.001925863,0.004531204,0.0003413163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009376168,0.0002732303,0.9846881,0.0008790834,0.001952698,0.0008290554,0.00004080093,0.00192129,0.00003956233],"genre_scores_gemma":[0.8531566,0.000008224662,0.1454503,0.0008531418,0.0002385766,0.0002259727,0.000007033019,0.00003636039,0.00002377053],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8437805,"threshold_uncertainty_score":0.9999738,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3144845068","doi":"10.1109/tsc.2021.3070746","title":"Resource Trading in Edge Computing-Enabled IoV: An Efficient Futures-Based Approach","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Futures contract; Edge computing; Resource (disambiguation); Negotiation; Low latency (capital markets); Algorithmic trading; Distributed computing; Enhanced Data Rates for GSM Evolution; Computer network; Telecommunications; Business","retraction":null,"screen_n_in":null,"score":{"opus":0.01384344171392852,"gpt":0.2321601459057042,"spread":0.2183167041917757,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003047046,0.0002667794,0.0002861691,0.0003472756,0.0003327573,0.0001038029,0.0002196636,0.0001375101,0.00003832504],"category_scores_gemma":[0.000001056651,0.0003170209,0.000112398,0.001376931,0.00002539543,0.00009906681,0.000001069476,0.0005380629,0.000007911864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001215393,"about_ca_system_score_gemma":0.00005420582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003870978,"about_ca_topic_score_gemma":0.0004412976,"domain_scores_codex":[0.9982301,0.00009069336,0.0005608885,0.000447486,0.0002601466,0.0004106722],"domain_scores_gemma":[0.9992096,0.0001244132,0.00005547405,0.0003984826,0.0000951289,0.0001169266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001357095,0.0003769059,0.00009039376,0.0002685283,0.00002900342,0.00001088986,0.00300369,0.9886568,0.001806334,0.0001393293,0.000004819422,0.005599791],"study_design_scores_gemma":[0.0009147458,0.00002566337,0.002540726,0.0001411022,0.00002669596,0.00000835117,0.002411844,0.9861789,0.007050534,0.000005871322,0.0003913129,0.0003042546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4908904,0.00002911556,0.5071686,0.00004019768,0.0002815182,0.0001536488,0.00001347353,0.0004594437,0.0009636161],"genre_scores_gemma":[0.9899302,0.000001353496,0.009464935,0.000364439,0.00007950611,0.00001001126,0.00008515245,0.0000522191,0.00001223838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4990397,"threshold_uncertainty_score":0.9999282,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3163561067","doi":"10.1109/tsc.2021.3081350","title":"Efficient Privacy-Preserving Similarity Range Query With Quadsector Tree in eHealthcare","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Natural Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Computer science; Encryption; Cloud computing; Outsourcing; Range query (database); Tree (set theory); Data mining; Information privacy; Computer security; Information retrieval; Web search query; Web query classification; Search engine","retraction":null,"screen_n_in":null,"score":{"opus":0.01654331399807334,"gpt":0.2533068914312266,"spread":0.2367635774331533,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004013696,0.0002836145,0.0003376928,0.0003084574,0.0004251545,0.0002670167,0.001125031,0.000124502,0.00002414373],"category_scores_gemma":[0.000004068276,0.0002779376,0.000135399,0.001938954,0.00003509543,0.0003334564,0.00004458169,0.0006212007,0.00001155199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008897459,"about_ca_system_score_gemma":0.0001446289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001003197,"about_ca_topic_score_gemma":0.01325131,"domain_scores_codex":[0.9973906,0.0002761961,0.0004317767,0.0008549677,0.0004920781,0.0005543513],"domain_scores_gemma":[0.9979769,0.000333294,0.0001313767,0.001203275,0.0001773502,0.0001777813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004245633,0.004148514,0.02426812,0.002976386,0.0002751267,0.001061555,0.04394262,0.7548437,0.002120698,0.005905862,0.00004924746,0.1599836],"study_design_scores_gemma":[0.001445411,0.0001382165,0.02524979,0.0006757336,0.00002005021,0.00006127238,0.0009864435,0.9666557,0.003666861,0.0003514417,0.0002041616,0.0005449353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4387796,0.0001467244,0.5596786,0.0005441912,0.0003267504,0.0001559453,0.0000187309,0.0002029493,0.0001465842],"genre_scores_gemma":[0.9620845,0.00001455186,0.03712945,0.0006820829,0.00005066173,0.0000106318,0.000007034141,0.00001829902,0.000002745739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5233049,"threshold_uncertainty_score":0.9999673,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2955869759","doi":"10.1109/tsc.2019.2924372","title":"Achieve Efficient and Verifiable Conjunctive and Fuzzy Queries over Encrypted Data in Cloud","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Computer science; Cloud computing; Encryption; Verifiable secret sharing; Fuzzy logic; Conjunctive query; Data mining; Computer security; Database; Information retrieval; Computer network; Artificial intelligence; Relational database; Set (abstract data type); Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.01049905107117793,"gpt":0.2371691104288975,"spread":0.2266700593577196,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003535125,0.0001876753,0.0002260652,0.000192464,0.0002058913,0.0002503252,0.0006217516,0.00007623274,0.000010175],"category_scores_gemma":[0.000001232427,0.0001863033,0.00002664893,0.0005597448,0.00006177076,0.000709652,0.00006518622,0.0002838914,0.00001173122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001825501,"about_ca_system_score_gemma":0.00002711536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006900667,"about_ca_topic_score_gemma":0.0004046607,"domain_scores_codex":[0.998473,0.00008850558,0.0002380445,0.0007115891,0.0002076491,0.0002811385],"domain_scores_gemma":[0.9987066,0.00024988,0.00008078693,0.0008404196,0.00003708967,0.00008518857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002072033,0.004409893,0.05091259,0.004054822,0.0009554293,0.0001544501,0.1075126,0.269694,0.0150953,0.1679489,0.0001710644,0.3770188],"study_design_scores_gemma":[0.001298914,0.0001802005,0.01146114,0.0002024772,0.00001704285,0.00001854987,0.0008981326,0.9833077,0.000635938,0.001064715,0.0005391519,0.0003760759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6255798,0.0001169646,0.3732065,0.00006517783,0.0004853735,0.0001842271,0.00005172801,0.00008243679,0.0002278019],"genre_scores_gemma":[0.9923651,0.00004148079,0.007269294,0.000276521,0.00002284558,0.000002295143,0.00001172729,0.000008353672,0.000002433041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7136137,"threshold_uncertainty_score":0.7597227,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2784203325","doi":"10.1109/tsc.2018.2793250","title":"RepNet: Cutting Latency with Flow Replication in Data Center Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computer network; Latency (audio); Testbed; Network packet; Handshaking; Distributed computing; Data center","retraction":null,"screen_n_in":null,"score":{"opus":0.02108565618563985,"gpt":0.255016751200999,"spread":0.2339310950153591,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007196971,0.0002199413,0.0001968233,0.0002080624,0.0004428604,0.0002335887,0.001809015,0.00007156567,0.000005685267],"category_scores_gemma":[0.000001679548,0.000195361,0.00003927613,0.0009920307,0.00004236823,0.0001384027,0.000072227,0.000325716,0.00003416821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000570316,"about_ca_system_score_gemma":0.00001970274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001777077,"about_ca_topic_score_gemma":0.0003525726,"domain_scores_codex":[0.9975952,0.0001109094,0.0004119982,0.001129786,0.0002991443,0.0004529617],"domain_scores_gemma":[0.997059,0.0001102069,0.0001845386,0.002476584,0.00009061547,0.0000790526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002052735,0.0001754768,0.0007517998,0.00004145833,0.00003595851,0.0000128839,0.001477525,0.680212,0.000015644,0.0000572186,0.00003676649,0.3171627],"study_design_scores_gemma":[0.000468298,0.0001022401,0.001171579,0.0003471946,0.00000989707,0.00002535137,0.0001028008,0.9965724,0.000122703,0.00002440452,0.0008275554,0.0002255499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1225019,0.00002041465,0.8748715,0.0006288052,0.0005161756,0.0002096622,0.00000207895,0.0003794894,0.0008700069],"genre_scores_gemma":[0.954632,0.000004257775,0.04431131,0.0007090567,0.0002386563,0.000004014751,0.000006648358,0.00002151022,0.00007253131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8321301,"threshold_uncertainty_score":0.796659,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4388757023","doi":"10.1109/tsc.2023.3333832","title":"Matching-Based Hybrid Service Trading for Task Assignment Over Dynamic Mobile Crowdsensing Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; Natural Science Foundation of Xiamen City","keywords":"Computer science; Crowdsensing; Task (project management); Matching (statistics); Computer network; Service (business); Distributed computing; Mobile computing; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.01106331737523972,"gpt":0.2518553316592037,"spread":0.2407920142839639,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0008314097,0.0004776799,0.0004467872,0.0004348713,0.00139091,0.0007060817,0.000778587,0.0001415806,0.00001011703],"category_scores_gemma":[0.000001461068,0.0005231298,0.000299165,0.001425346,0.00003438051,0.0004140042,0.00001900091,0.0004729884,0.00004965707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002201695,"about_ca_system_score_gemma":0.00008421169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001860098,"about_ca_topic_score_gemma":0.0002112179,"domain_scores_codex":[0.9966769,0.0001466862,0.0006289466,0.001058302,0.0004951192,0.000994108],"domain_scores_gemma":[0.9975597,0.0009046131,0.0002685335,0.0008971624,0.000154148,0.0002158699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002185175,0.00007949549,0.000004680371,0.0002055943,0.00005169162,0.00002108086,0.00110266,0.9327974,0.004794919,0.00001927775,0.00003961004,0.06086173],"study_design_scores_gemma":[0.0008579194,0.000121276,0.00004483787,0.0005127384,0.00004627835,0.00002897196,0.0003865415,0.9904499,0.006515576,0.0002121967,0.0002842521,0.000539513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2884153,0.00003168147,0.7080193,0.0002229966,0.001509682,0.0005052843,0.00001574525,0.001245114,0.00003489976],"genre_scores_gemma":[0.9856131,0.000004762224,0.01228437,0.001736474,0.0001321677,0.00005341029,0.00002594206,0.0000893454,0.00006041873],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6971979,"threshold_uncertainty_score":0.9999092,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4367016307","doi":"10.1109/tsc.2023.3270169","title":"Graph-Represented Computation-Intensive Task Scheduling Over Air-Ground Integrated Vehicular Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Natural Science Foundation of Xiamen City","keywords":"Computer science; Scheduling (production processes); Subgraph isomorphism problem; Distributed computing; Integer programming; Computation; Graph; Theoretical computer science; Mathematical optimization; Algorithm; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.00886397119217571,"gpt":0.2293588994665663,"spread":0.2204949282743906,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001395229,0.0002701597,0.0002399363,0.0003768749,0.0004026164,0.0001131552,0.00019652,0.0001513989,0.00002051678],"category_scores_gemma":[0.000001693167,0.0002944463,0.0001282175,0.002021203,0.0000317057,0.0002096369,0.000004208996,0.0004076115,0.0001022908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007739419,"about_ca_system_score_gemma":0.00001281057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002032869,"about_ca_topic_score_gemma":0.00009601624,"domain_scores_codex":[0.9985981,0.00004335422,0.000405907,0.0003629631,0.0002161222,0.0003735567],"domain_scores_gemma":[0.9990767,0.0001748453,0.00007552013,0.0002757357,0.000298122,0.00009902543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008870794,0.00002508415,0.000051556,0.00005503918,0.0001187918,0.000004513861,0.000420789,0.9926618,0.0006842911,0.00002080108,0.0000711981,0.005877254],"study_design_scores_gemma":[0.0004050299,0.00001895264,0.001007096,0.0001421752,0.00004854992,0.000005833499,0.0008306688,0.996541,0.0005380852,0.00005808215,0.0001283721,0.0002762194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3267755,0.00004123907,0.6709903,0.00005957336,0.0005379862,0.0002221193,0.00001272472,0.001287554,0.0000729965],"genre_scores_gemma":[0.9947547,0.00006750772,0.004534876,0.0002584479,0.00009232157,0.00002880689,0.0001652169,0.00007476476,0.00002330482],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6679793,"threshold_uncertainty_score":0.9999508,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2795633027","doi":"10.1109/tsc.2018.2821685","title":"Dependence-Based Data-Aware Process Conformance Checking","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","cited_by":20,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; National Key Research and Development Program of China; Deutsche Forschungsgemeinschaft; Alexander von Humboldt-Stiftung","keywords":"Computer science; Conformance checking; TRACE (psycholinguistics); Executable; Process (computing); Process mining; Consistency (knowledge bases); Leverage (statistics); Heuristics; Control flow; Data mining; Distributed computing; Programming language; Business process; Work in process; Business process management; Business process modeling; Operating system; Machine learning; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.04278380249166795,"gpt":0.2824952141329846,"spread":0.2397114116413167,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003741484,0.000285017,0.0002824705,0.0003842746,0.0009510825,0.0005468127,0.001001421,0.00009916401,0.0001400497],"category_scores_gemma":[0.000003443109,0.0002712259,0.00006852382,0.001251413,0.00007290064,0.001848176,0.00001496785,0.0002577924,0.0003391781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002070952,"about_ca_system_score_gemma":0.00005440376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005644365,"about_ca_topic_score_gemma":0.0008570527,"domain_scores_codex":[0.9980953,0.000008284667,0.0004131533,0.0006240103,0.0004467774,0.0004124296],"domain_scores_gemma":[0.9983518,0.00004079465,0.0003183641,0.0006429151,0.0006232661,0.00002284062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002033307,0.0004785209,0.002172322,0.003697098,0.0002394479,0.00001472589,0.000590827,0.8179839,0.000424445,0.00008678059,0.00008370358,0.1740249],"study_design_scores_gemma":[0.0004078812,0.00001071581,0.00008953427,0.0004170412,0.0001466671,0.000001742218,0.0003788318,0.9963909,0.0009606184,0.00008887313,0.0007709127,0.0003362172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1918137,0.00002092343,0.8054559,0.0003236851,0.0005956352,0.0001258727,0.00000941852,0.0005024645,0.001152345],"genre_scores_gemma":[0.9950355,0.000002686138,0.0006109429,0.003165598,0.001059191,0.000006575429,0.0000502124,0.00004213014,0.00002721965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.804845,"threshold_uncertainty_score":0.999974,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4387164682","doi":"10.1109/tsc.2023.3320674","title":"Offloading Dependent Tasks in Edge Computing With Unknown System-Side Information","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Computer science; Leverage (statistics); Dependency (UML); Task (project management); Artificial intelligence; Theoretical computer science; Machine learning; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.04869230615834702,"gpt":0.3553354334982302,"spread":0.3066431273398832,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00295028,0.0002858094,0.0004494101,0.001842278,0.0007436579,0.000606995,0.0009456521,0.0001271202,0.00001968242],"category_scores_gemma":[0.00003436649,0.0002434459,0.0001008648,0.004497803,0.00006108131,0.001439779,0.00002758094,0.0006633222,0.001157035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003343123,"about_ca_system_score_gemma":0.0001054209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003166644,"about_ca_topic_score_gemma":0.0008577079,"domain_scores_codex":[0.9950759,0.0002914327,0.001143703,0.0006075524,0.002109214,0.0007722368],"domain_scores_gemma":[0.9965525,0.001850339,0.000374804,0.0006049734,0.0004410724,0.0001762844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000049628,0.00003013536,0.0002729725,0.0001015005,0.00001918949,0.00003384308,0.002364004,0.8339481,0.00009148943,0.00002838429,0.000008162059,0.1630526],"study_design_scores_gemma":[0.0009682034,0.00009567379,0.00303521,0.0005016696,0.000009295578,0.00004832287,0.009673544,0.9829802,0.001982446,0.00008376901,0.0003302127,0.0002914679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3113183,0.00001049739,0.6864572,0.0001027939,0.0006678229,0.0003738105,0.00001502387,0.0003948261,0.0006597693],"genre_scores_gemma":[0.9967448,0.00000432452,0.002901937,0.0001031513,0.00009220037,0.00001277613,0.00000934493,0.00002843755,0.0001030297],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6854265,"threshold_uncertainty_score":0.9996207,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2064170343","doi":"10.1109/tsc.2012.15","title":"Simulating Service-Oriented Systems: A Survey and the Services-Aware Simulation Framework","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Alberta Innovates - Technology Futures","keywords":"Computer science; Software deployment; Service-oriented architecture; Distributed computing; Service (business); Software engineering; Software architecture; Systems engineering; Software; Web service; Operating system; World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.01262277792201211,"gpt":0.248545968061392,"spread":0.2359231901393798,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001012439,0.0006425441,0.000643891,0.0003331318,0.001488649,0.001293932,0.001747358,0.0002897337,0.0000287627],"category_scores_gemma":[0.00000425762,0.000484515,0.0001607757,0.002245173,0.00007452056,0.001260629,0.00008402095,0.0008522014,0.0001736826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006225115,"about_ca_system_score_gemma":0.00004832382,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01286235,"about_ca_topic_score_gemma":0.002429699,"domain_scores_codex":[0.9954111,0.0008959739,0.0009477774,0.001115734,0.0008126058,0.0008168083],"domain_scores_gemma":[0.9930845,0.003927029,0.0005266144,0.001395331,0.0007868891,0.0002795884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008306666,0.0001004331,0.0008000811,0.0006786517,0.0001540919,0.000003241928,0.01373353,0.9699656,0.00003556851,0.001120902,0.000001301913,0.01332355],"study_design_scores_gemma":[0.001443194,0.00007012775,0.003612472,0.0006384549,0.00004551564,0.00001830457,0.002125258,0.9905411,0.00008098209,0.000770378,0.0001445036,0.0005097038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.276864,0.0001688058,0.7192267,0.0008525499,0.001168274,0.001002035,0.00001716636,0.0005955966,0.0001049167],"genre_scores_gemma":[0.9882674,0.00001491272,0.005378288,0.005976657,0.0001967734,0.00006603864,0.00002130078,0.00006085018,0.00001784456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7138484,"threshold_uncertainty_score":0.9998113,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3027282887","doi":"10.1109/tsc.2020.2996382","title":"Joint Pricing and Security Investment in Cloud Security Service Market With User Interdependency","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Information and Cyber Security","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo; York University","funders":"Nanyang Technological University; National Research Foundation","keywords":"Cloud computing security; Cloud computing; Computer security; Stackelberg competition; Computer science; Security service; Service (business); Security controls; Business; Information security; Control (management); Economics; Marketing","retraction":null,"screen_n_in":null,"score":{"opus":0.01212346970754298,"gpt":0.2148862405040923,"spread":0.2027627707965494,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003680803,0.0002911277,0.0003040536,0.0001723759,0.0002597946,0.0002882121,0.0005673029,0.00009791475,0.00002875071],"category_scores_gemma":[0.000001599636,0.0002729482,0.00005369495,0.0009358049,0.00002632471,0.001028185,0.00004092663,0.0005970098,0.00002954498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007904526,"about_ca_system_score_gemma":0.00006146559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003912189,"about_ca_topic_score_gemma":0.00143424,"domain_scores_codex":[0.9980475,0.0001529235,0.000504673,0.0005328113,0.0003939199,0.0003681921],"domain_scores_gemma":[0.9990128,0.00007838182,0.0001830932,0.0003658301,0.0001231155,0.0002368099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007638654,0.001565667,0.00341648,0.006683938,0.0004827696,0.0002820638,0.8248112,0.08004802,0.0005864822,0.04138933,0.0004301012,0.03954006],"study_design_scores_gemma":[0.001267428,0.0001933104,0.001115075,0.00033656,0.00001577899,0.00004096907,0.002158903,0.9903776,0.00269862,0.0007516645,0.0006031274,0.0004409841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4303569,0.00003743046,0.5626792,0.002842139,0.0003256278,0.0004093864,0.000006873745,0.0003289821,0.003013479],"genre_scores_gemma":[0.9810939,0.0000100488,0.004851329,0.0139681,0.00004666093,0.000008858193,0.000001733828,0.00001446518,0.000004908394],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9103296,"threshold_uncertainty_score":0.9999723,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4389161177","doi":"10.1109/tsc.2023.3337873","title":"Decentralised Knowledge Graph Evolution via Blockchain","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Anhui Provincial Key Research and Development Plan; National Natural Science Foundation of China","keywords":"Computer science; Blockchain; Credibility; Context (archaeology); Computer security; Quality (philosophy); Smart contract; Process (computing); Publication; Database","retraction":null,"screen_n_in":null,"score":{"opus":0.0114818522716139,"gpt":0.2464807054216565,"spread":0.2349988531500426,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003896282,0.0002285254,0.0002078068,0.0005973785,0.0007916037,0.00008630888,0.001122765,0.0001982843,0.000008905085],"category_scores_gemma":[0.000001049657,0.0002454714,0.0001385888,0.003145495,0.00006188046,0.0001229072,0.00001911622,0.0003850261,0.0004238784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008380042,"about_ca_system_score_gemma":0.00003894876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000103719,"about_ca_topic_score_gemma":0.0002225974,"domain_scores_codex":[0.9982003,0.00009236377,0.0003463376,0.0006182767,0.0002174881,0.0005252309],"domain_scores_gemma":[0.9986747,0.000173987,0.0001099761,0.000805879,0.0001182594,0.0001172138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003665388,0.001649849,0.0004798771,0.0004028847,0.0003113057,0.00004030981,0.01070335,0.2519531,0.01073887,0.08086487,0.00046608,0.6423529],"study_design_scores_gemma":[0.0003728288,0.00005025148,0.0007870753,0.00004920691,0.00001418457,0.00001774708,0.0001426755,0.9827815,0.006344957,0.008769056,0.0003980904,0.0002724828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1716034,0.00005979783,0.8242723,0.000688571,0.0006078983,0.000234837,0.000004819922,0.002348064,0.0001803215],"genre_scores_gemma":[0.992977,0.0000149638,0.006664554,0.0001840321,0.00004738219,0.00003768586,0.000003089974,0.00001885712,0.00005240833],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8213736,"threshold_uncertainty_score":0.9999998,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2616077070","doi":"10.1109/tsc.2017.2705685","title":"Opportunistic Sharing of Continuous Mobile Sensing Data for Energy and Power Conservation","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Green IT and Sustainability","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Merge (version control); Mobile device; Real-time computing; Wearable computer; Energy consumption; Embedded system; Electrical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02745189341410605,"gpt":0.2631868117415357,"spread":0.2357349183274297,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002273517,0.0001254597,0.0002091267,0.00005798897,0.0003753711,0.0001045142,0.000307475,0.00006276685,0.000004101963],"category_scores_gemma":[0.000004819037,0.0001399424,0.00003473255,0.00003699394,0.0000472367,0.0002482265,0.00001137378,0.00007296644,2.525752e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002270201,"about_ca_system_score_gemma":0.00001374126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004161634,"about_ca_topic_score_gemma":0.0004555148,"domain_scores_codex":[0.999224,0.00001049818,0.0002562316,0.0002477893,0.00008193999,0.0001795079],"domain_scores_gemma":[0.998853,0.0001343182,0.00009835884,0.0007560027,0.0001065356,0.00005180039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002284904,0.0002609765,0.005784652,0.005697229,0.0006296671,0.00002939547,0.004678397,0.2030191,0.02278181,0.001003105,0.0001217957,0.7557654],"study_design_scores_gemma":[0.000357365,0.00004636273,0.0007759689,0.0001149742,0.00004188271,0.000005780045,0.0006185342,0.9949541,0.002097553,0.0001929742,0.0006442841,0.0001502105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5098557,0.00002395036,0.4894239,0.00002242292,0.0002604918,0.0001278301,0.00005301066,0.00007897686,0.0001537315],"genre_scores_gemma":[0.9975956,0.000008965801,0.00226554,0.00003531661,0.00002572271,0.000002948721,0.00001781062,0.00002270356,0.00002536311],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.791935,"threshold_uncertainty_score":0.5706685,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3043398049","doi":"10.1109/tsc.2020.3009084","title":"Cloud-Based Charging Management of Heterogeneous Electric Vehicles in a Network of Charging Stations: Price Incentive Versus Capacity Expansion","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut National de la Recherche Scientifique","funders":"National Science Foundation of Sri Lanka","keywords":"Computer science; Cloud computing; Incentive; Profit maximization; Quality of service; Queueing theory; Capacity planning; Computer network; Operations research; Distributed computing; Profit (economics); Operating system; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01321663262549375,"gpt":0.2120465808802586,"spread":0.1988299482547649,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001102368,0.0001942546,0.0002812869,0.0001301186,0.00009672404,0.00001568947,0.0001890489,0.0000716238,0.00001044166],"category_scores_gemma":[6.769977e-7,0.0002195372,0.00008560513,0.001224672,0.00001299909,0.0001006759,0.000003236728,0.0002933197,0.000002086544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001136586,"about_ca_system_score_gemma":0.00001552392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004547222,"about_ca_topic_score_gemma":0.00002338176,"domain_scores_codex":[0.9987081,0.00005147552,0.0004490845,0.0002340101,0.0002240982,0.0003332574],"domain_scores_gemma":[0.999478,0.0001191456,0.0001356005,0.0001425591,0.00006121612,0.00006342223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001335044,0.0000377108,0.0001050709,0.0007998236,0.0000980027,0.000004442974,0.001204519,0.9702537,0.009548602,0.00002780628,0.00000151801,0.01778532],"study_design_scores_gemma":[0.0009793825,0.0001514509,0.000594479,0.0003961642,0.00003872844,7.870763e-7,0.0002505414,0.9100503,0.08733574,0.00002267212,0.00001330457,0.0001664539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7473357,0.0001866461,0.2518827,0.00002380404,0.0001920194,0.0002140389,0.000008258979,0.000107675,0.00004911038],"genre_scores_gemma":[0.9957478,0.00008292876,0.003998734,0.0000760728,0.00005333929,0.000007022321,0.000003358537,0.00003038335,3.605785e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2484121,"threshold_uncertainty_score":0.8952469,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2779397283","doi":"10.1109/tsc.2017.2787152","title":"Interactive Refactoring of Web Service Interfaces Using Computational Search","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Code refactoring; Web service; User interface; Interface (matter); Web modeling; Mashup; World Wide Web; Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.03360290255296644,"gpt":0.3103840879793189,"spread":0.2767811854263524,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.000355255,0.0003735955,0.0004327099,0.0004135001,0.001305697,0.0005813343,0.002860921,0.0001241322,0.00002200982],"category_scores_gemma":[0.00000153096,0.0003692333,0.0001528366,0.0005156221,0.00006843269,0.001492675,0.0001079964,0.0005593373,0.00003750218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008971047,"about_ca_system_score_gemma":0.0001365329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002044326,"about_ca_topic_score_gemma":0.0009647205,"domain_scores_codex":[0.997357,0.000150505,0.0005963395,0.0007421076,0.0006516039,0.0005024443],"domain_scores_gemma":[0.9971102,0.0003889053,0.0005738724,0.00122631,0.0005396178,0.000161116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001145537,0.0003255734,0.0007122398,0.0006480081,0.0003001446,0.0000166096,0.01863319,0.914569,0.01933072,0.0005622215,0.000001083255,0.04478662],"study_design_scores_gemma":[0.0006944078,0.0001085471,0.001596482,0.0007149286,0.00003307829,0.00003270094,0.001124952,0.9350397,0.05995974,0.000287257,0.00005644413,0.0003517366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5276549,0.00001818676,0.4703897,0.0002947638,0.0008798235,0.0001442706,0.00001346802,0.0001417682,0.0004630903],"genre_scores_gemma":[0.9609911,0.000005439162,0.03827037,0.0005800659,0.000105331,0.000003223674,0.000002987081,0.0000313927,0.0000100776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4333362,"threshold_uncertainty_score":0.9999945,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4285221287","doi":"10.1109/tsc.2022.3184013","title":"CTL-Based Adaptive Service Composition in Edge Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Tech University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Quality of service; CTL*; Distributed computing; Edge device; Computer network; Cloud computing; Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.01085937768167918,"gpt":0.2204531903111779,"spread":0.2095938126294987,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004580493,0.0003949984,0.0003636973,0.0006191974,0.001134815,0.0001808803,0.001834342,0.00009790163,0.00005474378],"category_scores_gemma":[1.380432e-7,0.0004449219,0.0001547373,0.00305935,0.00002003943,0.0004409008,0.00005776764,0.001013147,0.00003548136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002158983,"about_ca_system_score_gemma":0.0000951413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001129491,"about_ca_topic_score_gemma":0.001851569,"domain_scores_codex":[0.9967655,0.0004641765,0.0005567541,0.0009007158,0.0006368468,0.0006759884],"domain_scores_gemma":[0.9982809,0.0003877505,0.0002439442,0.0007949573,0.0001347303,0.0001576647],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001062115,0.0003975962,0.00009638213,0.00007185174,0.00003080105,0.00003194131,0.003328273,0.9751455,0.0002768496,0.0004125861,0.000004192765,0.0200978],"study_design_scores_gemma":[0.001147015,0.0002585392,0.0007188203,0.0001327248,0.00002002492,0.00003072627,0.0008115673,0.9942369,0.001529855,0.0001600573,0.0005036738,0.0004500505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.124702,0.00009882723,0.8713171,0.001159773,0.00137804,0.0004109985,0.00001573656,0.0005089815,0.0004085286],"genre_scores_gemma":[0.9759728,0.000003463114,0.01007183,0.01371547,0.00009756886,0.00007047717,0.00002382132,0.00003772932,0.000006869547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8612452,"threshold_uncertainty_score":0.9998003,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3134872094","doi":"10.1109/tsc.2021.3065240","title":"Achieving Efficient and Privacy-Preserving Set Containment Search Over Encrypted Data","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Natural Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Computer science; Encryption; Bloom filter; Set operations; Cloud computing; Containment (computer programming); Set (abstract data type); Outsourcing; Tree (set theory); Computer security; Data mining; Database; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.0349615736369783,"gpt":0.2928322967017207,"spread":0.2578707230647425,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006532285,0.0002403844,0.0002517008,0.0001759911,0.0007028888,0.0006071363,0.001933778,0.00008364754,0.000051053],"category_scores_gemma":[0.000005803593,0.0002519832,0.0000725443,0.0008724393,0.00004318327,0.000664896,0.0003566629,0.0004416356,0.00001180892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004141765,"about_ca_system_score_gemma":0.00007382734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002392243,"about_ca_topic_score_gemma":0.000170385,"domain_scores_codex":[0.9973786,0.0002326883,0.0003612228,0.001024927,0.0005322801,0.000470227],"domain_scores_gemma":[0.9971147,0.0004102845,0.00008931968,0.002088512,0.0001081092,0.000189048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002963291,0.004056269,0.009204893,0.003639808,0.001465155,0.0008466502,0.07342182,0.3422635,0.04484499,0.02695568,0.000433433,0.4925715],"study_design_scores_gemma":[0.000663347,0.000048792,0.003454235,0.0002414146,0.00002799792,0.00004919655,0.0007279149,0.9895982,0.003999341,0.0001280988,0.0007425863,0.0003188451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3974036,0.0001711897,0.6014043,0.0002873934,0.0002966876,0.000121849,0.0000667757,0.0001538909,0.0000943331],"genre_scores_gemma":[0.9608773,0.00005097266,0.03843458,0.0005245943,0.00005067799,0.000002627307,0.00004206194,0.00001429796,0.000002876628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6473348,"threshold_uncertainty_score":0.9999933,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392980148","doi":"10.1109/tsc.2024.3377931","title":"Efficient and Privacy-Preserving Federated Learning Against Poisoning Adversaries","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; Higher Education Discipline Innovation Project; Ant Group","keywords":"Computer science; Upload; Federated learning; Computer security; Overhead (engineering); Confidentiality; Scale (ratio); Artificial intelligence; Machine learning; World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.01477667220527104,"gpt":0.254950718373431,"spread":0.2401740461681599,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0006115312,0.0003888226,0.0003102477,0.0005174309,0.001260021,0.001785101,0.007730184,0.0001888551,0.00001018467],"category_scores_gemma":[0.000238522,0.0003921405,0.0001015577,0.001352021,0.00009345123,0.0006962209,0.002623487,0.001107758,0.00005770792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001390494,"about_ca_system_score_gemma":0.00007253655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001076389,"about_ca_topic_score_gemma":0.00002907278,"domain_scores_codex":[0.9971296,0.0001585622,0.0004636766,0.001113459,0.000467836,0.0006668511],"domain_scores_gemma":[0.996567,0.000731718,0.0001178359,0.002359051,0.00009579239,0.0001286336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002530592,0.0001360921,0.0001766574,0.001095096,0.0002744199,0.0001894189,0.004589612,0.4979637,0.00589622,0.0005411959,0.0005422266,0.4885701],"study_design_scores_gemma":[0.0002306554,0.000066551,0.00009294243,0.0009891782,0.00001927234,0.00004059291,0.0004951099,0.9906424,0.005105902,0.001043261,0.0008777975,0.0003963142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2386006,0.0005835667,0.7519534,0.003771038,0.0009445386,0.0001843562,0.000005824842,0.003609328,0.0003473249],"genre_scores_gemma":[0.9020706,0.0000691772,0.09748262,0.0002386151,0.00004859192,0.000009223501,0.000004400946,0.00004172934,0.0000349889],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.66347,"threshold_uncertainty_score":0.9998531,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4387415142","doi":"10.1109/tsc.2023.3322432","title":"A Secure Satellite-Edge Computing Framework for Collaborative Line Outage Identification in Smart Grid","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China; Natural Science Foundation of Shenzhen City; Chinese University of Hong Kong, Shenzhen","keywords":"Computer science; Homomorphic encryption; Theoretical computer science; Encryption; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.02553592477067533,"gpt":0.296692298927999,"spread":0.2711563741573236,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009134315,0.0002909904,0.0003340745,0.0006390479,0.000614755,0.0004453531,0.0009406346,0.0001922341,0.000003907343],"category_scores_gemma":[0.00001081871,0.000321549,0.000162283,0.004321976,0.00004256396,0.0006220347,0.00002380839,0.0004936787,0.00006784567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006002831,"about_ca_system_score_gemma":0.00005807814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006382644,"about_ca_topic_score_gemma":0.0004616799,"domain_scores_codex":[0.9974688,0.0001594624,0.0006630204,0.0008278465,0.0003309208,0.0005499041],"domain_scores_gemma":[0.9976723,0.001045333,0.0002611375,0.0006824712,0.0002221535,0.0001165779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003075537,0.001548095,0.001933856,0.002058051,0.0003348233,0.00007875232,0.1027746,0.3799394,0.002982084,0.1061517,0.0001921535,0.4016989],"study_design_scores_gemma":[0.0007233278,0.0001320886,0.003414451,0.0004299639,0.00002129828,0.000004956475,0.00201068,0.97804,0.004010971,0.009478052,0.001274547,0.0004596242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1032005,0.00009002462,0.8927507,0.000500168,0.001979223,0.0006210016,0.0001587965,0.0006629144,0.00003674066],"genre_scores_gemma":[0.9280196,0.00005826383,0.07123524,0.0003703469,0.0001791654,0.00002916353,0.00007850429,0.00002508616,0.000004635082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8248191,"threshold_uncertainty_score":0.9999236,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2889144536","doi":"10.1109/tsc.2018.2867437","title":"Integrating Social Networks with Mobile Device-to-Device Services","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Computer network; Cellular traffic; Bluetooth; Cellular network; Exploit; Mobile device; Mobile social network; Mobile computing; Computer security; World Wide Web; Telecommunications; Wireless","retraction":null,"screen_n_in":null,"score":{"opus":0.01521958471411397,"gpt":0.2595537378677071,"spread":0.2443341531535932,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0004148802,0.0004625715,0.0004417998,0.0002210332,0.001528634,0.0006052296,0.001403271,0.0001954702,0.00004008762],"category_scores_gemma":[1.696646e-7,0.0004082974,0.000124551,0.00145663,0.00008354207,0.000591114,0.00003354057,0.0005655197,0.0001597789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007536757,"about_ca_system_score_gemma":0.00008285783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002976335,"about_ca_topic_score_gemma":0.001169911,"domain_scores_codex":[0.9970582,0.0001361208,0.0005652915,0.000910952,0.0005181349,0.0008112603],"domain_scores_gemma":[0.9981403,0.0002478372,0.0002735822,0.0006333106,0.0004052467,0.0002997547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001659411,0.0003430628,0.0002405334,0.0002590798,0.0002202872,0.00005548418,0.01748281,0.1836197,0.0001625693,0.0004625105,0.00009639986,0.7968916],"study_design_scores_gemma":[0.0004472835,0.0004943074,0.0001067392,0.0004212538,0.00004568976,0.00004043007,0.001265879,0.9949527,0.0002792506,0.00003506433,0.001402072,0.000509333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08616557,0.00003458843,0.9092483,0.0002043858,0.001082241,0.0003965775,0.000005217079,0.000645024,0.002218118],"genre_scores_gemma":[0.9414225,0.000004291577,0.05328194,0.004308133,0.0008262564,0.00003071949,0.000005183792,0.00004733123,0.00007362173],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8559663,"threshold_uncertainty_score":0.9998369,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4401387233","doi":"10.1109/tsc.2024.3440028","title":"Adaptive Network Management Service Based on Control Relation Graph for Software-Defined LEO Satellite Networks in 6G","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Satellite Communication Systems","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Relation (database); Computer network; Graph; Software; Software-defined networking; Distributed computing; Theoretical computer science; Data mining; Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.01522042072502382,"gpt":0.2271222429512799,"spread":0.211901822226256,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005895902,0.0003413646,0.000324953,0.0004244765,0.0002107988,0.0001561882,0.0003500567,0.0001809242,0.000009382618],"category_scores_gemma":[7.808435e-7,0.0003810955,0.000160739,0.001409743,0.00001276694,0.0001867969,0.000002923691,0.0004721074,0.00005618094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002152286,"about_ca_system_score_gemma":0.00001272362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004360238,"about_ca_topic_score_gemma":0.0004288806,"domain_scores_codex":[0.9980811,0.0001498003,0.0006321414,0.0004332764,0.0002389728,0.0004646488],"domain_scores_gemma":[0.9979081,0.001288189,0.00007356239,0.0005734373,0.00008001783,0.00007671255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001039871,0.00003688424,0.00009205551,0.0005809309,0.0001352579,0.000004122436,0.0003860805,0.941091,0.00001232763,0.0001726387,0.000009577184,0.05737511],"study_design_scores_gemma":[0.0009471113,0.00006269887,0.001166935,0.001712635,0.00006979241,0.000001885493,0.0001488933,0.9934615,0.00003783919,0.0001507845,0.001910825,0.0003290449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002758346,0.001915592,0.9902585,0.0001409417,0.001706171,0.001140097,0.00001978554,0.001337198,0.0007233321],"genre_scores_gemma":[0.9862034,0.0002147115,0.01246525,0.0006281988,0.0001541094,0.0001698972,0.00003477967,0.0001108662,0.00001876206],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9834451,"threshold_uncertainty_score":0.9998641,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392902703","doi":"10.1109/tsc.2024.3376203","title":"EPSet: Efficient and Privacy-Preserving Set Similarity Range Query Over Encrypted Data","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Range query (database); Encryption; Set (abstract data type); Similarity (geometry); Range (aeronautics); Information privacy; Query optimization; Data mining; Theoretical computer science; Information retrieval; Web search query; Sargable; Computer security; Search engine; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.03228511109238694,"gpt":0.2907963364408818,"spread":0.2585112253484949,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007299281,0.0002974938,0.0002568912,0.0003390746,0.0005432011,0.0009383724,0.002272268,0.0001263587,0.00004809881],"category_scores_gemma":[0.00000472636,0.0002909006,0.0001001479,0.001138728,0.00005857316,0.001151942,0.0002170382,0.0005610993,0.00002643494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003377744,"about_ca_system_score_gemma":0.00005403519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004581127,"about_ca_topic_score_gemma":0.0002763121,"domain_scores_codex":[0.9974369,0.0001495377,0.0003818149,0.001141582,0.0004448864,0.0004452532],"domain_scores_gemma":[0.997252,0.0005210016,0.00007263239,0.001926972,0.00005373629,0.0001735913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003606414,0.002969925,0.003343917,0.01165683,0.001991616,0.0009668937,0.08780506,0.1623294,0.006113508,0.04292436,0.004618008,0.6749198],"study_design_scores_gemma":[0.0003012657,0.0000384307,0.001214213,0.0003585887,0.00004147142,0.0000326369,0.0001246018,0.9935854,0.0003739075,0.0008036703,0.002801868,0.0003239615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1968797,0.0006385669,0.7996728,0.00043381,0.001049047,0.0002150245,0.0002989788,0.0006761525,0.0001358508],"genre_scores_gemma":[0.9787172,0.00006879176,0.02056847,0.0004755043,0.00009912332,0.000004272264,0.00004271849,0.00002126219,0.00000265612],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.831256,"threshold_uncertainty_score":0.9999543,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406983186","doi":"10.1109/tsc.2025.3536320","title":"DeFedGCN: Privacy-Preserving Decentralized Federated GCN for Recommender System","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Recommender system; Information privacy; Computer security; Internet privacy; World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.01909109743749904,"gpt":0.2816092261796781,"spread":0.2625181287421791,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006011497,0.0003467569,0.0004617947,0.0003530358,0.0009645976,0.0007500129,0.001531826,0.0001795438,0.000006818555],"category_scores_gemma":[0.000003287571,0.0003412676,0.0002437909,0.0008183662,0.00001388061,0.0004925676,0.00003794115,0.0002792551,0.00001361123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002298335,"about_ca_system_score_gemma":0.00008125989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002679099,"about_ca_topic_score_gemma":0.0001116543,"domain_scores_codex":[0.9975045,0.0002122227,0.0007138267,0.0007475324,0.0002318803,0.0005900432],"domain_scores_gemma":[0.9980894,0.0004631954,0.0002298463,0.0008555757,0.0002434753,0.0001185157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004414779,0.001766071,0.0007737912,0.0129059,0.00243946,0.00004810769,0.01004305,0.03432705,0.006119261,0.05774893,0.01434538,0.8590415],"study_design_scores_gemma":[0.001257607,0.00009156008,0.00006232379,0.001236824,0.00004255935,0.00002110677,0.0005420439,0.9627956,0.02152358,0.0006907561,0.01130645,0.0004296212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004476142,0.0001309536,0.9866519,0.001342183,0.002408152,0.0009530677,0.000008331855,0.001703185,0.002326103],"genre_scores_gemma":[0.9140447,0.00001521513,0.08464684,0.0009606708,0.0000476227,0.00008197522,0.000004084338,0.0000280032,0.0001708633],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9284685,"threshold_uncertainty_score":0.9999039,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2587329666","doi":"10.1109/tsc.2017.2662941","title":"Adaptable Context-Aware Cooking-Safe System","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Context (archaeology); Microcontroller; Ubiquitous computing; Context awareness; Fuzzy logic; Human–computer interaction; Embedded system; Computer security; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.02767501729170752,"gpt":0.2565122919103955,"spread":0.228837274618688,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005942148,0.0003995,0.0005373847,0.0002484291,0.00270692,0.001574771,0.002430411,0.000181312,0.00001784045],"category_scores_gemma":[0.000003816346,0.0004228496,0.0002459419,0.0002508519,0.00006907578,0.00177265,0.00003866684,0.0004681327,0.0003731302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002098817,"about_ca_system_score_gemma":0.0001002038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001587473,"about_ca_topic_score_gemma":0.001258427,"domain_scores_codex":[0.9971142,0.0001986669,0.0006003083,0.0008938891,0.0005843758,0.0006085294],"domain_scores_gemma":[0.9964696,0.0003529799,0.0006488681,0.001955999,0.0003422145,0.0002303427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008408314,0.0004755975,0.0006959577,0.001084195,0.0004527717,0.0001767802,0.004491399,0.01415969,0.001230445,0.002616079,0.0001626209,0.9743704],"study_design_scores_gemma":[0.001538757,0.0001470945,0.0009005504,0.001570599,0.00005104746,0.0002415933,0.001677364,0.9786414,0.01079967,0.00004822902,0.003578444,0.000805239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03350174,0.00004589392,0.9566653,0.0004001809,0.003589453,0.0004375876,0.00002379869,0.0009656592,0.004370323],"genre_scores_gemma":[0.9973749,0.00000374004,0.001565558,0.0004028803,0.0001944719,0.00002905595,0.000001836721,0.00003947707,0.0003880634],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9735652,"threshold_uncertainty_score":0.9998223,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2484289073","doi":"10.1109/tsc.2016.2594778","title":"Performance Evaluation and Optimization of Multi-dimensional Indexes in Hive","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Computer science; Bitmap; Search engine indexing; Inverted index; Index (typography); Skew; Database; Data mining; Information retrieval; Aggregate (composite); Query optimization; Data warehouse; Computer graphics (images); World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.01764642568734288,"gpt":0.2487210246469873,"spread":0.2310745989596445,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004891474,0.0001129698,0.0001301543,0.0002428724,0.0001259725,0.00002755239,0.0002445307,0.00004377368,0.000005146086],"category_scores_gemma":[0.000001783675,0.00008716709,0.00003003615,0.0003255614,0.00002781227,0.00008020012,0.00001467847,0.00007630516,0.000003534446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004889806,"about_ca_system_score_gemma":0.00002084228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004219724,"about_ca_topic_score_gemma":0.0000265131,"domain_scores_codex":[0.9988605,0.00009500754,0.0002736727,0.0003085084,0.0003008461,0.0001614753],"domain_scores_gemma":[0.9993696,0.0001499235,0.0001262708,0.0002188324,0.0000996365,0.00003576546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006340108,0.00006198917,0.0005172036,0.00002644736,0.00000845812,3.917038e-7,0.0005580006,0.7791852,0.0002441468,0.00001413234,3.228407e-7,0.2193774],"study_design_scores_gemma":[0.0008858187,0.00006285045,0.005880599,0.0003601067,0.000008238451,0.000003328747,0.00003871137,0.9909086,0.00172817,0.00001284318,0.000003507507,0.000107186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5070457,0.0000164853,0.4925348,0.0001035471,0.000129171,0.0001043519,5.033269e-7,0.00003852463,0.00002692524],"genre_scores_gemma":[0.9664391,0.000006651041,0.03343908,0.00007118931,0.00001357479,0.000004022264,2.875303e-7,0.000006278015,0.00001978239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4593934,"threshold_uncertainty_score":0.3554571,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4390533396","doi":"10.1109/tsc.2023.3349298","title":"Privacy-Preserving Convolutional Neural Network Classification Scheme With Multiple Keys","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Key Research and Development Projects of Shaanxi Province; National College Students Innovation and Entrepreneurship Training Program; Innovation Scientists and Technicians Troop Construction Projects of Henan Province; Natural Science Foundation of Henan Province; National Natural Science Foundation of China","keywords":"Computer science; Homomorphic encryption; Convolutional neural network; Activation function; Cryptosystem; Information privacy; Encryption; Functional encryption; Public-key cryptography; Theoretical computer science; Ciphertext; Artificial intelligence; Data mining; Computer security; Artificial neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.02040361260747392,"gpt":0.2485578780755709,"spread":0.228154265468097,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002875966,0.0002430186,0.0001766002,0.0001959776,0.0006282809,0.000613962,0.001078256,0.00009055642,0.00002427707],"category_scores_gemma":[0.000001646384,0.0002225285,0.0001227189,0.001334165,0.00005392365,0.001143476,0.00002774271,0.0004762302,0.00005238053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005016256,"about_ca_system_score_gemma":0.00006349674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001121411,"about_ca_topic_score_gemma":0.0001959363,"domain_scores_codex":[0.9980346,0.00009691223,0.0003158207,0.0007064687,0.0004073719,0.0004388391],"domain_scores_gemma":[0.9985564,0.0004054461,0.00008302229,0.0007259226,0.0001025789,0.0001265936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002154142,0.0007397599,0.00677589,0.001375977,0.0006031158,0.0001056583,0.007217829,0.7597214,0.003512772,0.08861743,0.0008969005,0.1302179],"study_design_scores_gemma":[0.000276564,0.00007056029,0.003411369,0.0002754161,0.00001785156,0.00003330706,0.00008661469,0.9927977,0.0002645756,0.000839206,0.001664031,0.0002628237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09228779,0.0002317773,0.9045174,0.0005861968,0.001045367,0.0002009216,0.00001984179,0.0008794758,0.0002312376],"genre_scores_gemma":[0.910868,0.00001127893,0.08857216,0.0002850277,0.0002065786,0.00001328744,0.00001824269,0.00001893417,0.000006502004],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8185802,"threshold_uncertainty_score":0.9074449,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3176140580","doi":"10.1109/tsc.2021.3090276","title":"Incremental Entity Summarization With Formal Concept Analysis","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"St. Francis Xavier University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Automatic summarization; Computer science; Notation; RDF; Linked data; Information retrieval; Semantic Web; Graph; Theoretical computer science; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.00954880000134104,"gpt":0.2235318969654613,"spread":0.2139830969641203,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001349523,0.0001527563,0.0002033325,0.0001616063,0.000487199,0.0003180659,0.000390107,0.00005291609,0.00004597091],"category_scores_gemma":[3.656423e-7,0.000136822,0.0001223043,0.001745238,0.00002316135,0.0006906966,0.00001207561,0.0001618099,0.00001624209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004692105,"about_ca_system_score_gemma":0.00004631055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001866251,"about_ca_topic_score_gemma":0.0006680047,"domain_scores_codex":[0.9986475,0.00008010177,0.000236664,0.0004182174,0.0003265536,0.0002909379],"domain_scores_gemma":[0.9992261,0.00005888243,0.0001043862,0.0004009277,0.0001309222,0.0000787767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002756152,0.0004812778,0.002758763,0.00007213395,0.0009536952,0.00007333452,0.004429877,0.8534113,0.0006427171,0.00175072,0.000008954544,0.1353897],"study_design_scores_gemma":[0.000519913,0.0001031673,0.004402484,0.00003288961,0.0001837125,0.00001980056,0.0004184073,0.9850012,0.008904271,0.00005581767,0.0001043941,0.0002539375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1191812,0.00002253336,0.8793544,0.0001216573,0.000251278,0.00007264709,0.000006813034,0.0001628162,0.0008266499],"genre_scores_gemma":[0.9543457,0.000005939491,0.0450023,0.0005631809,0.00003359058,0.000002515182,0.00001834898,0.000007043745,0.00002139895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8351645,"threshold_uncertainty_score":0.5579441,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}