{"meta":{"query_hash":"e1cbef1e675f","filters":{"venue":"Journal of Cloud Computing Advances Systems and Applications"},"cohort_total":43,"direct_labels_cover":0,"predictions_cover":43,"exported":43,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/e1cbef1e675f","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Cloud+Computing+Advances+Systems+and+Applications"},"results":[{"id":"W2023163864","doi":"10.1186/s13677-015-0032-x","title":"MapReduce for parallel trace validation of LTL properties","year":2015,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; TRACE (psycholinguistics); Cloud computing; Process (computing); Variety (cybernetics); Execution time; Parallel computing; Distributed computing; Theoretical computer science; Programming language; Artificial intelligence; Operating system","score_opus":0.06970177536661169,"score_gpt":0.3266746498857332,"score_spread":0.2569728745191215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023163864","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028658235,0.005296962,0.9648507,0.00014931551,0.00048378468,0.00039624094,0.0000014735751,0.00002148806,0.00014176368],"genre_scores_gemma":[0.5640093,0.000042840744,0.43558386,0.000006106907,0.0002984369,0.000024424206,3.9525716e-7,0.000004707414,0.000029955581],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988171,0.00008436206,0.000631044,0.00013653135,0.00022142801,0.00010955616],"domain_scores_gemma":[0.99794346,0.000112338595,0.0010047568,0.00021984361,0.0006396293,0.00007994929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012499955,0.00008276613,0.00023032627,0.0000729159,0.00009451574,0.0000764506,0.00040035672,0.00003560007,6.2085405e-8],"category_scores_gemma":[0.00010168905,0.00006645403,0.000048234146,0.00019410913,0.000047772388,0.00042081915,0.000043528245,0.00007567722,5.5793424e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008452052,0.00026952566,0.0005606768,0.0008567114,0.00007331941,5.252997e-7,0.0036354237,0.26053485,0.0077769123,0.5287706,0.00064909045,0.19678779],"study_design_scores_gemma":[0.0019019817,0.0009277013,0.0003650151,0.0006663246,0.00006842577,0.0004281228,0.0024071983,0.7774877,0.027472654,0.024597364,0.16320819,0.0004692977],"about_ca_topic_score_codex":0.000004354597,"about_ca_topic_score_gemma":1.013691e-7,"teacher_disagreement_score":0.53535104,"about_ca_system_score_codex":0.000032034426,"about_ca_system_score_gemma":0.000081216946,"threshold_uncertainty_score":0.27099165},"labels":[],"label_agreement":null},{"id":"W2111206802","doi":"10.1186/s13677-014-0015-3","title":"Genetic-based algorithms for resource management in virtualized IVR applications","year":2014,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; École de Technologie Supérieure; Hôpital Notre-Dame; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Virtualization; Cloud computing; Interactive voice response; Scheduling (production processes); Operating system; Software deployment; Distributed computing; Real-time computing","score_opus":0.012278879854938099,"score_gpt":0.27060615942945654,"score_spread":0.25832727957451845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111206802","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035630693,0.0023722476,0.9911628,0.00042968793,0.00020469444,0.0010128489,0.0000017436564,0.00006867359,0.0011842306],"genre_scores_gemma":[0.6785148,0.00006310378,0.31907922,0.00029622798,0.0015051841,0.00032292507,0.0000020278176,0.000029035602,0.00018750457],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797636,0.00012417416,0.00089287903,0.00039376158,0.00031735972,0.0002954491],"domain_scores_gemma":[0.99798864,0.00048736812,0.0007471802,0.0004928826,0.00015606,0.00012788584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011555062,0.00018629822,0.00037860437,0.0002749574,0.0003033421,0.0001922028,0.00082933635,0.000051245803,2.4640846e-7],"category_scores_gemma":[0.000014039845,0.00016643977,0.00011272862,0.0005273789,0.000053963813,0.00003240755,0.00017131386,0.00015356581,0.0000020439568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009544813,0.00014435912,0.00020091365,0.0002461846,0.000037748527,0.0000019203792,0.00011131482,0.3919472,0.000017469843,0.10679182,0.0002517145,0.5002398],"study_design_scores_gemma":[0.0009022962,0.000088748246,0.0003061231,0.00012756944,0.00002035763,0.000023811623,0.0001658653,0.5202769,0.000009224539,0.0028998323,0.47502267,0.00015659448],"about_ca_topic_score_codex":0.000006176718,"about_ca_topic_score_gemma":0.0000011222693,"teacher_disagreement_score":0.6749517,"about_ca_system_score_codex":0.000058867245,"about_ca_system_score_gemma":0.000023360628,"threshold_uncertainty_score":0.6787217},"labels":[],"label_agreement":null},{"id":"W2112623169","doi":"10.1186/s13677-014-0019-z","title":"Performance analysis model for big data applications in cloud computing","year":2014,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Cloud computing; Computer science; Big data; Virtualization; Cloud testing; Software; Distributed computing; Quality of service; Data science; Database; Cloud computing security; Operating system; Computer network","score_opus":0.034712789984352226,"score_gpt":0.28635807534397045,"score_spread":0.2516452853596182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112623169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05492189,0.0017854576,0.94167054,0.00021072912,0.00045777415,0.0005707479,0.000007239211,0.00006686208,0.00030877354],"genre_scores_gemma":[0.9602993,0.000071272596,0.037312247,0.000060955437,0.0021382335,0.000024712428,0.000009618143,0.00001536266,0.000068264795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974223,0.000102168146,0.0011558029,0.0005889402,0.00035715307,0.00037366067],"domain_scores_gemma":[0.99668866,0.000647716,0.0011514368,0.0010934023,0.0002717113,0.00014707544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022037874,0.00023240644,0.00062046415,0.00043540073,0.00050868397,0.0002529514,0.0018702891,0.000066460314,7.16833e-8],"category_scores_gemma":[0.00003923438,0.00020187821,0.00013716995,0.0013451291,0.00006495947,0.00010130062,0.00062751217,0.00024865466,0.0000011018111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003217491,0.00007855284,0.0030150556,0.00013380293,0.000072951116,2.1486991e-7,0.00024156978,0.8329994,0.000008408563,0.011923189,0.000081921826,0.1514417],"study_design_scores_gemma":[0.00042024456,0.00005525959,0.0007459864,0.00011278714,0.00011198318,0.000017934284,0.0001569105,0.9501636,0.0000035306064,0.00079917745,0.047198497,0.00021409211],"about_ca_topic_score_codex":0.000014328339,"about_ca_topic_score_gemma":0.000008587079,"teacher_disagreement_score":0.90537745,"about_ca_system_score_codex":0.000059472542,"about_ca_system_score_gemma":0.000057645055,"threshold_uncertainty_score":0.82323545},"labels":[],"label_agreement":null},{"id":"W2120113879","doi":"10.1186/2192-113x-2-22","title":"Data management in cloud environments: NoSQL and NewSQL data stores","year":2013,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":309,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"NoSQL; Computer science; Cloud computing; Database; Field (mathematics); Big data; Consistency (knowledge bases); Provisioning; Data management; The Internet; Cloud storage; Data science; Replication (statistics); Eventual consistency; Scalability; Distributed computing; World Wide Web; Data consistency; Data mining; Operating system","score_opus":0.02604106169121132,"score_gpt":0.2759074557414271,"score_spread":0.2498663940502158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120113879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05595672,0.01812374,0.92177486,0.0014660455,0.0007869841,0.0008800767,0.000013163943,0.000051226703,0.0009472029],"genre_scores_gemma":[0.95661134,0.0011872421,0.040373012,0.00017864333,0.0012814779,0.000018829774,0.00001150324,0.000019898256,0.0003180663],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979403,0.00010254253,0.00076077995,0.00057350995,0.0003532352,0.00026961393],"domain_scores_gemma":[0.9975524,0.00019012854,0.0006146458,0.0014691463,0.00003574865,0.00013795786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009935354,0.00018097658,0.00032552646,0.00015777202,0.00021462714,0.00035581694,0.0023704674,0.00003914858,0.0000012257647],"category_scores_gemma":[0.000014266971,0.00015078303,0.000022771666,0.00028089012,0.000076863944,0.00026261792,0.0028944463,0.00021019278,0.000008219865],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007456078,0.0003635847,0.0037076105,0.0003933319,0.0002102641,0.000033542357,0.00053437153,0.06828668,0.00005918719,0.045516595,0.008384936,0.87250245],"study_design_scores_gemma":[0.00063378917,0.000059537782,0.003362566,0.00025746753,0.000030661613,0.0001290739,0.0007977731,0.54548347,0.0000018769795,0.0017064635,0.4472993,0.000238005],"about_ca_topic_score_codex":0.00006633767,"about_ca_topic_score_gemma":0.0000050517483,"teacher_disagreement_score":0.9006546,"about_ca_system_score_codex":0.00003902299,"about_ca_system_score_gemma":0.000014595929,"threshold_uncertainty_score":0.6148754},"labels":[],"label_agreement":null},{"id":"W2123590764","doi":"10.1186/s13677-014-0023-3","title":"Fine-grained preemption analysis for latency investigation across virtual machines","year":2014,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Preemption; Virtual machine; Hypervisor; Operating system; Thread (computing); Latency (audio); Tracing; Interrupt; Real-time computing; Cloud computing; Embedded system; Virtualization","score_opus":0.012975199602674695,"score_gpt":0.2741907566374286,"score_spread":0.2612155570347539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123590764","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2548539,0.0006542007,0.74345577,0.00039007686,0.00030421038,0.00022563891,0.0000023839596,0.00005234799,0.00006148775],"genre_scores_gemma":[0.98192894,0.000010911477,0.016762778,0.000046890327,0.0010900324,0.000018753803,0.0000033114195,0.000008303347,0.0001300471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984184,0.00010712818,0.0007058021,0.00028800016,0.0002611116,0.00021958143],"domain_scores_gemma":[0.9979317,0.00040080483,0.00094810367,0.00030730563,0.000298035,0.00011402777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013361726,0.00015098281,0.00036898063,0.00017194882,0.00044433682,0.0002540524,0.0004947964,0.00004839587,2.0700638e-7],"category_scores_gemma":[0.000060653816,0.000122258,0.0001676554,0.00069391675,0.000052082665,0.000076066906,0.0001365318,0.000121193756,0.0000010466812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008921251,0.000057349207,0.008609291,0.00013275778,0.00022034287,4.802657e-7,0.0008861567,0.71432686,0.00015606375,0.0764087,0.00014118812,0.19905187],"study_design_scores_gemma":[0.00048441446,0.00020500882,0.0055132452,0.00008250498,0.00009761588,0.000025775767,0.00012142068,0.96977675,0.000023209943,0.0043883207,0.019112557,0.00016917038],"about_ca_topic_score_codex":0.000016194894,"about_ca_topic_score_gemma":0.000008755184,"teacher_disagreement_score":0.7270751,"about_ca_system_score_codex":0.000028994058,"about_ca_system_score_gemma":0.000017983437,"threshold_uncertainty_score":0.49855366},"labels":[],"label_agreement":null},{"id":"W2140437454","doi":"10.1186/s13677-015-0029-5","title":"A service oriented broker-based approach for dynamic resource discovery in virtual networks","year":2015,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Université du Québec à Montréal","funders":"","keywords":"Computer science; Network virtualization; Virtualization; Cloud computing; Proof of concept; Virtual network; Distributed computing; The Internet; Service (business); Resource (disambiguation); Network architecture; Virtual machine; World Wide Web; Computer network; Operating system","score_opus":0.015215412878207636,"score_gpt":0.25996479666391853,"score_spread":0.2447493837857109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140437454","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010562753,0.0053036734,0.983026,0.00020932082,0.00035491973,0.00042987193,0.0000037879493,0.000045537086,0.000064102336],"genre_scores_gemma":[0.94721067,0.000029915582,0.05175569,0.00020435223,0.0006954359,0.000052660085,0.000008416251,0.00001625277,0.000026625803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984664,0.00008822932,0.0006509094,0.0002850746,0.00024711856,0.00026231783],"domain_scores_gemma":[0.9982021,0.0004488457,0.0006341519,0.0002911527,0.00027223182,0.00015155412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008833031,0.0001548068,0.00035416914,0.00011999636,0.0001432073,0.0002102533,0.0005054075,0.00007065319,3.7468503e-8],"category_scores_gemma":[0.000033600423,0.00013065491,0.00006774329,0.0007171106,0.000032516622,0.00032129948,0.000096295444,0.00021846058,2.743217e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003191269,0.00009286659,0.0007041255,0.000049160542,0.0000112877815,0.0000012421328,0.00013544959,0.9743666,0.000004924185,0.009944042,0.00025147788,0.01440692],"study_design_scores_gemma":[0.0010598113,0.0001595943,0.0001254691,0.0001567049,0.00001038632,0.00006228534,0.0004633062,0.96739894,0.0000017078368,0.00037717397,0.030037925,0.0001467056],"about_ca_topic_score_codex":0.000018873443,"about_ca_topic_score_gemma":0.000008586792,"teacher_disagreement_score":0.9366479,"about_ca_system_score_codex":0.00007907001,"about_ca_system_score_gemma":0.000110032044,"threshold_uncertainty_score":0.53279525},"labels":[],"label_agreement":null},{"id":"W2470812362","doi":"10.1186/s13677-016-0058-8","title":"Green spine switch management for datacenter networks","year":2016,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Cloud computing; Energy consumption; CloudSim; Server; Virtualization; Computer network; Software-defined networking; Workload; Efficient energy use; Distributed computing; Operating system; Engineering","score_opus":0.01180760128625469,"score_gpt":0.2592881246260903,"score_spread":0.2474805233398356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2470812362","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034059843,0.0033716671,0.99028426,0.0012815432,0.00070857053,0.00046916577,0.000001943619,0.000055922326,0.00042092957],"genre_scores_gemma":[0.9458568,0.00034101316,0.049538676,0.00017506999,0.0027787364,0.000042541593,9.565899e-7,0.000021068507,0.0012451402],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985372,0.0000419728,0.00062335096,0.00029825594,0.00023126972,0.0002679887],"domain_scores_gemma":[0.9984972,0.00016768342,0.00064425374,0.00040756995,0.00016862217,0.00011467794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006960434,0.0001509824,0.00027774984,0.00010867712,0.00024928228,0.00014044199,0.00075890304,0.00003410557,3.6235963e-7],"category_scores_gemma":[0.0000067454316,0.00009620615,0.000094435556,0.00022112542,0.00003802862,0.00007084261,0.00033055214,0.00008127376,0.0000018682313],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010303019,0.00009244661,0.0003716052,0.00021819737,0.00012500059,0.000006113828,0.000072047944,0.034979206,0.000044536584,0.13917294,0.0016093855,0.8232982],"study_design_scores_gemma":[0.001360642,0.00018678095,0.0005417531,0.0006635852,0.00004629732,0.00014558718,0.00012010699,0.22988604,0.000014174802,0.0029033346,0.7638679,0.00026377072],"about_ca_topic_score_codex":0.0000034901336,"about_ca_topic_score_gemma":6.718362e-7,"teacher_disagreement_score":0.9424508,"about_ca_system_score_codex":0.000041665462,"about_ca_system_score_gemma":0.000010666753,"threshold_uncertainty_score":0.39231732},"labels":[],"label_agreement":null},{"id":"W2512816754","doi":"10.1186/s13677-016-0061-0","title":"Nearby live virtual machine migration using cloudlets and multipath TCP","year":2016,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Multipath TCP; Computer science; Live migration; Multipath propagation; Cloud computing; Virtual machine; Computer network; Operating system; Virtualization","score_opus":0.014920194446499887,"score_gpt":0.26621431612786417,"score_spread":0.2512941216813643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2512816754","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2791285,0.0040468313,0.71462744,0.00024685328,0.0017306895,0.00014506889,7.7651896e-7,0.000034279114,0.0000395569],"genre_scores_gemma":[0.9753599,0.0002384625,0.02075725,0.00003738118,0.0035561144,0.0000028545678,3.5458854e-7,0.000011529256,0.000036145702],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857163,0.00007962303,0.000620828,0.00026542446,0.00023508725,0.00022737833],"domain_scores_gemma":[0.9982801,0.00036542548,0.00075851404,0.0002124984,0.00023849915,0.00014498149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005834149,0.00016141334,0.00030232695,0.00011363599,0.00038181758,0.00020407044,0.00031833004,0.00005422935,2.437778e-7],"category_scores_gemma":[0.00003653143,0.0001115647,0.00005645492,0.0002050105,0.000072353316,0.0004196177,0.00016966603,0.00014126058,0.0000029226408],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003107074,0.00017860754,0.0136391865,0.00017754304,0.00010636597,0.000021056696,0.0026702543,0.007524448,0.016343128,0.022582024,0.0006261802,0.9361001],"study_design_scores_gemma":[0.0018508914,0.0004812095,0.0024929948,0.0012578195,0.00005552236,0.0018419989,0.00038566883,0.8553426,0.00062639295,0.0026325225,0.1323745,0.00065786607],"about_ca_topic_score_codex":0.00003216584,"about_ca_topic_score_gemma":0.0000016742642,"teacher_disagreement_score":0.93544227,"about_ca_system_score_codex":0.0000473215,"about_ca_system_score_gemma":0.000051141313,"threshold_uncertainty_score":0.45494768},"labels":[],"label_agreement":null},{"id":"W2552523179","doi":"10.1186/s13677-016-0068-6","title":"MDA: message digest-based authentication for mobile cloud computing","year":2016,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Data Security Solutions","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island; Dalhousie University","funders":"","keywords":"Computer science; Cloud computing; Mobile cloud computing; Server; Cloud computing security; Mobile computing; Computer security; Authentication (law); Mobile device; Mutual authentication; Computer network; Cloud testing; Operating system","score_opus":0.015147287253155789,"score_gpt":0.28623355855037136,"score_spread":0.2710862712972156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2552523179","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0200411,0.0036199894,0.97339463,0.0009147276,0.0010077831,0.00079334364,0.000046808458,0.00010578831,0.00007580505],"genre_scores_gemma":[0.95994425,0.000087530316,0.038366105,0.00006204599,0.0014155473,0.00006212563,0.0000054063094,0.00001834514,0.000038633785],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978166,0.0001084578,0.0009821792,0.0003981151,0.00034543464,0.00034916765],"domain_scores_gemma":[0.9961332,0.001323127,0.0012293091,0.00058732677,0.0005401571,0.0001868624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010906246,0.00020293545,0.0004043707,0.0001570478,0.00055740884,0.0002382972,0.0008863295,0.000076276265,9.946817e-7],"category_scores_gemma":[0.00010548384,0.00015319073,0.00014505118,0.00039523697,0.00011710287,0.00041919414,0.00016768418,0.00014863722,0.0000061396495],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000416571,0.0007300645,0.002109755,0.0006552259,0.00016877205,0.0000060935777,0.0013316404,0.022335494,0.008727632,0.71706873,0.0041056974,0.2427192],"study_design_scores_gemma":[0.003097859,0.00058777607,0.0009768618,0.0013703168,0.000120905606,0.0003454471,0.0005157811,0.32794288,0.0012848827,0.011891933,0.65105563,0.000809712],"about_ca_topic_score_codex":0.000009189656,"about_ca_topic_score_gemma":0.0000028344884,"teacher_disagreement_score":0.93990314,"about_ca_system_score_codex":0.000113150374,"about_ca_system_score_gemma":0.00013447207,"threshold_uncertainty_score":0.62469363},"labels":[],"label_agreement":null},{"id":"W2558110129","doi":"10.1186/s13677-016-0069-5","title":"Fine-grained multilayer virtualized systems analysis","year":2016,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science","score_opus":0.01132300534396516,"score_gpt":0.26265192924058467,"score_spread":0.2513289238966195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2558110129","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054188907,0.005570956,0.93817246,0.00057016034,0.0007108596,0.00028526087,0.000003244128,0.00010103222,0.00039712124],"genre_scores_gemma":[0.9921375,0.00006533631,0.0061883945,0.000029972065,0.000988937,0.0000150419955,3.7733662e-7,0.000011813996,0.00056266005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976631,0.00017782988,0.0010225031,0.00038189112,0.0004551275,0.00029956637],"domain_scores_gemma":[0.99711853,0.00058689754,0.0012064892,0.0005506787,0.0003504395,0.00018696234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001065805,0.0002108353,0.0006030711,0.00037322895,0.0003057598,0.0002766501,0.0008288881,0.000058984166,0.0000010500365],"category_scores_gemma":[0.000044577162,0.00013119572,0.00022805473,0.0009924239,0.000066310444,0.000082144616,0.00024648246,0.00012947446,0.000007040272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030975818,0.00030539354,0.0053999294,0.000280204,0.0016199773,0.000029080535,0.00079517224,0.49966606,0.0014546231,0.21816936,0.0011902424,0.27105898],"study_design_scores_gemma":[0.0019421559,0.00023934402,0.001604647,0.0005896924,0.0003900455,0.00029334435,0.0004352758,0.61305493,0.00005817182,0.0010142234,0.37978733,0.0005908026],"about_ca_topic_score_codex":0.000024156725,"about_ca_topic_score_gemma":0.0000025208446,"teacher_disagreement_score":0.9379486,"about_ca_system_score_codex":0.00006313425,"about_ca_system_score_gemma":0.000034523677,"threshold_uncertainty_score":0.5350006},"labels":[],"label_agreement":null},{"id":"W2596158739","doi":"10.1186/s13677-017-0073-4","title":"An autonomic prediction suite for cloud resource provisioning","year":2017,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Cloud computing; Suite; Computer science; Provisioning; Workload; Data mining; Resource (disambiguation); Test suite; Machine learning; Distributed computing; Artificial intelligence; Test case; Operating system","score_opus":0.014437718119059731,"score_gpt":0.284111495750988,"score_spread":0.26967377763192824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2596158739","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12145901,0.001577849,0.87412345,0.0005041256,0.0011068619,0.00056952756,0.0000045509555,0.00010106556,0.0005535484],"genre_scores_gemma":[0.9786023,0.000023021264,0.0177624,0.000042792304,0.003381403,0.000022341275,0.0000013546132,0.000016021859,0.00014836308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983278,0.000072395094,0.00071481336,0.00036771517,0.0002519374,0.0002653332],"domain_scores_gemma":[0.9969989,0.00021431908,0.0015863123,0.0008125175,0.00022408064,0.00016388358],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0013845329,0.00016603293,0.00032747295,0.000116157484,0.0016584609,0.0009722263,0.0013243603,0.000058118327,1.6155582e-7],"category_scores_gemma":[0.00004944039,0.00014114409,0.00010616092,0.00008978006,0.00007813865,0.00018399143,0.00025101352,0.00018732341,0.0000011236641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000298917,0.00020481445,0.0026803962,0.0002746767,0.00009087144,0.0000050602025,0.0010144502,0.44474664,0.00054119126,0.109391086,0.0011818946,0.43983904],"study_design_scores_gemma":[0.0006598555,0.0003159453,0.0016107578,0.0002735751,0.000028976756,0.0001190504,0.00031017276,0.7064347,0.000051522293,0.0018780455,0.2881301,0.00018725345],"about_ca_topic_score_codex":0.000012710995,"about_ca_topic_score_gemma":0.0000010368814,"teacher_disagreement_score":0.8571433,"about_ca_system_score_codex":0.00005953216,"about_ca_system_score_gemma":0.000052427327,"threshold_uncertainty_score":0.99964124},"labels":[],"label_agreement":null},{"id":"W2749299494","doi":"10.1186/s13677-017-0087-y","title":"Burstiness-aware service level planning for enterprise application clouds","year":2017,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Burstiness; Computer science; Cloud computing; Workload; Service level; Service-level agreement; Service (business); Plan (archaeology); Distributed computing; Resource allocation; Resource (disambiguation); Computer network; Operating system","score_opus":0.03374864244092402,"score_gpt":0.3135425792137882,"score_spread":0.2797939367728642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2749299494","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033016235,0.002094175,0.96200436,0.0012016118,0.0007587679,0.0005358476,0.0000057520115,0.00006771936,0.00031553002],"genre_scores_gemma":[0.98070514,0.00002317187,0.017114153,0.0001073213,0.0018820042,0.00004709561,0.0000015867319,0.000016024644,0.000103487844],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983696,0.00003962225,0.0006886664,0.00035121443,0.00028379934,0.00026712916],"domain_scores_gemma":[0.99658054,0.0002593516,0.0017662771,0.00075257116,0.00050595694,0.0001353316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008201901,0.00019067427,0.00036698082,0.00009888719,0.0012639894,0.00060440984,0.0015048047,0.000060088754,1.3397373e-7],"category_scores_gemma":[0.000040562438,0.0001648892,0.00010135606,0.00015315594,0.000051797644,0.00012176021,0.00039030297,0.00017521474,0.0000022198303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044647284,0.00026589827,0.0054264474,0.0011111902,0.0001740668,0.000009390602,0.0016080937,0.3312279,0.00034472515,0.08302375,0.0010840195,0.57567984],"study_design_scores_gemma":[0.00097283704,0.00011721878,0.0032377576,0.00057530374,0.00004670614,0.00017788826,0.00050272426,0.8068028,0.00004649085,0.0032019736,0.18399864,0.00031962918],"about_ca_topic_score_codex":0.00002987346,"about_ca_topic_score_gemma":0.0000022440456,"teacher_disagreement_score":0.94768894,"about_ca_system_score_codex":0.000043463053,"about_ca_system_score_gemma":0.000043901033,"threshold_uncertainty_score":0.9721712},"labels":[],"label_agreement":null},{"id":"W2760106693","doi":"10.1186/s13677-017-0091-2","title":"A resource management technique for processing deadline-constrained multi-stage workflows","year":2017,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cloud computing; Workflow; Workload; Distributed computing; Scheduling (production processes); Schedule; Service-level agreement; Resource Management System; Workflow management system; Middleware (distributed applications); Database; Resource allocation; Operating system; Computer network","score_opus":0.029407603523172555,"score_gpt":0.31476611946552396,"score_spread":0.2853585159423514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2760106693","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002558042,0.0025495277,0.99151254,0.00051689515,0.00026312209,0.0010683839,0.0000024303752,0.00007895777,0.0014501249],"genre_scores_gemma":[0.680013,0.000038660855,0.31818983,0.000056656627,0.00084976223,0.0001055053,6.1059717e-7,0.00001906568,0.0007269252],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982563,0.000050839484,0.00074057153,0.0003738284,0.00027009178,0.00030833815],"domain_scores_gemma":[0.9972329,0.00013780716,0.001606213,0.0006663544,0.00022446917,0.00013223398],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0012692402,0.00020231483,0.0003703679,0.00015177621,0.0013525748,0.0007509789,0.0013799622,0.000059305716,1.7635249e-7],"category_scores_gemma":[0.000030364288,0.00017027167,0.0001261877,0.00015246194,0.0001075842,0.00009899898,0.00041806564,0.00019683533,7.4914396e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024906465,0.00025724387,0.00044200307,0.0014331129,0.00012955394,0.000028613207,0.00047875612,0.10637452,0.00033471466,0.091023356,0.00030883923,0.79916435],"study_design_scores_gemma":[0.0017536598,0.00015904826,0.00041077207,0.0015491241,0.000064641834,0.00024710977,0.0008643229,0.5565612,0.00015000283,0.001632832,0.43615562,0.00045168522],"about_ca_topic_score_codex":0.0000050708113,"about_ca_topic_score_gemma":0.0000011003235,"teacher_disagreement_score":0.7987127,"about_ca_system_score_codex":0.00004658486,"about_ca_system_score_gemma":0.00003481711,"threshold_uncertainty_score":0.99994755},"labels":[],"label_agreement":null},{"id":"W2765481999","doi":"10.1186/s13677-017-0093-0","title":"Community clouds within M-commerce: a privacy by design perspective","year":2017,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Cloud computing; Perspective (graphical); Context (archaeology); Mobile commerce; Convergence (economics); Data science; Mobile device; Computer security; World Wide Web; Internet privacy; Artificial intelligence","score_opus":0.048664736856897026,"score_gpt":0.3602010476785805,"score_spread":0.3115363108216835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765481999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12024326,0.013553401,0.8490284,0.005874921,0.0017829895,0.0014468932,0.000036713853,0.00010428744,0.007929135],"genre_scores_gemma":[0.9948908,0.0005796217,0.0030193431,0.000039307288,0.0013460792,0.000015074923,0.0000012652021,0.000009656432,0.000098828954],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99825835,0.00064958795,0.00045279157,0.00014169796,0.00030127144,0.00019631004],"domain_scores_gemma":[0.99718845,0.00031436386,0.0013904873,0.00051818264,0.0004315218,0.00015702065],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0030418872,0.000117492265,0.0002801443,0.000048757098,0.004907624,0.0005053511,0.0011012726,0.00008080576,0.0000016264765],"category_scores_gemma":[0.0007823867,0.0001064226,0.000058832735,0.00010525087,0.0003744519,0.0005316831,0.00023625659,0.00053160614,0.0000026701778],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030694925,0.0017302921,0.010816429,0.00051003555,0.00045386006,0.000010723312,0.15625747,0.0035800235,0.0022189696,0.687395,0.03298731,0.103732966],"study_design_scores_gemma":[0.0017716781,0.00051919546,0.0025460895,0.0006279874,0.00013247736,0.00015250166,0.111469395,0.0037681586,0.00025332408,0.13225922,0.7458181,0.0006818997],"about_ca_topic_score_codex":0.0032203197,"about_ca_topic_score_gemma":0.00009369959,"teacher_disagreement_score":0.87464756,"about_ca_system_score_codex":0.00014597616,"about_ca_system_score_gemma":0.00013047739,"threshold_uncertainty_score":0.99638784},"labels":[],"label_agreement":null},{"id":"W2798060819","doi":"10.1186/s13677-018-0109-4","title":"An SVM-based framework for detecting DoS attacks in virtualized clouds under changing environment","year":2018,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Cloud computing; Hypervisor; Computer science; Virtual machine; Distributed computing; Denial-of-service attack; Virtualization; Computer security; Real-time computing; Operating system; The Internet","score_opus":0.017057794691434584,"score_gpt":0.2992770672192078,"score_spread":0.28221927252777324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2798060819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08178754,0.0011436549,0.91588575,0.0001502312,0.00063796045,0.00033793793,0.0000010751997,0.00003549625,0.000020334424],"genre_scores_gemma":[0.9132296,0.00004857602,0.084694594,0.00012528847,0.0018594214,0.000027916543,5.6534446e-7,0.000010360384,0.0000036861334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985543,0.00010849852,0.00060257013,0.00026699348,0.00020780614,0.00025982744],"domain_scores_gemma":[0.99843186,0.00041035732,0.0006593299,0.00027959672,0.000116981326,0.00010186826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010820276,0.00013018187,0.0002639336,0.00018367366,0.00045653537,0.00016686,0.00037255452,0.00008441926,0.000001374987],"category_scores_gemma":[0.000023910377,0.0001194704,0.00006443546,0.0003862235,0.000060377697,0.00027185297,0.000059876453,0.00021815595,0.0000020286266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011588793,0.00034755483,0.0006487146,0.00020754499,0.00005053383,0.000003153867,0.002631395,0.35678107,0.00321922,0.2833532,0.000034458193,0.35260728],"study_design_scores_gemma":[0.00076674845,0.00072410255,0.00013236675,0.0004089543,0.000012898686,0.00006023298,0.0006406416,0.95122266,0.0009822604,0.021475377,0.02334183,0.00023191176],"about_ca_topic_score_codex":0.0000052152777,"about_ca_topic_score_gemma":0.0000037539,"teacher_disagreement_score":0.83144206,"about_ca_system_score_codex":0.00007273035,"about_ca_system_score_gemma":0.000031574506,"threshold_uncertainty_score":0.48718616},"labels":[],"label_agreement":null},{"id":"W2808916808","doi":"10.1186/s13677-018-0111-x","title":"Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres","year":2018,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cloud computing; Computer science; Consolidation (business); Data center; Quality of service; Energy consumption; Virtual machine; Energy conservation; Distributed computing; Computer network; Operating system; Engineering; Business","score_opus":0.0175973067101441,"score_gpt":0.28547573251565667,"score_spread":0.2678784258055126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808916808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023946347,0.0021801144,0.97155374,0.000870471,0.00078157533,0.0004042092,0.000011966836,0.00004264254,0.00020890612],"genre_scores_gemma":[0.987339,0.00008700906,0.011282526,0.00012418201,0.0010444676,0.000016289992,0.000012614215,0.000010921892,0.000082991704],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983361,0.000113952185,0.0007250335,0.00037203883,0.00023261292,0.00022030312],"domain_scores_gemma":[0.997786,0.00043164473,0.00087494316,0.0004974841,0.00033387583,0.0000760713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009076816,0.0001484909,0.00029647662,0.00017524992,0.0002906412,0.00018041444,0.000834442,0.000047319387,3.2145186e-7],"category_scores_gemma":[0.0000597598,0.00012856889,0.000040903313,0.00039245535,0.00009102092,0.00013677706,0.00032823504,0.000119396165,8.989094e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009472407,0.00045727353,0.0015041267,0.00023005741,0.00019658986,0.000005499103,0.0013671078,0.07574318,0.0003666304,0.32377285,0.0021589983,0.594103],"study_design_scores_gemma":[0.0006299085,0.00019129102,0.00029054857,0.00016335654,0.000017750677,0.000050519404,0.0003129395,0.8713761,0.00004719587,0.0021492117,0.12462865,0.0001425253],"about_ca_topic_score_codex":0.00007047202,"about_ca_topic_score_gemma":0.000051067764,"teacher_disagreement_score":0.9633927,"about_ca_system_score_codex":0.00006922008,"about_ca_system_score_gemma":0.000066378205,"threshold_uncertainty_score":0.5242887},"labels":[],"label_agreement":null},{"id":"W2848003486","doi":"10.1186/s13677-018-0113-8","title":"A hybrid approach to automatic IaaS service selection","year":2018,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Software deployment; Cloud computing; Service (business); Service provider; Multiple-criteria decision analysis; Cluster analysis; Selection (genetic algorithm); Consolidation (business); Process (computing); Distributed computing; Operations research; Software engineering; Artificial intelligence; Operating system; Engineering","score_opus":0.00953107002022453,"score_gpt":0.255446671469364,"score_spread":0.2459156014491395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2848003486","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0680116,0.00088484614,0.92834246,0.0005044896,0.00047140676,0.0003152021,0.0000012727597,0.00009598432,0.001372753],"genre_scores_gemma":[0.8888477,0.000013257428,0.10836263,0.0007764591,0.0019526185,0.000019123545,8.284681e-7,0.0000109923185,0.000016369208],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857324,0.00007412662,0.0005484824,0.00028033732,0.0002961557,0.00022767388],"domain_scores_gemma":[0.99831444,0.0001144042,0.0005318783,0.00028148995,0.0005783471,0.00017941165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043342615,0.00015080026,0.00027674562,0.00017611904,0.0003647543,0.00023026795,0.000706829,0.000031608324,6.41787e-7],"category_scores_gemma":[0.0000056372983,0.00012264041,0.000047750364,0.0008416982,0.000023101496,0.00028970072,0.0001523231,0.00015592929,0.0000141390565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051305204,0.00090008223,0.0015188461,0.0021602802,0.0003529923,0.000007287485,0.01656363,0.12931708,0.004808499,0.27907395,0.0018745081,0.56337154],"study_design_scores_gemma":[0.00050728075,0.00040290307,0.00068930595,0.00037104546,0.000040763196,0.0015795054,0.0007508709,0.78096896,0.0007270169,0.004253105,0.20934373,0.00036547656],"about_ca_topic_score_codex":0.000045410285,"about_ca_topic_score_gemma":0.000009980065,"teacher_disagreement_score":0.8208361,"about_ca_system_score_codex":0.00003139214,"about_ca_system_score_gemma":0.00005913709,"threshold_uncertainty_score":0.50011307},"labels":[],"label_agreement":null},{"id":"W2884326256","doi":"10.1186/s13677-018-0115-6","title":"Performance of integrated workload scheduling and pre-fetching in multimedia mobile cloud computing","year":2018,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Cloud computing; Workload; Scheduling (production processes); Queueing theory; Real-time computing; Response time; Distributed computing; Dynamic priority scheduling; Multimedia; Computer network; Operating system; Quality of service; Mathematical optimization","score_opus":0.01009814310961994,"score_gpt":0.2720996067476056,"score_spread":0.26200146363798565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884326256","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6377958,0.0044955374,0.3552143,0.000023100552,0.0021207877,0.00022849177,2.769228e-7,0.000031707128,0.00008997647],"genre_scores_gemma":[0.9354766,0.00019782539,0.060973994,0.000017757317,0.0033083966,0.000004449063,5.351316e-7,0.000012437943,0.000007979951],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977783,0.000111534,0.0011640623,0.00033259369,0.00027261788,0.00034092317],"domain_scores_gemma":[0.99758565,0.00044885796,0.001160981,0.00026627825,0.0004093999,0.00012879884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014060156,0.0002108582,0.0005241156,0.0002527283,0.00035097424,0.00016303525,0.0005521019,0.00008042902,2.282675e-7],"category_scores_gemma":[0.000052094318,0.00018482548,0.000058493108,0.00071676046,0.00016023697,0.00038926236,0.0002825864,0.00040458437,0.0000012644017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049109145,0.00018533038,0.064700365,0.0005829071,0.00006547282,0.0000060520033,0.007673548,0.078319184,0.0021213852,0.0011433394,0.00006216528,0.84509116],"study_design_scores_gemma":[0.000574251,0.0002729818,0.0036435437,0.0017636303,0.000012194669,0.00018552098,0.0004860941,0.98803294,0.00037064444,0.00016490527,0.0042734123,0.00021987855],"about_ca_topic_score_codex":0.000041683103,"about_ca_topic_score_gemma":0.0000019444483,"teacher_disagreement_score":0.90971375,"about_ca_system_score_codex":0.000057445704,"about_ca_system_score_gemma":0.00009537866,"threshold_uncertainty_score":0.7536964},"labels":[],"label_agreement":null},{"id":"W2899636728","doi":"10.1186/s13677-018-0122-7","title":"Using genetic algorithms to find optimal solution in a search space for a cloud predictive cost-driven decision maker","year":2018,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cloud computing; Computer science; Scaling; Algorithm; Total cost; Mathematical optimization; Operating system; Mathematics; Economics","score_opus":0.03158742543083181,"score_gpt":0.32576689018220906,"score_spread":0.29417946475137724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899636728","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24546616,0.0007610374,0.7520421,0.00021791807,0.00057869067,0.0008637041,0.000003104894,0.000024117606,0.000043170894],"genre_scores_gemma":[0.65193486,0.000012593676,0.3461158,0.000037925565,0.0018321404,0.000024272105,2.9681163e-7,0.000012085738,0.0000300271],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980696,0.00009532899,0.00068672094,0.00039960846,0.00037906328,0.0003697221],"domain_scores_gemma":[0.99829715,0.00033443497,0.00042867404,0.00030478413,0.00046274313,0.00017222027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090628816,0.00016851444,0.00033429576,0.00027685973,0.0003992895,0.0002079304,0.0005517109,0.00006136553,3.101093e-7],"category_scores_gemma":[0.000038469174,0.00014913891,0.000080746555,0.0006265459,0.0000717909,0.00005758188,0.0003431675,0.00017992765,0.0000026377445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040986917,0.00007638232,0.0003515239,0.000049927352,0.000025031562,0.0000038203043,0.00097627036,0.8939789,0.00021718205,0.002039936,0.00014873849,0.10209132],"study_design_scores_gemma":[0.0007791349,0.00043883556,0.0013654211,0.00043051783,0.000016179973,0.00015229333,0.00036552144,0.981398,0.000051955718,0.00045264116,0.014386617,0.00016290873],"about_ca_topic_score_codex":0.000032731816,"about_ca_topic_score_gemma":0.0000039733886,"teacher_disagreement_score":0.40646872,"about_ca_system_score_codex":0.00016077541,"about_ca_system_score_gemma":0.0000784544,"threshold_uncertainty_score":0.6081708},"labels":[],"label_agreement":null},{"id":"W3107379691","doi":"10.1186/s13677-020-00213-7","title":"A cloud priority-based dynamic online double auction mechanism (PB-DODAM)","year":2020,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Double auction; Cloud computing; Computer science; Common value auction; Resource allocation; Scheduling (production processes); Market mechanism; Operations research; Microeconomics; Mathematical optimization; Computer network; Economics","score_opus":0.052962928245127605,"score_gpt":0.36625877816198166,"score_spread":0.31329584991685405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107379691","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052739993,0.0015631228,0.9406328,0.003600685,0.0007356053,0.00047720596,0.000031253086,0.000066341054,0.00015299076],"genre_scores_gemma":[0.9889528,0.00008060236,0.008421872,0.0002839725,0.0020763304,0.000021342257,0.0000052356877,0.000017455446,0.0001403472],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99707794,0.00017129867,0.0014027328,0.00040886397,0.0007322666,0.00020690182],"domain_scores_gemma":[0.9963085,0.00061664864,0.0017592668,0.00035313645,0.00067743653,0.00028499527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015381173,0.0001887939,0.0004886115,0.00015935193,0.0006041797,0.00025017833,0.00059733255,0.000085363245,0.000015461019],"category_scores_gemma":[0.00014578398,0.0001500332,0.00016503176,0.000974531,0.0001143384,0.00027678962,0.00007871929,0.00035822007,0.000035899684],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005305577,0.00080498535,0.0006685917,0.00026620628,0.00014758379,0.000010120195,0.0017443821,0.3231806,0.0097506,0.52617383,0.0016682248,0.13505433],"study_design_scores_gemma":[0.0026045346,0.00053343346,0.00023446868,0.00017692553,0.00011664653,0.00038674634,0.0070979353,0.22642833,0.00085786154,0.103758134,0.65726876,0.0005361893],"about_ca_topic_score_codex":0.0000058394153,"about_ca_topic_score_gemma":0.0000039283173,"teacher_disagreement_score":0.93621284,"about_ca_system_score_codex":0.000052754218,"about_ca_system_score_gemma":0.00012394512,"threshold_uncertainty_score":0.61181766},"labels":[],"label_agreement":null},{"id":"W3107607987","doi":"10.1186/s13677-020-00206-6","title":"Multi-level host-based intrusion detection system for Internet of things","year":2020,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":105,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Intrusion detection system; Host (biology); Overhead (engineering); Host-based intrusion detection system; Computer security; Cloud computing; Address space; Automation; The Internet; Internet of Things; Embedded system; Computer network; World Wide Web; Operating system; Intrusion prevention system","score_opus":0.026107601613144658,"score_gpt":0.26201596053728365,"score_spread":0.235908358924139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107607987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013644559,0.00156459,0.98344964,0.00020048583,0.0006867952,0.00037700363,0.00000279125,0.000054026405,0.00002010176],"genre_scores_gemma":[0.9280874,0.000028643626,0.07122716,0.000072909745,0.0005581404,0.000013553674,5.5690236e-7,0.0000074599075,0.0000041696776],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870217,0.00006736843,0.00070544844,0.00020255876,0.00019966358,0.00012277918],"domain_scores_gemma":[0.99803805,0.00020884561,0.0011180488,0.00013549098,0.00039526558,0.00010431901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048348622,0.0001118991,0.00030239113,0.000086904176,0.00016538787,0.00008983134,0.0003620613,0.000061831866,2.5513026e-7],"category_scores_gemma":[0.00004112468,0.000096328375,0.00009745713,0.00031010294,0.000032674,0.00030638033,0.00007691673,0.0001622014,8.571503e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003197272,0.00032862736,0.0002974388,0.0042928285,0.00015533368,0.0000048956035,0.0042798445,0.061171666,0.09740402,0.05861563,0.00031338795,0.7728166],"study_design_scores_gemma":[0.0006624114,0.0003918373,0.00006512509,0.00036053956,0.000017688366,0.000059134665,0.00023833946,0.9706808,0.012157081,0.00008497522,0.015172758,0.00010931621],"about_ca_topic_score_codex":0.000015318394,"about_ca_topic_score_gemma":0.0000013359993,"teacher_disagreement_score":0.91444284,"about_ca_system_score_codex":0.00004130577,"about_ca_system_score_gemma":0.000035441466,"threshold_uncertainty_score":0.3928157},"labels":[],"label_agreement":null},{"id":"W3112885745","doi":"10.1186/s13677-020-00224-4","title":"WISE: a computer system performance index scoring framework","year":2021,"lang":"en","type":"preprint","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Workload; Computer science; Idle; Distributed computing; Resource (disambiguation); Resource allocation; Index (typography); Operating system; Computer network; World Wide Web","score_opus":0.012014485312611583,"score_gpt":0.25338591586123604,"score_spread":0.24137143054862445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112885745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15281458,0.015248387,0.8267,0.00017301699,0.0040875426,0.00046667928,0.000001769253,0.00018412879,0.00032395494],"genre_scores_gemma":[0.9243594,0.00029705415,0.07006837,0.00005438685,0.0051119174,0.000028129993,0.0000016813141,0.000031847772,0.00004724066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961755,0.00021601975,0.0015692977,0.0007890594,0.00078904233,0.00046107266],"domain_scores_gemma":[0.99516773,0.0003818021,0.0024394612,0.0011081645,0.0006490241,0.00025378674],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001246449,0.00046804073,0.0010412247,0.00030507846,0.0005408514,0.0012296169,0.0018040952,0.00026401735,4.242003e-7],"category_scores_gemma":[0.000019139894,0.00042025422,0.00027702827,0.0005692379,0.00008577413,0.000095779265,0.0025136806,0.0013838352,0.0000029838932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005106863,0.00008946594,0.0014940472,0.0027445045,0.00017749907,0.000038810835,0.0007454686,0.8594115,0.000004269848,0.016238408,0.00011024854,0.11894071],"study_design_scores_gemma":[0.00031684412,0.000104574356,0.0009148303,0.008593627,0.00006715423,0.00073976343,0.0007802639,0.9723211,0.000010807838,0.0003742215,0.015266417,0.0005103744],"about_ca_topic_score_codex":0.000022501104,"about_ca_topic_score_gemma":6.12036e-7,"teacher_disagreement_score":0.7715448,"about_ca_system_score_codex":0.0002168528,"about_ca_system_score_gemma":0.00019871448,"threshold_uncertainty_score":0.99982494},"labels":[],"label_agreement":null},{"id":"W3129010358","doi":"10.1186/s13677-020-00223-5","title":"Generic SDE and GA-based workload modeling for cloud systems","year":2021,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Workload; Cloud computing; Computer science; Kalman filter; Overhead (engineering); Real-time computing; Distributed computing; Operating system; Artificial intelligence","score_opus":0.02101204403142701,"score_gpt":0.2648088372187086,"score_spread":0.24379679318728162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129010358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06924309,0.041763198,0.8868686,0.00044063572,0.0011288638,0.00035100567,0.000002201576,0.000063426836,0.00013897913],"genre_scores_gemma":[0.96847916,0.00021050965,0.029012978,0.00009517285,0.002056451,0.000029672543,0.0000010943706,0.00001814369,0.00009683918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980138,0.00011541816,0.0008333901,0.00041440138,0.00031408796,0.00030887916],"domain_scores_gemma":[0.99805933,0.00031497958,0.0005972087,0.00039253998,0.0004609934,0.00017492156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008497505,0.00020400106,0.00046373636,0.00012091156,0.0005009963,0.00049812347,0.00044697727,0.000063996,1.5173472e-7],"category_scores_gemma":[0.000033250413,0.00017802836,0.00011772329,0.0004123239,0.00003607201,0.000053185628,0.00020034438,0.00018712893,7.8558116e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000050004496,0.000053271087,0.000085116815,0.00031269717,0.000042985837,0.0000068945824,0.00011135457,0.9560996,0.00007001773,0.015678998,0.00010162621,0.027432429],"study_design_scores_gemma":[0.00055558945,0.000072984534,0.000010096935,0.0005209677,0.00003619777,0.0002459524,0.00045820596,0.9663101,0.000015572714,0.00069955015,0.030876657,0.00019812629],"about_ca_topic_score_codex":0.000014855245,"about_ca_topic_score_gemma":0.0000012640085,"teacher_disagreement_score":0.899236,"about_ca_system_score_codex":0.000051602543,"about_ca_system_score_gemma":0.00009704872,"threshold_uncertainty_score":0.72597855},"labels":[],"label_agreement":null},{"id":"W3149220685","doi":"10.1186/s13677-021-00238-6","title":"DRMaestro: orchestrating disaggregated resources on virtualized data-centers","year":2021,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"H2020 European Research Council; Institució Catalana de Recerca i Estudis Avançats; Generalitat de Catalunya; European Commission; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Provisioning; Computer science; Workload; Data center; Distributed computing; Software deployment; Scalability; Resource (disambiguation); Quality of service; Resource allocation; Computer network; Database; Operating system","score_opus":0.02409135462519495,"score_gpt":0.28655901533644146,"score_spread":0.2624676607112465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149220685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27480873,0.009532785,0.7106644,0.0014923887,0.0010800324,0.00030333182,0.000008489001,0.00015712083,0.0019527415],"genre_scores_gemma":[0.9829895,0.00012560173,0.015269993,0.00017675506,0.0012039819,0.0000046568553,0.000004850229,0.00001589265,0.00020880628],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99777514,0.00021854082,0.0008192575,0.00046529094,0.0004546064,0.0002671477],"domain_scores_gemma":[0.99738044,0.00045557244,0.00094385596,0.0008332257,0.00022739806,0.00015951032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008668323,0.00019073945,0.00038579098,0.00010410301,0.0004147161,0.00046898384,0.0011582543,0.000046671812,6.9337614e-7],"category_scores_gemma":[0.00007944375,0.00015826109,0.00008022201,0.0005064825,0.00006562518,0.00009552266,0.00058288214,0.00029703835,0.000003611211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047210644,0.00079523626,0.0021476129,0.0005139065,0.00041104868,0.00021775307,0.0022170355,0.37042502,0.0010553473,0.13379289,0.0026374164,0.48573953],"study_design_scores_gemma":[0.0014681106,0.00028868287,0.0005449811,0.0012642676,0.00006539695,0.00088283623,0.0022363004,0.5434155,0.00017846149,0.001048518,0.44812962,0.00047731728],"about_ca_topic_score_codex":0.000010990723,"about_ca_topic_score_gemma":0.0000014963032,"teacher_disagreement_score":0.7081807,"about_ca_system_score_codex":0.000040618605,"about_ca_system_score_gemma":0.000066317996,"threshold_uncertainty_score":0.64537},"labels":[],"label_agreement":null},{"id":"W4213430637","doi":"10.1186/s13677-022-00280-y","title":"KeyPIn – mitigating the free rider problem in the distributed cloud based on Key, Participation, and Incentive","year":2022,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IBM (Canada)","funders":"Research Foundation of The City University of New York; City University of New York","keywords":"Cloud computing; Incentive; Key (lock); Free rider problem; Scheme (mathematics); Computer science; Resource (disambiguation); Limiting; Free riding; Computer security; Environmental economics; Computer network; Microeconomics; Engineering; Economics; Public good","score_opus":0.009706813936772335,"score_gpt":0.2543796975633796,"score_spread":0.24467288362660725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213430637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14155437,0.002979188,0.84062225,0.01346566,0.00018623553,0.0009318037,0.000026171083,0.000048716058,0.00018559494],"genre_scores_gemma":[0.99540716,0.00002720512,0.0038959875,0.00028306732,0.0001545318,0.0002225171,0.0000023501582,0.000004606581,0.0000025955758],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985187,0.00028610625,0.00051989313,0.00021357086,0.0002886756,0.00017304317],"domain_scores_gemma":[0.9980703,0.0007117546,0.0006191478,0.0004427092,0.00011790382,0.000038183724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014106823,0.00010584202,0.00017967407,0.00007297682,0.0010070559,0.00012972121,0.0009141903,0.000031044245,5.578215e-7],"category_scores_gemma":[0.000045482207,0.00006882924,0.00003654286,0.0006768268,0.00012381331,0.00008013839,0.0002388166,0.00045856083,2.7590167e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012578551,0.00035201272,0.0065298476,0.00006270002,0.00002725521,0.000004975724,0.0028444005,0.12104302,0.00004656781,0.84270734,0.0010839907,0.025285298],"study_design_scores_gemma":[0.0011677218,0.00032746058,0.00784506,0.00014069657,0.0000322271,0.00014296139,0.0045775943,0.78348345,0.000054514014,0.082339756,0.11960948,0.00027908955],"about_ca_topic_score_codex":0.000022478924,"about_ca_topic_score_gemma":0.000007455267,"teacher_disagreement_score":0.85385275,"about_ca_system_score_codex":0.00004569882,"about_ca_system_score_gemma":0.000044322533,"threshold_uncertainty_score":0.77455616},"labels":[],"label_agreement":null},{"id":"W4291178743","doi":"10.1186/s13677-022-00296-4","title":"Anomaly detection in microservice environments using distributed tracing data analysis and NLP","year":2022,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ciena (Canada); Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Microservices; Tracing; Agile software development; Scalability; TRACE (psycholinguistics); Anomaly detection; DevOps; Data mining; Visualization; Overfitting; Artificial intelligence; Cloud computing; Machine learning; Database; Artificial neural network; Software engineering","score_opus":0.01553001782085877,"score_gpt":0.27015264019657786,"score_spread":0.2546226223757191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291178743","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4244488,0.0022430343,0.57303154,0.000022186277,0.00012565621,0.0001080091,0.000012040649,0.000006735513,0.0000020285079],"genre_scores_gemma":[0.9949782,0.000073804506,0.0047978964,0.000011514077,0.00012055809,0.000006460938,0.0000056203535,0.0000038704816,0.0000020865214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998629,0.00012926475,0.0005901116,0.00029135452,0.00022153469,0.00013878547],"domain_scores_gemma":[0.9987515,0.00012443987,0.00062311953,0.000409068,0.0000345579,0.000057282177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010993467,0.00009276736,0.00028275963,0.00018167355,0.00042661227,0.000098964825,0.0004926705,0.00002483804,3.6671875e-7],"category_scores_gemma":[0.000009320618,0.00008519912,0.00003704788,0.00090161426,0.000027235352,0.00040330566,0.00040905728,0.00018785975,1.5817875e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018425704,0.00020194467,0.23876293,0.00025220602,0.0002049835,0.000008866361,0.00086540135,0.7013874,0.0041759494,0.0001918753,0.000005018682,0.053925008],"study_design_scores_gemma":[0.00039903432,0.0000681462,0.045958422,0.000053519692,0.000099961966,0.00037017555,0.0008076142,0.94527894,0.00012972021,0.00013121827,0.0065350938,0.00016816553],"about_ca_topic_score_codex":0.0001396995,"about_ca_topic_score_gemma":0.00001863973,"teacher_disagreement_score":0.5705294,"about_ca_system_score_codex":0.00010016674,"about_ca_system_score_gemma":0.00002734423,"threshold_uncertainty_score":0.34743193},"labels":[],"label_agreement":null},{"id":"W4293061092","doi":"10.1186/s13677-022-00290-w","title":"Joint task offloading and resource allocation in mobile edge computing with energy harvesting","year":2022,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; EnodeB; Mobile edge computing; Computation offloading; Energy consumption; Resource allocation; Telecommunications link; Server; Lyapunov optimization; Computer network; Real-time computing; Enhanced Data Rates for GSM Evolution; Distributed computing; Edge computing; Base station; User equipment; Engineering; Telecommunications","score_opus":0.011325944907850743,"score_gpt":0.23331654862432,"score_spread":0.22199060371646925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293061092","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1736023,0.009350668,0.81517583,0.00017128968,0.00096245954,0.00025702096,4.4920145e-7,0.000062263956,0.00041774166],"genre_scores_gemma":[0.9844168,0.000048753238,0.013801278,0.00007821111,0.0015771814,0.00002120366,0.0000017219143,0.000016895267,0.000037980415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793196,0.00020114293,0.000830725,0.00036836686,0.00035479973,0.00031298285],"domain_scores_gemma":[0.9981069,0.00036222074,0.0010263969,0.00023815579,0.00014511801,0.000121196965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013828645,0.00018125647,0.00039164766,0.00027166097,0.00084347546,0.00026704156,0.00043163626,0.000031873344,1.6948101e-7],"category_scores_gemma":[0.000019639427,0.00016759266,0.00004226664,0.000710564,0.00005795962,0.00030673057,0.00044552973,0.0003745241,2.4491897e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021273529,0.00017944604,0.0058557806,0.00025843436,0.000052801366,0.000036590114,0.0042640143,0.4891684,0.0015431362,0.022714142,0.00044452847,0.47546145],"study_design_scores_gemma":[0.00093285856,0.00043626234,0.0012256566,0.0005689087,0.000019943485,0.0019477506,0.0019905798,0.7296642,0.0001057251,0.00076765986,0.2619093,0.000431153],"about_ca_topic_score_codex":0.000057525463,"about_ca_topic_score_gemma":0.0000023724579,"teacher_disagreement_score":0.8108145,"about_ca_system_score_codex":0.00011680987,"about_ca_system_score_gemma":0.00009240454,"threshold_uncertainty_score":0.68342304},"labels":[],"label_agreement":null},{"id":"W4296448983","doi":"10.1186/s13677-022-00322-5","title":"CLQLMRS: improving cache locality in MapReduce job scheduling using Q-learning","year":2022,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Locality; Cache; Locality of reference; Parallel computing; Cache algorithms; Scheduling (production processes); FIFO and LIFO accounting; FIFO (computing and electronics); Distributed computing; Principle of locality; CPU cache; Operating system","score_opus":0.015603006816162793,"score_gpt":0.2672680328503246,"score_spread":0.2516650260341618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296448983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4325273,0.004740899,0.56177974,0.00013467587,0.00044476415,0.00018679348,4.90675e-7,0.00004445051,0.00014089313],"genre_scores_gemma":[0.9781171,0.000016019321,0.021158794,0.0000444711,0.0006008895,0.000010879252,2.968943e-7,0.00001287089,0.000038683225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99758744,0.0004182971,0.0008712426,0.00035794656,0.0004468145,0.00031824294],"domain_scores_gemma":[0.998185,0.00030493047,0.000990807,0.00029088007,0.000121916855,0.00010648294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027677626,0.0001633751,0.00036112533,0.00024358674,0.0008372902,0.0002227789,0.0007552111,0.000033022505,8.183925e-7],"category_scores_gemma":[0.000049050814,0.00015907438,0.00008853097,0.0007529952,0.00004197716,0.00007964925,0.0007817407,0.00068937725,5.615649e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005053336,0.000052957606,0.0026740052,0.000088500834,0.00001537118,0.0000110277115,0.00051187887,0.94856787,0.00027121234,0.0048185783,0.000005967403,0.042977583],"study_design_scores_gemma":[0.00038367245,0.0000887763,0.00046802557,0.00013402019,0.000013795773,0.0003577863,0.0026686261,0.9822303,0.000015650658,0.000453957,0.01299519,0.00019020712],"about_ca_topic_score_codex":0.00012488954,"about_ca_topic_score_gemma":0.0000017145621,"teacher_disagreement_score":0.5455898,"about_ca_system_score_codex":0.00021708083,"about_ca_system_score_gemma":0.00009153788,"threshold_uncertainty_score":0.64868647},"labels":[],"label_agreement":null},{"id":"W4306168742","doi":"10.1186/s13677-022-00338-x","title":"Complex event processing for physical and cyber security in datacentres - recent progress, challenges and recommendations","year":2022,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Research Chairs","funders":"Universiti Kebangsaan Malaysia","keywords":"Computer science; Complex event processing; Cloud computing; Big data; Cyber-physical system; Event (particle physics); Computer security; Internet of Things; Intrusion detection system; The Internet; Data science; World Wide Web; Process (computing); Data mining","score_opus":0.028546551343360038,"score_gpt":0.3120620850335768,"score_spread":0.28351553369021676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306168742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20342818,0.28456992,0.48630384,0.021243736,0.00128531,0.0027356893,0.000050331055,0.00012309506,0.0002599113],"genre_scores_gemma":[0.98905236,0.0052056634,0.005239976,0.00003501955,0.00038550515,0.00006920449,0.0000034453285,0.0000054849725,0.0000033448068],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990317,0.00010676316,0.00035749274,0.0002174749,0.00015298268,0.00013358238],"domain_scores_gemma":[0.9991431,0.00015724965,0.0004295244,0.00010736582,0.00009934994,0.00006336997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005450812,0.00008707354,0.00020789445,0.00007627912,0.00047630703,0.00011178923,0.00017555541,0.000016870663,5.498673e-7],"category_scores_gemma":[0.000009945078,0.0000803014,0.000021200718,0.00017795071,0.00004759338,0.00028123707,0.00021312834,0.00018235252,5.093129e-8],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001868176,0.00020408878,0.00013758626,0.00018416885,0.000011801454,8.1905074e-7,0.0026824453,0.002144405,0.000023180393,0.042344335,0.00024045643,0.952008],"study_design_scores_gemma":[0.00058030104,0.00026504815,0.000588182,0.0001579325,0.000011567761,0.00021117878,0.0014513667,0.465623,0.000009763333,0.013459912,0.51749825,0.0001434858],"about_ca_topic_score_codex":0.0000026775135,"about_ca_topic_score_gemma":0.000009045744,"teacher_disagreement_score":0.95186454,"about_ca_system_score_codex":0.00003742216,"about_ca_system_score_gemma":0.00002735481,"threshold_uncertainty_score":0.36634168},"labels":[],"label_agreement":null},{"id":"W4308000139","doi":"10.1186/s13677-022-00349-8","title":"A malware detection system using a hybrid approach of multi-heads attention-based control flow traces and image visualization","year":2022,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Malware; Android (operating system); Artificial intelligence; Android malware; Support vector machine; Machine learning; Pattern recognition (psychology); Operating system","score_opus":0.011815562399076888,"score_gpt":0.2725403864438092,"score_spread":0.2607248240447323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308000139","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034060426,0.0027328995,0.9622297,0.000013016506,0.00024303685,0.00056777697,0.000020504269,0.00012323303,0.0000093999215],"genre_scores_gemma":[0.8619141,0.000017533976,0.13785395,0.000009728011,0.00012713954,0.00005986491,0.0000016654512,0.000013324115,0.0000027037845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820316,0.00023494911,0.0007840374,0.00028375158,0.00034008388,0.00015404675],"domain_scores_gemma":[0.9977006,0.00015503676,0.0014493831,0.00021794291,0.00040144034,0.00007562276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077803014,0.00015121451,0.00037848385,0.00026812148,0.00057008484,0.00010298778,0.00027765005,0.000031179883,2.701123e-7],"category_scores_gemma":[0.000021987807,0.0001493553,0.00008121736,0.00045767523,0.000062665225,0.00036936466,0.000070793365,0.00019277385,7.769014e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007641193,0.0003985829,0.0010244715,0.0016999415,0.00009290313,0.000009367563,0.0003542951,0.8559727,0.05543261,0.007279516,0.000011134792,0.07764804],"study_design_scores_gemma":[0.0007489309,0.00018650388,0.00010443061,0.00012285257,0.00003288669,0.00065497943,0.00068311545,0.9938487,0.0027055605,0.000121926,0.000648417,0.00014171464],"about_ca_topic_score_codex":0.00001818691,"about_ca_topic_score_gemma":6.963767e-7,"teacher_disagreement_score":0.8278537,"about_ca_system_score_codex":0.0001373025,"about_ca_system_score_gemma":0.000053968426,"threshold_uncertainty_score":0.60905325},"labels":[],"label_agreement":null},{"id":"W4313452571","doi":"10.1186/s13677-022-00363-w","title":"Low-power multi-cloud deployment of large distributed service applications with response-time constraints","year":2023,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; IBM (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cloud computing; Software deployment; Computer science; Distributed computing; Response time; Heuristic; Quality of service; Mathematical optimization; Computer network; Operating system; Mathematics","score_opus":0.01035178616180404,"score_gpt":0.26248656876223314,"score_spread":0.2521347826004291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313452571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16292219,0.0007524536,0.83448356,0.00070488616,0.00019319727,0.00065032695,0.000032684053,0.00017245754,0.00008821228],"genre_scores_gemma":[0.9912298,0.000029206756,0.008185297,0.00007482765,0.0002959196,0.00005062056,0.0000059156987,0.000021575846,0.00010686493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976676,0.00017728277,0.0009207417,0.00037862937,0.00048470614,0.00037102355],"domain_scores_gemma":[0.99700433,0.00054745696,0.001151838,0.000556939,0.0005558566,0.0001836006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014465172,0.00022476832,0.0004753588,0.00022474071,0.00039165738,0.00013167613,0.00083969044,0.000061722414,0.000001597084],"category_scores_gemma":[0.000025529414,0.00017980077,0.00009392222,0.0014956711,0.000116820316,0.00005823607,0.00033844172,0.00022097028,0.000020570034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003094453,0.0023346553,0.0030092585,0.0017190636,0.00090037746,0.00008399586,0.0041970653,0.85674566,0.006095084,0.068497054,0.004284832,0.051823523],"study_design_scores_gemma":[0.004594255,0.00072363997,0.0048917634,0.0017806068,0.00014506829,0.0006309252,0.00406499,0.70987487,0.00075564685,0.0006520357,0.2709206,0.00096559466],"about_ca_topic_score_codex":0.000005718705,"about_ca_topic_score_gemma":0.0000014791165,"teacher_disagreement_score":0.82830757,"about_ca_system_score_codex":0.000059016606,"about_ca_system_score_gemma":0.00010332972,"threshold_uncertainty_score":0.7332063},"labels":[],"label_agreement":null},{"id":"W4313680298","doi":"10.1186/s13677-022-00358-7","title":"Load balancing and service discovery using Docker Swarm for microservice based big data applications","year":2023,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Microservices; Computer science; Load balancing (electrical power); Big data; Distributed computing; Cloud computing; Server; Swarm behaviour; Scheduling (production processes); Workload; Operating system","score_opus":0.04269904710130417,"score_gpt":0.29903745092955997,"score_spread":0.2563384038282558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313680298","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06012838,0.0037976934,0.9336256,0.0011256472,0.00049804215,0.0006514868,0.000024491492,0.00010575246,0.00004294554],"genre_scores_gemma":[0.9321125,0.00014143856,0.06344408,0.00063351326,0.0034268363,0.000067929854,0.000019066047,0.000040744177,0.00011389054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814135,0.000059581937,0.0006805323,0.00049298175,0.0003203241,0.00030523556],"domain_scores_gemma":[0.99744457,0.0005629834,0.000738368,0.0007590555,0.00036837673,0.00012664974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012625852,0.00018913696,0.00034509148,0.00016394806,0.0006335884,0.00051097834,0.0010838836,0.000050927643,5.6770876e-8],"category_scores_gemma":[0.000022158461,0.00016606842,0.00005615931,0.0009551801,0.00004101053,0.00014661626,0.00072854816,0.00015564961,0.0000019437039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021292692,0.00017047071,0.001201177,0.0022073858,0.00015563764,0.0000067608607,0.00066868647,0.7645462,0.001883907,0.010266037,0.00082659174,0.21804583],"study_design_scores_gemma":[0.00049656624,0.000030839492,0.00022712802,0.0002766715,0.00004428797,0.00007883529,0.00038067874,0.8412209,0.00004200614,0.00068247505,0.15632904,0.00019055037],"about_ca_topic_score_codex":0.00005862214,"about_ca_topic_score_gemma":0.000013162818,"teacher_disagreement_score":0.8719841,"about_ca_system_score_codex":0.00006105176,"about_ca_system_score_gemma":0.00013349166,"threshold_uncertainty_score":0.67720735},"labels":[],"label_agreement":null},{"id":"W4317390695","doi":"10.1186/s13677-022-00387-2","title":"Deep learning approach to security enforcement in cloud workflow orchestration","year":2023,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Data Security Solutions","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Concordia University","funders":"","keywords":"Cloud computing; Computer science; Workflow; Orchestration; Cloud computing security; Anomaly detection; Computer security; Distributed computing; Data mining; Database; Operating system","score_opus":0.02015942445982682,"score_gpt":0.2833356406579219,"score_spread":0.26317621619809506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317390695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034079522,0.0012694249,0.96230847,0.0002884031,0.0004921629,0.00048529237,0.0000024359217,0.00010747255,0.0009668409],"genre_scores_gemma":[0.9807692,0.00018163878,0.018123962,0.00003671341,0.0008001799,0.000043463617,0.000008922771,0.000009992315,0.000025894144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998061,0.00014098703,0.00078289676,0.00032343387,0.00037649667,0.00031516538],"domain_scores_gemma":[0.998606,0.00025652003,0.00047413816,0.0003198681,0.00016957065,0.00017388641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013301894,0.00014999509,0.00030552165,0.00030536676,0.00030875223,0.0002509639,0.00061648,0.00006032386,4.527185e-7],"category_scores_gemma":[0.000078625024,0.00014662679,0.000060205257,0.0016172511,0.000034565615,0.00034821266,0.00024893155,0.00037332778,0.000012915721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053568283,0.00011256817,0.000973213,0.000107024185,0.000017271646,0.0000040736927,0.0033874025,0.7957602,0.00006327467,0.17679352,0.0003012426,0.022474809],"study_design_scores_gemma":[0.00037940344,0.00010506817,0.0007391503,0.00017557057,0.000009279213,0.000119719116,0.001230631,0.90023476,0.000015725942,0.006039816,0.09072784,0.00022304979],"about_ca_topic_score_codex":0.00003719553,"about_ca_topic_score_gemma":0.000014489204,"teacher_disagreement_score":0.9466897,"about_ca_system_score_codex":0.00010770233,"about_ca_system_score_gemma":0.000052933337,"threshold_uncertainty_score":0.5979267},"labels":[],"label_agreement":null},{"id":"W4317716297","doi":"10.1186/s13677-022-00356-9","title":"Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment","year":2023,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial neural network; Cloud computing; Cyber-attack; Artificial intelligence; Machine learning; Bayesian network; Naive Bayes classifier; Data mining; Computer security; Support vector machine; Operating system","score_opus":0.04837057785187663,"score_gpt":0.30902654409993446,"score_spread":0.2606559662480578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317716297","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14909068,0.0025887904,0.8455214,0.0002913526,0.0016593587,0.00070216606,0.0000104240335,0.000111652604,0.000024209063],"genre_scores_gemma":[0.9825564,0.00037177536,0.013944432,0.00007661132,0.0028443497,0.000036894453,0.000009189346,0.00002028867,0.00014003832],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997907,0.00012506482,0.0009674022,0.00035096845,0.0002794075,0.00037017913],"domain_scores_gemma":[0.99820435,0.00038591772,0.0009143233,0.00025820752,0.00012005913,0.000117167474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010979618,0.00018476434,0.00035433643,0.00015127806,0.0004751622,0.00017788388,0.0003936757,0.00008563275,0.0000010043086],"category_scores_gemma":[0.000022176573,0.0001710969,0.000101084246,0.0006426474,0.000054637578,0.00036831206,0.00017668495,0.00030144237,0.0000049843115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001639053,0.000048178128,0.0010550277,0.00007651798,0.000017817034,0.0000040139043,0.00039755218,0.9625056,0.00011973227,0.0017105419,0.0008150481,0.033233613],"study_design_scores_gemma":[0.00069422997,0.00011220506,0.002788042,0.00019398106,0.000010048177,0.00006297355,0.00022790971,0.9206193,0.000017580262,0.00021612267,0.074910566,0.0001470336],"about_ca_topic_score_codex":0.000012337765,"about_ca_topic_score_gemma":0.000006580108,"teacher_disagreement_score":0.83346575,"about_ca_system_score_codex":0.000080750724,"about_ca_system_score_gemma":0.00002680033,"threshold_uncertainty_score":0.6977129},"labels":[],"label_agreement":null},{"id":"W4327893913","doi":"10.1186/s13677-023-00412-y","title":"An artificial intelligence lightweight blockchain security model for security and privacy in IIoT systems","year":2023,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":155,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Blockchain; Cloud computing; Computer security; Anomaly detection; Authentication (law); Artificial intelligence","score_opus":0.022181034803496553,"score_gpt":0.2987557569866778,"score_spread":0.27657472218318124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327893913","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25108844,0.0028977578,0.74443823,0.0005460869,0.00019931371,0.0006788621,0.000011671232,0.000122018755,0.000017639128],"genre_scores_gemma":[0.99262136,0.00032082864,0.0065049296,0.00001817689,0.00037890294,0.00013628215,0.0000023419632,0.000012365348,0.000004827775],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794215,0.00008849919,0.0009679128,0.00045309347,0.00022203544,0.00032630734],"domain_scores_gemma":[0.99822104,0.00028925712,0.00056378456,0.0004899588,0.00028182464,0.0001541234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015514095,0.00019266816,0.00044493852,0.00033171542,0.0003889059,0.00021500957,0.00078984234,0.00015494596,1.01254166e-7],"category_scores_gemma":[0.000038677885,0.00017711938,0.000057672067,0.0008416863,0.00010264529,0.0002027368,0.00018613866,0.00033440895,0.0000011183802],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000937833,0.00017565144,0.00022471376,0.00022400546,0.000017885995,0.0000032178366,0.002980756,0.14902882,0.00016818101,0.8316891,0.000041377356,0.015436869],"study_design_scores_gemma":[0.00011526057,0.0000810587,0.000013777505,0.00008432021,0.000008177078,0.000079008474,0.0008119484,0.82450014,0.00005907852,0.17249598,0.0015969544,0.00015430828],"about_ca_topic_score_codex":0.000024204575,"about_ca_topic_score_gemma":0.000016759337,"teacher_disagreement_score":0.7415329,"about_ca_system_score_codex":0.00004523591,"about_ca_system_score_gemma":0.000072008865,"threshold_uncertainty_score":0.72227186},"labels":[],"label_agreement":null},{"id":"W4387498943","doi":"10.1186/s13677-023-00497-5","title":"Resource allocation strategy for blockchain-enabled NOMA-based MEC networks","year":2023,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Mobile edge computing; Blockchain; Distributed computing; Resource allocation; Quality of service; Cloud computing; Computer network; Cluster analysis; Energy consumption; Server; Artificial intelligence","score_opus":0.01800759526780883,"score_gpt":0.26644629492613375,"score_spread":0.24843869965832494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387498943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020478232,0.0076495362,0.97043705,0.00020122841,0.00016093306,0.0004449132,0.000005636876,0.00047511127,0.00014736972],"genre_scores_gemma":[0.99361473,0.0003367945,0.005469556,0.000012420545,0.00037964052,0.00010419829,0.000009066406,0.0000304527,0.00004313619],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893516,0.000028083543,0.00058176514,0.00012432822,0.00012325865,0.00020741283],"domain_scores_gemma":[0.9985684,0.0005099077,0.00037584009,0.0002968749,0.00019193624,0.000057020254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004065388,0.00013473137,0.00026957653,0.00016468411,0.00022632489,0.000071013375,0.0003178173,0.000081385806,2.7123997e-7],"category_scores_gemma":[0.000032607608,0.00012916917,0.000058706246,0.0004928851,0.00005234456,0.00006316645,0.00003370452,0.00020545679,0.0000011158024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046935793,0.000009959385,0.000036114478,0.00011373686,0.000019745501,3.4340454e-7,0.000019429639,0.9510447,0.0002894434,0.008717546,0.00029073574,0.039453547],"study_design_scores_gemma":[0.00034180153,0.00004897017,0.000057158453,0.00013103073,0.000012589773,0.000013601644,0.0006923756,0.9268713,0.0002899525,0.0011787304,0.07022957,0.00013294614],"about_ca_topic_score_codex":0.0000013910511,"about_ca_topic_score_gemma":0.0000015478565,"teacher_disagreement_score":0.9731365,"about_ca_system_score_codex":0.00005382923,"about_ca_system_score_gemma":0.000021338774,"threshold_uncertainty_score":0.5267366},"labels":[],"label_agreement":null},{"id":"W4389086879","doi":"10.1186/s13677-023-00551-2","title":"Correction: Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment","year":2023,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"E-Learning and Knowledge Management","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cloud computing; Computer science; Artificial neural network; Stage (stratigraphy); Artificial intelligence; Computer security; Machine learning; Operating system; Geology","score_opus":0.04936781900401968,"score_gpt":0.3131338985939717,"score_spread":0.263766079589952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389086879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02859174,0.0023884182,0.9640456,0.00037378175,0.003567634,0.000699162,0.000005081695,0.00015082952,0.0001777853],"genre_scores_gemma":[0.98045325,0.00020080445,0.014631723,0.00005556694,0.0028307373,0.00005227522,0.000011179572,0.00002679747,0.0017376748],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997978,0.00013512038,0.0008843559,0.0003760036,0.00026141093,0.00036510965],"domain_scores_gemma":[0.99816066,0.0004544717,0.0008709986,0.00027295106,0.00013120844,0.00010969122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012063337,0.00019220007,0.00035291974,0.00016540778,0.00047439497,0.00020178092,0.00041297023,0.00006137402,8.1113564e-7],"category_scores_gemma":[0.0000354206,0.00017840281,0.000096873475,0.0006194932,0.00006330445,0.00023407405,0.00022053422,0.00029607242,0.000009358714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009543074,0.000055750068,0.003286571,0.00009414295,0.000025676767,0.0000047942394,0.0004940117,0.953298,0.000019469251,0.0013392047,0.0030168097,0.03835608],"study_design_scores_gemma":[0.00064875756,0.0001003338,0.004635404,0.00025474242,0.000014239126,0.000040812178,0.0007604649,0.8889389,0.0000047737153,0.000048159127,0.10440701,0.00014641315],"about_ca_topic_score_codex":0.000012263463,"about_ca_topic_score_gemma":0.000007309051,"teacher_disagreement_score":0.9518615,"about_ca_system_score_codex":0.000101342404,"about_ca_system_score_gemma":0.000026960099,"threshold_uncertainty_score":0.72750556},"labels":[],"label_agreement":null},{"id":"W4391899496","doi":"10.1186/s13677-024-00610-2","title":"Multiple objectives dynamic VM placement for application service availability in cloud networks","year":2024,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Cloud computing; Computer science; Distributed computing; Service (business); Operating system; Business","score_opus":0.008551592260182813,"score_gpt":0.2663357467579314,"score_spread":0.2577841544977486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391899496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04109308,0.012187864,0.9440099,0.0005981671,0.00095856976,0.00094541686,0.0000032174953,0.00010701628,0.0000967629],"genre_scores_gemma":[0.9900323,0.000099004894,0.008735566,0.000058506033,0.0009053013,0.00010088843,0.0000023067366,0.000015426955,0.000050684568],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807966,0.00009585399,0.0008452296,0.0004611798,0.0002388602,0.00027921962],"domain_scores_gemma":[0.9980769,0.0008032906,0.00044344825,0.00036438223,0.00021799497,0.00009396244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013766185,0.00018773956,0.00033881277,0.00017680076,0.00023384219,0.00028690608,0.0005637273,0.000064066095,2.2331578e-7],"category_scores_gemma":[0.000024679559,0.00016152687,0.00009843681,0.00071435526,0.00003737103,0.00008090439,0.00019652174,0.00025529182,0.0000023959722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001369895,0.000102533566,0.00051919994,0.0005057305,0.00004246905,0.0000017887087,0.000581376,0.882643,0.00006660321,0.013695878,0.000109980174,0.10171778],"study_design_scores_gemma":[0.00038341078,0.000080632184,0.00041404695,0.00033972,0.000017184104,0.000045714925,0.00047752057,0.9436531,0.0000069279845,0.0015325285,0.05288792,0.00016130523],"about_ca_topic_score_codex":0.000030886793,"about_ca_topic_score_gemma":0.000023340148,"teacher_disagreement_score":0.9489392,"about_ca_system_score_codex":0.00016396589,"about_ca_system_score_gemma":0.00005585328,"threshold_uncertainty_score":0.6586875},"labels":[],"label_agreement":null},{"id":"W4393232060","doi":"10.1186/s13677-024-00637-5","title":"PMNet: a multi-branch and multi-scale semantic segmentation approach to water extraction from high-resolution remote sensing images with edge-cloud computing","year":2024,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Government of Jiangsu Province; National Office for Philosophy and Social Sciences; National Natural Science Foundation of China","keywords":"Cloud computing; Computer science; Segmentation; Scale (ratio); Enhanced Data Rates for GSM Evolution; High resolution; Image segmentation; Artificial intelligence; Edge computing; Remote sensing; Computer vision; Geology; Operating system; Cartography; Geography","score_opus":0.011501600910498101,"score_gpt":0.26726636712817564,"score_spread":0.2557647662176775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393232060","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30077168,0.0010989665,0.6971541,0.000105851694,0.0003449248,0.00041618178,0.0000029815703,0.000036251888,0.000069050424],"genre_scores_gemma":[0.7892461,0.000101737714,0.2100812,0.000022141856,0.00044601556,0.0000022481038,0.0000074987865,0.000016484339,0.00007657629],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863553,0.00007561744,0.00045483475,0.00035569325,0.0002659043,0.00021242454],"domain_scores_gemma":[0.9994321,0.00007869994,0.00023837952,0.00012542967,0.00003484579,0.00009056691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044164198,0.00017268852,0.00024181855,0.00007964586,0.0003393713,0.00025352833,0.00008946005,0.000042240423,0.0000017383167],"category_scores_gemma":[0.0000026355629,0.00012309471,0.000035121513,0.00018634245,0.00006624053,0.0002847674,0.00008510557,0.00018426676,0.000009380384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038909606,0.0001733195,0.001936498,0.00039656332,0.00012496709,0.0000097820575,0.0028484408,0.5614985,0.081902295,0.000056657995,0.00029514512,0.3507189],"study_design_scores_gemma":[0.00067079643,0.00009020699,0.005221747,0.00045718407,0.000122039964,0.00012618856,0.0014357502,0.9852747,0.0017174789,0.00009824482,0.0045379386,0.0002477612],"about_ca_topic_score_codex":0.00067790755,"about_ca_topic_score_gemma":0.00004221541,"teacher_disagreement_score":0.4884744,"about_ca_system_score_codex":0.00010810937,"about_ca_system_score_gemma":0.000006804268,"threshold_uncertainty_score":0.50196564},"labels":[],"label_agreement":null},{"id":"W4396578820","doi":"10.1186/s13677-024-00654-4","title":"Enhancing patient healthcare with mobile edge computing and 5G: challenges and solutions for secure online health tools","year":2024,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"University of Johannesburg","keywords":"Computer science; Health care; Cloud computing; Edge computing; Enhanced Data Rates for GSM Evolution; Mobile edge computing; Multimedia; Internet privacy; Telecommunications; Operating system","score_opus":0.032423477029099784,"score_gpt":0.3127658532853731,"score_spread":0.28034237625627334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396578820","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04005107,0.24933363,0.7062265,0.0019623782,0.0016293137,0.00067680445,0.000005623817,0.000092217495,0.0000224033],"genre_scores_gemma":[0.94360876,0.0040185587,0.0485002,0.00012540737,0.0036888067,0.000022608476,0.000004345172,0.0000243917,0.0000069482066],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981039,0.00008878603,0.00075835886,0.0004184435,0.00021038199,0.00042011918],"domain_scores_gemma":[0.9980846,0.0006770122,0.0005575958,0.00019026677,0.00027275746,0.00021778334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008851257,0.00020519564,0.00045550172,0.00015030624,0.0007829439,0.00038690952,0.00020531272,0.000055266526,3.0746016e-8],"category_scores_gemma":[0.000016541124,0.00016423331,0.00005294916,0.00024191257,0.00006560036,0.0003442477,0.00016501191,0.00029075093,2.2339522e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066944945,0.00006822401,0.000046243436,0.002013576,0.000053291493,0.0000043398954,0.007259908,0.003445157,0.000055396093,0.027887676,0.00017317552,0.95898634],"study_design_scores_gemma":[0.0012316528,0.003458271,0.0008041561,0.009201041,0.0000721477,0.0032856083,0.011190544,0.6276752,0.00006662982,0.004581867,0.33752856,0.0009043417],"about_ca_topic_score_codex":0.000015471363,"about_ca_topic_score_gemma":0.000012712706,"teacher_disagreement_score":0.95808196,"about_ca_system_score_codex":0.000071069815,"about_ca_system_score_gemma":0.00017894062,"threshold_uncertainty_score":0.669724},"labels":[],"label_agreement":null},{"id":"W4401887817","doi":"10.1186/s13677-024-00696-8","title":"Using blockchain and AI technologies for sustainable, biodiverse, and transparent fisheries of the future","year":2024,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Blockchain; Biodiversity; Business; Fishery; Environmental resource management; Natural resource economics; Computer science; Environmental science; Computer security; Economics; Ecology; Biology","score_opus":0.014478127469289448,"score_gpt":0.2681458556738041,"score_spread":0.25366772820451466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401887817","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09657299,0.071159385,0.82671046,0.0047027823,0.000198407,0.0005377569,0.000010304934,0.00009007113,0.000017838483],"genre_scores_gemma":[0.9862731,0.0007319508,0.012817601,0.000016567254,0.00012300703,0.000021721742,1.4233996e-7,0.000004360873,0.000011560134],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.999246,0.000020736123,0.00033024518,0.00018202583,0.000092439725,0.00012857493],"domain_scores_gemma":[0.9992183,0.00013254078,0.00024024115,0.00019776513,0.00018610718,0.000025088346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003219724,0.00009560161,0.00020688401,0.00010227059,0.00034213098,0.00012431788,0.00031794023,0.000081274484,5.5335587e-8],"category_scores_gemma":[0.000011217128,0.00006519279,0.000042534975,0.00037811964,0.00022963743,0.000104419836,0.00013882437,0.000172431,1.9022165e-8],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003929734,0.000030827112,0.0010173798,0.0008384735,0.000047495545,0.000001753481,0.00057330565,0.0012649747,0.00035485934,0.9046615,0.00018767908,0.09101785],"study_design_scores_gemma":[0.00060683885,0.00023985674,0.0006424414,0.00066888705,0.00013223516,0.0008616177,0.015354779,0.40232915,0.0012970994,0.15710469,0.4203958,0.00036659787],"about_ca_topic_score_codex":0.000007135983,"about_ca_topic_score_gemma":0.0000023142359,"teacher_disagreement_score":0.8897001,"about_ca_system_score_codex":0.00001679706,"about_ca_system_score_gemma":0.000042224423,"threshold_uncertainty_score":0.2658485},"labels":[],"label_agreement":null},{"id":"W4406178268","doi":"10.1186/s13677-024-00724-7","title":"Virtual machine scheduling and migration management across multi-cloud data centers: blockchain-based versus centralized frameworks","year":2025,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Blockchain; Cloud computing; Computer science; Scheduling (production processes); Live migration; Virtual machine; Distributed computing; Operating system; Computer security; Virtualization; Engineering; Operations management","score_opus":0.01966948055937254,"score_gpt":0.30973959163544246,"score_spread":0.2900701110760699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406178268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12022487,0.0074708117,0.86924094,0.0010627024,0.0014490094,0.00039993995,0.000008647308,0.000078033336,0.00006504238],"genre_scores_gemma":[0.93718576,0.00016532867,0.061905563,0.00016027263,0.00048231025,0.00000920161,0.0000051967495,0.0000110815145,0.00007527194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792016,0.00012453516,0.0007842986,0.0005135309,0.00032188275,0.00033559115],"domain_scores_gemma":[0.9979487,0.0003783938,0.0006716337,0.00071807095,0.0001530723,0.00013009504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000990808,0.00022787806,0.00037250202,0.00015412296,0.000577636,0.0004553278,0.0010801058,0.00009700872,2.652367e-7],"category_scores_gemma":[0.00003738255,0.00019983534,0.00006873906,0.0004921152,0.00008681346,0.00004479439,0.00080773566,0.0004104762,8.3689343e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008107498,0.0002909954,0.0013226034,0.0003807081,0.00026800844,0.00001355891,0.00053491787,0.74917495,0.000030754254,0.029974846,0.00019821004,0.21772939],"study_design_scores_gemma":[0.0022656121,0.00007982394,0.0002930024,0.00065507554,0.000055859484,0.000016636333,0.0009515154,0.957641,0.000011930353,0.00013519378,0.03771087,0.00018347871],"about_ca_topic_score_codex":0.00003365631,"about_ca_topic_score_gemma":0.00001190183,"teacher_disagreement_score":0.8169609,"about_ca_system_score_codex":0.00006352857,"about_ca_system_score_gemma":0.00003270052,"threshold_uncertainty_score":0.8149048},"labels":[],"label_agreement":null},{"id":"W4417292807","doi":"10.1186/s13677-025-00821-1","title":"Security-aware computation offloading in internet of vehicles: a multi-agent reinforcement learning algorithm with attention mechanism","year":2025,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computation offloading; Computation; Task (project management); Reinforcement learning; Energy consumption; Layer (electronics); Latency (audio); Feature (linguistics); Key (lock); Application layer","score_opus":0.010039877850642125,"score_gpt":0.2699538571386763,"score_spread":0.25991397928803417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417292807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048792277,0.0010154132,0.9486767,0.00005848005,0.0010541509,0.00029066426,1.2539174e-7,0.00003140051,0.00008075555],"genre_scores_gemma":[0.96728146,0.000048939404,0.03230014,0.000016781738,0.00029332956,0.000008150032,0.0000019287697,0.0000067736373,0.00004252034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998372,0.00010434373,0.000854164,0.00023018841,0.0002466188,0.00019264747],"domain_scores_gemma":[0.9983895,0.00016465913,0.0009449695,0.00012788692,0.00031803912,0.00005498839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075147627,0.00014329396,0.0003504315,0.00030492677,0.00015518442,0.000119068856,0.00030890718,0.000048046906,8.532024e-8],"category_scores_gemma":[0.000012147057,0.00012585642,0.000058403868,0.0005471692,0.0000336585,0.00025063034,0.00015243114,0.00027390313,5.7359847e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003561206,0.000365646,0.009296501,0.0012567154,0.00020904465,0.000021050737,0.0045892186,0.5589199,0.0010316485,0.032932788,0.00017125078,0.39117065],"study_design_scores_gemma":[0.00078949623,0.0001521337,0.00047397098,0.0013651038,0.000016264794,0.000055246473,0.00054101256,0.9938063,0.00020563112,0.00082721113,0.001645707,0.00012192679],"about_ca_topic_score_codex":0.000042844935,"about_ca_topic_score_gemma":0.0000013528706,"teacher_disagreement_score":0.91848916,"about_ca_system_score_codex":0.000097410084,"about_ca_system_score_gemma":0.000066521345,"threshold_uncertainty_score":0.5132275},"labels":[],"label_agreement":null}]}