{"meta":{"query_hash":"f642fb22983b","filters":{"venue":"Sustainable Computing Informatics and Systems"},"cohort_total":13,"direct_labels_cover":0,"predictions_cover":13,"exported":13,"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/f642fb22983b","api":"https://metacan.xera.ac/api/v1/cohort?venue=Sustainable+Computing+Informatics+and+Systems"},"results":[{"id":"W2028641944","doi":"10.1016/j.suscom.2011.11.001","title":"Optimizing Cloud providers revenues via energy efficient server allocation","year":2011,"lang":"en","type":"article","venue":"Sustainable Computing Informatics and Systems","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"European Regional Development Fund","keywords":"Cloud computing; Server; Data center; Lease; Revenue; Computer science; Service provider; Renting; Environmental economics; Operations research; Order (exchange); Database; Service (business); Computer network; Business; Finance; Economics; Operating system; Engineering; Marketing","score_opus":0.012324258606661906,"score_gpt":0.200490233173688,"score_spread":0.1881659745670261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028641944","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":[0.20526749,0.00045528976,0.78676295,0.000051189167,0.00058108906,0.0003181768,1.7848647e-7,0.00025820895,0.006305429],"genre_scores_gemma":[0.9862974,0.000003949903,0.012659569,0.00012428041,0.00011634269,0.000008846976,0.0000014912948,0.000012630591,0.0007754563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981284,0.00009681627,0.00064622145,0.00023111896,0.00032914555,0.00056830305],"domain_scores_gemma":[0.99866056,0.00006119295,0.00038328816,0.0005247853,0.00024419097,0.00012599656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012062126,0.0002117503,0.0002548177,0.00020380331,0.00050178054,0.00048883253,0.00059557706,0.00007324043,7.3413804e-7],"category_scores_gemma":[0.000027224976,0.00018926227,0.000058261714,0.0003958219,0.00004544173,0.00007232268,0.0008446247,0.00012262078,0.0000054363386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000068758877,0.000078782985,0.00015125841,0.0013554536,0.00007265806,0.000034684002,0.051918093,0.7214936,0.0000032913097,0.20362493,0.0005921749,0.020668212],"study_design_scores_gemma":[0.00024946642,0.00007655531,0.000096587464,0.00014333564,0.000009791359,0.000033205353,0.0086093545,0.9861961,0.000022452,0.00034353058,0.0039741164,0.0002454979],"about_ca_topic_score_codex":0.0007367764,"about_ca_topic_score_gemma":0.0000012486383,"teacher_disagreement_score":0.78102994,"about_ca_system_score_codex":0.0001113849,"about_ca_system_score_gemma":0.000053810276,"threshold_uncertainty_score":0.77178913},"labels":[],"label_agreement":null},{"id":"W2274590954","doi":"10.1016/j.suscom.2016.01.004","title":"A simple and robust approach to energy disaggregation in the presence of outliers","year":2016,"lang":"en","type":"article","venue":"Sustainable Computing Informatics and Systems","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Ningbo","keywords":"Computer science; Outlier; Overhead (engineering); Cluster analysis; Data mining; TRACE (psycholinguistics); Energy consumption; Energy (signal processing); Event (particle physics); Data cleansing; Anomaly detection; Hidden Markov model; Markov chain; Artificial intelligence; Machine learning; Engineering; Data quality; Mathematics","score_opus":0.008811287612282077,"score_gpt":0.18599460527200934,"score_spread":0.17718331765972725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2274590954","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":[0.39372906,0.00019502445,0.5946753,0.000026610227,0.000107976564,0.00034026426,0.0000014434615,0.0000498998,0.010874409],"genre_scores_gemma":[0.9993164,0.00002180397,0.00049645046,0.000015501733,0.000028001916,0.000016983293,0.0000016880666,0.0000071051304,0.00009607195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992411,0.000027469481,0.00031899626,0.00005751886,0.00013469001,0.00022021597],"domain_scores_gemma":[0.9996015,0.00010664969,0.000054840955,0.00016011983,0.000041783387,0.000035147346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005691001,0.000086969296,0.00013378875,0.00011685579,0.000052009396,0.00007697835,0.00011964194,0.000027844184,1.6936647e-7],"category_scores_gemma":[0.000044587527,0.00005371909,0.000010131863,0.00019782671,0.000024577465,0.00014564833,0.00009655988,0.000030325848,2.0770227e-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":[0.0000017129033,0.000005500112,0.001387339,0.0007740083,0.00001083212,7.353314e-7,0.0045934473,0.9560995,0.000002714558,0.034341622,0.0005976417,0.002184915],"study_design_scores_gemma":[0.00019159724,0.00001977227,0.0013488638,0.00008737734,0.0000035712728,0.000003958001,0.021021565,0.9727366,0.000006723652,0.000087616456,0.0044052806,0.00008707678],"about_ca_topic_score_codex":0.00018489177,"about_ca_topic_score_gemma":0.0000037927457,"teacher_disagreement_score":0.6055873,"about_ca_system_score_codex":0.000046862548,"about_ca_system_score_gemma":0.000006893912,"threshold_uncertainty_score":0.2190601},"labels":[],"label_agreement":null},{"id":"W3217655315","doi":"10.1016/j.suscom.2021.100617","title":"Modeling and evaluation of dispatching policies in IaaS cloud data centers using SANs","year":2021,"lang":"en","type":"article","venue":"Sustainable Computing Informatics and Systems","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"CloudSim; Cloud computing; Computer science; Data center; Distributed computing; Service-level agreement; Scheduling (production processes); Quality of service; Operating system; Computer network; Operations management; Engineering","score_opus":0.051090300136524915,"score_gpt":0.3003796007054221,"score_spread":0.24928930056889717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217655315","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":[0.7339885,0.00083494076,0.26435414,0.00004655587,0.00016778611,0.00019098513,0.0000010863985,0.000024367713,0.00039161695],"genre_scores_gemma":[0.99414724,0.000011147714,0.0057287323,0.000033471784,0.00005197866,8.8073364e-7,0.0000050944263,0.0000061060014,0.000015346139],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811614,0.00018063908,0.0006816007,0.00021399649,0.00045096388,0.00035663933],"domain_scores_gemma":[0.9987423,0.00008693501,0.00021354455,0.0005825703,0.00031674316,0.000057933805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033943066,0.00013345336,0.00026904116,0.00019416174,0.00023399347,0.0004310307,0.000427951,0.00004353808,1.6124585e-7],"category_scores_gemma":[0.000118330834,0.00012757312,0.000020679377,0.00039788993,0.000028048544,0.00012100111,0.002123881,0.00011794959,1.0588587e-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":[9.53252e-7,0.00001569722,0.001558401,0.0007786941,0.000017956789,0.000005444413,0.00987308,0.9739137,0.0000047169897,0.009380301,0.0000070031324,0.004444044],"study_design_scores_gemma":[0.00040006405,0.000011768844,0.00024222895,0.00040506292,0.000018022572,0.000033082062,0.037106194,0.9613584,0.0000029026432,0.00024187015,0.0000501118,0.00013024348],"about_ca_topic_score_codex":0.0015944826,"about_ca_topic_score_gemma":0.000014171009,"teacher_disagreement_score":0.26015872,"about_ca_system_score_codex":0.00010498414,"about_ca_system_score_gemma":0.0001438429,"threshold_uncertainty_score":0.5202281},"labels":[],"label_agreement":null},{"id":"W4224267687","doi":"10.1016/j.suscom.2022.100743","title":"Improving sugarcane production in saline soils with Machine Learning and the Internet of Things","year":2022,"lang":"en","type":"article","venue":"Sustainable Computing Informatics and Systems","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":25,"is_retracted":false,"has_abstract":false,"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":"Sugar; Agriculture; Leaching (pedology); Hectare; Agricultural engineering; Sustainability; Soil salinity; Environmental science; Business; Soil water; Geography; Engineering; Biology","score_opus":0.0046415002942509385,"score_gpt":0.17352669600674653,"score_spread":0.16888519571249558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224267687","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":[0.99834013,0.00057289726,0.00004015494,0.0002705794,0.000054073826,0.00033338455,7.609059e-7,0.000018713206,0.00036931914],"genre_scores_gemma":[0.99926436,0.000013159055,0.00004578934,0.000033852848,0.000046740486,0.000009391035,0.000015431135,6.241807e-7,0.00057062757],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917966,0.00009990531,0.00030523448,0.00008157154,0.00015396389,0.00017968693],"domain_scores_gemma":[0.9995051,0.00012807395,0.00024598915,0.000024763098,0.00007377111,0.00002230333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012912969,0.00008207653,0.00018132632,0.000019811347,0.00030969526,0.0000946468,0.00008603875,0.000018934797,0.0000034168288],"category_scores_gemma":[0.000052824944,0.000025345484,0.000015514097,0.00025711735,0.00005228462,0.00012685807,0.00022886616,0.00019793118,7.381103e-8],"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":[0.001606157,0.000347173,0.53786516,0.006557349,0.000250567,0.00006356659,0.18550383,0.07837247,0.0034245052,0.039717253,0.0013848577,0.14490709],"study_design_scores_gemma":[0.0010949229,0.0006669001,0.007382722,0.00013325705,0.000021608334,0.00021330266,0.25131306,0.7304331,0.0000853338,0.00008746047,0.008309553,0.00025874175],"about_ca_topic_score_codex":0.0041611805,"about_ca_topic_score_gemma":0.000045400586,"teacher_disagreement_score":0.6520607,"about_ca_system_score_codex":0.000024277619,"about_ca_system_score_gemma":0.000007438788,"threshold_uncertainty_score":0.62904876},"labels":[],"label_agreement":null},{"id":"W4362580023","doi":"10.1016/j.suscom.2023.100867","title":"An efficient edge computing management mechanism for sustainable smart cities","year":2023,"lang":"en","type":"article","venue":"Sustainable Computing Informatics and Systems","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Server; Edge computing; Enhanced Data Rates for GSM Evolution; Smart city; Service (business); Mechanism (biology); Computer security; Internet of Things; Computer network; Telecommunications; Business","score_opus":0.013717480090452169,"score_gpt":0.24421826744848463,"score_spread":0.23050078735803245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362580023","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":[0.1727099,0.000095502655,0.8147464,0.000067617795,0.0033114783,0.0015920129,6.2022417e-7,0.0010843114,0.006392176],"genre_scores_gemma":[0.97213423,0.000011422696,0.02290442,0.0001867135,0.00094315805,0.0000469364,0.00003276277,0.00004988634,0.003690475],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99580187,0.00010990033,0.0011170412,0.00046942837,0.00053422234,0.001967544],"domain_scores_gemma":[0.997182,0.00033366232,0.00044818447,0.00077547145,0.0009872526,0.00027342825],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0038218158,0.00042203467,0.0005453515,0.00080304063,0.0021468564,0.0025897736,0.0011212137,0.00014027637,2.8885526e-7],"category_scores_gemma":[0.000084974636,0.0004146502,0.00011953163,0.001301072,0.00006432654,0.00074436615,0.0016061492,0.00021397606,0.000018790292],"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":[0.000007621974,0.000044100954,0.00013896512,0.0056219497,0.00006759248,0.00010490696,0.013786561,0.083238,0.0000023971118,0.88715196,0.004826419,0.0050095622],"study_design_scores_gemma":[0.0006913532,0.00017981608,0.00015744442,0.00017510247,0.000017431992,0.000038128062,0.055072002,0.92924076,0.00001952166,0.004648118,0.009268599,0.0004917041],"about_ca_topic_score_codex":0.00013024035,"about_ca_topic_score_gemma":2.181448e-7,"teacher_disagreement_score":0.8825038,"about_ca_system_score_codex":0.00024980606,"about_ca_system_score_gemma":0.00013721727,"threshold_uncertainty_score":0.99983054},"labels":[],"label_agreement":null},{"id":"W4362672441","doi":"10.1016/j.suscom.2023.100868","title":"Multivariate time-series sensor vital sign forecasting of cardiovascular and chronic respiratory diseases","year":2023,"lang":"en","type":"article","venue":"Sustainable Computing Informatics and Systems","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Høgskulen på Vestlandet","keywords":"Computer science; Machine learning; Artificial intelligence; Feature (linguistics); Random forest; Vital signs; Support vector machine; Naive Bayes classifier; Feature engineering; Medicine; Deep learning","score_opus":0.07856979135498487,"score_gpt":0.36249202610573084,"score_spread":0.28392223475074596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362672441","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":[0.9890209,0.0033342063,0.003916114,0.000038431874,0.0005078042,0.001966846,0.000042037896,0.00024720628,0.0009264847],"genre_scores_gemma":[0.9985429,0.000064400556,0.00028371098,0.000028645309,0.0003198188,0.000039608716,0.000015517553,0.000029062625,0.00067630445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972426,0.00038376517,0.0011880334,0.00015897624,0.00029482393,0.000731771],"domain_scores_gemma":[0.9975585,0.00091943843,0.00045256194,0.00030364192,0.00060224766,0.00016361274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019815967,0.00017992166,0.00053709204,0.0002161164,0.0012198273,0.00005164915,0.00011010668,0.00015365047,0.0000050705967],"category_scores_gemma":[0.0008110892,0.00016093638,0.000075825206,0.00038514074,0.00012917063,0.0002869963,0.0003775863,0.00027474746,0.000029063302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018363394,0.000051569605,0.07663854,0.1375032,0.0009388223,0.00024837302,0.1411617,0.57931614,0.00011463125,0.015103254,0.003655191,0.045084957],"study_design_scores_gemma":[0.00023697231,0.00015210136,0.00058021065,0.00091033103,0.000041590876,0.000006008743,0.13875529,0.84600484,0.000014690148,0.00012394387,0.012983248,0.0001907728],"about_ca_topic_score_codex":0.001116345,"about_ca_topic_score_gemma":0.0000064194883,"teacher_disagreement_score":0.2666887,"about_ca_system_score_codex":0.00018072086,"about_ca_system_score_gemma":0.0003878845,"threshold_uncertainty_score":0.9382049},"labels":[],"label_agreement":null},{"id":"W4381384545","doi":"10.1016/j.suscom.2023.100888","title":"Energy and carbon-aware initial VM placement in geographically distributed cloud data centers","year":2023,"lang":"en","type":"article","venue":"Sustainable Computing Informatics and Systems","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"CloudSim; Cloud computing; Data center; Computer science; Energy consumption; Efficient energy use; Greenhouse gas; Environmental economics; Carbon fibers; Virtual machine; Environmental science; Algorithm; Operating system; Engineering","score_opus":0.0156882160483962,"score_gpt":0.24205759500177326,"score_spread":0.22636937895337705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381384545","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":[0.77317286,0.00033939368,0.22283484,0.00036526352,0.00080705015,0.00051169697,0.000018402168,0.0004821896,0.0014682839],"genre_scores_gemma":[0.9991926,0.000027678385,0.00034307485,0.00008998707,0.00010698623,0.0000067251294,0.00007328095,0.000010636261,0.00014901761],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976625,0.00011405009,0.00074291654,0.00034722246,0.0003880323,0.00074531947],"domain_scores_gemma":[0.99857414,0.00018380675,0.00022851388,0.000755969,0.00010411303,0.00015346789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016390237,0.0002275861,0.0003283292,0.00043982905,0.00031720122,0.0007210902,0.00091650814,0.00008247047,2.3178728e-7],"category_scores_gemma":[0.00004174251,0.0002088286,0.000027327093,0.0010808507,0.00006726776,0.0000702373,0.004158578,0.00015677114,8.2118976e-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":[0.000058611167,0.00023434807,0.026983812,0.007184147,0.00037632202,0.0010475103,0.022981185,0.741477,0.0000026591918,0.14357302,0.010592958,0.045488406],"study_design_scores_gemma":[0.00064766314,0.000066986366,0.0009019935,0.0001966108,0.0000068505615,0.000024370693,0.011082978,0.9787763,5.35513e-7,0.00016500999,0.007888791,0.0002419204],"about_ca_topic_score_codex":0.00095759396,"about_ca_topic_score_gemma":0.000012129499,"teacher_disagreement_score":0.23729926,"about_ca_system_score_codex":0.000074605676,"about_ca_system_score_gemma":0.000061610575,"threshold_uncertainty_score":0.85157835},"labels":[],"label_agreement":null},{"id":"W4403583265","doi":"10.1016/j.suscom.2024.101044","title":"Energy-efficient and fault-tolerant routing mechanism for WSN using optimizer based deep learning model","year":2024,"lang":"en","type":"article","venue":"Sustainable Computing Informatics and Systems","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Mechanism (biology); Fault tolerance; Routing (electronic design automation); Distributed computing; Energy (signal processing); Parallel computing; Embedded system","score_opus":0.012923741848462691,"score_gpt":0.23003708456954486,"score_spread":0.21711334272108218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403583265","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":[0.046515282,0.00084461714,0.9510224,0.000032115466,0.0004612698,0.00035950623,0.0000014040298,0.0003763996,0.0003870201],"genre_scores_gemma":[0.8548567,0.000010915926,0.14472151,0.000078963334,0.00008176106,0.000013240697,0.0000063117927,0.000031468226,0.00019913336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99753183,0.00008328092,0.00078434066,0.00037384356,0.00036486305,0.00086186506],"domain_scores_gemma":[0.9984697,0.00041226234,0.0002558049,0.00029993328,0.00039555848,0.00016675734],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0014262984,0.0003175977,0.00040111507,0.00032822802,0.0009098367,0.0020262187,0.0003347373,0.00015101103,2.2884677e-7],"category_scores_gemma":[0.00005563885,0.00029040745,0.000085444946,0.00044068898,0.000040283063,0.00034831298,0.00044283588,0.00024779214,4.009525e-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":[0.000002603976,0.000008029557,0.000003275484,0.000690809,0.000016609187,0.000010301025,0.0020830524,0.69567853,0.0000133528,0.29918215,0.0000059726544,0.0023053205],"study_design_scores_gemma":[0.00039915618,0.00005675531,3.063704e-7,0.00048631223,0.000021975227,0.000054738488,0.0037779901,0.9942386,0.00003504326,0.00031685206,0.00024963883,0.00036259438],"about_ca_topic_score_codex":0.0000912494,"about_ca_topic_score_gemma":6.4578126e-7,"teacher_disagreement_score":0.80834144,"about_ca_system_score_codex":0.00017802391,"about_ca_system_score_gemma":0.00014544747,"threshold_uncertainty_score":0.9999548},"labels":[],"label_agreement":null},{"id":"W4408107987","doi":"10.1016/j.suscom.2025.101107","title":"Secured user authentication and data sharing for mobile cloud computing using 2C-Cubehash and PWCC","year":2025,"lang":"en","type":"article","venue":"Sustainable Computing Informatics and Systems","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"PotashCorp (Canada)","funders":"","keywords":"Computer science; Cloud computing; Authentication (law); Data sharing; Mobile cloud computing; Computer network; Computer security; Operating system","score_opus":0.02831750202727509,"score_gpt":0.31018741919642046,"score_spread":0.2818699171691454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408107987","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":[0.31258,0.0010186344,0.6851897,0.000021892722,0.00028752146,0.0006982254,0.00001993866,0.00008289372,0.00010120373],"genre_scores_gemma":[0.95152396,0.000031698837,0.048206314,0.000063020314,0.000075523276,0.0000070223327,0.00006110456,0.000008267978,0.000023095385],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825096,0.000042341944,0.000668492,0.00041481102,0.00015934049,0.00046405944],"domain_scores_gemma":[0.99823046,0.00026925,0.0002881104,0.00087095273,0.00024042957,0.000100822625],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002060339,0.00019781028,0.00032060783,0.00024997912,0.00087951095,0.0018218461,0.00079184497,0.000095259944,1.8380412e-7],"category_scores_gemma":[0.000102775186,0.00019441737,0.000026735845,0.00043188824,0.000081456084,0.0010885956,0.0029909564,0.0001415515,1.7453475e-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":[0.00001503104,0.000058451446,0.008650035,0.00832615,0.00011888315,0.0000045200436,0.01756638,0.0041515003,0.0000265956,0.9472345,0.00085888477,0.0129890805],"study_design_scores_gemma":[0.00053181255,0.0000374719,0.00044021633,0.0002878119,0.000030753028,0.000025677162,0.008907054,0.9811469,0.0000062469107,0.002507322,0.005873367,0.0002053708],"about_ca_topic_score_codex":0.00016029163,"about_ca_topic_score_gemma":0.000002632671,"teacher_disagreement_score":0.9769954,"about_ca_system_score_codex":0.00004191138,"about_ca_system_score_gemma":0.0000820814,"threshold_uncertainty_score":0.99921435},"labels":[],"label_agreement":null},{"id":"W4412507335","doi":"10.1016/j.suscom.2025.101166","title":"Does faster mean greener? Runtime and energy trade-offs in iOS applications with compiler optimizations","year":2025,"lang":"en","type":"article","venue":"Sustainable Computing Informatics and Systems","topic":"Green IT and Sustainability","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Queen's University","funders":"","keywords":"Computer science; Compiler; Parallel computing; Optimizing compiler; Embedded system; Operating system","score_opus":0.0030346550180378384,"score_gpt":0.1843689082150246,"score_spread":0.18133425319698676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412507335","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":[0.22309324,0.0008259643,0.750925,0.0001680281,0.00019488434,0.0012152011,0.000011901121,0.00035903632,0.023206756],"genre_scores_gemma":[0.9977183,0.00002813793,0.0011591105,0.00003892362,0.000026782787,0.000063292275,0.00001648552,0.0000147654455,0.0009342203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882466,0.00003065863,0.000527441,0.00012653029,0.000112562746,0.0003781606],"domain_scores_gemma":[0.99941385,0.00008859617,0.00006451862,0.00024015932,0.00011880524,0.0000740475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029444686,0.00019287896,0.00030232072,0.0002729974,0.00023477619,0.00024876327,0.00011603383,0.00008780273,0.0000013921001],"category_scores_gemma":[0.0000073809924,0.00013608314,0.000020473535,0.00046083,0.00007974069,0.00022072556,0.00009572825,0.00013588887,2.9724674e-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":[0.000012471888,0.000043075037,0.030764835,0.006133988,0.000099108634,0.000008707856,0.0072773397,0.8579687,0.0000019277493,0.08768871,0.00034462914,0.009656514],"study_design_scores_gemma":[0.0004891374,0.000019966068,0.0025594148,0.00011721382,0.0000169279,0.0000066527996,0.026282135,0.95658785,0.0000032976318,0.00029255147,0.013421251,0.00020359519],"about_ca_topic_score_codex":0.00022889546,"about_ca_topic_score_gemma":0.00006227367,"teacher_disagreement_score":0.77462506,"about_ca_system_score_codex":0.00011632777,"about_ca_system_score_gemma":0.000053347878,"threshold_uncertainty_score":0.5549309},"labels":[],"label_agreement":null},{"id":"W4414583580","doi":"10.1016/j.suscom.2025.101214","title":"A two-stage spatio-temporal flexibility-based energy optimization of internet data centers in active distribution networks based on robust control and transformer machine learning strategy","year":2025,"lang":"en","type":"article","venue":"Sustainable Computing Informatics and Systems","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Mean squared error; Robustness (evolution); Mean absolute percentage error; Approximation error; Feature selection; Efficient energy use; Extreme learning machine; Renewable energy; Gradient boosting","score_opus":0.010361380630286141,"score_gpt":0.2174166966273537,"score_spread":0.20705531599706756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414583580","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":[0.032074887,0.00011413156,0.9663274,0.00001181806,0.00012355062,0.00034471988,0.000038350943,0.000068032576,0.00089708646],"genre_scores_gemma":[0.998056,0.000012606377,0.00043133143,0.000023978311,0.000014451359,0.000010130285,0.001396769,0.000012006116,0.000042713815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987501,0.00008829724,0.0006047088,0.00014051482,0.00013642378,0.00027995423],"domain_scores_gemma":[0.99939924,0.00011402738,0.00013906127,0.00022344607,0.00008219385,0.000042045427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073172606,0.0001793109,0.00029468772,0.00021307908,0.00009518465,0.000115116556,0.00014375937,0.00006644451,0.000001556173],"category_scores_gemma":[0.000031724903,0.00017920889,0.000021004398,0.000279288,0.000034544966,0.00022597707,0.00008214492,0.0001654123,3.152779e-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":[0.00009014658,0.000021152886,0.0041118176,0.0014698053,0.000039647024,0.0000024453238,0.00015287795,0.9908971,2.3725218e-7,0.002373182,0.00003147116,0.0008101078],"study_design_scores_gemma":[0.001894111,0.00007440414,0.00020748442,0.00031553762,0.00001790534,3.0722538e-7,0.001785583,0.99491304,0.000007650747,0.000002909687,0.00064479065,0.0001362624],"about_ca_topic_score_codex":0.0013630536,"about_ca_topic_score_gemma":0.00009372714,"teacher_disagreement_score":0.9659811,"about_ca_system_score_codex":0.00020141175,"about_ca_system_score_gemma":0.00004193727,"threshold_uncertainty_score":0.73079264},"labels":[],"label_agreement":null},{"id":"W4417085887","doi":"10.1016/j.suscom.2025.101279","title":"Challenges of IoT sensors in smart buildings ecosystems and integration of blockchain for enhanced security and efficiency","year":2025,"lang":"en","type":"article","venue":"Sustainable Computing Informatics and Systems","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Internet of Things; Blockchain; Building automation; Context (archaeology); Confidentiality; Building management system; Big data; Data management; Smart objects","score_opus":0.008416564498445127,"score_gpt":0.2441935399049309,"score_spread":0.23577697540648576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417085887","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":[0.76092213,0.0016942801,0.23602651,0.0001209746,0.000052202504,0.0006836296,0.0000019153365,0.000032991444,0.00046535512],"genre_scores_gemma":[0.9956111,0.00010349386,0.0042244443,0.000007999667,0.000005513105,0.000028388546,6.943065e-7,0.000003056245,0.000015315905],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998735,0.000045388126,0.0007121703,0.00017972216,0.000093118215,0.00023458856],"domain_scores_gemma":[0.9987687,0.00029491985,0.0003454452,0.00026261728,0.0002910671,0.000037283855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015781563,0.00012742053,0.00037868912,0.00038298906,0.0001334733,0.000058064707,0.00021741135,0.000118861324,4.7024063e-8],"category_scores_gemma":[0.00015473721,0.00011819472,0.000020487676,0.00038174167,0.00009948987,0.00006569085,0.00025780618,0.00011556566,2.4548322e-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":[0.000009192045,0.000051064035,0.0003314889,0.004757757,0.000016413434,3.532227e-7,0.013453553,0.00082867895,0.00020770343,0.97000164,0.000009039073,0.010333147],"study_design_scores_gemma":[0.0005135236,0.0001763453,0.00019271304,0.00037872422,0.0000058514483,0.000007506487,0.021152163,0.968274,0.0013351014,0.0076953904,0.000161329,0.000107338754],"about_ca_topic_score_codex":0.00020366056,"about_ca_topic_score_gemma":0.000028274664,"teacher_disagreement_score":0.9674453,"about_ca_system_score_codex":0.00003245169,"about_ca_system_score_gemma":0.000058494858,"threshold_uncertainty_score":0.48198408},"labels":[],"label_agreement":null},{"id":"W7115564490","doi":"10.1016/j.suscom.2025.101284","title":"Explainable and counterfactual lasso regression for resilient micro gas turbine power prediction in smart grids","year":2025,"lang":"en","type":"article","venue":"Sustainable Computing Informatics and Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Lasso (programming language); Microgrid; Smart grid; Counterfactual thinking; Turbine; Power (physics); Electric power system; Grid; Energy (signal processing)","score_opus":0.005467601936135394,"score_gpt":0.20940605431126838,"score_spread":0.20393845237513297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115564490","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":[0.97239065,0.0017495657,0.014006401,0.00001760081,0.000763166,0.0004977924,0.000010674804,0.00013423791,0.0104299085],"genre_scores_gemma":[0.99740845,0.00005462862,0.0004987596,0.000019485537,0.000041694642,0.000020619484,0.000021944034,0.000015010931,0.0019194178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987644,0.000019279109,0.00056888873,0.00011169889,0.000102067104,0.00043369652],"domain_scores_gemma":[0.99946696,0.00014545856,0.00007942544,0.00013414421,0.00011727976,0.000056761466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006933098,0.00018107434,0.00026707494,0.00025478765,0.00023323798,0.00021155314,0.00007415496,0.00011306802,0.0000010534746],"category_scores_gemma":[0.000059097027,0.00016105454,0.000024216468,0.00019992371,0.000026676347,0.00023289342,0.00009151731,0.00014915396,4.3949825e-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":[0.00017970167,0.00007437361,0.03971464,0.02702758,0.00018417771,0.000031885564,0.03415691,0.8469595,0.0004493151,0.026082529,0.017649712,0.0074897115],"study_design_scores_gemma":[0.0009264654,0.00008313552,0.00056486204,0.0011297453,0.000010070088,0.000017767103,0.016196337,0.9309871,0.00022132613,0.00007715178,0.049614403,0.00017162049],"about_ca_topic_score_codex":0.0000911282,"about_ca_topic_score_gemma":0.0000055354035,"teacher_disagreement_score":0.084027655,"about_ca_system_score_codex":0.00015665477,"about_ca_system_score_gemma":0.000037126636,"threshold_uncertainty_score":0.65676135},"labels":[],"label_agreement":null}]}