{"meta":{"query_hash":"f1b03580179b","filters":{"venue":"Cluster Computing"},"cohort_total":99,"direct_labels_cover":0,"predictions_cover":99,"exported":99,"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/f1b03580179b","api":"https://metacan.xera.ac/api/v1/cohort?venue=Cluster+Computing"},"results":[{"id":"W1509547215","doi":"10.1023/a:1011454709330","title":"A Heuristic-Based Wormhole Routing Algorithm for Hypercube Multicomputer Networks","year":2001,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"King Fahd University of Petroleum and Minerals; University of Toronto; University of Saskatchewan","keywords":"Computer science; Hypercube; Node (physics); Routing (electronic design automation); Fault tolerance; Path (computing); Fault (geology); Routing algorithm; Adaptive routing; Algorithm; Parallel computing; Distributed computing; Computer network; Link-state routing protocol; Routing protocol","score_opus":0.018128057815828175,"score_gpt":0.2501036426428538,"score_spread":0.2319755848270256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1509547215","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05733782,0.000657031,0.93535775,0.00034903744,0.00024360884,0.0001963171,0.000073268006,0.0010429878,0.004742239],"genre_scores_gemma":[0.4559712,0.00034366766,0.53881603,0.00016011657,0.00007598197,0.00024282782,0.0002131904,0.00011125465,0.004065647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999731,0.00008427437,0.000013285287,0.00003547794,0.000093842245,0.000042227683],"domain_scores_gemma":[0.9994293,0.00021635236,0.000058542602,0.00008124061,0.00017012247,0.000044433727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006921735,0.00042507867,0.0007654743,0.0008261753,0.0008388746,0.0006569358,0.001531143,0.0008206555,0.0021973546],"category_scores_gemma":[0.0018898452,0.00038381136,0.00030015956,0.00093252043,0.0004627791,0.0010257981,0.00075785565,0.0004626801,0.00027132037],"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":[0.000467193,0.00016902255,0.00079899403,0.0001701675,0.000088921624,0.00009159131,0.00010091104,0.6612029,0.008177295,0.020128205,0.0051933825,0.30341133],"study_design_scores_gemma":[0.000055888173,0.00008243274,0.00012985904,0.000007231891,0.000018173552,0.00003738114,0.000020691094,0.9914437,0.0015238385,0.005264136,0.0014048022,0.000011745437],"about_ca_topic_score_codex":0.002962881,"about_ca_topic_score_gemma":0.0046276967,"teacher_disagreement_score":0.002962881,"about_ca_system_score_codex":0.0006346633,"about_ca_system_score_gemma":0.0010521219,"threshold_uncertainty_score":0.007350862},"labels":[],"label_agreement":null},{"id":"W1514196962","doi":"10.1023/a:1011808526481","title":"Exploiting Locality in Single Assignment Data Structures Updated Through Split-Phase Transactions","year":2001,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":5,"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 Alberta","funders":"International Social Science Council; Defense Advanced Research Projects Agency; Australian Centre for Advanced Photovoltaics; University of Delaware","keywords":"Computer science; Cache; Locality; Speedup; Porting; Exploit; Latency (audio); Parallel computing; Locality of reference; Software; Operating system","score_opus":0.0872778500453001,"score_gpt":0.3401463011643994,"score_spread":0.25286845111909934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1514196962","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52010965,0.00073649164,0.4695503,0.00057398085,0.00020624652,0.00012640982,0.00019973186,0.003078225,0.0054189814],"genre_scores_gemma":[0.9069949,0.0001603894,0.08880572,0.000082392435,0.00007141033,0.00008452604,0.00024205676,0.00029668814,0.0032619627],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990208,0.00025488413,0.00006424733,0.00013094048,0.0003788654,0.00015031555],"domain_scores_gemma":[0.9940904,0.0020044926,0.00045523813,0.0023884892,0.00085421815,0.00020707912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013666748,0.00035945655,0.0007390227,0.0009453182,0.0010476705,0.0010903546,0.0016786883,0.0004581205,0.0028233686],"category_scores_gemma":[0.008299644,0.00064704166,0.00027129994,0.002269204,0.0008772735,0.003950468,0.0016839547,0.00090795534,0.0005212302],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0047063706,0.0006753356,0.013388969,0.00034490597,0.00017049791,0.00036844407,0.0013441889,0.1845027,0.089965895,0.06801559,0.012423408,0.6240937],"study_design_scores_gemma":[0.00020469702,0.00062055845,0.0021860497,0.000024632904,0.00012221992,0.00023670343,0.00041224627,0.8570461,0.06682162,0.066331916,0.0059387297,0.00005452175],"about_ca_topic_score_codex":0.0015543337,"about_ca_topic_score_gemma":0.0042638467,"teacher_disagreement_score":0.0028233686,"about_ca_system_score_codex":0.0004465337,"about_ca_system_score_gemma":0.0011546039,"threshold_uncertainty_score":0.009445131},"labels":[],"label_agreement":null},{"id":"W1549249462","doi":"10.1023/a:1025713926030","title":"Using System Emulation to Model Next-Generation Shared Virtual Memory Clusters","year":2003,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Emulation; Shared memory; Distributed shared memory; Cache coherence; Distributed computing; Abstraction; Software; Cache; Leverage (statistics); Abstraction layer; Scalability; Computer architecture; Embedded system; Operating system; CPU cache; Memory management; Uniform memory access; Overlay; Cache algorithms","score_opus":0.09454530633250477,"score_gpt":0.29453342677653127,"score_spread":0.1999881204440265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1549249462","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25600454,0.0004174354,0.7145727,0.00087021344,0.0002217908,0.00015541796,0.00032443946,0.0010658413,0.026367538],"genre_scores_gemma":[0.9617088,0.00015945923,0.034171395,0.0000752009,0.000026239672,0.00013703549,0.00020312786,0.00016530219,0.0033534372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995658,0.00020244534,0.0000134741895,0.00004222976,0.00007948268,0.00009657818],"domain_scores_gemma":[0.99894994,0.0005591373,0.00009603489,0.00011346539,0.00020186404,0.00007964929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079918903,0.00058716204,0.000826123,0.00043170003,0.0007542885,0.0011968742,0.0018210557,0.0011806424,0.0024829237],"category_scores_gemma":[0.003097203,0.0004868126,0.0007519277,0.00045652475,0.0009148669,0.0014416244,0.000930508,0.0012034596,0.00026357433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001196511,0.0000074243967,0.00016707754,0.0000056988,0.0000067249975,0.000015765354,0.0000142665995,0.9949768,0.000144726,0.0039732223,0.00010617421,0.00057008193],"study_design_scores_gemma":[0.0000036693648,0.00000312522,0.00002680381,8.660886e-7,0.00000171318,0.0000022883116,0.0000040093314,0.9986349,0.00007392756,0.0011322869,0.000114880124,0.000001524779],"about_ca_topic_score_codex":0.014682931,"about_ca_topic_score_gemma":0.008254584,"teacher_disagreement_score":0.014682931,"about_ca_system_score_codex":0.0013212438,"about_ca_system_score_gemma":0.0013254685,"threshold_uncertainty_score":0.029194891},"labels":[],"label_agreement":null},{"id":"W1563694061","doi":"10.1023/a:1012705007864","title":"A Gracefully Degradable Declustered RAID Architecture","year":2002,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Advanced Data Storage Technologies","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Computer science; RAID; Prime (order theory); Control reconfiguration; Parallel computing; Computer hardware; Combinatorics; Mathematics; Embedded system","score_opus":0.02393951936800257,"score_gpt":0.23457954180919635,"score_spread":0.21064002244119379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1563694061","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17834808,0.005192002,0.7671325,0.0011394931,0.00103012,0.000272102,0.000502477,0.028343534,0.018039526],"genre_scores_gemma":[0.7209677,0.00083748356,0.25527108,0.00037516214,0.00018792455,0.00013149425,0.00044902932,0.00040296413,0.021377139],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992047,0.000113393275,0.000062094405,0.00011925997,0.0003395943,0.0001609909],"domain_scores_gemma":[0.99831045,0.00013751963,0.00011027285,0.00092263246,0.00037121875,0.0001479129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008528816,0.00065187085,0.00062381325,0.00088886893,0.0013023751,0.0020862133,0.0030665377,0.0007902526,0.004366628],"category_scores_gemma":[0.0014537189,0.0005430849,0.00031419494,0.0007066224,0.0008576624,0.0020510752,0.002159807,0.0012456492,0.0023580235],"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":[0.004261803,0.00031961937,0.004592483,0.0007390914,0.00021329774,0.0010589566,0.0009836233,0.09435704,0.2585829,0.09812844,0.03936536,0.49739745],"study_design_scores_gemma":[0.00024494124,0.0011251003,0.0012046708,0.000083038285,0.00020922141,0.0015800244,0.0003171036,0.6908781,0.17618757,0.058475934,0.06947422,0.0002201556],"about_ca_topic_score_codex":0.0013348185,"about_ca_topic_score_gemma":0.0020850135,"teacher_disagreement_score":0.004366628,"about_ca_system_score_codex":0.0009563531,"about_ca_system_score_gemma":0.0013958812,"threshold_uncertainty_score":0.014607847},"labels":[],"label_agreement":null},{"id":"W1573408443","doi":"10.1023/a:1025718026938","title":"Behavior and Performance of Interactive Multi-Player Game Servers","year":2003,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":37,"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":"Computer science; Server; Bottleneck; Benchmarking; Scalability; Online transaction processing; Class (philosophy); Architecture; The Internet; Software deployment; Transaction processing; Database transaction; World Wide Web; Software engineering; Database; Artificial intelligence; Embedded system","score_opus":0.016981213291262624,"score_gpt":0.24824137192620366,"score_spread":0.23126015863494104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1573408443","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99848795,0.000012976449,0.0005996599,0.000027409671,0.000003414152,0.000013456067,0.000048146045,0.000025662637,0.000781331],"genre_scores_gemma":[0.99884915,0.000008165645,0.000256855,0.000011585319,0.0000019532224,0.000011926046,0.00008606872,0.000012379147,0.0007619088],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992428,0.00031436043,0.00003514636,0.00010971006,0.000115087605,0.00018294176],"domain_scores_gemma":[0.98839885,0.008185814,0.0006066207,0.00044405615,0.0007894094,0.0015753136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012006229,0.00057373854,0.00042604416,0.0005577684,0.0004924912,0.001357276,0.0007275112,0.0007252421,0.0035994705],"category_scores_gemma":[0.014444688,0.00020434533,0.0002000455,0.0003755748,0.0004398813,0.0006494855,0.00072298694,0.00083516433,0.00060228724],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.031893685,0.013050898,0.40779686,0.0004312441,0.0010025707,0.00169448,0.0074174786,0.27283525,0.16864303,0.011806213,0.0059954994,0.07743284],"study_design_scores_gemma":[0.00035324524,0.0048488355,0.15271161,0.00002387526,0.000116117015,0.00041411072,0.0027910627,0.821094,0.011823943,0.0047172545,0.0010113154,0.00009464062],"about_ca_topic_score_codex":0.005055914,"about_ca_topic_score_gemma":0.0036605906,"teacher_disagreement_score":0.005055914,"about_ca_system_score_codex":0.00060005696,"about_ca_system_score_gemma":0.0005661057,"threshold_uncertainty_score":0.01204139},"labels":[],"label_agreement":null},{"id":"W1599674946","doi":"10.1023/a:1013985610753","title":"Internal Node and Shortcut Based Routing with Guaranteed Delivery in Wireless Networks","year":2002,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":140,"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 Ottawa","funders":"","keywords":"Computer science; Greedy algorithm; Node (physics); Hop (telecommunications); Packet forwarding; Computer network; Algorithm; Network packet; Geographic routing; Communication source; Routing protocol; Static routing","score_opus":0.010729929712024047,"score_gpt":0.2009819713228837,"score_spread":0.19025204161085965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1599674946","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043571133,0.0013801774,0.9496698,0.00045441402,0.000270955,0.00008549036,0.00006988908,0.00071743946,0.003780763],"genre_scores_gemma":[0.6819173,0.001868307,0.30207464,0.00023793259,0.00041984365,0.00021951385,0.0002888229,0.00047921043,0.012494441],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989002,0.0003635222,0.000049004648,0.00014560267,0.00040580067,0.00013585907],"domain_scores_gemma":[0.99700826,0.0017315253,0.00027608883,0.00036427693,0.0004106621,0.00020922664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022243995,0.00079750165,0.0013773096,0.001161073,0.0009232782,0.0017458276,0.0025600793,0.0011590547,0.0023692416],"category_scores_gemma":[0.0070241704,0.0006898495,0.0006032041,0.0014215424,0.001316986,0.0027062048,0.0016700507,0.0014719457,0.0004692473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011409282,0.00021760144,0.0013049508,0.00049943314,0.00014161352,0.00027719836,0.0006242962,0.4532919,0.011462805,0.29756048,0.0122294435,0.2212494],"study_design_scores_gemma":[0.00006467141,0.00020854757,0.00025698927,0.00002374176,0.00006874231,0.00013925877,0.00005623076,0.92046195,0.0022969318,0.07332273,0.003074917,0.000025226496],"about_ca_topic_score_codex":0.00219205,"about_ca_topic_score_gemma":0.0023964902,"teacher_disagreement_score":0.0025600793,"about_ca_system_score_codex":0.0012287094,"about_ca_system_score_gemma":0.0010059912,"threshold_uncertainty_score":0.011763871},"labels":[],"label_agreement":null},{"id":"W1963959738","doi":"10.1007/s10586-015-0446-8","title":"Congestion-aware TCP-friendly system for multimedia transmission based on UDP","year":2015,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Computer network; Transmission (telecommunications); Network congestion; Transmission Control Protocol; Multimedia; Telecommunications; Network packet","score_opus":0.019740784220199235,"score_gpt":0.24706646756602482,"score_spread":0.2273256833458256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963959738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27832493,0.0019744707,0.6367868,0.0004609807,0.00078004,0.00069802575,0.00054595,0.066295214,0.014133518],"genre_scores_gemma":[0.9539806,0.0002562336,0.039992105,0.00017031404,0.00015229132,0.00017698278,0.00041437597,0.00050176954,0.004355412],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994135,0.00015058358,0.00004852484,0.000103652914,0.00018511362,0.000098664284],"domain_scores_gemma":[0.9989649,0.00014547096,0.000056359695,0.00020571213,0.00047593852,0.00015165297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094807695,0.0008030556,0.0011318447,0.0016225236,0.0011968076,0.0011493061,0.001946771,0.0005252951,0.0032581773],"category_scores_gemma":[0.0018856208,0.00038015968,0.00028333635,0.00069416355,0.00031762142,0.000979389,0.0010347886,0.00081203226,0.0008641713],"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":[0.006109587,0.0018274358,0.015463841,0.00081948854,0.0005733632,0.0019953314,0.0015278652,0.044328246,0.36554462,0.018877348,0.06027556,0.48265734],"study_design_scores_gemma":[0.0003482942,0.0010684191,0.0051287827,0.000065250286,0.00060326996,0.0012425358,0.00016022763,0.82315,0.14302424,0.004761646,0.020197453,0.00024992533],"about_ca_topic_score_codex":0.0020270785,"about_ca_topic_score_gemma":0.0015041527,"teacher_disagreement_score":0.0032581773,"about_ca_system_score_codex":0.0006319634,"about_ca_system_score_gemma":0.000853634,"threshold_uncertainty_score":0.010899663},"labels":[],"label_agreement":null},{"id":"W1991774940","doi":"10.1007/s10586-011-0157-8","title":"A smart spaces architecture based on heterogeneous contexts, particularly social contexts","year":2011,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Ubiquitous computing; Smart environment; Architecture; Context (archaeology); Smart objects; Ambient intelligence; Wireless sensor network; Human–computer interaction; Context-aware pervasive systems; Data science; Context awareness; World Wide Web; Internet of Things; Computer network","score_opus":0.03434927812730705,"score_gpt":0.24872590352701995,"score_spread":0.21437662539971292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991774940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09630582,0.00043569223,0.877243,0.0010605578,0.00020008757,0.00024121405,0.00037763492,0.0063552503,0.017780825],"genre_scores_gemma":[0.7263136,0.00030234156,0.2591849,0.00029791804,0.00006335846,0.00022492213,0.0005790629,0.00016345481,0.012870549],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99965703,0.00006433664,0.00002503586,0.000109662426,0.00008466301,0.00005938449],"domain_scores_gemma":[0.99960107,0.00005197962,0.000028315837,0.00009624993,0.00009844536,0.00012391486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034466374,0.00035707702,0.00041150962,0.0005748782,0.0010209759,0.0018654055,0.0013491432,0.0006434544,0.0037734858],"category_scores_gemma":[0.000613869,0.00028413112,0.0003329485,0.00068770465,0.0006967099,0.0023999172,0.0025850702,0.0006442343,0.0012507222],"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":[0.0010245779,0.00078574196,0.016069267,0.0004477992,0.00028500368,0.00093277986,0.0035905426,0.12900104,0.11709677,0.21729586,0.039700583,0.47377002],"study_design_scores_gemma":[0.000058861402,0.0003416096,0.0061918297,0.0000889331,0.00017649002,0.00053780363,0.0015715376,0.74310225,0.037377603,0.10856803,0.10186446,0.00012057712],"about_ca_topic_score_codex":0.004772839,"about_ca_topic_score_gemma":0.009865141,"teacher_disagreement_score":0.004772839,"about_ca_system_score_codex":0.0005883589,"about_ca_system_score_gemma":0.0010239976,"threshold_uncertainty_score":0.0126235485},"labels":[],"label_agreement":null},{"id":"W1993015214","doi":"10.1023/b:clus.0000028005.97093.ec","title":"Performance Evaluation of Shared Mesh Protection in WDM Networks","year":2004,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Exploit; Scalability; Path (computing); Selection (genetic algorithm); Node (physics); Distributed computing; Resource (disambiguation); Mathematical optimization; Computer network; Artificial intelligence","score_opus":0.02261660445200595,"score_gpt":0.24440444342156312,"score_spread":0.22178783896955717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993015214","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9843853,0.0005038992,0.011550951,0.00014475307,0.000043882912,0.000025936664,0.00007099685,0.00030870672,0.0029655835],"genre_scores_gemma":[0.9983975,0.00004370244,0.0011251417,0.0000077940895,0.000009530586,0.0000027446356,0.000040150986,0.000009496291,0.0003638667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984372,0.0004795037,0.00006599492,0.00015861524,0.00051685254,0.0003418765],"domain_scores_gemma":[0.9931512,0.0040418473,0.0004981685,0.00094049156,0.0011390828,0.00022915205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017697152,0.0005490742,0.0004629916,0.00073423836,0.00062967156,0.0007983282,0.001053336,0.00057803997,0.0024318474],"category_scores_gemma":[0.0058448436,0.00017412196,0.00020445835,0.0006527176,0.0007182888,0.001352222,0.00058977545,0.0003414835,0.00017901295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.015546179,0.00097195554,0.015992563,0.0006419891,0.00037975254,0.0005720784,0.00046675405,0.6151561,0.14690106,0.009331463,0.0039051461,0.19013502],"study_design_scores_gemma":[0.00024224477,0.002649965,0.004923215,0.000021288903,0.00014422876,0.00031627956,0.00018305662,0.91101044,0.076958425,0.0024659098,0.0010551856,0.000029776604],"about_ca_topic_score_codex":0.0028761975,"about_ca_topic_score_gemma":0.0020899912,"teacher_disagreement_score":0.0028761975,"about_ca_system_score_codex":0.0017882481,"about_ca_system_score_gemma":0.0006960177,"threshold_uncertainty_score":0.0129746795},"labels":[],"label_agreement":null},{"id":"W1996451519","doi":"10.1007/s10586-011-0188-1","title":"Energy cost evaluation of parallel algorithms for multiprocessor systems","year":2011,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Parallel computing; Scalability; Energy consumption; Multiprocessing; CUDA; Matrix multiplication; Computation; Scheduling (production processes); Algorithm; Operating system","score_opus":0.10519926558241342,"score_gpt":0.31863618000184163,"score_spread":0.2134369144194282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996451519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42509067,0.003224181,0.5357252,0.0012779871,0.0003124766,0.0002055384,0.00029614806,0.0005440969,0.03332371],"genre_scores_gemma":[0.9236532,0.00045899837,0.07048543,0.00007304293,0.00005727538,0.00011685136,0.00020629965,0.00024327589,0.0047057224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999244,0.00033823782,0.0000232374,0.000036211855,0.00027914214,0.000079229176],"domain_scores_gemma":[0.99821293,0.0011954474,0.000074844145,0.00012787711,0.0003380706,0.000050854935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011611134,0.0007381186,0.0008295667,0.0008019181,0.0005595053,0.00094427,0.0009894134,0.0008494767,0.0037344974],"category_scores_gemma":[0.0056414646,0.00026875507,0.00050147675,0.0013196893,0.00054119824,0.0011017827,0.0006806594,0.0005136076,0.00022380013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021216183,0.000052016498,0.00045942984,0.00007202214,0.00002449797,0.000033426528,0.000012692809,0.97415876,0.0010177422,0.0060736104,0.0005065186,0.017377106],"study_design_scores_gemma":[0.000012291294,0.000023421875,0.0001794394,0.0000033409783,0.0000059174104,0.000008186136,0.000008007493,0.9969733,0.0005687615,0.0020353014,0.00017994699,0.0000020965313],"about_ca_topic_score_codex":0.0042680623,"about_ca_topic_score_gemma":0.0044119623,"teacher_disagreement_score":0.0042680623,"about_ca_system_score_codex":0.0013358345,"about_ca_system_score_gemma":0.0010551251,"threshold_uncertainty_score":0.012493134},"labels":[],"label_agreement":null},{"id":"W1997051250","doi":"10.1007/s10586-011-0165-8","title":"Exploiting application buffer reuse to improve MPI small message transfer protocols over RDMA-enabled networks","year":2011,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Remote direct memory access; InfiniBand; Computer science; Communication source; Message passing; Computer network; Transfer (computing); Protocol (science); Latency (audio); Reuse; Message queue; Operating system; Distributed computing; Telecommunications","score_opus":0.033918410432737106,"score_gpt":0.2669658850689768,"score_spread":0.23304747463623968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997051250","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39484653,0.00107709,0.58611923,0.00065106584,0.00030972323,0.00015926185,0.000122069316,0.009531089,0.007183967],"genre_scores_gemma":[0.9238028,0.00014832459,0.07257026,0.000107278625,0.000047705405,0.00008006245,0.00013093908,0.00028127016,0.0028314157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991258,0.0002490888,0.00006358271,0.00012211366,0.0002644804,0.00017485302],"domain_scores_gemma":[0.9961993,0.0013503379,0.00025293764,0.0013568485,0.0006496449,0.00019096065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020186133,0.0008548407,0.0007061888,0.0008235081,0.0009872937,0.001482582,0.0027670746,0.00060919893,0.0020437196],"category_scores_gemma":[0.00759637,0.00046192345,0.00031373667,0.0008912176,0.0006723849,0.0028958363,0.0015201776,0.0011527801,0.00054636475],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023482763,0.0010295187,0.0081720855,0.0003370439,0.00026530385,0.00029710736,0.00058498024,0.4512429,0.20627736,0.033296622,0.0071022385,0.2890466],"study_design_scores_gemma":[0.00007353793,0.00018030332,0.00047916573,0.0000075124585,0.000066490305,0.000035987418,0.00004621237,0.92787963,0.06383971,0.005376539,0.0019877874,0.00002714621],"about_ca_topic_score_codex":0.0037509599,"about_ca_topic_score_gemma":0.0067872987,"teacher_disagreement_score":0.0037509599,"about_ca_system_score_codex":0.00092120277,"about_ca_system_score_gemma":0.0015012188,"threshold_uncertainty_score":0.010675609},"labels":[],"label_agreement":null},{"id":"W1998489369","doi":"10.1007/s10586-015-0448-6","title":"Cloud-based rehabilitation and recovery prediction system for stroke patients","year":2015,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; University of Ottawa","funders":"","keywords":"Rehabilitation; Stroke (engine); Physical medicine and rehabilitation; Physical therapy; Medicine; Hemiparesis; Kinematics; Computer science; Test (biology); Surgery","score_opus":0.016084099823223553,"score_gpt":0.2568216504819465,"score_spread":0.24073755065872293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998489369","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88709867,0.0018934029,0.06481736,0.00274812,0.00065310963,0.00095201685,0.022375915,0.010308351,0.009152991],"genre_scores_gemma":[0.98110205,0.000401242,0.011870694,0.0001632939,0.00010072226,0.00012657509,0.004862368,0.00003651385,0.0013365743],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997688,0.000023098371,0.0000330926,0.000068629655,0.000053216645,0.000053061976],"domain_scores_gemma":[0.99933714,0.00010699074,0.00008767886,0.00007472157,0.00020926863,0.00018408072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045401417,0.00056807214,0.00080079446,0.0012501392,0.00056538236,0.000711369,0.00072638824,0.0004615722,0.002960849],"category_scores_gemma":[0.0011813206,0.00016833883,0.00038317617,0.0010135542,0.00008410721,0.0005944289,0.0005639967,0.00045294192,0.0008756784],"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":[0.00507645,0.0016797798,0.48935023,0.00036970704,0.0004492339,0.0016560432,0.0002554663,0.040124852,0.008961984,0.0016458909,0.07796407,0.3724664],"study_design_scores_gemma":[0.00021810105,0.0005520129,0.1862492,0.000101820966,0.00036508165,0.00075320865,0.00044541294,0.79070204,0.0087748915,0.0024044958,0.009335939,0.00009775903],"about_ca_topic_score_codex":0.01630878,"about_ca_topic_score_gemma":0.0170465,"teacher_disagreement_score":0.01630878,"about_ca_system_score_codex":0.0008112152,"about_ca_system_score_gemma":0.0011903113,"threshold_uncertainty_score":0.03242773},"labels":[],"label_agreement":null},{"id":"W1999004644","doi":"10.1007/s10586-009-0087-x","title":"Job co-allocation strategies for multiple high performance computing clusters","year":2009,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Scalability; Distributed computing; Cluster (spacecraft); USable; Bandwidth (computing); Bandwidth allocation; Computation; Process (computing); Control (management); Resource allocation; Set (abstract data type); Computer network; Algorithm; Operating system","score_opus":0.0211842272790483,"score_gpt":0.26592446903246025,"score_spread":0.24474024175341197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999004644","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6095829,0.0018106863,0.3687308,0.0008722042,0.0002754785,0.00045323186,0.000087689885,0.00067880057,0.017508157],"genre_scores_gemma":[0.96900594,0.000105588406,0.028005907,0.000045117693,0.000049736875,0.000110570465,0.000027699061,0.00004867061,0.0026006484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991947,0.00029360398,0.000043135613,0.00009904717,0.00015606922,0.0002135548],"domain_scores_gemma":[0.99657947,0.0017992577,0.0002311455,0.0002826199,0.00057772244,0.00052969914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019565811,0.0007234301,0.00115703,0.001050576,0.0014792666,0.0013908505,0.002505829,0.0009052499,0.0033908982],"category_scores_gemma":[0.0042089075,0.00060811476,0.00045532634,0.0012754445,0.0006110639,0.0010958742,0.0014224593,0.00053901627,0.0004295986],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025108897,0.00071129936,0.0039649336,0.00021263755,0.0002361379,0.00041089812,0.00033512982,0.8449948,0.012100295,0.014180725,0.00481337,0.115529],"study_design_scores_gemma":[0.00005428054,0.000117806376,0.000631791,0.0000046846303,0.000030264644,0.00004785971,0.000071026654,0.9941963,0.0012349888,0.0031876094,0.00041107173,0.000012314528],"about_ca_topic_score_codex":0.0040230514,"about_ca_topic_score_gemma":0.0052351924,"teacher_disagreement_score":0.0040230514,"about_ca_system_score_codex":0.0012816064,"about_ca_system_score_gemma":0.0013641457,"threshold_uncertainty_score":0.011343718},"labels":[],"label_agreement":null},{"id":"W2008903345","doi":"10.1007/s10586-007-0020-0","title":"An exact parallel algorithm to compare very long biological sequences in clusters of workstations","year":2007,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":20,"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 Ottawa","funders":"","keywords":"Computer science; Workstation; Algorithm; Heuristic; Sequence (biology); Smith–Waterman algorithm; Computation; Dynamic programming; Cluster (spacecraft); Quadratic equation; Time complexity; Parallel computing; Parallel algorithm; Sequence alignment; Mathematics; Artificial intelligence; Biology","score_opus":0.03297818096897284,"score_gpt":0.31452972213610314,"score_spread":0.2815515411671303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008903345","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018108122,0.00031740396,0.9763282,0.00010421184,0.00012683129,0.0001143739,0.00016252384,0.0031255146,0.0016128713],"genre_scores_gemma":[0.07943055,0.00015312682,0.91650116,0.0000799522,0.000062131476,0.00030460398,0.00050487876,0.00024952338,0.0027140074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878854,0.00022109188,0.00011700383,0.00024069387,0.00052807614,0.00010460865],"domain_scores_gemma":[0.9981122,0.0007339665,0.00008479328,0.00049064035,0.00050403515,0.00007435285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015289605,0.0009590709,0.0011142227,0.0019907344,0.0014774308,0.001272293,0.002906069,0.0010746978,0.00521914],"category_scores_gemma":[0.0053360104,0.0007175482,0.00066765514,0.0035639415,0.0008888617,0.0021440806,0.0019080762,0.00093434186,0.0014421461],"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":[0.0007472371,0.00024547617,0.0016171974,0.00027072078,0.00018656152,0.0002180391,0.00024723876,0.17618793,0.024619596,0.023877872,0.010154848,0.76162714],"study_design_scores_gemma":[0.00019381077,0.00019000341,0.00087630795,0.000014763389,0.00007063763,0.00030125544,0.0000774181,0.93571997,0.01730573,0.03864811,0.0065655634,0.00003642305],"about_ca_topic_score_codex":0.003811317,"about_ca_topic_score_gemma":0.004487255,"teacher_disagreement_score":0.00521914,"about_ca_system_score_codex":0.0008904269,"about_ca_system_score_gemma":0.0017369468,"threshold_uncertainty_score":0.01745975},"labels":[],"label_agreement":null},{"id":"W2016191648","doi":"10.1007/s10586-010-0141-8","title":"Service control with the preemptive parallel job scheduler Scojo-PECT","year":2010,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":5,"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":"Technische Universität Berlin","keywords":"Computer science; Predictability; Preemption; Scheduling (production processes); Quality of service; Distributed computing; Job scheduler; Job queue; Service quality; Service (business); Computer network; Mathematical optimization; Operating system","score_opus":0.009007964121247419,"score_gpt":0.2221063133733864,"score_spread":0.213098349252139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016191648","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10424472,0.00065670745,0.8674796,0.0006676486,0.0006620702,0.00021686521,0.00013812189,0.0056304256,0.020303775],"genre_scores_gemma":[0.90493184,0.00017267375,0.083820485,0.00022181263,0.00019775027,0.000078079574,0.00015089885,0.00026315468,0.0101633025],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992366,0.000110013425,0.000043643835,0.00012266355,0.00033403788,0.00015312356],"domain_scores_gemma":[0.998126,0.0004485986,0.00012671552,0.00038201764,0.0006014199,0.0003152525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014720276,0.00040850317,0.000629996,0.0005393277,0.0009363693,0.0019224379,0.0014297413,0.00046728103,0.003092122],"category_scores_gemma":[0.003651515,0.00026757005,0.00027574142,0.00050420855,0.000676329,0.00076478993,0.00092294137,0.0011672998,0.0009302314],"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":[0.004262514,0.0011367896,0.0047665015,0.00033958774,0.00012634283,0.0006005183,0.00042684734,0.33646098,0.08028089,0.12358908,0.023472708,0.4245372],"study_design_scores_gemma":[0.000116636824,0.00013114644,0.00027788838,0.0000061055534,0.000016728778,0.000068258196,0.000018436911,0.9727861,0.011141655,0.011848457,0.0035733473,0.000015237594],"about_ca_topic_score_codex":0.0033682147,"about_ca_topic_score_gemma":0.0030360478,"teacher_disagreement_score":0.0033682147,"about_ca_system_score_codex":0.00083650544,"about_ca_system_score_gemma":0.0024549048,"threshold_uncertainty_score":0.010344148},"labels":[],"label_agreement":null},{"id":"W2016399888","doi":"10.1007/s10586-011-0154-y","title":"An architecture for P2P bag-of-tasks execution with multiple task allocation policies in desktop grids","year":2011,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":4,"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 Ottawa","funders":"","keywords":"Computer science; Overlay; Chord (peer-to-peer); Distributed computing; Task (project management); Architecture; Grid; FIFO (computing and electronics); Queue; Speedup; Computer network; Operating system","score_opus":0.02567746571945842,"score_gpt":0.25422284128571326,"score_spread":0.22854537556625484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016399888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13626088,0.00091967004,0.80347586,0.0011097121,0.00061335776,0.0004686621,0.00039727756,0.0452365,0.011517972],"genre_scores_gemma":[0.5883943,0.00038781128,0.39692482,0.0002941031,0.00012287864,0.00033408156,0.00078133063,0.0008832771,0.011877371],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992423,0.00017383619,0.00006905605,0.00013246671,0.00023142232,0.00015090739],"domain_scores_gemma":[0.9988368,0.0001959002,0.000041498755,0.00040337275,0.00026990817,0.0002524343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011451588,0.00057017116,0.00078493665,0.00064012635,0.0015661833,0.0020863088,0.003689074,0.0009466832,0.0057160584],"category_scores_gemma":[0.0021557636,0.0007184932,0.00042188587,0.0011659224,0.0006747774,0.0017348051,0.0021588607,0.0015283099,0.0019169234],"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":[0.005567824,0.0015274326,0.008986882,0.00058045285,0.0003610163,0.0014609403,0.0016144624,0.20253223,0.091903925,0.06464735,0.10445074,0.5163667],"study_design_scores_gemma":[0.0002756111,0.000321291,0.0010748424,0.000023775878,0.000083672574,0.00021593759,0.00018874652,0.9363614,0.021214707,0.020038018,0.02012228,0.00007967147],"about_ca_topic_score_codex":0.010973116,"about_ca_topic_score_gemma":0.009575508,"teacher_disagreement_score":0.010973116,"about_ca_system_score_codex":0.0012215722,"about_ca_system_score_gemma":0.0021321715,"threshold_uncertainty_score":0.021818519},"labels":[],"label_agreement":null},{"id":"W2017300675","doi":"10.1007/s10586-007-0009-8","title":"Enabling ad-hoc collaboration between mobile users in the $\\mathcal{MESSENGER}$ project","year":2007,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Mobile Ad Hoc Networks","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":"University of Guelph","funders":"","keywords":"Computer science; World Wide Web; Wireless ad hoc network; Web service; Telecommunications; Wireless","score_opus":0.022454425732741777,"score_gpt":0.29919943913432806,"score_spread":0.2767450134015863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017300675","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5759799,0.0006206662,0.37241974,0.0022767943,0.0003128842,0.0004151805,0.00016944153,0.004709199,0.043096133],"genre_scores_gemma":[0.9176295,0.00016618702,0.073073566,0.00013669168,0.000043883123,0.00014818877,0.00013702006,0.00011279307,0.008552243],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993112,0.00028593699,0.000018670326,0.00008169458,0.0001646459,0.00013783798],"domain_scores_gemma":[0.9990146,0.0003470178,0.000055630735,0.00014589571,0.00012288298,0.00031399206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011699989,0.0002818427,0.00032020485,0.00031331132,0.0012426926,0.0010551317,0.00082382327,0.0009091084,0.002773693],"category_scores_gemma":[0.0014713346,0.00019212159,0.00017730115,0.00039190968,0.00055296306,0.0015286752,0.0021628148,0.0006971618,0.0006683319],"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":[0.004353659,0.0021293683,0.0073528676,0.00042134683,0.000095288924,0.0018890777,0.0045183385,0.045208275,0.20272136,0.18106785,0.048374146,0.5018684],"study_design_scores_gemma":[0.0005980816,0.0014702778,0.0036863312,0.00008261756,0.00013216154,0.0009075672,0.0032957576,0.6404467,0.12475679,0.0831166,0.1413745,0.00013270375],"about_ca_topic_score_codex":0.0018907243,"about_ca_topic_score_gemma":0.002828796,"teacher_disagreement_score":0.002773693,"about_ca_system_score_codex":0.00032439813,"about_ca_system_score_gemma":0.0009123729,"threshold_uncertainty_score":0.009278953},"labels":[],"label_agreement":null},{"id":"W2017811850","doi":"10.1007/s10586-009-0090-2","title":"Using ant-based agents for congestion control in ad-hoc wireless sensor networks","year":2009,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Canadian Centre for Applied Research in Cancer Control","keywords":"Computer science; ANT; Wireless ad hoc network; Computer network; Network congestion; Wireless sensor network; Wireless; Telecommunications; Network packet","score_opus":0.029282627456746757,"score_gpt":0.28007145399687017,"score_spread":0.2507888265401234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017811850","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08566348,0.0014232757,0.9073095,0.00041102883,0.00035239602,0.0001015125,0.000024090326,0.00096712576,0.0037475855],"genre_scores_gemma":[0.8913207,0.0005438527,0.10539503,0.000077263336,0.00008094093,0.00006171807,0.000027553371,0.0000806746,0.0024121583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953234,0.00019411022,0.000035746845,0.00004927361,0.00015654074,0.000032006097],"domain_scores_gemma":[0.99880135,0.0005929118,0.0001046507,0.00010740577,0.00032221992,0.00007152952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010017491,0.00041525598,0.0005551258,0.00058855186,0.00067947916,0.0009956638,0.001057902,0.00063837745,0.0005688694],"category_scores_gemma":[0.002866912,0.00031155362,0.00020944209,0.0006104818,0.00060096843,0.0010181516,0.00059803436,0.0006217474,0.0001310183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005322239,0.00022752905,0.0020550806,0.00022380723,0.00017183411,0.00018257315,0.00027889834,0.7407868,0.028790878,0.025700452,0.0031493434,0.19790053],"study_design_scores_gemma":[0.000020120951,0.00004904427,0.00010873851,0.0000036222496,0.000021972875,0.000028007385,0.000014001376,0.9914863,0.0035652984,0.0034103196,0.0012845653,0.000007940384],"about_ca_topic_score_codex":0.001997188,"about_ca_topic_score_gemma":0.00260909,"teacher_disagreement_score":0.001997188,"about_ca_system_score_codex":0.0003182713,"about_ca_system_score_gemma":0.00047677552,"threshold_uncertainty_score":0.00529778},"labels":[],"label_agreement":null},{"id":"W2022402912","doi":"10.1023/b:clus.0000028003.65775.8d","title":"Combined Ring–Mesh Optical Transport Networks","year":2004,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":3,"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 Alberta","funders":"","keywords":"Computer science; Optical mesh network; Multiplexer; Synchronous optical networking; Mesh networking; Ring network; Computer network; Network architecture; Network planning and design; Optical Transport Network; Wavelength-division multiplexing; Ring (chemistry); Traffic grooming; Wireless mesh network; Telecommunications; Network topology; Multiplexing; Passive optical network; Optics; Wavelength; Physics","score_opus":0.007360152963897589,"score_gpt":0.2074705969096531,"score_spread":0.20011044394575553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022402912","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24711296,0.0020199397,0.71587974,0.00055650424,0.00059460563,0.00021930055,0.00049102027,0.0031309768,0.029994916],"genre_scores_gemma":[0.83209413,0.0004732444,0.14269696,0.00018912787,0.00028551844,0.0001551301,0.00048484097,0.00013863995,0.023482453],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938107,0.00014407081,0.000025888405,0.00011748596,0.00021195566,0.00011959802],"domain_scores_gemma":[0.99883837,0.00025507173,0.000074331765,0.00036101302,0.00038392344,0.00008740966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085107685,0.00051736494,0.00062829786,0.00068001886,0.0005997507,0.00092686247,0.0018535411,0.0005895537,0.007832976],"category_scores_gemma":[0.0010289497,0.00040557553,0.00040755523,0.0006936626,0.0003044124,0.002009181,0.0012391849,0.0005107961,0.0013348496],"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":[0.0044122324,0.0005721071,0.0033096464,0.0005542255,0.000568557,0.000414127,0.00014345694,0.1428475,0.2618272,0.047084466,0.018619534,0.519647],"study_design_scores_gemma":[0.00015691857,0.0006576095,0.0012521747,0.00003529747,0.00022909383,0.0002622304,0.000046535468,0.8987965,0.06008356,0.015473616,0.02295308,0.00005332846],"about_ca_topic_score_codex":0.00081790634,"about_ca_topic_score_gemma":0.00383804,"teacher_disagreement_score":0.007832976,"about_ca_system_score_codex":0.00058272167,"about_ca_system_score_gemma":0.00054599514,"threshold_uncertainty_score":0.02620393},"labels":[],"label_agreement":null},{"id":"W2025713912","doi":"10.1007/s10586-012-0243-6","title":"Special issue on energy-aware computing and communications","year":2013,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Energy (signal processing); Distributed computing; Telecommunications; Computer security","score_opus":0.01632456257095322,"score_gpt":0.24672398629097048,"score_spread":0.23039942372001726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025713912","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009474971,0.028392509,0.0024880029,0.014022711,0.8966516,0.00014819532,0.00031458246,0.00031171698,0.056723095],"genre_scores_gemma":[0.003373887,0.022689918,0.00069097034,0.0036431153,0.8092439,0.000107102744,0.00060789386,0.00032705307,0.15931618],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981887,0.00017782589,0.00015815467,0.0003654472,0.00086177955,0.00024809854],"domain_scores_gemma":[0.9937896,0.0013938695,0.00052757387,0.0005278879,0.0021827952,0.001578273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002329565,0.0034233076,0.004117716,0.005619743,0.0024598972,0.0066529796,0.002682937,0.0050033974,0.09989152],"category_scores_gemma":[0.0037456,0.0009665885,0.0020619163,0.00345788,0.0011854034,0.0045829043,0.0027594499,0.00403534,0.040166646],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009591251,0.00010444271,0.00028092804,0.0007086759,0.000050190883,0.00024250308,0.000017635435,0.0002926136,0.00089376466,0.0026654438,0.9428508,0.051797118],"study_design_scores_gemma":[0.000017363232,0.00011106763,0.0008492684,0.00016938602,0.00003906869,0.0004129795,0.000024548746,0.0006476356,0.00032251445,0.002060902,0.9953259,0.00001926695],"about_ca_topic_score_codex":0.0005954547,"about_ca_topic_score_gemma":0.001890996,"teacher_disagreement_score":0.09989152,"about_ca_system_score_codex":0.001598681,"about_ca_system_score_gemma":0.001743328,"threshold_uncertainty_score":0.3341704},"labels":[],"label_agreement":null},{"id":"W2030619444","doi":"10.1007/s10586-012-0213-z","title":"Research trends in cloud, cluster and grid computing","year":2012,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Cloud computing; Cluster (spacecraft); Grid; Distributed computing; Grid computing; Computational science; Operating system; Geology","score_opus":0.057190164526805316,"score_gpt":0.3430372837340717,"score_spread":0.2858471192072664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030619444","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024449492,0.6381972,0.05136736,0.08090365,0.0139013855,0.00020782124,0.00082766404,0.0010996943,0.18904579],"genre_scores_gemma":[0.1507119,0.70272034,0.06619766,0.016586017,0.018929731,0.00021182947,0.0014796087,0.00046383607,0.042699073],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977562,0.00043093215,0.00016249728,0.0003853639,0.0010058064,0.00025933233],"domain_scores_gemma":[0.98343325,0.007125145,0.0009363026,0.00066788204,0.0063072145,0.0015301784],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0035260478,0.0005946272,0.0009263777,0.0034264636,0.0009008466,0.0055948547,0.0017420613,0.0021382885,0.020805491],"category_scores_gemma":[0.0086397175,0.0003267861,0.0005629427,0.013043552,0.0017723452,0.0077176467,0.0010467147,0.0027056618,0.005321215],"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":[0.00019581338,0.0002545579,0.007515796,0.004570974,0.000054498658,0.00016744989,0.0003817333,0.0012689198,0.0056445748,0.1434059,0.077426575,0.75911313],"study_design_scores_gemma":[0.000047682417,0.00024148436,0.0076708924,0.0015510044,0.000120036944,0.0010574969,0.0019817727,0.006807808,0.0047565517,0.058291595,0.9174005,0.00007322919],"about_ca_topic_score_codex":0.0026563243,"about_ca_topic_score_gemma":0.003188534,"teacher_disagreement_score":0.9965735,"about_ca_system_score_codex":0.0022714108,"about_ca_system_score_gemma":0.005481798,"threshold_uncertainty_score":0.06960124},"labels":[],"label_agreement":null},{"id":"W2039244387","doi":"10.1007/s10586-013-0247-x","title":"Enabling richer statistical MANET simulations through cluster computing","year":2013,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Mobile Ad Hoc Networks","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 Victoria","funders":"","keywords":"Computer science; Mobile ad hoc network; Quality of service; Computer network; Throughput; Ergodicity; Distributed computing; Cluster analysis; Vehicular ad hoc network; Wireless ad hoc network; Wireless; Network packet; Machine learning; Telecommunications","score_opus":0.020639056912619146,"score_gpt":0.2736194149382785,"score_spread":0.2529803580256594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039244387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15092018,0.0002012762,0.8296947,0.00083836063,0.00019695371,0.00011219462,0.0002951066,0.003212026,0.01452917],"genre_scores_gemma":[0.852835,0.0002708048,0.14366549,0.00019728806,0.00008553908,0.00013926349,0.00024584806,0.000539753,0.0020210305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999374,0.00028913896,0.000018584486,0.00006540853,0.00017894006,0.00007393459],"domain_scores_gemma":[0.99775875,0.001329708,0.000085878724,0.00048131603,0.00019983175,0.00014454334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009788101,0.00059991283,0.0011612074,0.00056870736,0.0009491479,0.001629972,0.002019973,0.0010740975,0.0043650484],"category_scores_gemma":[0.0061885393,0.0006842656,0.00055709603,0.00091256935,0.0009771343,0.0026666196,0.0023626462,0.0019405715,0.0006502504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008212828,0.00008275278,0.0008241656,0.000055321463,0.000042394517,0.0000810811,0.000107253174,0.9432801,0.0031413815,0.04506957,0.0010848842,0.0061488696],"study_design_scores_gemma":[0.000007987906,0.0000064909827,0.000044409073,0.0000020380457,0.0000025726854,0.000006334068,0.000009614529,0.9893261,0.00037158767,0.009749317,0.0004693625,0.000004262086],"about_ca_topic_score_codex":0.006228711,"about_ca_topic_score_gemma":0.006053828,"teacher_disagreement_score":0.006228711,"about_ca_system_score_codex":0.00081571104,"about_ca_system_score_gemma":0.0012419707,"threshold_uncertainty_score":0.014602542},"labels":[],"label_agreement":null},{"id":"W2049096082","doi":"10.1007/s10586-008-0059-6","title":"Translating Service Level Objectives to lower level policies for multi-tier services","year":2008,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Service-level agreement; Service level objective; Service level; Service (business); Domain (mathematical analysis); Service provider; Quality (philosophy); Quality of service; Service design; Process management; Software engineering; Computer network","score_opus":0.09111181423032377,"score_gpt":0.2979178296390462,"score_spread":0.20680601540872245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049096082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05387767,0.00022808897,0.92504096,0.001880654,0.00026271585,0.0003469775,0.00038747303,0.0016626741,0.01631274],"genre_scores_gemma":[0.7510997,0.00023007463,0.24391252,0.00045111965,0.00009894773,0.00019440427,0.00032925347,0.00049479975,0.0031892427],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953034,0.0016325756,0.0003403148,0.00039969798,0.0014816567,0.00084235426],"domain_scores_gemma":[0.99220663,0.0038019584,0.0007030468,0.00092987076,0.0019778397,0.0003806192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059934026,0.0009049782,0.0010976456,0.0011576947,0.0008233971,0.00701516,0.0011649061,0.0014258518,0.0051040347],"category_scores_gemma":[0.016749108,0.0007575037,0.0009808177,0.0011660197,0.0015220686,0.0037350457,0.0018906271,0.0042338986,0.0013474989],"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":[0.00036924914,0.000509968,0.0032909217,0.00041518707,0.00010273543,0.00024004368,0.00070963474,0.57723975,0.010441181,0.3025581,0.005810678,0.098312564],"study_design_scores_gemma":[0.00002715553,0.000049685466,0.00039920633,0.00006022552,0.000027608461,0.00001797118,0.00014921762,0.88234174,0.0053537274,0.10887905,0.0026721105,0.000022364025],"about_ca_topic_score_codex":0.005709994,"about_ca_topic_score_gemma":0.00627646,"teacher_disagreement_score":0.00701516,"about_ca_system_score_codex":0.00379438,"about_ca_system_score_gemma":0.004756064,"threshold_uncertainty_score":0.0316965},"labels":[],"label_agreement":null},{"id":"W2055595274","doi":"10.1007/s10586-005-6185-5","title":"Walk-In Bridge Scheduling in Bluetooth Scatternets","year":2005,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Bluetooth and Wireless Communication Technologies","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":"University of Manitoba","funders":"","keywords":"Piconet; Computer science; Bluetooth; Scheduling (production processes); Queue; Scalability; Scatternet; Network packet; Computer network; Bridge (graph theory); Locality; Distributed computing; Queueing theory; Wireless; Telecommunications; Mathematical optimization; Operating system","score_opus":0.021749068211897947,"score_gpt":0.2676898813785566,"score_spread":0.24594081316665864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055595274","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51091564,0.00053405133,0.47094306,0.00076539384,0.00033382047,0.00039515167,0.00029513869,0.0018421477,0.013975609],"genre_scores_gemma":[0.94664675,0.00007660725,0.0495327,0.00007893447,0.00004668745,0.0000837089,0.00018173143,0.00015724635,0.0031955205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987669,0.00036438444,0.00006142752,0.00017420728,0.00020192696,0.00043114368],"domain_scores_gemma":[0.99715924,0.0011752279,0.00023890371,0.0004475747,0.00034722118,0.00063180807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019136654,0.0006224509,0.0013987701,0.00093353254,0.002129899,0.0014468452,0.0020413615,0.00070064096,0.005223038],"category_scores_gemma":[0.0043718633,0.0005698954,0.00041750816,0.0012097405,0.0005750728,0.0015347032,0.0013877229,0.0007129588,0.00054307014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034626112,0.0011325428,0.0075484538,0.00023671747,0.00014979928,0.00034925403,0.000668308,0.7491941,0.014546153,0.037753716,0.014272762,0.17068562],"study_design_scores_gemma":[0.000097455784,0.00026366644,0.00085184636,0.000010037073,0.00003075797,0.00006409783,0.00027182163,0.9855964,0.0023572103,0.008986195,0.0014571829,0.000013315031],"about_ca_topic_score_codex":0.004320251,"about_ca_topic_score_gemma":0.011016687,"teacher_disagreement_score":0.005223038,"about_ca_system_score_codex":0.0010002409,"about_ca_system_score_gemma":0.0020897225,"threshold_uncertainty_score":0.017472804},"labels":[],"label_agreement":null},{"id":"W2059124748","doi":"10.1007/s10586-008-0065-8","title":"Efficient shared memory and RDMA based collectives on multi-rail QsNetII SMP clusters","year":2008,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Remote direct memory access; Computer science; Parallel computing; Operating system","score_opus":0.029210776239220186,"score_gpt":0.24045570847511624,"score_spread":0.21124493223589605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059124748","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7325435,0.0007668219,0.24770704,0.0006748136,0.00025326165,0.0001309027,0.00021624606,0.0052515334,0.012455862],"genre_scores_gemma":[0.9352425,0.000078184166,0.057173193,0.00003478897,0.00003256069,0.00006672361,0.00018486126,0.000106495965,0.0070807245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99951506,0.00011456511,0.000029697216,0.00007812372,0.00015540326,0.00010724467],"domain_scores_gemma":[0.9988738,0.00029370317,0.000056957535,0.0004231746,0.00024984835,0.0001026396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083014823,0.00036745184,0.0008545874,0.00047232804,0.0013688437,0.0012432188,0.0015802898,0.00051636604,0.003204743],"category_scores_gemma":[0.001653658,0.0003396183,0.00029763137,0.00077499286,0.0005415889,0.0012250784,0.0012771209,0.00052255194,0.00055722165],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035722065,0.0004813193,0.005775887,0.00030693616,0.00020251083,0.00050981814,0.0013896291,0.5210101,0.15496266,0.03770709,0.018321902,0.25575984],"study_design_scores_gemma":[0.00015264895,0.00027172238,0.0009391169,0.000009817047,0.00003685684,0.00005420178,0.00021310779,0.96576035,0.021928329,0.006617231,0.003990841,0.000025716996],"about_ca_topic_score_codex":0.0044219266,"about_ca_topic_score_gemma":0.0077692866,"teacher_disagreement_score":0.0044219266,"about_ca_system_score_codex":0.000920553,"about_ca_system_score_gemma":0.0008655154,"threshold_uncertainty_score":0.010720909},"labels":[],"label_agreement":null},{"id":"W2064570018","doi":"10.1007/s10586-012-0229-4","title":"The impact of heterogeneous multi-core clusters on graph partitioning: an empirical study","year":2012,"lang":"en","type":"article","venue":"Cluster Computing","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":11,"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 New Brunswick","funders":"","keywords":"Computer science; Graph partition; Graph; Core (optical fiber); Empirical research; Theoretical computer science; Statistics; Telecommunications","score_opus":0.06011409574486494,"score_gpt":0.34687051547067943,"score_spread":0.2867564197258145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064570018","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9987532,0.000063012674,0.0006455655,0.000026972697,0.0000020412542,0.00002635408,0.000045328707,0.000009836172,0.0004276111],"genre_scores_gemma":[0.9989536,0.000038295093,0.00069075223,0.000010001863,0.0000032313874,0.000012259823,0.00010478251,0.000009110883,0.00017802225],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975068,0.0013558794,0.000102223355,0.0003090021,0.0005066592,0.00021944278],"domain_scores_gemma":[0.87674075,0.11022858,0.0046634357,0.0038461515,0.0031432265,0.0013778282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003232566,0.00035668167,0.00027412595,0.00084410777,0.00067591626,0.0008246133,0.0008639268,0.0006051268,0.00223276],"category_scores_gemma":[0.045620073,0.00023956371,0.00038616426,0.0012612963,0.0008955521,0.0016322496,0.00062182586,0.0007023274,0.00020254984],"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":[0.0054834248,0.0049738693,0.7938639,0.00041252226,0.0005200943,0.00075722806,0.0023799757,0.08461737,0.0074196043,0.0018471451,0.0017951565,0.09592965],"study_design_scores_gemma":[0.00023709254,0.007037859,0.82399654,0.000069646274,0.0005522854,0.0016612342,0.008148642,0.14823166,0.0050669517,0.0023706846,0.002571477,0.00005587031],"about_ca_topic_score_codex":0.004332771,"about_ca_topic_score_gemma":0.006420999,"teacher_disagreement_score":0.004332771,"about_ca_system_score_codex":0.00076269277,"about_ca_system_score_gemma":0.00055684784,"threshold_uncertainty_score":0.017095685},"labels":[],"label_agreement":null},{"id":"W2064808093","doi":"10.1007/s10586-009-0088-9","title":"Sensor scheduling for p-percent coverage in wireless sensor networks","year":2009,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Energy Efficient Wireless Sensor Networks","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":"Athabasca University; University of Alberta","funders":"National Science Foundation","keywords":"Computer science; Wireless sensor network; Scheduling (production processes); Cover (algebra); Distributed algorithm; Computer network; Distributed computing; Set cover problem; Real-time computing; Algorithm; Set (abstract data type); Mathematical optimization; Mathematics","score_opus":0.013046304723211073,"score_gpt":0.24679719007164624,"score_spread":0.23375088534843516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064808093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13672814,0.00072181615,0.8538065,0.00081935595,0.00022650309,0.00016353319,0.000156469,0.000877828,0.0064998935],"genre_scores_gemma":[0.9505747,0.00015337317,0.047218867,0.00008766753,0.00005874001,0.000079711135,0.000058023503,0.00010087864,0.0016680795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993038,0.00024149052,0.00003707738,0.00011596813,0.00013798835,0.00016369607],"domain_scores_gemma":[0.9984195,0.00094197545,0.00012226334,0.00020519983,0.00018615235,0.00012486683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016268479,0.0004152962,0.00077688316,0.00049535435,0.0010157862,0.00080453657,0.0012312832,0.00047763757,0.0025244805],"category_scores_gemma":[0.005071751,0.00033409285,0.00034647784,0.0006755952,0.0005321063,0.0011269109,0.00087201194,0.00057967164,0.00023604195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012378708,0.00020504798,0.0017970601,0.00019839997,0.0000704419,0.0001682813,0.00025719072,0.79967517,0.015515428,0.055937283,0.0067470106,0.118190795],"study_design_scores_gemma":[0.000022318842,0.00009475901,0.00024605656,0.00000474253,0.000009145782,0.000033927663,0.00003489761,0.9866187,0.0020656344,0.010239036,0.0006257842,0.000005029156],"about_ca_topic_score_codex":0.0032833216,"about_ca_topic_score_gemma":0.004597002,"teacher_disagreement_score":0.0032833216,"about_ca_system_score_codex":0.0011600057,"about_ca_system_score_gemma":0.0014350414,"threshold_uncertainty_score":0.008603692},"labels":[],"label_agreement":null},{"id":"W2070292256","doi":"10.1007/s10586-006-9738-3","title":"Finding a suitable system scale to optimize program performance on software DSM systems","year":2006,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Overhead (engineering); Distributed computing; Software; Interface (matter); Embedded system; Parallel computing; Operating system","score_opus":0.013565794297711252,"score_gpt":0.24879841619857646,"score_spread":0.2352326219008652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070292256","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76306593,0.00063746533,0.22406434,0.0007682446,0.000071379756,0.000144582,0.00015853562,0.0020550864,0.009034429],"genre_scores_gemma":[0.96896845,0.00005997653,0.030356288,0.000030769952,0.000012345264,0.000041707168,0.00004326862,0.00014054628,0.00034659734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997278,0.00009287105,0.000012621631,0.000040226227,0.0000659015,0.000060525555],"domain_scores_gemma":[0.9989697,0.00050627335,0.00006948014,0.000120567915,0.00026224504,0.00007181541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009105326,0.00039352136,0.0006058025,0.00047093641,0.00041113497,0.0005487776,0.0004922748,0.00047912754,0.0014983345],"category_scores_gemma":[0.0044978335,0.00028988047,0.0002501555,0.00043809027,0.00041366613,0.00085156073,0.0005660963,0.0004915356,0.00022603337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057493185,0.00024207009,0.008118055,0.00032407485,0.00008174017,0.00017104682,0.00019992175,0.78819853,0.07637562,0.01113436,0.0044347243,0.11014492],"study_design_scores_gemma":[0.000053371055,0.00014319399,0.002052258,0.000007450805,0.000021933849,0.000022066282,0.00010781094,0.98273534,0.010045617,0.0041278703,0.00067254854,0.000010551331],"about_ca_topic_score_codex":0.0013154048,"about_ca_topic_score_gemma":0.0024739795,"teacher_disagreement_score":0.0014983345,"about_ca_system_score_codex":0.00050654926,"about_ca_system_score_gemma":0.0009170089,"threshold_uncertainty_score":0.0050124526},"labels":[],"label_agreement":null},{"id":"W2071880963","doi":"10.1023/b:clus.0000003942.73875.29","title":"A Synchronous Co-Allocation Mechanism for Grid Computing Systems","year":2003,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Victoria; Purdue University","keywords":"Computer science; Quality of service; Reservation; Resource allocation; Distributed computing; Grid; Queueing theory; Grid computing; Computer network; Scheme (mathematics); Service (business); Variety (cybernetics)","score_opus":0.020168566578802734,"score_gpt":0.26402301979823023,"score_spread":0.2438544532194275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071880963","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021321703,0.00042873994,0.95926934,0.0003555355,0.0005143059,0.00028144842,0.00018386176,0.011728908,0.005916137],"genre_scores_gemma":[0.57185936,0.0003728546,0.41134202,0.00036744672,0.00063976063,0.0007762017,0.00057891896,0.001251575,0.01281178],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972699,0.000810751,0.000294258,0.0004263281,0.0007505081,0.0004482579],"domain_scores_gemma":[0.991037,0.0016857245,0.0005156799,0.004605101,0.001321857,0.0008346206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004661924,0.0009231054,0.001392207,0.0014780739,0.0028712486,0.0036905573,0.004471508,0.0012927743,0.008190645],"category_scores_gemma":[0.0084755365,0.00089836895,0.00077082,0.002397545,0.0013063337,0.004194945,0.003979105,0.0016699019,0.0023546617],"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":[0.0046496163,0.0010080754,0.0040826355,0.0006469038,0.00037112157,0.0005581788,0.0010410298,0.103005275,0.04530184,0.3328426,0.061072215,0.4454205],"study_design_scores_gemma":[0.00079124275,0.0005328755,0.00095078704,0.00005090208,0.00032404586,0.00042720913,0.00016364259,0.780922,0.03598009,0.11429664,0.06538682,0.00017385972],"about_ca_topic_score_codex":0.0023656557,"about_ca_topic_score_gemma":0.002855866,"teacher_disagreement_score":0.008190645,"about_ca_system_score_codex":0.0013420467,"about_ca_system_score_gemma":0.0028879095,"threshold_uncertainty_score":0.027400374},"labels":[],"label_agreement":null},{"id":"W2085059901","doi":"10.1007/s10586-009-0076-0","title":"On GPU’s viability as a middleware accelerator","year":2009,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":9,"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 British Columbia","funders":"Nvidia","keywords":"Computer science; Implementation; Exploit; Middleware (distributed applications); Erasure code; Graphics; Embedded system; Distributed computing; Operating system; Decoding methods; Software engineering","score_opus":0.018697008819549554,"score_gpt":0.2505568737334214,"score_spread":0.23185986491387184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085059901","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50620127,0.031461705,0.09575655,0.04576877,0.002398199,0.000066116285,0.00048728153,0.0019865981,0.31587356],"genre_scores_gemma":[0.9679731,0.0021962824,0.011723258,0.00042674076,0.00024982772,0.000022018463,0.00009597057,0.00023054113,0.017082276],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896395,0.0002933386,0.000034758647,0.00012809614,0.00033485595,0.00024494334],"domain_scores_gemma":[0.9959674,0.0018507347,0.0001383357,0.0007138193,0.0009936186,0.0003361601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019144472,0.0003634501,0.0005690728,0.00087016553,0.0014614648,0.005175795,0.0017350982,0.001989827,0.010066557],"category_scores_gemma":[0.007799776,0.00035099982,0.0002582641,0.0012822255,0.0018694558,0.0056201695,0.001382611,0.0015184907,0.001695059],"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":[0.001066281,0.00019668687,0.007989842,0.0003964902,0.00009554303,0.00072835153,0.0010202691,0.02740778,0.027761009,0.74076223,0.047140513,0.14543499],"study_design_scores_gemma":[0.00011767327,0.00034399287,0.0075885393,0.00033560698,0.0002863083,0.0010059979,0.002926503,0.43337914,0.05876459,0.2801489,0.2149748,0.00012801227],"about_ca_topic_score_codex":0.008478161,"about_ca_topic_score_gemma":0.0069281417,"teacher_disagreement_score":0.010066557,"about_ca_system_score_codex":0.0029820632,"about_ca_system_score_gemma":0.0017989401,"threshold_uncertainty_score":0.03367603},"labels":[],"label_agreement":null},{"id":"W2089800424","doi":"10.1007/s10586-005-6181-9","title":"Average Waiting Time of Clusterhead Controlled Token for Virtual Base Station On-Demand in MANETs","year":2005,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Mobile Ad Hoc Networks","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":"University of Ottawa","funders":"","keywords":"Polling; Computer science; Computer network; Mobile ad hoc network; Security token; Base station; Wireless ad hoc network; Network packet; Wireless; Telecommunications","score_opus":0.011011729859760656,"score_gpt":0.2436293415535245,"score_spread":0.23261761169376385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089800424","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87894756,0.0013467403,0.114073515,0.0003221131,0.00028678295,0.000078274345,0.00033340562,0.00091322314,0.003698386],"genre_scores_gemma":[0.9968105,0.00007464055,0.0022578533,0.000021327156,0.000016654496,0.000009368467,0.00007741903,0.000034530385,0.00069778966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855465,0.00021567727,0.00007389976,0.0002647611,0.00039529765,0.0004956746],"domain_scores_gemma":[0.99404293,0.0035204035,0.00043738488,0.0004308281,0.0010538488,0.00051469513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022267015,0.00058412825,0.00088943733,0.0008621805,0.001119076,0.0012173529,0.0016258314,0.00065711327,0.0025332358],"category_scores_gemma":[0.0072970423,0.0004124213,0.00031947638,0.0010774964,0.00065877713,0.0013141744,0.000672597,0.0006125394,0.0001957837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0057489444,0.00036569862,0.013957431,0.0003981435,0.00018119473,0.0005123619,0.00050107203,0.8653735,0.026823279,0.018985864,0.005893041,0.061259527],"study_design_scores_gemma":[0.00003701546,0.00028816907,0.0019121799,0.000007880151,0.00007111426,0.00011868477,0.00016900712,0.99042845,0.0043061194,0.0022191196,0.00041348246,0.000028797002],"about_ca_topic_score_codex":0.0074615683,"about_ca_topic_score_gemma":0.0077320477,"teacher_disagreement_score":0.0074615683,"about_ca_system_score_codex":0.0020796906,"about_ca_system_score_gemma":0.002276333,"threshold_uncertainty_score":0.015089333},"labels":[],"label_agreement":null},{"id":"W2104464824","doi":"10.1007/s10586-014-0349-0","title":"Recent trends in interactive multimedia computing for industry","year":2014,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Multimedia Communication and Technology","field":"Social Sciences","cited_by":25,"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 Waterloo","funders":"","keywords":"Computer science; Multimedia; Interactive media; Human–computer interaction","score_opus":0.04951955399076109,"score_gpt":0.38102367480029126,"score_spread":0.3315041208095302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104464824","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026043281,0.73021966,0.028233668,0.04220567,0.005557901,0.000116119474,0.0005922565,0.0008150567,0.16621642],"genre_scores_gemma":[0.17806618,0.6752778,0.04585455,0.014698975,0.016180146,0.0001442788,0.0014715443,0.00031041066,0.06799613],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99930346,0.00014188598,0.00005088797,0.00013985447,0.00028007475,0.00008382426],"domain_scores_gemma":[0.9934309,0.003339557,0.0005237334,0.00019127887,0.0018480557,0.00066659827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013934226,0.0004191154,0.000393236,0.0022018023,0.00051836524,0.0028528816,0.001059583,0.001403463,0.023002105],"category_scores_gemma":[0.004200905,0.00018450728,0.00032348212,0.0052747824,0.0007337326,0.0027178247,0.0008384019,0.0014370956,0.003838091],"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":[0.00013981425,0.00014488552,0.0033787845,0.0030405244,0.000032914555,0.00014142483,0.00033148724,0.00067976624,0.0045712334,0.032775052,0.076184906,0.87857926],"study_design_scores_gemma":[0.000017944894,0.0001844151,0.0071288124,0.00095468306,0.00006749083,0.00083283754,0.0009963321,0.0037874486,0.0026493168,0.01149859,0.9718421,0.00004004232],"about_ca_topic_score_codex":0.0015289772,"about_ca_topic_score_gemma":0.0030787268,"teacher_disagreement_score":0.023002105,"about_ca_system_score_codex":0.0011490143,"about_ca_system_score_gemma":0.0013583285,"threshold_uncertainty_score":0.076949775},"labels":[],"label_agreement":null},{"id":"W2120741525","doi":"10.1007/s10586-006-9746-3","title":"Finding Hamiltonian paths in tournaments on clusters","year":2006,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Complexity and Algorithms in Graphs","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Scalability; Tournament; Computation; Parallel computing; Computer cluster; Cluster (spacecraft); Parallel algorithm; Graph; Running time; Hamiltonian (control theory); Reuse; Theoretical computer science; Algorithm; Distributed computing; Combinatorics; Mathematical optimization; Mathematics; Operating system","score_opus":0.018802860021966208,"score_gpt":0.25354114527614796,"score_spread":0.23473828525418175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120741525","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.758136,0.0006945514,0.22408542,0.0014375321,0.00010389323,0.0002943719,0.0006873738,0.0005316618,0.014029137],"genre_scores_gemma":[0.8998905,0.00030152022,0.09177774,0.00016101873,0.000069918235,0.00012176234,0.0007684679,0.00015107913,0.006757992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988331,0.0003822391,0.000058579593,0.0002800062,0.00017636477,0.00026966663],"domain_scores_gemma":[0.99133635,0.0061526494,0.0007496109,0.00045070896,0.00036917286,0.0009414643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011204776,0.0006708024,0.0015463495,0.0015171625,0.0023133087,0.0037674839,0.0022612766,0.0015152573,0.007971914],"category_scores_gemma":[0.0100638345,0.0009849637,0.0009245987,0.0026340643,0.0018023726,0.0033633297,0.0020005237,0.0014594544,0.00040854735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014301349,0.0006557061,0.013431806,0.0008226358,0.00036865077,0.0005761113,0.001398256,0.6006111,0.005681261,0.2705435,0.017610518,0.086870395],"study_design_scores_gemma":[0.00019413434,0.0001237637,0.0015937691,0.000037110527,0.00006153426,0.00013132204,0.00055979757,0.6454397,0.0010689727,0.34855568,0.0022038314,0.000030394574],"about_ca_topic_score_codex":0.0061288746,"about_ca_topic_score_gemma":0.00865258,"teacher_disagreement_score":0.007971914,"about_ca_system_score_codex":0.0015810325,"about_ca_system_score_gemma":0.0012400452,"threshold_uncertainty_score":0.026668727},"labels":[],"label_agreement":null},{"id":"W2140520484","doi":"10.1023/b:clus.0000028000.94688.a1","title":"Performance of Fast Routing Algorithms in Large Optical Switches Built on the Vertical Stacking of Banyan Structures","year":2004,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":11,"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 Ottawa; University of Waterloo","funders":"Japan Science Society","keywords":"Computer science; Banyan; Stacking; Routing (electronic design automation); Routing algorithm; Algorithm; Optical switch; Parallel computing; Computer network; Optoelectronics; Materials science; Routing protocol; Physics","score_opus":0.012753337222787445,"score_gpt":0.23314641847679926,"score_spread":0.2203930812540118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140520484","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98202467,0.00028512595,0.01348146,0.00013350042,0.000050947685,0.000010106548,0.000060078404,0.00042121747,0.0035329913],"genre_scores_gemma":[0.9892163,0.00007172705,0.00960341,0.000020946609,0.000010282693,0.000007853982,0.000091611684,0.000031723925,0.0009460787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974805,0.000082072555,0.000009126098,0.00003670267,0.000054209362,0.00006980869],"domain_scores_gemma":[0.99816006,0.0012759418,0.00013040242,0.00014493696,0.0002204087,0.00006832588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062322075,0.00048281913,0.00025751587,0.00032513967,0.00052588375,0.00062543637,0.00048301244,0.0004924097,0.0017656476],"category_scores_gemma":[0.0018540829,0.00019135319,0.00012678622,0.00044591288,0.000369001,0.000960185,0.00026601943,0.00045837628,0.00015890645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004326723,0.00058595295,0.006009922,0.00020382342,0.00013980152,0.00012883346,0.00025117878,0.73457104,0.10160087,0.023136808,0.0054494063,0.12359563],"study_design_scores_gemma":[0.00007331347,0.00038741107,0.001471858,0.0000063914263,0.000021598176,0.000034305012,0.0000656085,0.9591985,0.03511515,0.0032548548,0.00035672032,0.000014338613],"about_ca_topic_score_codex":0.0023720542,"about_ca_topic_score_gemma":0.0030967365,"teacher_disagreement_score":0.0023720542,"about_ca_system_score_codex":0.0007060176,"about_ca_system_score_gemma":0.0007067483,"threshold_uncertainty_score":0.0059066415},"labels":[],"label_agreement":null},{"id":"W2156080250","doi":"10.1007/s10586-014-0359-y","title":"Carbon-aware distributed cloud: multi-level grouping genetic algorithm","year":2014,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Carbon footprint; Cloud computing; Greenhouse gas; Genetic algorithm; Distributed computing; Bin packing problem; Algorithm; Reduction (mathematics); Bin; Machine learning","score_opus":0.02141251415636504,"score_gpt":0.2360213925604552,"score_spread":0.21460887840409015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156080250","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10930463,0.0003083172,0.8822118,0.00035758736,0.00011978904,0.00011052925,0.000058103906,0.00071248325,0.0068167257],"genre_scores_gemma":[0.73379207,0.00012038589,0.26334712,0.00013061684,0.00003311872,0.00013612917,0.00010341653,0.00007613786,0.0022609422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997241,0.00007134423,0.0000103118355,0.00005716205,0.00008646675,0.000050505503],"domain_scores_gemma":[0.99959403,0.00017348591,0.000045046458,0.000043418037,0.00011086825,0.00003315606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061856426,0.00056434865,0.0010250795,0.0008523595,0.0010105112,0.00096641725,0.0015971824,0.0015831852,0.0013038819],"category_scores_gemma":[0.001346295,0.000365776,0.000798346,0.0011616455,0.0005620407,0.00096201914,0.0009885728,0.00066828856,0.00019995282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069424255,0.00007752099,0.00069160847,0.000018480632,0.000038494396,0.000031569467,0.000036554364,0.9673949,0.0013759944,0.0029609916,0.0005675287,0.026736934],"study_design_scores_gemma":[0.000013188129,0.00001500317,0.000071584924,0.0000018131715,0.0000065351505,0.0000065308454,0.000007537164,0.9986632,0.00021985626,0.0008929111,0.00009888202,0.0000029528683],"about_ca_topic_score_codex":0.009926769,"about_ca_topic_score_gemma":0.008860968,"teacher_disagreement_score":0.009926769,"about_ca_system_score_codex":0.0012652845,"about_ca_system_score_gemma":0.0014954347,"threshold_uncertainty_score":0.019737959},"labels":[],"label_agreement":null},{"id":"W2180880586","doi":"10.1007/s10586-015-0477-1","title":"A distributed frequent itemset mining algorithm using Spark for Big Data analytics","year":2015,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Computer science; SPARK (programming language); Pruning; Scalability; Data mining; Association rule learning; Big data; Benchmark (surveying); GSP Algorithm; Computation; Process (computing); Algorithm; Apriori algorithm; Database","score_opus":0.256721916407233,"score_gpt":0.34482071353202187,"score_spread":0.08809879712478885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2180880586","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025723642,0.0003693204,0.95938665,0.00033882842,0.00027992832,0.00030228205,0.00090365193,0.011412805,0.0012829613],"genre_scores_gemma":[0.09833704,0.00015787079,0.89602256,0.00012276712,0.00012210252,0.00027580856,0.0025616372,0.00042647807,0.001973806],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985098,0.00018734022,0.00017085492,0.0004260218,0.0005816038,0.00012435415],"domain_scores_gemma":[0.9976533,0.0006126609,0.000101918384,0.00060349784,0.0007906689,0.0002379656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017911318,0.0010505875,0.0024002574,0.0020306644,0.0016768355,0.0016261775,0.003363443,0.00082075683,0.0031595568],"category_scores_gemma":[0.0045116404,0.0007699398,0.0013969517,0.0038581644,0.00042042648,0.0019955568,0.0017533111,0.0012095643,0.001940976],"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":[0.0022690836,0.0012305431,0.0065235808,0.00052216795,0.000670569,0.00077846885,0.00031256097,0.07169991,0.025128078,0.008766173,0.04796354,0.83413535],"study_design_scores_gemma":[0.0004897538,0.0003033849,0.0015418667,0.000016626049,0.00013894835,0.00071482995,0.00015428514,0.95880926,0.008574192,0.017978502,0.011219678,0.00005872591],"about_ca_topic_score_codex":0.0029105006,"about_ca_topic_score_gemma":0.0048303474,"teacher_disagreement_score":0.003363443,"about_ca_system_score_codex":0.00052421,"about_ca_system_score_gemma":0.0021590188,"threshold_uncertainty_score":0.010569751},"labels":[],"label_agreement":null},{"id":"W2195171817","doi":"10.1007/s10586-015-0498-9","title":"An efficient approach to collaborative simulation of variable structure systems on multi-core machines","year":2015,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Thread (computing); Speedup; Multi-core processor; Distributed computing; Core (optical fiber); Variable (mathematics); Parallel computing; Operating system","score_opus":0.042559065971394326,"score_gpt":0.300148674526515,"score_spread":0.2575896085551207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2195171817","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045308854,0.00013302213,0.94363993,0.0002348953,0.0000998787,0.00015696783,0.000074388096,0.00074455334,0.009607467],"genre_scores_gemma":[0.62663573,0.00013984778,0.36769235,0.0001309408,0.000084787025,0.0005605251,0.00017734342,0.00029394942,0.004284524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917126,0.0002512491,0.000033261917,0.00007222295,0.0003360084,0.00013610994],"domain_scores_gemma":[0.9982469,0.00090331264,0.000095380805,0.00025403124,0.00032773762,0.00017263954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012204755,0.0007669412,0.0016560993,0.00075654744,0.0015729916,0.0012833984,0.0031548578,0.0020412907,0.004040443],"category_scores_gemma":[0.004108216,0.00068073533,0.001109501,0.001066796,0.0010535402,0.0013791728,0.0024154156,0.0014213434,0.0006299948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074859105,0.00006785322,0.0003240673,0.000030568564,0.000028462917,0.00007081775,0.0000592156,0.977768,0.0008779863,0.01125713,0.00049612357,0.0089448895],"study_design_scores_gemma":[0.000011803158,0.000005908959,0.000016675052,8.623141e-7,0.0000018287391,0.0000032878838,0.000004143535,0.9981171,0.00012928768,0.0015594044,0.0001482504,0.0000015377865],"about_ca_topic_score_codex":0.008770776,"about_ca_topic_score_gemma":0.009176704,"teacher_disagreement_score":0.008770776,"about_ca_system_score_codex":0.00096362265,"about_ca_system_score_gemma":0.0017868589,"threshold_uncertainty_score":0.017439425},"labels":[],"label_agreement":null},{"id":"W2343650655","doi":"10.1007/s10586-016-0565-x","title":"Incorporating service and user information and latent features to predict QoS for selecting and recommending cloud service compositions","year":2016,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"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; Quality of service; Mobile QoS; Software as a service; Service provider; Service (business); The Internet; Software; Computer network; World Wide Web; Software development; Operating system","score_opus":0.009644635896766519,"score_gpt":0.23200064133424436,"score_spread":0.22235600543747785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2343650655","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5443831,0.00047706856,0.45183656,0.00037177635,0.00005955835,0.000087596716,0.00072204473,0.0010804302,0.0009818426],"genre_scores_gemma":[0.9633972,0.00012236713,0.035103127,0.000043344142,0.00004034208,0.000030807758,0.0006563218,0.000031611216,0.0005749641],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996158,0.000079125435,0.000030961564,0.00007182761,0.00013832556,0.00006386091],"domain_scores_gemma":[0.99860305,0.0008319488,0.00011948269,0.000086965396,0.00023834324,0.00012022944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007183186,0.00058964,0.0007026835,0.0018650795,0.00033362527,0.00073856016,0.00049683166,0.00075314107,0.000586469],"category_scores_gemma":[0.0026502665,0.00024201375,0.0005966662,0.0015130256,0.00024678095,0.0010248193,0.00035500363,0.0009618075,0.00032038224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001470143,0.0017650843,0.17421892,0.00017718332,0.00047763818,0.0002273361,0.00022679905,0.41173738,0.02640051,0.003038023,0.0051495004,0.37511152],"study_design_scores_gemma":[0.000005866061,0.000023317773,0.0024318865,0.000002472488,0.000017956669,0.000012107374,0.000013886533,0.996212,0.0007428866,0.00045134276,0.00008125603,0.0000049513114],"about_ca_topic_score_codex":0.015485965,"about_ca_topic_score_gemma":0.019534366,"teacher_disagreement_score":0.015485965,"about_ca_system_score_codex":0.00063498394,"about_ca_system_score_gemma":0.00093323115,"threshold_uncertainty_score":0.03079164},"labels":[],"label_agreement":null},{"id":"W2588816312","doi":"10.1007/s10586-017-0770-2","title":"Efficient and robust frame-synchronized blind audio watermarking by featuring multilevel DWT and DCT","year":2017,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan; McGill University; University of Florida","keywords":"Digital watermarking; Computer science; Discrete cosine transform; Discrete wavelet transform; Watermark; Payload (computing); Frame (networking); Robustness (evolution); Artificial intelligence; Algorithm; Speech recognition; Wavelet; Wavelet transform; Image (mathematics); Telecommunications","score_opus":0.019899407363536747,"score_gpt":0.2601512023865371,"score_spread":0.24025179502300037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588816312","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062963784,0.0004341784,0.9309236,0.000124414,0.000125082,0.000049591752,0.00011020168,0.0008311327,0.004438052],"genre_scores_gemma":[0.510311,0.0005000926,0.47963703,0.00007601761,0.00012348717,0.000051392006,0.00027703808,0.00012987462,0.008894063],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998381,0.000017530918,0.000008021907,0.000028834185,0.000087882334,0.000019708152],"domain_scores_gemma":[0.9998227,0.000043988057,0.00002302084,0.000040200477,0.000057289337,0.000012837234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013321901,0.00037201497,0.00030681468,0.0004302662,0.00022959229,0.0004431046,0.00037427185,0.00030448902,0.0020961391],"category_scores_gemma":[0.00055266696,0.000143788,0.0002361464,0.0004849244,0.00017131734,0.0006462495,0.00039645104,0.00036230258,0.000788796],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031983835,0.00006542463,0.00041327917,0.00009398424,0.000026960986,0.0001079697,0.00003883941,0.01604458,0.71902215,0.0071013607,0.0017082979,0.2550573],"study_design_scores_gemma":[0.000039938197,0.00027308892,0.0011701705,0.000023999544,0.000055121858,0.00039991425,0.00003760422,0.5469776,0.4376939,0.0027992295,0.010497314,0.000032269978],"about_ca_topic_score_codex":0.0008669751,"about_ca_topic_score_gemma":0.0021489798,"teacher_disagreement_score":0.0020961391,"about_ca_system_score_codex":0.00020594495,"about_ca_system_score_gemma":0.00036614895,"threshold_uncertainty_score":0.0070123076},"labels":[],"label_agreement":null},{"id":"W2588845958","doi":"10.1007/s10586-017-0767-x","title":"Towards product customization and personalization in IoT-enabled cloud manufacturing","year":2017,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":130,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Cloud computing; Personalization; Product (mathematics); Production (economics); Internet of Things; World Wide Web","score_opus":0.015945802531172896,"score_gpt":0.23314995918181483,"score_spread":0.21720415665064194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588845958","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2688481,0.0011751403,0.6811606,0.0010272772,0.00011161676,0.00018584468,0.00016809469,0.0018629796,0.045460336],"genre_scores_gemma":[0.8807269,0.00086282135,0.11119704,0.00018431526,0.000038761787,0.000035395075,0.000199967,0.00013417956,0.0066205803],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99933606,0.00013161892,0.00003808496,0.00014961175,0.00025317544,0.00009139236],"domain_scores_gemma":[0.9990815,0.00017018591,0.00009899015,0.00045442506,0.00015061813,0.000044295844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000776482,0.00035136775,0.00030608047,0.00061417074,0.0005436049,0.0019899162,0.00076603796,0.00064596965,0.0022044291],"category_scores_gemma":[0.001306149,0.00027889168,0.000572429,0.0012561626,0.00055763,0.0023002,0.0015817388,0.0006954571,0.0006696529],"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":[0.00036213538,0.0006061221,0.014111663,0.00026928226,0.00010328055,0.0005503379,0.0014395777,0.0868398,0.13735735,0.076315224,0.006026046,0.67601925],"study_design_scores_gemma":[0.000027593349,0.00024067106,0.018546278,0.0001301699,0.000087622604,0.000940651,0.00176845,0.6861076,0.14821385,0.07144396,0.072411105,0.00008201292],"about_ca_topic_score_codex":0.0021818317,"about_ca_topic_score_gemma":0.0030902706,"teacher_disagreement_score":0.0022044291,"about_ca_system_score_codex":0.00065158977,"about_ca_system_score_gemma":0.000760792,"threshold_uncertainty_score":0.007374525},"labels":[],"label_agreement":null},{"id":"W2589580626","doi":"10.1007/s10586-017-0749-z","title":"A user behavior prediction model based on parallel neural network and k-nearest neighbor algorithms","year":2017,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":18,"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 Waterloo","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; k-nearest neighbors algorithm; Artificial neural network; Machine learning; Data mining; Artificial intelligence; Algorithm","score_opus":0.03270691804910043,"score_gpt":0.3126841976612784,"score_spread":0.279977279612178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2589580626","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15787886,0.0006015542,0.83493537,0.0006648893,0.00021497013,0.0001363446,0.00041932464,0.0015573761,0.00359126],"genre_scores_gemma":[0.94368684,0.0003169527,0.05132408,0.00008395965,0.00009011456,0.00014953586,0.00032580842,0.000042662487,0.0039801574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951303,0.000087327004,0.000027513484,0.00017848908,0.00012366372,0.00006989366],"domain_scores_gemma":[0.9991768,0.00034947356,0.00006947268,0.00004476472,0.0003196051,0.000039876468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077896327,0.00074427045,0.0013110209,0.00092668185,0.0006410439,0.00079303735,0.0017151084,0.00089608255,0.0016512376],"category_scores_gemma":[0.0018574974,0.00043146737,0.00069827924,0.0010864725,0.00035931557,0.0014686364,0.00042887332,0.0009652711,0.00050786644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002455095,0.00030292387,0.006463334,0.000052121803,0.00011520579,0.000088865,0.00004943573,0.91115654,0.0008567909,0.0021229058,0.0018123194,0.07673403],"study_design_scores_gemma":[0.000002145375,0.0000070657998,0.00021910256,8.457723e-7,0.0000051678126,0.000005857301,0.00000207684,0.9993388,0.0000642771,0.00031991064,0.00003261629,0.0000021528558],"about_ca_topic_score_codex":0.050410062,"about_ca_topic_score_gemma":0.030681055,"teacher_disagreement_score":0.050410062,"about_ca_system_score_codex":0.0013833125,"about_ca_system_score_gemma":0.0013489228,"threshold_uncertainty_score":0.10023326},"labels":[],"label_agreement":null},{"id":"W2592666947","doi":"10.1007/s10586-017-0815-6","title":"Collision detection for virtual environment using particle swarm optimization with adaptive cauchy mutation","year":2017,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Particle swarm optimization; Computer science; Multi-swarm optimization; Collision detection; Algorithm; Mathematical optimization; Adaptive mutation; Cauchy distribution; Mutation; Collision; Swarm behaviour; Genetic algorithm; Mathematics; Artificial intelligence","score_opus":0.03538594963211208,"score_gpt":0.2655798658036606,"score_spread":0.23019391617154852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592666947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017852822,0.000058062975,0.9808493,0.000067593406,0.000036934252,0.000027803659,0.000009489835,0.00023404893,0.00086395675],"genre_scores_gemma":[0.6391513,0.000073421324,0.3584946,0.00005756303,0.000021538675,0.00014145975,0.000052476404,0.000081811224,0.0019258212],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995857,0.00008629927,0.000015460304,0.00008122574,0.00017964146,0.00005165002],"domain_scores_gemma":[0.9993506,0.00032431397,0.000064925116,0.000065384855,0.0001502495,0.00004450457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006807726,0.00074260694,0.0010502683,0.00094640726,0.0009852449,0.0007971951,0.0016622768,0.0010957572,0.0012255079],"category_scores_gemma":[0.0026353637,0.0005141856,0.00069793203,0.0008481842,0.0008677937,0.0011689673,0.0018162187,0.00094687985,0.00020110155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013137929,0.00006919592,0.0007365273,0.000036402325,0.00004494376,0.00008649993,0.00009298683,0.91030794,0.003653961,0.010277016,0.000895189,0.073667936],"study_design_scores_gemma":[0.000004128538,0.0000074351897,0.000039771203,9.278962e-7,0.0000017582536,0.000007643137,0.0000041813214,0.9986284,0.00028229522,0.0009467788,0.000074391304,0.0000023383884],"about_ca_topic_score_codex":0.008885311,"about_ca_topic_score_gemma":0.0049163927,"teacher_disagreement_score":0.008885311,"about_ca_system_score_codex":0.0008227928,"about_ca_system_score_gemma":0.0014571303,"threshold_uncertainty_score":0.017667234},"labels":[],"label_agreement":null},{"id":"W2782246985","doi":"10.1007/s10586-017-1672-z","title":"The simulation model on delay time of road accessibility based on intelligent traffic control system","year":2018,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Computer science; Transport engineering; Traffic congestion; Residential area; Analytic hierarchy process; Operations research; Civil engineering; Mathematics; Engineering; Geography","score_opus":0.01252483556359828,"score_gpt":0.24751372783804063,"score_spread":0.23498889227444233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782246985","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75302666,0.000541497,0.20543842,0.0010263692,0.00034896669,0.00009975886,0.0012363833,0.000494625,0.037787374],"genre_scores_gemma":[0.99399877,0.0001383954,0.0020381343,0.000021792104,0.000015892892,0.000037026162,0.00019281315,0.000014420827,0.0035427413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997868,0.00004237527,0.000010496381,0.00005188205,0.00004307334,0.000065413275],"domain_scores_gemma":[0.9995432,0.00019686682,0.000043841468,0.000022641552,0.00014711736,0.00004636671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032418387,0.0004584149,0.00051247986,0.0005864783,0.00057118665,0.0006844279,0.0010884668,0.00091879396,0.0034973626],"category_scores_gemma":[0.0011977433,0.00027973476,0.0006722913,0.00085952,0.0004475965,0.0011030025,0.000405731,0.00062894163,0.00018455372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023902798,0.000017511209,0.0010600012,0.00001264303,0.0000072141006,0.000035302368,0.000018955396,0.99457955,0.00018672922,0.0027433326,0.00028030094,0.0010344706],"study_design_scores_gemma":[0.000003548231,0.000006073588,0.00024714557,0.0000011064518,0.000004857138,0.000004229406,0.000008618851,0.99915683,0.00006365488,0.00040639142,0.000094969764,0.0000025868712],"about_ca_topic_score_codex":0.074555725,"about_ca_topic_score_gemma":0.022352535,"teacher_disagreement_score":0.074555725,"about_ca_system_score_codex":0.0014016471,"about_ca_system_score_gemma":0.0013729844,"threshold_uncertainty_score":0.14824355},"labels":[],"label_agreement":null},{"id":"W2786800158","doi":"10.1007/s10586-018-1854-3","title":"Cost-sensitive large margin distribution machine for fault detection of wind turbines","year":2018,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":22,"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 Waterloo","funders":"Natural Science Foundation of Hainan Province; National Natural Science Foundation of China","keywords":"Margin (machine learning); Wind power; Computer science; Support vector machine; Benchmark (surveying); Turbine; Sensitivity (control systems); Fault (geology); Fault detection and isolation; Machine learning; Artificial intelligence; Engineering; Geology","score_opus":0.012201347228585293,"score_gpt":0.2825349271661472,"score_spread":0.2703335799375619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786800158","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21591324,0.0004233379,0.78042114,0.00032732537,0.000063687294,0.000037512058,0.00011703368,0.0011089137,0.0015878442],"genre_scores_gemma":[0.9680369,0.000038154845,0.03110403,0.000019168341,0.000010889925,0.000008101323,0.000043833104,0.000015550824,0.0007232843],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985087,0.00004464859,0.000004903377,0.00002698535,0.000057372585,0.000015230329],"domain_scores_gemma":[0.99945027,0.0002899273,0.00007420147,0.00006214812,0.00009655238,0.000026908352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023865931,0.00034011158,0.00034177498,0.00037900067,0.00023898836,0.00024207422,0.00039943977,0.00022536692,0.0013881914],"category_scores_gemma":[0.0012957044,0.000108949884,0.00009521432,0.00028147345,0.00017949236,0.00046469984,0.00027698773,0.0003533569,0.00016688176],"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":[0.0018301595,0.00020698343,0.0058031203,0.00025261607,0.000049016387,0.0003116059,0.00008761706,0.43426028,0.096787535,0.012273805,0.005715713,0.44242144],"study_design_scores_gemma":[0.000008546061,0.00007601717,0.0010277082,0.000003193637,0.0000049472274,0.000050531635,0.000008628668,0.98659724,0.009169955,0.0026456949,0.00040373742,0.000003921066],"about_ca_topic_score_codex":0.0006998879,"about_ca_topic_score_gemma":0.0014509659,"teacher_disagreement_score":0.0013881914,"about_ca_system_score_codex":0.0003712495,"about_ca_system_score_gemma":0.00027851542,"threshold_uncertainty_score":0.0046439767},"labels":[],"label_agreement":null},{"id":"W2966314110","doi":"10.1007/s10586-019-02954-w","title":"Optimizing virtual machine placement in IaaS data centers: taxonomy, review and open issues","year":2019,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Virtual machine; Energy consumption; Virtualization; Data center; Distributed computing; Live migration; Process (computing); Cloud computing; Operating system","score_opus":0.049818494569923204,"score_gpt":0.2964128105983675,"score_spread":0.24659431602844428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966314110","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018819065,0.9878078,0.007752851,0.0010102858,0.00024004928,0.000022206317,0.000028652248,0.000041996514,0.0012141715],"genre_scores_gemma":[0.02150369,0.96586066,0.010975876,0.00034134154,0.00084266183,0.000028147735,0.000101199395,0.000030722847,0.00031580194],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99837494,0.0003980867,0.00024550612,0.00036241006,0.00050975534,0.00010924556],"domain_scores_gemma":[0.99454653,0.0033403959,0.0006151963,0.00018044838,0.0011719374,0.00014546579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002443481,0.001032202,0.0017068288,0.002133968,0.00043706456,0.0027117971,0.0025917261,0.0014272878,0.0013894611],"category_scores_gemma":[0.0074417344,0.00054021546,0.000647265,0.0062674875,0.0008297375,0.003345825,0.0009196991,0.0011588312,0.00046726855],"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":[0.00011042521,0.00015967258,0.0018095484,0.016931295,0.00016564193,0.000042439067,0.00012439002,0.009279534,0.0013074127,0.013355128,0.009140675,0.94757384],"study_design_scores_gemma":[0.00017719074,0.001554749,0.009654759,0.03314226,0.0015548602,0.0020405385,0.0019020672,0.08603619,0.008953,0.07202479,0.7826613,0.00029834328],"about_ca_topic_score_codex":0.0021577105,"about_ca_topic_score_gemma":0.0025763505,"teacher_disagreement_score":0.0027117971,"about_ca_system_score_codex":0.0010169641,"about_ca_system_score_gemma":0.003420475,"threshold_uncertainty_score":0.012922525},"labels":[],"label_agreement":null},{"id":"W3000966336","doi":"10.1007/s10586-020-03046-w","title":"A game theoretic analysis of resource mining in blockchain","year":2020,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University; University of Waterloo","funders":"Narodowym Centrum Nauki; University of Waterloo; Narodowe Centrum Nauki","keywords":"Blockchain; Computer science; Resource (disambiguation); Theoretical computer science; Data mining; Computer security; Computer network","score_opus":0.013451679883116265,"score_gpt":0.23824495205200588,"score_spread":0.2247932721688896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000966336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3441718,0.0006317673,0.597228,0.0032277228,0.000080018435,0.00026388108,0.00027160873,0.00012478163,0.054000463],"genre_scores_gemma":[0.98377025,0.00018681676,0.010573298,0.00006597971,0.000025013425,0.00007132536,0.000043190485,0.000017837872,0.0052462756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897325,0.00055425905,0.000026158223,0.00008926259,0.0001729729,0.00018418083],"domain_scores_gemma":[0.99625105,0.002617199,0.00035287096,0.00013921045,0.00029231052,0.00034748786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001655569,0.0005786725,0.00083433907,0.00077876844,0.0009793008,0.0020548746,0.0013871421,0.0015719808,0.0052347356],"category_scores_gemma":[0.005217175,0.0005227729,0.0006383434,0.000855791,0.0022660561,0.003164088,0.0013414021,0.001460623,0.0002149236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005490045,0.000083471474,0.00059901085,0.000048205024,0.000029959167,0.0001720544,0.00012071749,0.5077511,0.0007760312,0.4863955,0.0011180957,0.0028509318],"study_design_scores_gemma":[0.000012245269,0.000016839977,0.000092020826,0.0000057864404,0.0000038614,0.000014952267,0.000047555564,0.9172304,0.0000677997,0.082069814,0.00043260786,0.0000061617607],"about_ca_topic_score_codex":0.010403659,"about_ca_topic_score_gemma":0.0072954046,"teacher_disagreement_score":0.010403659,"about_ca_system_score_codex":0.0032200406,"about_ca_system_score_gemma":0.0023531602,"threshold_uncertainty_score":0.023363113},"labels":[],"label_agreement":null},{"id":"W3003707605","doi":"10.1007/s10586-020-03064-8","title":"Securing the LoRaWAN join procedure using blockchains","year":2020,"lang":"en","type":"article","venue":"Cluster Computing","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Network packet; Computer security; Computer network; Authentication (law); Replay attack; Distributed computing","score_opus":0.026268906866458788,"score_gpt":0.2362873881786082,"score_spread":0.2100184813121494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003707605","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17193246,0.00053608074,0.793517,0.0014441015,0.0004823535,0.00082318287,0.00044968954,0.0054643117,0.0253508],"genre_scores_gemma":[0.9511062,0.00014792735,0.040969595,0.000108506654,0.00007991125,0.00020928094,0.00022558896,0.00015674582,0.0069962153],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99202543,0.0023573118,0.00056110637,0.00091891905,0.0027990993,0.0013381069],"domain_scores_gemma":[0.97586185,0.0076237745,0.001959877,0.010975261,0.0026076687,0.00097160443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007041363,0.0005667158,0.0012492518,0.0012218539,0.0025046184,0.0035092384,0.0016262053,0.0015535591,0.005323919],"category_scores_gemma":[0.017168371,0.00066367676,0.0005097548,0.00087012234,0.002353261,0.005731855,0.005642976,0.0026873043,0.0023273705],"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":[0.0041260766,0.0006879599,0.009082426,0.00051268167,0.00026074733,0.0011348465,0.0023395107,0.19622017,0.05698021,0.36170995,0.013260516,0.35368484],"study_design_scores_gemma":[0.00023412799,0.00038508853,0.000887431,0.0001259499,0.00006819572,0.00028538043,0.00032394062,0.8054394,0.04321354,0.12546527,0.023446081,0.00012559236],"about_ca_topic_score_codex":0.0030209136,"about_ca_topic_score_gemma":0.0033207438,"teacher_disagreement_score":0.007041363,"about_ca_system_score_codex":0.0014379874,"about_ca_system_score_gemma":0.005494077,"threshold_uncertainty_score":0.037238717},"labels":[],"label_agreement":null},{"id":"W3005560537","doi":"10.1007/s10586-020-03058-6","title":"A decentralized lightweight blockchain-based authentication mechanism for IoT systems","year":2020,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":296,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Blockchain; Internet of Things; Authentication (law); Computer security; Access control; Confidentiality; Smart objects; Mechanism (biology); The Internet; World Wide Web","score_opus":0.01976899737891828,"score_gpt":0.24507620672889213,"score_spread":0.22530720934997386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005560537","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06023568,0.00036060688,0.9259368,0.00063996465,0.00026966768,0.00047306245,0.0002998021,0.0042381193,0.0075463275],"genre_scores_gemma":[0.92913693,0.00016431777,0.06225149,0.000099698365,0.000090858586,0.00026850897,0.00028177924,0.00009740781,0.007608988],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831843,0.00041197147,0.00012850115,0.00021996822,0.00064142706,0.0002797157],"domain_scores_gemma":[0.99654835,0.0007762231,0.0002835073,0.0014344457,0.0006518277,0.00030565765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020230177,0.00042802296,0.000893169,0.00077778276,0.0017770681,0.0017250995,0.001918494,0.0013300386,0.0077222413],"category_scores_gemma":[0.0037013977,0.00039381295,0.00032483385,0.000923988,0.00091167726,0.0032083842,0.0034364623,0.0014702211,0.0019094788],"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":[0.0035551295,0.0007644715,0.0033276065,0.0005808645,0.00018799945,0.000935925,0.0007814612,0.21164675,0.10857615,0.2920231,0.023835188,0.3537854],"study_design_scores_gemma":[0.00027391379,0.0003344129,0.00049365225,0.000042980577,0.00005484062,0.0002376563,0.00009683533,0.8877657,0.02442231,0.06873504,0.017462634,0.00008011869],"about_ca_topic_score_codex":0.0016673665,"about_ca_topic_score_gemma":0.0021963876,"teacher_disagreement_score":0.0077222413,"about_ca_system_score_codex":0.00095734955,"about_ca_system_score_gemma":0.0029182702,"threshold_uncertainty_score":0.025833428},"labels":[],"label_agreement":null},{"id":"W3012262040","doi":"10.1007/s10586-020-03086-2","title":"Energy-efficient offloading of real-time tasks using cloud computing","year":2020,"lang":"en","type":"article","venue":"Cluster Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Cloudlet; Cloud computing; Distributed computing; Computation offloading; Computation; Energy consumption; Power (physics); Efficient energy use; Embedded system; Real-time computing; Edge computing; Operating system; Algorithm","score_opus":0.02841494371782515,"score_gpt":0.2512993307724237,"score_spread":0.22288438705459857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012262040","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49895328,0.0028674244,0.440168,0.00087022677,0.0012496167,0.000225106,0.0005002699,0.0024203993,0.052745666],"genre_scores_gemma":[0.9736875,0.00030253065,0.020963263,0.000089493326,0.000056800312,0.0000235335,0.00021936483,0.000116811505,0.004540657],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995751,0.000033003747,0.000015482743,0.00006364164,0.00011074018,0.0002020915],"domain_scores_gemma":[0.9996358,0.000073480755,0.000025475845,0.000090370544,0.000114315255,0.000060521663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021795115,0.0006113866,0.00054293795,0.0005073664,0.001118802,0.0011416418,0.0007368466,0.00028322832,0.0025779977],"category_scores_gemma":[0.0006159108,0.00017422259,0.00031324202,0.0010023816,0.00026376316,0.0009087806,0.0007468047,0.0004125658,0.0005716098],"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":[0.00258672,0.0012459967,0.0062824073,0.00036517778,0.00012614466,0.0009545288,0.00034328317,0.30207986,0.2025313,0.030379266,0.027998125,0.42510724],"study_design_scores_gemma":[0.000050579605,0.00011774744,0.0035720437,0.00002102901,0.00003108734,0.00017454999,0.00018083601,0.9421711,0.034693405,0.010392603,0.008569149,0.000025957755],"about_ca_topic_score_codex":0.0064805215,"about_ca_topic_score_gemma":0.012541181,"teacher_disagreement_score":0.0064805215,"about_ca_system_score_codex":0.00048388386,"about_ca_system_score_gemma":0.0013063212,"threshold_uncertainty_score":0.0128855705},"labels":[],"label_agreement":null},{"id":"W3045595071","doi":"10.1007/s10586-020-03156-5","title":"Design and evaluation of a user authentication model for IoT networks based on app event patterns","year":2020,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Internet Traffic Analysis and Secure E-voting","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":"Ontario Tech University","funders":"","keywords":"Computer science; Usability; Authentication (law); Computer security; Multi-factor authentication; Authentication protocol; Computer network; Human–computer interaction","score_opus":0.053350187115428174,"score_gpt":0.28743706252022055,"score_spread":0.2340868754047924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045595071","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1380484,0.00015200417,0.85291845,0.00038478922,0.000109463406,0.0007701497,0.00007878559,0.0026810216,0.004856958],"genre_scores_gemma":[0.9188026,0.00009058249,0.078741565,0.000076864046,0.00002002745,0.00016499076,0.000061544335,0.00006447392,0.0019773787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987564,0.00039076604,0.000096246586,0.00018306209,0.0004125423,0.00016099434],"domain_scores_gemma":[0.9979563,0.00059617794,0.00016525915,0.00034694507,0.0007577384,0.00017760722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001548972,0.0005417877,0.0004747425,0.0006140147,0.00065338885,0.0016648354,0.0017273945,0.0007067106,0.002118536],"category_scores_gemma":[0.0033755023,0.00029379912,0.00039862687,0.00029013486,0.0006257718,0.0018280265,0.0007617667,0.00068077,0.00043856868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031185427,0.0020598767,0.017310822,0.00063780794,0.0003956257,0.00058909756,0.00065608183,0.47294673,0.11711804,0.09523798,0.0059789876,0.28395045],"study_design_scores_gemma":[0.00003607821,0.0002816114,0.0004614656,0.000008923623,0.000052776693,0.00007938148,0.000049940525,0.980026,0.014790598,0.002569815,0.0016291232,0.0000143296675],"about_ca_topic_score_codex":0.0031545667,"about_ca_topic_score_gemma":0.0027496284,"teacher_disagreement_score":0.0031545667,"about_ca_system_score_codex":0.0011212432,"about_ca_system_score_gemma":0.0013976801,"threshold_uncertainty_score":0.008191884},"labels":[],"label_agreement":null},{"id":"W3082025521","doi":"10.1007/s10586-020-03153-8","title":"TempoCode-IoT: temporal codebook-based encoding of flow features for intrusion detection in Internet of Things","year":2020,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":33,"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 Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Codebook; Discriminative model; Intrusion detection system; Internet of Things; Botnet; Feature (linguistics); Artificial intelligence; Encoding (memory); Data mining; State (computer science); Representation (politics); The Internet; Machine learning; Code (set theory); Pattern recognition (psychology); Computer security; Algorithm; World Wide Web","score_opus":0.018654792774376094,"score_gpt":0.24056577732456388,"score_spread":0.2219109845501878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082025521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023164488,0.00073218683,0.963308,0.00021865951,0.00053191936,0.00017944275,0.0031998393,0.006343448,0.0023219914],"genre_scores_gemma":[0.4305471,0.0010045543,0.54779613,0.0004206494,0.000291411,0.0005768238,0.011166138,0.0007793704,0.007417814],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996438,0.000040009258,0.000023517492,0.00006633328,0.00017602059,0.000050390125],"domain_scores_gemma":[0.9992217,0.00019889884,0.00007436654,0.00018034216,0.00025077627,0.00007396225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042089968,0.0006653481,0.0007494493,0.0011100406,0.0004017634,0.0006704975,0.0010971162,0.000518668,0.003977826],"category_scores_gemma":[0.0028850015,0.00019135702,0.0004239318,0.0018025031,0.00035555012,0.0013557464,0.0012094738,0.0010538307,0.0012900131],"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":[0.0017366672,0.00037803318,0.002939191,0.0002969518,0.00006952215,0.00023108993,0.00014155588,0.047265623,0.068975165,0.02655731,0.041417427,0.8099915],"study_design_scores_gemma":[0.00010035065,0.00036717422,0.0021300446,0.00007467561,0.000037267637,0.00027969046,0.00008603212,0.9268784,0.03713993,0.013800749,0.019015169,0.00009066876],"about_ca_topic_score_codex":0.0058929846,"about_ca_topic_score_gemma":0.008732101,"teacher_disagreement_score":0.0058929846,"about_ca_system_score_codex":0.00068310095,"about_ca_system_score_gemma":0.0012683348,"threshold_uncertainty_score":0.013307214},"labels":[],"label_agreement":null},{"id":"W3096318863","doi":"10.1007/s10586-020-03199-8","title":"MMHGE: detecting mild cognitive impairment based on multi-atlas multi-view hybrid graph convolutional networks and ensemble learning","year":2020,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":28,"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":"Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Computer science; Discriminative model; Cognitive impairment; Graph; Artificial intelligence; Pattern recognition (psychology); Convolutional neural network; Cognition; Machine learning; Medicine; Theoretical computer science","score_opus":0.03560893031235769,"score_gpt":0.30679036735635823,"score_spread":0.27118143704400055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096318863","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2621653,0.0044848067,0.683978,0.0014057268,0.0005603475,0.0004731656,0.014744881,0.027631115,0.004556623],"genre_scores_gemma":[0.7203309,0.0011483644,0.24977002,0.00062713755,0.00022013024,0.000283497,0.0183063,0.00091849134,0.008395199],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994319,0.00011609509,0.000017956114,0.00017826859,0.0001393003,0.000116402734],"domain_scores_gemma":[0.99960047,0.000118025244,0.000030887364,0.00008820886,0.00011593133,0.000046563197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013577272,0.0016486791,0.0014624428,0.0023857176,0.00064408814,0.0010895941,0.0016893334,0.0014763471,0.0014437215],"category_scores_gemma":[0.0023679323,0.0005501673,0.0016455451,0.0013322897,0.00027752196,0.00096068915,0.002028773,0.0013516537,0.0008538097],"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":[0.0013098151,0.00083510025,0.03914369,0.0003109283,0.002897094,0.00072877645,0.00018730653,0.13790861,0.019922907,0.0031200284,0.067565,0.72607076],"study_design_scores_gemma":[0.000053312095,0.00016018562,0.012686873,0.000038782782,0.00036479277,0.000634255,0.00007794252,0.9650593,0.009769407,0.0061669047,0.004922308,0.00006607154],"about_ca_topic_score_codex":0.029975059,"about_ca_topic_score_gemma":0.04988379,"teacher_disagreement_score":0.029975059,"about_ca_system_score_codex":0.0007855693,"about_ca_system_score_gemma":0.0011905126,"threshold_uncertainty_score":0.059601188},"labels":[],"label_agreement":null},{"id":"W3123965728","doi":"10.1007/s10586-021-03231-5","title":"Towards the design of real-time autonomous IoT NIDS","year":2021,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Botnet; Internet of Things; Scalability; Intrusion detection system; Feature selection; Classifier (UML); Sliding window protocol; Data mining; Network packet; Computer network; Artificial intelligence; Real-time computing; Machine learning; Computer security; The Internet; Window (computing); Operating system","score_opus":0.02480977710524583,"score_gpt":0.24126189873652187,"score_spread":0.21645212163127603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123965728","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010320981,0.00037339196,0.98325944,0.00024570266,0.00011323225,0.00013133242,0.00003567601,0.0011164539,0.0044038245],"genre_scores_gemma":[0.4472313,0.000625115,0.54249495,0.0003074879,0.00006150591,0.000266982,0.00014147053,0.00023440213,0.00863672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994844,0.00009014806,0.00004148505,0.000108163425,0.0002077759,0.000068006644],"domain_scores_gemma":[0.99914956,0.00018351739,0.00010650089,0.00015196376,0.00033135893,0.00007712865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009992627,0.0003672757,0.00046169566,0.00026372314,0.00041512755,0.0018372218,0.001925356,0.00072529184,0.0018909214],"category_scores_gemma":[0.0018367157,0.0003835213,0.00030737917,0.00017594443,0.00056961895,0.0011808336,0.0010545016,0.0009505602,0.00072289945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039504573,0.00031580124,0.003477918,0.00078537705,0.00013922728,0.0003313744,0.00053016614,0.4537004,0.11625457,0.15861125,0.011044619,0.2544142],"study_design_scores_gemma":[0.000029270805,0.0001623041,0.00031354078,0.000036574344,0.00003559783,0.00012898071,0.00007885915,0.94334906,0.018324366,0.013886506,0.023636473,0.000018509098],"about_ca_topic_score_codex":0.0011045813,"about_ca_topic_score_gemma":0.002338243,"teacher_disagreement_score":0.001925356,"about_ca_system_score_codex":0.0007242713,"about_ca_system_score_gemma":0.0011505855,"threshold_uncertainty_score":0.0063257813},"labels":[],"label_agreement":null},{"id":"W3173206457","doi":"10.1007/s10586-021-03339-8","title":"A classification of hadoop job schedulers based on performance optimization approaches","year":2021,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Job scheduler; Scheduling (production processes); Job queue; Execution time; Task (project management); Distributed computing; Job performance; Cloud computing; Operating system; Mathematical optimization; Job satisfaction; Management","score_opus":0.044410813730767905,"score_gpt":0.23115552274408324,"score_spread":0.18674470901331533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173206457","genre_codex":"empirical","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48977274,0.056491185,0.39766902,0.0039583836,0.002760368,0.0027021326,0.0021109555,0.01074933,0.033785917],"genre_scores_gemma":[0.77654886,0.009976057,0.20012788,0.0007667317,0.0007917964,0.0004406679,0.002824224,0.0008154518,0.0077083674],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99751496,0.00024997958,0.0002817106,0.00033204764,0.0010570305,0.00056426035],"domain_scores_gemma":[0.99437016,0.0013354837,0.0006115527,0.0006200202,0.0021755148,0.0008873159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003851161,0.0011604948,0.0014033107,0.0046696616,0.0015374611,0.002472862,0.0025112103,0.0008476094,0.0016059903],"category_scores_gemma":[0.005898893,0.0006184947,0.0016603771,0.0055843694,0.00066084665,0.0013955269,0.0009179324,0.0011252343,0.00062879355],"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":[0.0029685802,0.0015937333,0.07395825,0.0020770864,0.00064815173,0.00038007318,0.0006240507,0.06606319,0.030536776,0.029774716,0.02436773,0.7670076],"study_design_scores_gemma":[0.00066920946,0.0038391957,0.12962727,0.0008191279,0.0009822896,0.0016517979,0.001261556,0.69290346,0.032675147,0.042965546,0.092138894,0.00046657416],"about_ca_topic_score_codex":0.0043368996,"about_ca_topic_score_gemma":0.0038944136,"teacher_disagreement_score":0.0046696616,"about_ca_system_score_codex":0.0016057319,"about_ca_system_score_gemma":0.0038492116,"threshold_uncertainty_score":0.020367146},"labels":[],"label_agreement":null},{"id":"W3192838559","doi":"10.1007/s10586-021-03361-w","title":"ProBlock: a novel approach for fake news detection","year":2021,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Upload; Correctness; Voting; Social media; Fake news; Internet privacy; News media; Probabilistic logic; The Internet; World Wide Web; Computer security; Artificial intelligence; Algorithm; Advertising","score_opus":0.0530046895499609,"score_gpt":0.31844115176218396,"score_spread":0.26543646221222306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192838559","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010184438,0.00021833956,0.9845355,0.0005704771,0.000080614715,0.00020182,0.00027255327,0.0013039019,0.0026324275],"genre_scores_gemma":[0.728122,0.00066654594,0.25415775,0.00038762752,0.00028673033,0.00055273355,0.0009674399,0.00030811763,0.014551145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99529403,0.0013289391,0.00025059798,0.0010632335,0.0016041788,0.00045901598],"domain_scores_gemma":[0.98403615,0.0082225,0.00200305,0.0027958287,0.0023089498,0.0006336396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004393999,0.0012314222,0.002227458,0.0028343042,0.0020220168,0.0037301357,0.003913203,0.002396,0.0051013473],"category_scores_gemma":[0.020337649,0.00096752186,0.0011318141,0.0018546396,0.0025977101,0.0075578955,0.005879254,0.0026805701,0.0018935375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012530208,0.0004958349,0.011318075,0.0006330996,0.0002640121,0.0012426937,0.0010076837,0.41006497,0.009546942,0.23555528,0.014610653,0.31400776],"study_design_scores_gemma":[0.000017081578,0.000055980247,0.00019573828,0.000016267504,0.000019101582,0.00014584803,0.000049724473,0.95050156,0.0014042798,0.045634493,0.0019387832,0.000021115557],"about_ca_topic_score_codex":0.0043824175,"about_ca_topic_score_gemma":0.004052492,"teacher_disagreement_score":0.0051013473,"about_ca_system_score_codex":0.0021493542,"about_ca_system_score_gemma":0.0037559473,"threshold_uncertainty_score":0.023237944},"labels":[],"label_agreement":null},{"id":"W3198861897","doi":"10.1007/s10586-021-03402-4","title":"Toward integrating software defined networks with the Internet of Things: a review","year":2021,"lang":"en","type":"review","venue":"Cluster Computing","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":45,"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 Ottawa","funders":"","keywords":"Computer science; Software deployment; The Internet; Orchestration; Software-defined networking; World Wide Web; Telecommunications; Computer network","score_opus":0.03871512774760118,"score_gpt":0.2824244045812103,"score_spread":0.24370927683360916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198861897","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010525712,0.9971011,0.00075331586,0.0005399743,0.00048674736,0.000007952041,0.000016971442,0.000010720499,0.0009778964],"genre_scores_gemma":[0.00052706327,0.9977888,0.00067696633,0.0003700648,0.00029856266,0.000007352444,0.000023260902,0.0000021489634,0.00030580413],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995747,0.000065823006,0.000054060998,0.00007489352,0.00019252204,0.000037849546],"domain_scores_gemma":[0.99841845,0.00089822087,0.00014886378,0.00003460195,0.0004010558,0.00009877391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013397587,0.0011302475,0.0015187314,0.0029750383,0.00035199453,0.0015296079,0.0012033553,0.0016838033,0.0027475045],"category_scores_gemma":[0.0018342851,0.00045852328,0.00062362925,0.0042080143,0.0006528657,0.0031746016,0.001120174,0.0020741294,0.0014262627],"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":[0.000052786785,0.00010639262,0.0002674404,0.024979638,0.000108571134,0.00016026382,0.0000664499,0.0010103083,0.0017059987,0.013739699,0.03353183,0.92427063],"study_design_scores_gemma":[0.000019006588,0.00013361314,0.00059795793,0.00751206,0.00020761756,0.00054600235,0.0000951915,0.00043693918,0.00057022757,0.0055905045,0.9842569,0.000034058532],"about_ca_topic_score_codex":0.0015879438,"about_ca_topic_score_gemma":0.0029434438,"teacher_disagreement_score":0.0029750383,"about_ca_system_score_codex":0.0009938689,"about_ca_system_score_gemma":0.0025309958,"threshold_uncertainty_score":0.009191334},"labels":[],"label_agreement":null},{"id":"W3200962337","doi":"10.1007/s10586-021-03395-0","title":"Replication management in peer-to-peer cloud storage systems","year":2021,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"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; Replication (statistics); Cloud computing; Cloud storage; Distributed computing; Load balancing (electrical power); Computer data storage; Peer-to-peer; Popularity; Computer network; Data management; Database; Operating system","score_opus":0.02324836695857902,"score_gpt":0.2641101334585464,"score_spread":0.24086176649996738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200962337","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32198393,0.011565961,0.64422333,0.0040375274,0.0010980397,0.0005595547,0.00048306584,0.0045946427,0.011454015],"genre_scores_gemma":[0.96125114,0.001048138,0.03364386,0.00009136091,0.00024117932,0.00008500924,0.0001522848,0.00012822224,0.0033587771],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973781,0.0007383335,0.00027316337,0.00042173782,0.000879757,0.00030897753],"domain_scores_gemma":[0.99193794,0.0030013844,0.000719693,0.0017813696,0.0021278355,0.00043178885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003404343,0.00043982427,0.0013148254,0.0009388778,0.0026013511,0.0034434933,0.0028593743,0.0014932296,0.0014454921],"category_scores_gemma":[0.010863671,0.00058173423,0.0003475568,0.0015517126,0.0010653842,0.0037756485,0.0014363431,0.000987108,0.0004857761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011442987,0.0005605233,0.010417573,0.00093894836,0.00034209865,0.0016627376,0.0025793072,0.4362907,0.05465577,0.15573423,0.027875714,0.307798],"study_design_scores_gemma":[0.000068804206,0.00015359186,0.001448034,0.000042410553,0.00009387789,0.0005321162,0.0006103015,0.9244345,0.010930892,0.05224753,0.009368399,0.00006957543],"about_ca_topic_score_codex":0.009160543,"about_ca_topic_score_gemma":0.006639584,"teacher_disagreement_score":0.009160543,"about_ca_system_score_codex":0.0019636312,"about_ca_system_score_gemma":0.0023119575,"threshold_uncertainty_score":0.018214464},"labels":[],"label_agreement":null},{"id":"W4292259733","doi":"10.1007/s10586-022-03659-3","title":"When explainable AI meets IoT applications for supervised learning","year":2022,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Høgskulen på Vestlandet","keywords":"Computer science; Internet of Things; Field (mathematics); Artificial intelligence; Process (computing); Deep learning; Big data; Machine learning; Interpretation (philosophy); Computation; Data mining; Algorithm; Embedded system","score_opus":0.015019502098149678,"score_gpt":0.259273588772887,"score_spread":0.2442540866747373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292259733","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12380271,0.00038110776,0.8660369,0.002993557,0.00011003898,0.000091754955,0.0002794348,0.002061403,0.004243087],"genre_scores_gemma":[0.8903452,0.00015371873,0.10670649,0.00030180713,0.00012463432,0.000091373135,0.00043979398,0.0001389846,0.0016981047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990119,0.00026917475,0.000052692638,0.0003395483,0.00022907002,0.000097720134],"domain_scores_gemma":[0.9950826,0.0030227506,0.00040598758,0.0008573031,0.00047652528,0.00015484239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013884181,0.00051136734,0.0005525155,0.00044574542,0.00041763636,0.0013546029,0.0011428262,0.0013137463,0.002596044],"category_scores_gemma":[0.00850231,0.00024043852,0.00047471307,0.00051941304,0.0011451511,0.0027275002,0.0014885496,0.0015832636,0.00039708533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038214872,0.0003424847,0.016677355,0.00034067966,0.00017758158,0.00073302333,0.0005090459,0.5938756,0.015586078,0.09005645,0.011312449,0.270007],"study_design_scores_gemma":[0.000005861858,0.000016917196,0.0005490225,0.000007650796,0.000004681635,0.000032915756,0.000046557867,0.9589421,0.0020088155,0.037529867,0.0008517111,0.000003917286],"about_ca_topic_score_codex":0.0024124403,"about_ca_topic_score_gemma":0.00266384,"teacher_disagreement_score":0.002596044,"about_ca_system_score_codex":0.00078957283,"about_ca_system_score_gemma":0.0008526118,"threshold_uncertainty_score":0.008684635},"labels":[],"label_agreement":null},{"id":"W4308791329","doi":"10.1007/s10586-022-03797-8","title":"Modeling and simulation of smart grid-aware edge computing federations","year":2022,"lang":"en","type":"article","venue":"Cluster Computing","topic":"IoT and Edge/Fog Computing","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":"Carleton University","funders":"Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España; Google","keywords":"Computer science; Edge computing; Smart grid; Distributed computing; Dimensioning; Enhanced Data Rates for GSM Evolution; Energy consumption; Grid; Computer network; Telecommunications","score_opus":0.030313186692413164,"score_gpt":0.26954846889463774,"score_spread":0.23923528220222456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308791329","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8436436,0.0002596972,0.118920736,0.00092076976,0.000106031155,0.00013602806,0.0007396822,0.0007386192,0.03453488],"genre_scores_gemma":[0.99116546,0.000074820215,0.006887847,0.000038065627,0.0000060024713,0.00004804655,0.00016640373,0.000025000523,0.0015882157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997373,0.00008334266,0.0000109332095,0.000041682797,0.00004429399,0.00008236721],"domain_scores_gemma":[0.99939644,0.00028269182,0.00007060168,0.00006538194,0.00009770081,0.000087086795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005331963,0.0004396084,0.0007078335,0.00046071547,0.000896495,0.0012641948,0.00080477895,0.0011319774,0.0023810521],"category_scores_gemma":[0.0012441196,0.00032171307,0.0007193684,0.00072969345,0.0008254243,0.0009270771,0.00083288626,0.00063104194,0.0001995576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020226558,0.000015867518,0.0007444314,0.000003886141,0.000004986236,0.000027861004,0.000014442607,0.9965714,0.00020540153,0.0018186336,0.00012666218,0.00044621417],"study_design_scores_gemma":[0.0000032382306,0.0000033759925,0.000096690324,8.1948565e-7,9.981651e-7,0.0000018714115,0.000009525135,0.99933124,0.00005699335,0.00038861606,0.00010559952,0.0000011331844],"about_ca_topic_score_codex":0.036106165,"about_ca_topic_score_gemma":0.018026706,"teacher_disagreement_score":0.036106165,"about_ca_system_score_codex":0.0014544098,"about_ca_system_score_gemma":0.0013423868,"threshold_uncertainty_score":0.07179201},"labels":[],"label_agreement":null},{"id":"W4313463169","doi":"10.1007/s10586-022-03913-8","title":"A near-optimal & load balanced resilient system design for high-performance computing platform","year":2023,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":5,"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 Calgary","funders":"","keywords":"Computer science; Architecture; Component (thermodynamics); Distributed computing; Cluster (spacecraft); Systems architecture; Operating system","score_opus":0.02975731426517,"score_gpt":0.24914656247507452,"score_spread":0.21938924820990452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313463169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041476518,0.0004577145,0.94308025,0.00031724432,0.00011956102,0.00010278437,0.00008046438,0.0010787443,0.013286713],"genre_scores_gemma":[0.9043046,0.00017161321,0.09010863,0.00014024692,0.00004627381,0.00007559812,0.00005721452,0.00007148201,0.0050244313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974555,0.000057285844,0.000008835322,0.00006080341,0.000079649886,0.000047873706],"domain_scores_gemma":[0.99984837,0.00003266789,0.000026946243,0.000018326948,0.00005943614,0.000014226226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002462561,0.000569758,0.00053009175,0.00032858865,0.0007057949,0.00083047856,0.0008280066,0.00048044717,0.00521499],"category_scores_gemma":[0.00048251252,0.0002719951,0.0003439085,0.00025980792,0.00028423054,0.00068501884,0.00052932685,0.0004012813,0.0006735749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025718377,0.00009217014,0.0005125619,0.00022531168,0.00005577393,0.00012922901,0.00013317462,0.8280665,0.05105715,0.011007758,0.004724099,0.10373906],"study_design_scores_gemma":[0.000010667201,0.00014588049,0.00013086486,0.000008057326,0.000015080367,0.00003704992,0.000024221696,0.9935387,0.0029142366,0.0017534262,0.0014138689,0.000007898183],"about_ca_topic_score_codex":0.0023313907,"about_ca_topic_score_gemma":0.004203621,"teacher_disagreement_score":0.00521499,"about_ca_system_score_codex":0.0005998825,"about_ca_system_score_gemma":0.0008536031,"threshold_uncertainty_score":0.017445862},"labels":[],"label_agreement":null},{"id":"W4318699532","doi":"10.1007/s10586-023-03975-2","title":"Pricing the cloud based on multi-attribute auction mechanism","year":2023,"lang":"en","type":"article","venue":"Cluster Computing","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":"New York Institute of Technology","funders":"","keywords":"Cloud computing; Computer science; Quality of service; Revenue; Service provider; Scalability; The Internet; Service (business); Computer network; Database; Business; World Wide Web; Marketing; Finance; Operating system","score_opus":0.027586872532665497,"score_gpt":0.25344511757744576,"score_spread":0.22585824504478025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318699532","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10917319,0.0009400934,0.8720806,0.0008525187,0.00055931706,0.00029437768,0.00013225611,0.00056364277,0.015403943],"genre_scores_gemma":[0.9591091,0.00026701766,0.03694199,0.0000593387,0.00012494618,0.00004882735,0.000048654852,0.000030522697,0.0033696755],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967063,0.0011084329,0.00014802717,0.00032885862,0.0011564165,0.0005519276],"domain_scores_gemma":[0.99808013,0.0006209984,0.00015921796,0.00039112533,0.0004775352,0.00027093623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002214186,0.00047323862,0.0016124255,0.00095442706,0.001446996,0.0042492165,0.002352906,0.0012500832,0.0035281344],"category_scores_gemma":[0.0044883955,0.00036486858,0.0011617683,0.0020986074,0.00074896205,0.0036933338,0.0013492698,0.0014016778,0.00040354827],"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":[0.001370144,0.0008503439,0.0038180419,0.00031466986,0.00037556753,0.0007681807,0.00026495208,0.34762344,0.01706158,0.47121537,0.010431209,0.14590655],"study_design_scores_gemma":[0.00007906203,0.000112803056,0.00047042937,0.000010418046,0.00004562792,0.00033632046,0.00004551023,0.943398,0.001991098,0.051406,0.0020574324,0.00004732383],"about_ca_topic_score_codex":0.0016898409,"about_ca_topic_score_gemma":0.0011446942,"teacher_disagreement_score":0.0042492165,"about_ca_system_score_codex":0.0015314551,"about_ca_system_score_gemma":0.002025603,"threshold_uncertainty_score":0.011802852},"labels":[],"label_agreement":null},{"id":"W4366496383","doi":"10.1007/s10586-023-03991-2","title":"Task scheduling in the internet of things: challenges, solutions, and future trends","year":2023,"lang":"en","type":"article","venue":"Cluster Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Internet of Things; Scheduling (production processes); Task (project management); The Internet; Data science; World Wide Web; Systems engineering","score_opus":0.03479724700029281,"score_gpt":0.25837364196410856,"score_spread":0.22357639496381576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366496383","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050660804,0.57280576,0.2464341,0.089786366,0.0085042585,0.0001730786,0.00043853305,0.0011774418,0.030019606],"genre_scores_gemma":[0.45336935,0.35324246,0.16141935,0.0083832145,0.010768054,0.00017597267,0.0009673044,0.00030124575,0.011372996],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99909186,0.00020520958,0.000057693214,0.00017256188,0.00028187194,0.00019086455],"domain_scores_gemma":[0.9965508,0.0016294436,0.0002530662,0.0001505971,0.00086711097,0.00054901995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003089946,0.0006685048,0.0010450045,0.0008775222,0.0007902767,0.0036905045,0.002126108,0.0016608561,0.0031097874],"category_scores_gemma":[0.0034488002,0.0003581352,0.00052713085,0.0028353527,0.000925298,0.005749295,0.0013814067,0.002215481,0.00089645066],"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":[0.00042204847,0.0004884252,0.00393164,0.002860497,0.00010950054,0.00011558174,0.000282249,0.046575468,0.0058229635,0.10561783,0.06393094,0.76984286],"study_design_scores_gemma":[0.00007910183,0.0004932623,0.0028597713,0.001216447,0.00014175396,0.00047782823,0.0030144916,0.43299037,0.003289154,0.28215665,0.27314857,0.00013253711],"about_ca_topic_score_codex":0.0029137738,"about_ca_topic_score_gemma":0.0049148137,"teacher_disagreement_score":0.0036905045,"about_ca_system_score_codex":0.0010201674,"about_ca_system_score_gemma":0.0030149221,"threshold_uncertainty_score":0.016341388},"labels":[],"label_agreement":null},{"id":"W4376138134","doi":"10.1007/s10586-023-04000-2","title":"Secure itemset hiding in smart city sensor data","year":2023,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":1,"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":"Computer science; USable; Private information retrieval; Information sensitivity; Internet of Things; Binary number; Data mining; Computer security; World Wide Web","score_opus":0.09783609782403217,"score_gpt":0.322279729449814,"score_spread":0.2244436316257818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376138134","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2936652,0.00062868115,0.69801134,0.0013123662,0.00016688176,0.0001069921,0.0007146053,0.0009969883,0.0043969164],"genre_scores_gemma":[0.9660236,0.00029142573,0.029769825,0.00008711379,0.000038413666,0.000033870532,0.00025129152,0.000044186458,0.0034602152],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985141,0.00024471933,0.0000928942,0.00017469567,0.00066510704,0.00030850014],"domain_scores_gemma":[0.9957705,0.001123761,0.00035079205,0.0024038253,0.0002632829,0.000087815264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011205706,0.0003559761,0.00077935384,0.00060410827,0.00093827647,0.0014702694,0.001007451,0.0008600656,0.0014378177],"category_scores_gemma":[0.0036578716,0.00039579888,0.0005770056,0.0017909782,0.001357881,0.0037104082,0.0026956904,0.0013398111,0.00041914335],"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":[0.0033371956,0.00029391385,0.005945737,0.00043881228,0.00025017475,0.001496058,0.0015872121,0.32035092,0.073832884,0.41008458,0.008256187,0.17412642],"study_design_scores_gemma":[0.00005054372,0.00015302146,0.0011419554,0.00003933073,0.000050692404,0.0004948288,0.0003800812,0.7892979,0.052082364,0.14951573,0.006746494,0.000047133726],"about_ca_topic_score_codex":0.0012398863,"about_ca_topic_score_gemma":0.0012631094,"teacher_disagreement_score":0.0014702694,"about_ca_system_score_codex":0.0008840486,"about_ca_system_score_gemma":0.0009432583,"threshold_uncertainty_score":0.006414175},"labels":[],"label_agreement":null},{"id":"W4379191672","doi":"10.1007/s10586-023-04035-5","title":"A deep learning approach for detecting covert timing channel attacks using sequential data","year":2023,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Convolutional neural network; Deep learning; Covert; Artificial intelligence; Byte; Machine learning; Identification (biology); Channel (broadcasting); Artificial neural network; Computer network","score_opus":0.09715234867049774,"score_gpt":0.31639493210071495,"score_spread":0.2192425834302172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379191672","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14989981,0.000455105,0.8454224,0.00049037824,0.00009082885,0.00005755928,0.00035013116,0.0012910496,0.001942816],"genre_scores_gemma":[0.9220723,0.00015934331,0.07380114,0.00012683608,0.0000792076,0.000040840914,0.00047235965,0.000038082373,0.0032097853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948525,0.000089423214,0.000026898004,0.00012628605,0.00013202704,0.00014010927],"domain_scores_gemma":[0.9987141,0.00060477364,0.00016747863,0.0001769251,0.00025015813,0.000086528555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008322135,0.0007624123,0.00076832913,0.0011308183,0.00042125434,0.0007158212,0.0011813528,0.00077230635,0.0011900619],"category_scores_gemma":[0.0021452142,0.0003840705,0.00043035788,0.0009780886,0.00052554323,0.0011043298,0.0009189038,0.001242548,0.0003333363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063366845,0.00066747575,0.012780554,0.00008103332,0.00017310979,0.00019788434,0.00006877694,0.5261447,0.017544033,0.013224159,0.004529214,0.4239554],"study_design_scores_gemma":[0.0000025223676,0.000019685169,0.00026383565,0.00000153369,0.000004419781,0.0000088572115,0.0000040768064,0.9964964,0.0009982786,0.0020492533,0.00014894735,0.0000022536067],"about_ca_topic_score_codex":0.0071989656,"about_ca_topic_score_gemma":0.009847285,"teacher_disagreement_score":0.0071989656,"about_ca_system_score_codex":0.00080560066,"about_ca_system_score_gemma":0.0012652092,"threshold_uncertainty_score":0.014314115},"labels":[],"label_agreement":null},{"id":"W4381734648","doi":"10.1007/s10586-023-04088-6","title":"A BERT-based recommender system for secure blockchain-based cyber physical drug supply chain management","year":2023,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":20,"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; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer security; Supply chain; Autoencoder; Blockchain; Artificial intelligence; Deep learning; Business","score_opus":0.012037259370700982,"score_gpt":0.2480337018784461,"score_spread":0.2359964425077451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381734648","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13959621,0.0018794858,0.8204992,0.0013579244,0.0006588844,0.0008129194,0.0024032318,0.016697228,0.016094893],"genre_scores_gemma":[0.8842154,0.00039053534,0.10059728,0.00025175136,0.0001574786,0.00017977488,0.001992757,0.00009373206,0.012121336],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990164,0.00020748361,0.0000883986,0.00021859551,0.00035926254,0.00010980232],"domain_scores_gemma":[0.9980246,0.0005604382,0.0001507738,0.00044176672,0.00060660817,0.00021584274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012259503,0.0007273004,0.0016065141,0.0012376845,0.0012268195,0.0012006641,0.00207975,0.0015621269,0.007362549],"category_scores_gemma":[0.0027394851,0.00042858292,0.0004817792,0.0012787727,0.0002658939,0.0020812459,0.0014168075,0.00096950075,0.0029579124],"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":[0.0027489953,0.0018457546,0.017423209,0.0004887308,0.0004680102,0.0014284012,0.00030464053,0.2087574,0.029095374,0.013175774,0.0414559,0.68280786],"study_design_scores_gemma":[0.00005695407,0.00016765503,0.0012720868,0.000011829209,0.000044940447,0.00017567498,0.000036103123,0.9896348,0.0024746459,0.0019854968,0.0041019237,0.00003780491],"about_ca_topic_score_codex":0.010759568,"about_ca_topic_score_gemma":0.021591391,"teacher_disagreement_score":0.010759568,"about_ca_system_score_codex":0.00073187257,"about_ca_system_score_gemma":0.001296302,"threshold_uncertainty_score":0.024630189},"labels":[],"label_agreement":null},{"id":"W4384824314","doi":"10.1007/s10586-023-04107-6","title":"Correction: A BERT-based recommender system for secure blockchain-based cyber physical drug supply chain management","year":2023,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University; University of Guelph","funders":"","keywords":"Computer science; Blockchain; Recommender system; Supply chain; Cyber-physical system; Supply chain management; World Wide Web; Computer security; Operating system","score_opus":0.01113111552402549,"score_gpt":0.24493478397687024,"score_spread":0.23380366845284475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384824314","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007359875,0.0016764118,0.004661272,0.114783466,0.8660834,0.000064987005,0.0061038206,0.0015817889,0.0043090293],"genre_scores_gemma":[0.08778896,0.011012428,0.023107862,0.15827012,0.3183092,0.00045283162,0.010028948,0.002486472,0.3885432],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967327,0.0005712419,0.00046677145,0.0005649745,0.0013701369,0.00029420273],"domain_scores_gemma":[0.9522903,0.011619322,0.0017648101,0.002918637,0.029601168,0.0018057276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003511896,0.0014662078,0.0015140163,0.0023059852,0.0018827934,0.0026595742,0.0030587735,0.006805453,0.06823884],"category_scores_gemma":[0.0779995,0.00072608574,0.0013430697,0.0022748357,0.0018122274,0.0021003445,0.0015953036,0.0075771986,0.034389187],"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":[0.00011081198,0.00000786191,0.00023354072,0.00027857942,0.00003769332,0.000337004,0.000056631452,0.00021257252,0.00016942958,0.0016305185,0.9882648,0.008660573],"study_design_scores_gemma":[0.0001578737,0.000056041994,0.002081223,0.0004140058,0.00010208638,0.00092818233,0.00016302158,0.0023133445,0.0012000203,0.0036553328,0.9888365,0.00009230611],"about_ca_topic_score_codex":0.025415504,"about_ca_topic_score_gemma":0.02120808,"teacher_disagreement_score":0.06823884,"about_ca_system_score_codex":0.003098701,"about_ca_system_score_gemma":0.005064557,"threshold_uncertainty_score":0.22828168},"labels":[],"label_agreement":null},{"id":"W4386988816","doi":"10.1007/s10586-023-04135-2","title":"Cost-optimized cloud resource management for video streaming: ARIMA predictive approach","year":2023,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Image and Video Quality Assessment","field":"Computer Science","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":"Ontario Tech University","funders":"","keywords":"Autoregressive integrated moving average; Computer science; Cloud computing; Resource allocation; Resource (disambiguation); Real-time computing; Autoregressive model; Resource management (computing); Time series; Distributed computing; Machine learning; Computer network; Statistics; Operating system","score_opus":0.056776951360685286,"score_gpt":0.32123585804828514,"score_spread":0.26445890668759986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386988816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.079518944,0.00094902643,0.9138437,0.0008219966,0.00015556511,0.00008130699,0.00027311587,0.0006652142,0.0036911634],"genre_scores_gemma":[0.9465131,0.000491939,0.048542,0.00013708026,0.00015648965,0.00008722846,0.00021639465,0.000106346866,0.0037493894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935216,0.00019898439,0.000026903195,0.0001349598,0.0001527135,0.0001341922],"domain_scores_gemma":[0.9978331,0.0015182783,0.00016016682,0.00007777543,0.00033713525,0.00007369462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020826824,0.0009843877,0.0017391292,0.00089906296,0.00064274826,0.0014032539,0.0019797327,0.0012005378,0.0022155577],"category_scores_gemma":[0.0047495435,0.0007652088,0.00091424293,0.0012195684,0.0005512925,0.0012796657,0.00065402273,0.0019097005,0.00028405068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004317969,0.000038046714,0.00034571564,0.000016780168,0.000030002171,0.000022310714,0.000011603017,0.98798126,0.0002550058,0.0027308732,0.00047352727,0.008051691],"study_design_scores_gemma":[0.0000010192102,0.000002602977,0.000041451807,7.520074e-7,0.0000033859947,0.0000014249705,0.0000017498826,0.99950767,0.000026438633,0.00039039785,0.000021868225,0.0000012947064],"about_ca_topic_score_codex":0.048719913,"about_ca_topic_score_gemma":0.027584242,"teacher_disagreement_score":0.048719913,"about_ca_system_score_codex":0.001680824,"about_ca_system_score_gemma":0.0021009361,"threshold_uncertainty_score":0.09687269},"labels":[],"label_agreement":null},{"id":"W4390112344","doi":"10.1007/s10586-023-04205-5","title":"An ensemble clustering approach for modeling hidden categorization perspectives for cloud workloads","year":2023,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Cluster analysis; Cloud computing; Data mining; Categorization; Workload; Conceptual clustering; Data stream clustering; Fuzzy clustering; Bottleneck; Machine learning; Artificial intelligence; CURE data clustering algorithm","score_opus":0.03532039395177234,"score_gpt":0.2766147355894165,"score_spread":0.24129434163764418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390112344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05948757,0.00041364698,0.9378186,0.0002400318,0.00007344655,0.000057758527,0.00041855406,0.00056186627,0.0009285394],"genre_scores_gemma":[0.7551201,0.00042580522,0.23908246,0.00013657946,0.00015575212,0.0001693129,0.001979642,0.00017426965,0.0027561344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992483,0.00023993176,0.000042823904,0.0001990106,0.0001547257,0.00011508067],"domain_scores_gemma":[0.99814093,0.0010333308,0.00012249926,0.00023086277,0.00039494818,0.00007739094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016359814,0.000770426,0.0008409959,0.0017120691,0.00078248564,0.0012251099,0.0014408956,0.0010261582,0.0010939228],"category_scores_gemma":[0.003803161,0.00040040282,0.0015642466,0.0018056564,0.00027211363,0.0016580384,0.0008319875,0.0015103387,0.00043971447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041837659,0.0003596792,0.012243909,0.000111315145,0.0006649814,0.00020395608,0.0007374443,0.6710184,0.007821889,0.025718346,0.0058677066,0.27483395],"study_design_scores_gemma":[0.0000015259803,0.000011236855,0.00047295733,0.0000036985066,0.000012738029,0.000009042156,0.000016736352,0.9940282,0.0001830979,0.0050734896,0.00018195774,0.0000052729506],"about_ca_topic_score_codex":0.013871317,"about_ca_topic_score_gemma":0.021732816,"teacher_disagreement_score":0.013871317,"about_ca_system_score_codex":0.0009907089,"about_ca_system_score_gemma":0.00085200917,"threshold_uncertainty_score":0.027581155},"labels":[],"label_agreement":null},{"id":"W4390727139","doi":"10.1007/s10586-023-04225-1","title":"Convergence of the Harris hawks optimization algorithm and fuzzy system for cloud-based task scheduling enhancement","year":2024,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":3,"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":"Computer science; Fuzzy logic; CloudSim; Algorithm; Job shop scheduling; Mathematical optimization; Local optimum; Energy consumption; Scheduling (production processes); Cloud computing; Artificial intelligence; Schedule; Mathematics","score_opus":0.010101862025957404,"score_gpt":0.22877278919860108,"score_spread":0.2186709271726437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390727139","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076618604,0.00026358073,0.9173804,0.00014579651,0.00009818391,0.00004503949,0.000017634457,0.00012265559,0.0053080805],"genre_scores_gemma":[0.9121165,0.00011619256,0.085076466,0.000042334017,0.00003073381,0.0000402818,0.00002285566,0.00003756005,0.002516924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997789,0.000066123444,0.00000820299,0.0000359241,0.00007525524,0.000035656845],"domain_scores_gemma":[0.99952495,0.00024833594,0.000032081738,0.00002404003,0.0001448003,0.000025911597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000819627,0.00034488656,0.00053070777,0.00038051247,0.00045255458,0.00049671606,0.0006153985,0.00046773872,0.0016224224],"category_scores_gemma":[0.002108335,0.00020347566,0.0003906575,0.000371215,0.00051620044,0.0005588529,0.00058882707,0.0005455701,0.00012597807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011906747,0.00003822546,0.0005122901,0.000040938383,0.000025794066,0.000027538097,0.000057555688,0.94325423,0.004209967,0.014924512,0.00062045275,0.036169495],"study_design_scores_gemma":[0.0000026350444,0.00001000577,0.00005279773,0.0000010216597,0.0000014201097,0.0000027637927,0.000002916834,0.9987883,0.00020375704,0.00086933014,0.0000632731,0.0000016893614],"about_ca_topic_score_codex":0.008331668,"about_ca_topic_score_gemma":0.004164711,"teacher_disagreement_score":0.008331668,"about_ca_system_score_codex":0.0007429131,"about_ca_system_score_gemma":0.0011211326,"threshold_uncertainty_score":0.016566336},"labels":[],"label_agreement":null},{"id":"W4391538996","doi":"10.1007/s10586-023-04230-4","title":"Ovcosim: an open-source versatile computation offloading simulator for cloud and fog computing","year":2024,"lang":"en","type":"article","venue":"Cluster Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cloud computing; Open source; Computation; Fog computing; Computation offloading; Distributed computing; Operating system; Edge computing; Algorithm; Software","score_opus":0.030811059258252466,"score_gpt":0.31200434804295424,"score_spread":0.2811932887847018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391538996","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22229035,0.0020599184,0.5396701,0.0014821406,0.0021775072,0.0012269253,0.01536852,0.12299414,0.092730485],"genre_scores_gemma":[0.8037417,0.0008981169,0.15001191,0.0008214653,0.000147105,0.0008349858,0.0134121515,0.0114204865,0.0187121],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976,0.00004105503,0.000013152998,0.00003709487,0.000084687395,0.000063991334],"domain_scores_gemma":[0.9995521,0.00016530037,0.000022539378,0.000059718423,0.0001234469,0.00007690319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033659578,0.0010680488,0.0007688631,0.00041031154,0.00058983197,0.0008557416,0.002382534,0.0008324692,0.010095569],"category_scores_gemma":[0.0016708673,0.00048013957,0.00082807714,0.00053512957,0.00039192764,0.00096245646,0.001043555,0.0016876728,0.0014012514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007623553,0.00038963673,0.003764782,0.00057046255,0.00028396203,0.00025099205,0.00026588203,0.8243409,0.01195725,0.015398154,0.09431703,0.04769865],"study_design_scores_gemma":[0.000116756455,0.00004810021,0.00036226682,0.000016879947,0.000024485105,0.000027555981,0.000021310838,0.9772595,0.004134018,0.0028371438,0.015127914,0.000023994262],"about_ca_topic_score_codex":0.015192317,"about_ca_topic_score_gemma":0.016765796,"teacher_disagreement_score":0.015192317,"about_ca_system_score_codex":0.000849814,"about_ca_system_score_gemma":0.0019345953,"threshold_uncertainty_score":0.033773005},"labels":[],"label_agreement":null},{"id":"W4392230051","doi":"10.1007/s10586-024-04318-5","title":"Improving resource utilization and fault tolerance in large simulations via actors","year":2024,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":5,"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 Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Fault tolerance; Distributed computing; Computation; Resource (disambiguation); Scale (ratio); Supercomputer; Load balancing (electrical power); Intervention (counseling); Parallel computing; Algorithm","score_opus":0.019779325445825984,"score_gpt":0.2717865230535286,"score_spread":0.25200719760770257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392230051","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59712845,0.000350415,0.38905492,0.0007986084,0.00014701737,0.00008884417,0.000091366244,0.0016619255,0.010678492],"genre_scores_gemma":[0.9669777,0.00008433388,0.031859916,0.0000397736,0.000011139829,0.000038354476,0.000044967943,0.00012260751,0.0008211572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992324,0.00040059193,0.000029949562,0.00009496463,0.00012438599,0.00011776056],"domain_scores_gemma":[0.9964849,0.0023312552,0.00021769709,0.0004667199,0.00026595106,0.00023342753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001401838,0.00057041354,0.0007252455,0.0005022644,0.00073505257,0.0012666911,0.001277542,0.0008632167,0.0022490944],"category_scores_gemma":[0.008536122,0.0003926131,0.00036253012,0.0006989817,0.0009970289,0.0017944879,0.0013557514,0.0011101041,0.00025581784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003115676,0.00008202589,0.001687868,0.00003332877,0.0000230551,0.00004678894,0.000084758656,0.9741096,0.0035876124,0.00869942,0.00032502753,0.011008965],"study_design_scores_gemma":[0.000014819848,0.000023959628,0.00006934357,0.0000025479035,0.000004956246,0.0000041423978,0.000013771092,0.99541,0.0015262114,0.0027308506,0.00019624527,0.0000030911926],"about_ca_topic_score_codex":0.003132838,"about_ca_topic_score_gemma":0.0026599814,"teacher_disagreement_score":0.003132838,"about_ca_system_score_codex":0.0008860678,"about_ca_system_score_gemma":0.001159597,"threshold_uncertainty_score":0.007523954},"labels":[],"label_agreement":null},{"id":"W4392348357","doi":"10.1007/s10586-024-04317-6","title":"Improving the quality of real-time data transmission service in VANETS by balancing the load on road side units","year":2024,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","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":"University of Regina","funders":"","keywords":"Computer science; Computer network; Quality of service; Vehicular ad hoc network; Scalability; Network congestion; Network packet; Packet loss; Load balancing (electrical power); Wireless ad hoc network; Wireless; Telecommunications","score_opus":0.021932957093232005,"score_gpt":0.25944999305400707,"score_spread":0.23751703596077506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392348357","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35437182,0.0030855767,0.62504965,0.0012835077,0.00050837256,0.00012152,0.00029931086,0.0018564991,0.013423768],"genre_scores_gemma":[0.98714256,0.00037752397,0.010557628,0.000057039626,0.000055393208,0.000012346018,0.00011942009,0.00006109994,0.0016170293],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987469,0.00029362328,0.00005390018,0.00018612001,0.00041988216,0.00029948732],"domain_scores_gemma":[0.9982241,0.000473778,0.00019795694,0.00024363732,0.0007457797,0.00011475962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001016704,0.00075724314,0.00068132207,0.0006725576,0.00081011554,0.0019978979,0.0016898183,0.00070896605,0.0021246609],"category_scores_gemma":[0.004135433,0.00031641216,0.000216202,0.0017351734,0.00055920595,0.0021895876,0.0011763453,0.0007227352,0.00050951896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075327494,0.00024378297,0.00629196,0.00026655762,0.0001232015,0.00013001534,0.00021529658,0.76189315,0.03201516,0.011736665,0.0057401937,0.1805908],"study_design_scores_gemma":[0.000012594863,0.0001599659,0.0013226167,0.000009901918,0.000027239457,0.00005832548,0.00018181567,0.9847061,0.006914598,0.0041464767,0.002443284,0.00001708835],"about_ca_topic_score_codex":0.0066471295,"about_ca_topic_score_gemma":0.009034809,"teacher_disagreement_score":0.0066471295,"about_ca_system_score_codex":0.0009885931,"about_ca_system_score_gemma":0.0010653218,"threshold_uncertainty_score":0.013216913},"labels":[],"label_agreement":null},{"id":"W4396214135","doi":"10.1007/s10586-024-04504-5","title":"Forest fire monitoring system supported by unmanned aerial vehicles and edge computing: a performance evaluation using petri nets","year":2024,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nortel (Canada)","funders":"","keywords":"Computer science; Petri net; Firefighting; Drone; Fire detection; Key (lock); Enhanced Data Rates for GSM Evolution; Edge computing; Variety (cybernetics); Systems engineering; Real-time computing; Distributed computing; Computer security; Artificial intelligence; Architectural engineering","score_opus":0.02002648129740907,"score_gpt":0.2476591701216259,"score_spread":0.22763268882421683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396214135","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9441174,0.00017439989,0.050191723,0.00006895317,0.000063551306,0.0001271582,0.00020782459,0.0019366974,0.0031122107],"genre_scores_gemma":[0.9928699,0.000043330903,0.0063280202,0.000011198113,0.0000037521752,0.00001699614,0.00009977752,0.000013225202,0.0006138278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996902,0.000062463136,0.000020245036,0.00006837921,0.000088269626,0.0000704931],"domain_scores_gemma":[0.99925333,0.00033220154,0.00006463943,0.00006123581,0.00019741598,0.000091120746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005721661,0.0004996698,0.00034983526,0.0004926766,0.0004062827,0.0005245626,0.0006437926,0.00029892928,0.0013108411],"category_scores_gemma":[0.0009448059,0.00012052511,0.00022821515,0.00031408385,0.00020501687,0.00048358418,0.0002562323,0.00024895585,0.00015399467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0075192633,0.0012290163,0.028166668,0.00042972303,0.00026577595,0.00090339896,0.00018106033,0.6775572,0.09740335,0.002685714,0.0026220114,0.18103689],"study_design_scores_gemma":[0.000058840837,0.0005415022,0.00535375,0.0000061202963,0.000073762414,0.000067225075,0.000059077836,0.96936786,0.023733057,0.00027082046,0.00045415654,0.000013918025],"about_ca_topic_score_codex":0.012394975,"about_ca_topic_score_gemma":0.008114866,"teacher_disagreement_score":0.012394975,"about_ca_system_score_codex":0.0006843014,"about_ca_system_score_gemma":0.0007699768,"threshold_uncertainty_score":0.024645627},"labels":[],"label_agreement":null},{"id":"W4399047587","doi":"10.1007/s10586-024-04539-8","title":"Improvement in task allocation for VM and reduction of Makespan in IaaS model for cloud computing","year":2024,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"New York Institute of Technology","funders":"","keywords":"Computer science; Cloud computing; Reduction (mathematics); Task (project management); Job shop scheduling; Distributed computing; Operating system; Schedule","score_opus":0.01944828629269905,"score_gpt":0.26467066563322206,"score_spread":0.24522237934052302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399047587","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6335573,0.0018233678,0.32614097,0.0014090843,0.00048777455,0.0002538159,0.0004114499,0.0011575246,0.0347588],"genre_scores_gemma":[0.9895839,0.00013402583,0.009055924,0.00003023438,0.000016384702,0.00002327057,0.00005489488,0.000020435573,0.0010808088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925333,0.00019867688,0.00002639206,0.00012059479,0.00014421144,0.00025663545],"domain_scores_gemma":[0.99937147,0.00016493339,0.00008097965,0.00009253116,0.00019266564,0.00009749565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066826434,0.00056203053,0.00055825105,0.0003613826,0.00066266616,0.0012995866,0.001044679,0.00046325556,0.002410902],"category_scores_gemma":[0.0015121864,0.00014304214,0.00054771337,0.00066377735,0.00031721048,0.0010513804,0.00044107146,0.00059386605,0.0001877797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006170025,0.00030110416,0.002874961,0.00019266199,0.0000799427,0.00018478607,0.00009703909,0.9059128,0.015432139,0.013572999,0.0035461942,0.0571884],"study_design_scores_gemma":[0.0000115520415,0.00020856882,0.0014141883,0.000012094202,0.000028331953,0.00008269548,0.00009589294,0.99227774,0.0016726856,0.0031268185,0.0010611182,0.000008237775],"about_ca_topic_score_codex":0.0071825925,"about_ca_topic_score_gemma":0.0066871285,"teacher_disagreement_score":0.0071825925,"about_ca_system_score_codex":0.0010986757,"about_ca_system_score_gemma":0.0018854664,"threshold_uncertainty_score":0.014281571},"labels":[],"label_agreement":null},{"id":"W4399211318","doi":"10.1007/s10586-024-04575-4","title":"Correction: Improving the quality of real-time data transmission service in VANETS by balancing the load on road side units","year":2024,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Vehicular Ad Hoc Networks (VANETs)","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 Regina","funders":"","keywords":"Computer science; Service quality; Quality of service; Transmission (telecommunications); Quality (philosophy); Service (business); Computer network; Telecommunications","score_opus":0.020301277570784423,"score_gpt":0.2571193320094003,"score_spread":0.23681805443861587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399211318","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002018282,0.0012448002,0.0011671794,0.06283094,0.9307178,0.000028137489,0.0016675299,0.00061003765,0.0015317708],"genre_scores_gemma":[0.030157125,0.014210488,0.008515196,0.17331712,0.5916324,0.00037918036,0.0035244795,0.0023378578,0.17592607],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99357975,0.0010533566,0.0011340496,0.0011432536,0.0025030072,0.0005866378],"domain_scores_gemma":[0.9291564,0.0128586525,0.0037229091,0.004019172,0.047812507,0.0024303799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042208857,0.0029590602,0.0025451982,0.00441174,0.0029848842,0.004454125,0.0043196524,0.0098346975,0.03901365],"category_scores_gemma":[0.09123618,0.0014267742,0.0021753914,0.0038985407,0.0034255143,0.0029424713,0.0023552768,0.014907239,0.032551687],"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":[0.00006084423,0.000007371324,0.00013632276,0.00031989432,0.00003164119,0.00021291984,0.000058859598,0.00013821149,0.000082941246,0.00078476244,0.9927867,0.005379521],"study_design_scores_gemma":[0.000098677556,0.000046560308,0.0017126687,0.0006225472,0.00010288946,0.0007210429,0.00019643344,0.0009168907,0.0008065692,0.001827926,0.99285203,0.00009574753],"about_ca_topic_score_codex":0.03381781,"about_ca_topic_score_gemma":0.022396157,"teacher_disagreement_score":0.03901365,"about_ca_system_score_codex":0.004179544,"about_ca_system_score_gemma":0.0069935853,"threshold_uncertainty_score":0.13051367},"labels":[],"label_agreement":null},{"id":"W4403536375","doi":"10.1007/s10586-024-04693-z","title":"A distributed algorithm with dynamic event-triggered mechanism for resource allocation problems","year":2024,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":3,"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 Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Distributed computing; Mechanism (biology); Resource allocation; Algorithm; Event (particle physics); Resource (disambiguation); Computer network","score_opus":0.010314172859187436,"score_gpt":0.24470259354462703,"score_spread":0.2343884206854396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403536375","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062775286,0.00008126054,0.9920482,0.00012341668,0.00005768481,0.000054641037,0.000015679365,0.00027085686,0.0010706682],"genre_scores_gemma":[0.32129484,0.00014854435,0.67503595,0.00016239307,0.00007122717,0.0004463057,0.00010563884,0.000104367995,0.0026307816],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922514,0.00026614504,0.000051173,0.00016054053,0.00020450508,0.00009247605],"domain_scores_gemma":[0.99891925,0.00065237016,0.000061573555,0.00011153358,0.00016742552,0.00008786155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018291736,0.0007252843,0.0012796518,0.00060961425,0.0007694613,0.001210502,0.0025839317,0.0017689269,0.0028957755],"category_scores_gemma":[0.003501177,0.00045215982,0.00056673685,0.0008845815,0.0009248749,0.0012796908,0.001920556,0.0012495153,0.00040036734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005969382,0.00025662142,0.00041800988,0.00014319127,0.000102488106,0.00011182973,0.0001123426,0.7780084,0.0061863316,0.063515514,0.004191774,0.14635655],"study_design_scores_gemma":[0.00010653508,0.000046970865,0.000031480482,0.0000033256974,0.00000912667,0.000018262752,0.000005847746,0.99090683,0.0004343505,0.007816143,0.0006145765,0.000006562156],"about_ca_topic_score_codex":0.0017957363,"about_ca_topic_score_gemma":0.0015751468,"teacher_disagreement_score":0.0028957755,"about_ca_system_score_codex":0.00078514055,"about_ca_system_score_gemma":0.0017196253,"threshold_uncertainty_score":0.0096873045},"labels":[],"label_agreement":null},{"id":"W4403767616","doi":"10.1007/s10586-024-04687-x","title":"An auto-scaling multi-tiered node-organizing blockchain design model for the internet of things","year":2024,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":3,"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 du Québec à Montréal","funders":"","keywords":"Blockchain; Computer science; Node (physics); Internet of Things; The Internet; Scaling; Computer network; World Wide Web; Distributed computing; Theoretical computer science; Computer security","score_opus":0.03532494149082179,"score_gpt":0.27876315994559775,"score_spread":0.24343821845477595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403767616","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0597884,0.00039762515,0.91957027,0.000548796,0.000092833216,0.00016653391,0.00013991367,0.00022844823,0.019067215],"genre_scores_gemma":[0.9394775,0.0002963204,0.05305331,0.00007944428,0.000023476923,0.00013634344,0.000089909765,0.00003293268,0.0068106255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995204,0.00017009012,0.000022503138,0.00007103007,0.00015710795,0.000058764366],"domain_scores_gemma":[0.99946016,0.00016880731,0.00005249348,0.00008277924,0.00017787793,0.000057824447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079538045,0.00030857156,0.00057900464,0.00028256752,0.00065543427,0.0011600461,0.001182197,0.0008885674,0.004638111],"category_scores_gemma":[0.0012752102,0.00025960265,0.0003982702,0.00049664505,0.0005795559,0.0013119514,0.00094965135,0.0006329218,0.00041538486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010703428,0.000065060325,0.00041947118,0.000098142744,0.00003129719,0.00016899232,0.00009566679,0.8937355,0.0062185912,0.07448545,0.0012491655,0.023325557],"study_design_scores_gemma":[0.0000105864865,0.000038288857,0.000044325672,0.0000062058953,0.000007687164,0.000025590838,0.000011376092,0.9854512,0.00042316777,0.012987614,0.0009889381,0.0000051227867],"about_ca_topic_score_codex":0.0024210624,"about_ca_topic_score_gemma":0.00407789,"teacher_disagreement_score":0.004638111,"about_ca_system_score_codex":0.000754373,"about_ca_system_score_gemma":0.001286689,"threshold_uncertainty_score":0.015515983},"labels":[],"label_agreement":null},{"id":"W4404024662","doi":"10.1007/s10586-024-04802-y","title":"Optimizing support vector regression using grey wolf optimizer for enhancing energy efficiency and building prototype architecture","year":2024,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Building Energy and Comfort Optimization","field":"Engineering","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":"McGill University","funders":"","keywords":"Computer science; Architecture; Regression; Efficient energy use; Support vector machine; Energy (signal processing); Artificial intelligence; Data mining; Statistics; Electrical engineering","score_opus":0.010450131055919467,"score_gpt":0.2430712678616497,"score_spread":0.23262113680573024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404024662","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21140483,0.0002208313,0.78063256,0.00014438562,0.000040618736,0.000036574875,0.0000651989,0.0014269374,0.006028056],"genre_scores_gemma":[0.913711,0.000041294254,0.08375762,0.000022886294,0.000006718145,0.000036875168,0.00007195722,0.000095109346,0.002256582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984646,0.000035677964,0.0000054881575,0.0000281465,0.000057910715,0.000026313977],"domain_scores_gemma":[0.999803,0.00007526021,0.00001802401,0.000018687657,0.000074818454,0.000010226553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034569768,0.00051865214,0.00048850744,0.00026902786,0.00020971047,0.00046427565,0.00053424836,0.0004999774,0.0017769734],"category_scores_gemma":[0.0008111274,0.00021354093,0.00035876466,0.00034863118,0.00018910065,0.0003694644,0.00027471175,0.0005007622,0.00032053137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069826616,0.00004675391,0.00053620676,0.000031738873,0.000017107586,0.000034473975,0.000015525098,0.94286406,0.009944069,0.0011047327,0.00064002664,0.0446955],"study_design_scores_gemma":[0.0000013548337,0.000012245115,0.00006724949,5.8026444e-7,0.0000016535066,0.000001945422,0.0000018011782,0.9987833,0.0009519677,0.00011238115,0.00006478259,7.428068e-7],"about_ca_topic_score_codex":0.0038734751,"about_ca_topic_score_gemma":0.0037684792,"teacher_disagreement_score":0.0038734751,"about_ca_system_score_codex":0.00037887294,"about_ca_system_score_gemma":0.00049214833,"threshold_uncertainty_score":0.007701814},"labels":[],"label_agreement":null},{"id":"W4405350980","doi":"10.1007/s10586-024-04878-6","title":"Metaheuristic-based task scheduling for latency-sensitive IoT applications in edge computing","year":2024,"lang":"en","type":"article","venue":"Cluster Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":26,"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 Alberta","funders":"","keywords":"Computer science; Cloud computing; Distributed computing; Scheduling (production processes); Job shop scheduling; Edge computing; Particle swarm optimization; Energy consumption; Metaheuristic; Latency (audio); Internet of Things; Real-time computing; Mathematical optimization; Computer network; Embedded system; Algorithm; Operating system","score_opus":0.020997561866417703,"score_gpt":0.2827724796412178,"score_spread":0.2617749177748001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405350980","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13788597,0.0012188143,0.8516324,0.0005314315,0.00030732105,0.00014000648,0.00013231473,0.0003602128,0.0077915904],"genre_scores_gemma":[0.87086636,0.00032275243,0.12601657,0.00018130825,0.00006777644,0.00011372804,0.000117916316,0.00007990344,0.0022336172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996501,0.00011657881,0.000014279673,0.000050129693,0.00007287181,0.00009590847],"domain_scores_gemma":[0.99950826,0.0002610902,0.000055734552,0.000025497508,0.0001019314,0.000047500427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089136075,0.0007039889,0.0009189173,0.00070314977,0.00058507745,0.00085100956,0.00115158,0.0008007937,0.0012213145],"category_scores_gemma":[0.0014628043,0.0003030436,0.0008811653,0.00086805335,0.00034560295,0.0005758788,0.000547014,0.0008734526,0.000119820725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088020075,0.000068307214,0.00024945103,0.000036644393,0.000044728367,0.00002424059,0.000018111661,0.97956413,0.0017153625,0.0024678912,0.000673178,0.01504984],"study_design_scores_gemma":[0.000006013467,0.000018464438,0.000047117468,0.000001941294,0.000005489854,0.0000038021305,0.00000792633,0.99880207,0.00016593814,0.0008026965,0.00013673502,0.0000017340434],"about_ca_topic_score_codex":0.007832369,"about_ca_topic_score_gemma":0.0073544024,"teacher_disagreement_score":0.007832369,"about_ca_system_score_codex":0.001026372,"about_ca_system_score_gemma":0.0017984376,"threshold_uncertainty_score":0.015573561},"labels":[],"label_agreement":null},{"id":"W4405662613","doi":"10.1007/s10586-024-04831-7","title":"Time delay-based routing protocol using genetic algorithm in vehicular Ad Hoc networks","year":2024,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Computer network; Routing protocol; Node (physics); Scalability; Distributed computing; Wireless ad hoc network; Routing (electronic design automation)","score_opus":0.008539897106455684,"score_gpt":0.23615969378482013,"score_spread":0.22761979667836443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405662613","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051267788,0.00093241007,0.9429779,0.0002576452,0.0002028804,0.00008982712,0.000051182436,0.00060097117,0.003619384],"genre_scores_gemma":[0.813616,0.00071803504,0.18201542,0.0000822588,0.000048088557,0.000119925106,0.00012538114,0.000047489797,0.0032273943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966216,0.00010542024,0.000016284086,0.00006015119,0.000115344934,0.000040612587],"domain_scores_gemma":[0.99962306,0.00016162932,0.00003430634,0.000028353563,0.00013577935,0.000016856478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060428935,0.00037161438,0.0005867908,0.0007507871,0.00058176107,0.00075512176,0.001134363,0.0006233577,0.0005061822],"category_scores_gemma":[0.0011011972,0.0001884516,0.00035736238,0.001147817,0.0004717251,0.0007193184,0.00041772236,0.0005786855,0.00010269169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009112029,0.00008509219,0.00075873244,0.000052523763,0.00006366046,0.000061517756,0.000072559145,0.8873101,0.005253614,0.013874402,0.0012378497,0.09113892],"study_design_scores_gemma":[0.000014112186,0.00005658063,0.00018265896,0.0000054066,0.00001647649,0.00003061485,0.000016627553,0.9943821,0.0013382836,0.0032203367,0.00072713423,0.000009784665],"about_ca_topic_score_codex":0.01013297,"about_ca_topic_score_gemma":0.0057184706,"teacher_disagreement_score":0.01013297,"about_ca_system_score_codex":0.0009452002,"about_ca_system_score_gemma":0.0012879383,"threshold_uncertainty_score":0.02014798},"labels":[],"label_agreement":null},{"id":"W4406629874","doi":"10.1007/s10586-024-04871-z","title":"Multilevel learning for enhanced traffic congestion prediction using anomaly detection and ensemble learning","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Ensemble learning; Anomaly detection; Machine learning; Artificial intelligence; Anomaly (physics); Learning network","score_opus":0.010644909819243454,"score_gpt":0.23596112725073531,"score_spread":0.22531621743149186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406629874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09755465,0.00021183051,0.89969194,0.00015787508,0.00009187429,0.000029527997,0.0001618795,0.0011120136,0.0009884589],"genre_scores_gemma":[0.8893887,0.000095883035,0.10902303,0.000052887095,0.000064965505,0.00004896442,0.00029080056,0.000057842102,0.0009768698],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953175,0.000104626604,0.000024697047,0.00011862714,0.00014697935,0.000073308554],"domain_scores_gemma":[0.99860805,0.0006162685,0.000097812444,0.0002230835,0.00038813805,0.000066706234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084646535,0.00049154006,0.0011926185,0.0008196388,0.00045756254,0.0006790435,0.0011714993,0.0005008054,0.0012562518],"category_scores_gemma":[0.0027692663,0.0002927149,0.00071543903,0.0009419538,0.00024073581,0.0013289681,0.0010628863,0.0015547836,0.0002853234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002709776,0.00043253272,0.010157455,0.000048447393,0.0002652523,0.00007454849,0.000092995884,0.56611484,0.008940833,0.0047685257,0.003297813,0.40553573],"study_design_scores_gemma":[0.0000012494226,0.000013013137,0.00029428108,8.117023e-7,0.000005084328,0.0000028891518,0.0000027034073,0.99860066,0.00034527056,0.0006737831,0.000058185557,0.0000020253494],"about_ca_topic_score_codex":0.006307944,"about_ca_topic_score_gemma":0.008706317,"teacher_disagreement_score":0.006307944,"about_ca_system_score_codex":0.000460402,"about_ca_system_score_gemma":0.0006090231,"threshold_uncertainty_score":0.012542427},"labels":[],"label_agreement":null},{"id":"W4406679155","doi":"10.1007/s10586-024-04885-7","title":"Qaas: hybrid cryptocurrency wallet-as-a-service based on Quantum RNG","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","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 Sherbrooke; École de Technologie Supérieure","funders":"","keywords":"Computer science; Cryptocurrency; Service (business); Quantum; Computer security; Computer network; Business","score_opus":0.009841690002237493,"score_gpt":0.2572373635055686,"score_spread":0.24739567350333108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406679155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15748857,0.0010395128,0.7783277,0.0010667914,0.0010299779,0.0009609875,0.0006885125,0.017968249,0.041429747],"genre_scores_gemma":[0.9303967,0.00016638296,0.055367243,0.000208224,0.00007827569,0.00016137528,0.00041203218,0.00019958284,0.013010179],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992386,0.00018723181,0.00003609819,0.00011209846,0.00022539735,0.00020062574],"domain_scores_gemma":[0.9994609,0.00006974738,0.000033018172,0.00020677433,0.00012017698,0.000109405366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057916384,0.00054716313,0.0009965844,0.0006950615,0.0011931714,0.0019399981,0.0015657552,0.0010719039,0.014411667],"category_scores_gemma":[0.00097613776,0.000246001,0.0003931779,0.0008417824,0.0009832962,0.0020896262,0.0020119243,0.0011899677,0.0029592658],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0059105516,0.0011346202,0.0024657592,0.0006498727,0.00032566758,0.0014503628,0.00042398006,0.100954495,0.110093616,0.43317956,0.050899178,0.29251233],"study_design_scores_gemma":[0.00038099958,0.0006355491,0.0004827976,0.000042534168,0.000071490686,0.00036711775,0.000108266504,0.86113685,0.039741546,0.0671617,0.029770423,0.00010065518],"about_ca_topic_score_codex":0.0019222834,"about_ca_topic_score_gemma":0.0025251338,"teacher_disagreement_score":0.014411667,"about_ca_system_score_codex":0.00066846726,"about_ca_system_score_gemma":0.0015314486,"threshold_uncertainty_score":0.048211813},"labels":[],"label_agreement":null},{"id":"W4407910880","doi":"10.1007/s10586-024-05061-7","title":"A robust federated biased learning algorithm for time series forecasting","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":3,"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 Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Series (stratigraphy); Algorithm; Time series; Artificial intelligence; Machine learning","score_opus":0.0170275305282568,"score_gpt":0.21454584863847265,"score_spread":0.19751831811021586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407910880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010613055,0.00033661412,0.98760927,0.00010627163,0.00008546106,0.000025421534,0.000053367625,0.0006953978,0.0004751846],"genre_scores_gemma":[0.37478822,0.00032293148,0.6196,0.00030840296,0.00025280204,0.0001770761,0.0005459782,0.00021049404,0.0037939837],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874914,0.00035889237,0.000081244725,0.00026942394,0.00040764658,0.00013367798],"domain_scores_gemma":[0.99698323,0.0013556221,0.00019516889,0.00041809992,0.0009550913,0.000092835406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031569295,0.0008071578,0.0019511479,0.001129911,0.00072571664,0.0012580341,0.0025747837,0.0017685917,0.002013543],"category_scores_gemma":[0.009658506,0.0005340251,0.00085172505,0.0015393953,0.0006749446,0.0015740028,0.0015949368,0.0017841035,0.00087992684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045040602,0.00015980778,0.0015518933,0.00006148232,0.00016709534,0.00005997137,0.000042479376,0.6189609,0.0039553395,0.008988662,0.003909942,0.36169198],"study_design_scores_gemma":[0.000007318421,0.000012814375,0.0000666441,0.0000024967155,0.0000052521423,0.000008215322,0.000001333475,0.99774736,0.00045422872,0.0015029463,0.00018844564,0.0000028902643],"about_ca_topic_score_codex":0.00788043,"about_ca_topic_score_gemma":0.0054359,"teacher_disagreement_score":0.00788043,"about_ca_system_score_codex":0.0010460169,"about_ca_system_score_gemma":0.0020339834,"threshold_uncertainty_score":0.016695619},"labels":[],"label_agreement":null},{"id":"W4407937593","doi":"10.1007/s10586-024-04933-2","title":"Forecasting workload in cloud computing: towards uncertainty-aware predictions and transfer learning","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Horizon 2020 Framework Programme; Science Foundation Ireland","keywords":"Computer science; Workload; Cloud computing; Transfer of learning; Transfer (computing); Distributed computing; Artificial intelligence; Operating system","score_opus":0.013282937208747455,"score_gpt":0.23042878469417238,"score_spread":0.21714584748542493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407937593","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21151002,0.0005101121,0.7834381,0.0012815358,0.00005804187,0.000066457054,0.0001784562,0.00078740594,0.0021698172],"genre_scores_gemma":[0.97566766,0.0001324815,0.023221567,0.00011632839,0.000052277832,0.000035769815,0.00014066114,0.000032378357,0.00060096633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995005,0.0001744317,0.000024666806,0.000110482986,0.00011509742,0.00007476736],"domain_scores_gemma":[0.9971666,0.0018737238,0.00024455276,0.00016334814,0.0004366925,0.000115151604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001588101,0.00088806346,0.00073796004,0.00079569017,0.00034495347,0.0010272518,0.0012359045,0.00088928104,0.0007499469],"category_scores_gemma":[0.007308792,0.00042571776,0.000424158,0.00079465006,0.00064115174,0.0015598016,0.0011161264,0.0016016306,0.00020484549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030516243,0.000035694018,0.0012311648,0.000009283419,0.000012091627,0.000015396861,0.000015834563,0.98390204,0.00026533825,0.0011036021,0.00026912123,0.013109855],"study_design_scores_gemma":[3.9854643e-7,0.0000017509974,0.000045912024,7.5150973e-7,4.5634866e-7,5.158312e-7,0.0000012479978,0.9992101,0.000045953155,0.00067885197,0.000013397423,6.222367e-7],"about_ca_topic_score_codex":0.0144143365,"about_ca_topic_score_gemma":0.0067231036,"teacher_disagreement_score":0.0144143365,"about_ca_system_score_codex":0.0012814184,"about_ca_system_score_gemma":0.0010744587,"threshold_uncertainty_score":0.028660893},"labels":[],"label_agreement":null},{"id":"W4409885889","doi":"10.1007/s10586-024-04981-8","title":"SPINEX-clustering: similarity-based predictions with explainable neighbors exploration for clustering problems","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Clemson University","keywords":"Computer science; Cluster analysis; Similarity (geometry); Data mining; Artificial intelligence; Information retrieval; Image (mathematics)","score_opus":0.03347734993308603,"score_gpt":0.3032434677044574,"score_spread":0.2697661177713714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409885889","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03152567,0.00044275913,0.9635216,0.0004908072,0.00005986396,0.000164699,0.00021176145,0.0017817143,0.0018010455],"genre_scores_gemma":[0.36246505,0.0003117215,0.63262105,0.00030975437,0.00011193873,0.00042544198,0.0011011331,0.00037653357,0.0022773582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988211,0.00045984334,0.000050756127,0.0002474301,0.00034041717,0.00008046443],"domain_scores_gemma":[0.99678934,0.001997332,0.00023801747,0.00041385638,0.00043998647,0.00012145838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002193979,0.0013203187,0.0014199222,0.001452127,0.0011358811,0.0014871625,0.002366071,0.0017134487,0.0027514112],"category_scores_gemma":[0.009187346,0.00052063115,0.00097387814,0.0013754135,0.0012251723,0.0022624268,0.002744128,0.0021314197,0.0007902537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018941659,0.00013682542,0.002662188,0.0001861404,0.000106307496,0.00008883434,0.00025794774,0.82323843,0.0015144579,0.03314367,0.0060281977,0.1324475],"study_design_scores_gemma":[0.000014412129,0.000032557436,0.00010378079,0.00000817487,0.000005153153,0.000016915805,0.000024260253,0.98351824,0.0004650804,0.015201042,0.00060483126,0.0000056141307],"about_ca_topic_score_codex":0.0048649744,"about_ca_topic_score_gemma":0.0057440503,"teacher_disagreement_score":0.0048649744,"about_ca_system_score_codex":0.0012185867,"about_ca_system_score_gemma":0.0022880677,"threshold_uncertainty_score":0.011602998},"labels":[],"label_agreement":null},{"id":"W4411341629","doi":"10.1007/s10586-024-05088-w","title":"Aqua garbage collector: utilizing AI and IoT for efficient underwater garbage classification","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Garbage; Internet of Things; Garbage collection; Underwater; Artificial intelligence; Embedded system; Real-time computing; Programming language; Geology","score_opus":0.03608731604708054,"score_gpt":0.2960237010436957,"score_spread":0.25993638499661514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411341629","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18361947,0.0020038243,0.74666774,0.0007277154,0.0006240129,0.0003476631,0.0020219416,0.04474325,0.0192444],"genre_scores_gemma":[0.6847927,0.0007741555,0.2987098,0.00040451012,0.000113235,0.00017337837,0.002014095,0.00052278506,0.012495357],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998134,0.00001553491,0.0000118667185,0.00005582653,0.00007216908,0.000031102532],"domain_scores_gemma":[0.9997588,0.00005473107,0.000023531395,0.000045583747,0.00008774514,0.00002968438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025246677,0.0007408967,0.000557504,0.0011571881,0.0005862447,0.00091335055,0.0010617726,0.00041529103,0.002588405],"category_scores_gemma":[0.00045239239,0.00024208183,0.0003726679,0.0013151653,0.00025511635,0.0010561706,0.0008255324,0.00042390413,0.0009006895],"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":[0.0011290024,0.00043871402,0.015112315,0.00047067055,0.00020500593,0.00044623943,0.00037938327,0.032940533,0.15625481,0.0040236614,0.025141886,0.76345783],"study_design_scores_gemma":[0.00004160085,0.00024899098,0.007688338,0.000052309344,0.000108206834,0.00025534845,0.00039434948,0.8699095,0.09555952,0.004988057,0.020677522,0.00007622355],"about_ca_topic_score_codex":0.005880054,"about_ca_topic_score_gemma":0.012494324,"teacher_disagreement_score":0.005880054,"about_ca_system_score_codex":0.0004842027,"about_ca_system_score_gemma":0.000859914,"threshold_uncertainty_score":0.0116916895},"labels":[],"label_agreement":null},{"id":"W4411343010","doi":"10.1007/s10586-024-05093-z","title":"Seraph: a stochastic operational approach for the deployment of multi-antenna UAV-assisted networks","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"UAV Applications and Optimization","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":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Software deployment; Antenna (radio); Telecommunications; Real-time computing; Computer network; Software engineering","score_opus":0.017658255415634073,"score_gpt":0.2440005425451968,"score_spread":0.22634228712956272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411343010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050137076,0.000120013356,0.9917401,0.00020122447,0.00006405781,0.000049455917,0.00005451535,0.00017072374,0.002586188],"genre_scores_gemma":[0.67338073,0.00054329244,0.3177387,0.00026757858,0.00019446411,0.00031708195,0.00021536283,0.0002968663,0.0070458124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999268,0.000351531,0.000017022314,0.00007718518,0.0001847722,0.000101543],"domain_scores_gemma":[0.99877757,0.0007849036,0.00009470458,0.0000756433,0.00018133258,0.00008594125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016631752,0.00088590494,0.0008211914,0.00055492745,0.0005324208,0.00086200837,0.0014353209,0.0008787417,0.0022762832],"category_scores_gemma":[0.0044499575,0.0005142667,0.00063070736,0.00055120524,0.0007143663,0.0008112132,0.0016733436,0.0010838968,0.00028205855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028473749,0.000014723451,0.0001991157,0.000033504428,0.000016774044,0.00003570642,0.000023297893,0.95869815,0.0006421657,0.028699758,0.0016587445,0.009949554],"study_design_scores_gemma":[0.0000023255407,0.0000054351895,0.000020898373,0.000001740909,0.0000012682342,0.0000047219046,0.0000033415558,0.99675107,0.000060381204,0.0028517765,0.00029569445,0.0000013051086],"about_ca_topic_score_codex":0.0059829047,"about_ca_topic_score_gemma":0.0076273326,"teacher_disagreement_score":0.0059829047,"about_ca_system_score_codex":0.0012642653,"about_ca_system_score_gemma":0.0016291135,"threshold_uncertainty_score":0.011896133},"labels":[],"label_agreement":null},{"id":"W4413846978","doi":"10.1007/s10586-025-05213-3","title":"Energy service selection method based on blockchain smart contract and improved polar bear algorithm","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Blockchain; Smart contract; Selection (genetic algorithm); Algorithm; Service (business); Polar; Energy (signal processing); Computer security; Artificial intelligence; Statistics","score_opus":0.005890502163549605,"score_gpt":0.24432712386969435,"score_spread":0.23843662170614474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413846978","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056660015,0.00024547853,0.9336555,0.00029659228,0.00011782424,0.00017535362,0.00009443186,0.00034031138,0.0084146345],"genre_scores_gemma":[0.8216144,0.00026289123,0.16526318,0.00010864252,0.00009136707,0.00020693649,0.00027459534,0.000085907246,0.012092102],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993155,0.00014010887,0.000037483478,0.000110449146,0.00030290164,0.000093434326],"domain_scores_gemma":[0.99944085,0.00022425051,0.00004185803,0.00007002264,0.00016582603,0.000057178455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074683415,0.00039588622,0.0011153771,0.00089090137,0.0010926479,0.0010553221,0.0012416082,0.00063801557,0.006512449],"category_scores_gemma":[0.0015026955,0.00022671111,0.00045509398,0.0013035634,0.00052638375,0.0015688519,0.0010253805,0.0006891234,0.0006306875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008151613,0.00032325485,0.002702492,0.00023503548,0.00009321022,0.00029891485,0.00025538472,0.49438196,0.01656795,0.12245198,0.0073976456,0.35447705],"study_design_scores_gemma":[0.000048977014,0.000033685395,0.00016516994,0.0000052695536,0.000011021651,0.000055746794,0.000030790765,0.98523337,0.0018974013,0.011406539,0.0011021956,0.000009802654],"about_ca_topic_score_codex":0.0035227537,"about_ca_topic_score_gemma":0.0033906826,"teacher_disagreement_score":0.006512449,"about_ca_system_score_codex":0.0006787669,"about_ca_system_score_gemma":0.0019363142,"threshold_uncertainty_score":0.021786332},"labels":[],"label_agreement":null},{"id":"W4413943652","doi":"10.1007/s10586-025-05326-9","title":"L-xaids: A LIME-based eXplainable AI framework for intrusion detection systems","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Mitacs","keywords":"Computer science; Intrusion detection system; Lime; Artificial intelligence; Geology","score_opus":0.010833975656361544,"score_gpt":0.28906552911999034,"score_spread":0.2782315534636288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413943652","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021878236,0.0003420932,0.9904392,0.0013672399,0.000045890705,0.00019384643,0.000751124,0.0025793496,0.0020933675],"genre_scores_gemma":[0.12508537,0.0005325603,0.86809933,0.0006520568,0.00008624121,0.0006658481,0.0020396353,0.0002741451,0.002564763],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797934,0.0009787587,0.00018336278,0.00033896827,0.0004089631,0.00011065137],"domain_scores_gemma":[0.99610317,0.0027989747,0.00029208238,0.0002937344,0.00039228602,0.00011970415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002963391,0.0013254938,0.000550482,0.0023722202,0.0007375168,0.002877374,0.0024565705,0.0016739347,0.0076550795],"category_scores_gemma":[0.009861457,0.0005830153,0.002745607,0.00083147077,0.001845633,0.0028217647,0.0033773521,0.0037044887,0.0010613682],"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":[0.00018862223,0.00020766887,0.003887115,0.0010765169,0.0002543648,0.0007986071,0.001588891,0.27581507,0.0030418565,0.5185552,0.011746254,0.18283983],"study_design_scores_gemma":[0.000043995366,0.000062927,0.00040530835,0.0001702932,0.00006120682,0.00013269861,0.00013573798,0.7098716,0.0013904414,0.2623806,0.02530828,0.0000368541],"about_ca_topic_score_codex":0.0076534827,"about_ca_topic_score_gemma":0.012113075,"teacher_disagreement_score":0.0076550795,"about_ca_system_score_codex":0.0019759368,"about_ca_system_score_gemma":0.002244593,"threshold_uncertainty_score":0.025608778},"labels":[],"label_agreement":null},{"id":"W4413953215","doi":"10.1007/s10586-025-05445-3","title":"A multi-strategy improved gazelle optimization algorithm for solving numerical optimization and engineering applications","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Optimization algorithm; Engineering optimization; Mathematical optimization; Optimization problem; Algorithm; Mathematics","score_opus":0.01689102579222413,"score_gpt":0.2881112256679542,"score_spread":0.2712201998757301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413953215","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012583391,0.0003938726,0.9787926,0.00018130263,0.0001289774,0.00007584454,0.000035633628,0.00053955347,0.0072688065],"genre_scores_gemma":[0.22920129,0.00031410225,0.75720245,0.00026897583,0.00010607642,0.00061729096,0.0002047859,0.0002916338,0.011793397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997273,0.00009489177,0.000011425634,0.00003390654,0.000110781664,0.000021642441],"domain_scores_gemma":[0.99968016,0.00014290764,0.00002330655,0.000029818144,0.00010531662,0.00001845098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007317428,0.00081207365,0.0010239702,0.0007422069,0.00062875944,0.00077556947,0.0015850986,0.001546974,0.0054160827],"category_scores_gemma":[0.0014019822,0.0004446287,0.0007555266,0.0008405385,0.0005284993,0.0007423228,0.0012614537,0.0010567432,0.0010890715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001800712,0.00014556892,0.0006084832,0.00016525743,0.000096642245,0.00010827123,0.0000919976,0.81985754,0.008833316,0.016222151,0.0048186085,0.14887202],"study_design_scores_gemma":[0.000022828235,0.000043352076,0.000067695844,0.0000057862553,0.000006212353,0.000013276436,0.0000038127655,0.99733526,0.0004331028,0.0007857129,0.0012769748,0.000005989354],"about_ca_topic_score_codex":0.0038011558,"about_ca_topic_score_gemma":0.0048024496,"teacher_disagreement_score":0.0054160827,"about_ca_system_score_codex":0.00054568116,"about_ca_system_score_gemma":0.00094395597,"threshold_uncertainty_score":0.01811862},"labels":[],"label_agreement":null},{"id":"W4414114940","doi":"10.1007/s10586-025-05504-9","title":"Multiobjective pruning for sparse deep neural networks: leveraging $$L_0$$ regularization on batch normalization scaling factors","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Normalization (sociology); Regularization (linguistics); Pruning; Convolutional neural network; Deep neural networks; Computational complexity theory; Benchmark (surveying); Scaling","score_opus":0.018154779156090133,"score_gpt":0.26386150018222976,"score_spread":0.24570672102613964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414114940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010154381,0.00037460515,0.9862077,0.00029758603,0.00008109955,0.000056405024,0.000053714062,0.0006647654,0.0021096566],"genre_scores_gemma":[0.30337793,0.00039709316,0.6858889,0.0006384921,0.0002074866,0.00035203967,0.0004223684,0.000654157,0.008061438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988482,0.00043124787,0.000060170863,0.00014734955,0.00040420354,0.000108747015],"domain_scores_gemma":[0.99733907,0.0015435049,0.00017545826,0.00028866058,0.000536954,0.00011634403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00351275,0.0014503105,0.0014996999,0.00079424854,0.00059999747,0.0012089739,0.0025526942,0.0022232842,0.0038307197],"category_scores_gemma":[0.01036824,0.0006636186,0.0006923444,0.00086625415,0.0011532716,0.0016739029,0.0021479812,0.0025030035,0.00087299326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025706377,0.00028310285,0.0011086133,0.00021414178,0.00011219954,0.00016396395,0.00013517917,0.6040971,0.009931582,0.045150705,0.0129488725,0.32559744],"study_design_scores_gemma":[0.00000920172,0.000015867443,0.000056040302,0.000010014326,0.000005692571,0.000010488009,0.0000049089645,0.99405426,0.000771948,0.0046950374,0.00036318722,0.000003425611],"about_ca_topic_score_codex":0.004693578,"about_ca_topic_score_gemma":0.012915396,"teacher_disagreement_score":0.004693578,"about_ca_system_score_codex":0.0009976209,"about_ca_system_score_gemma":0.0018683653,"threshold_uncertainty_score":0.018577456},"labels":[],"label_agreement":null},{"id":"W4414126261","doi":"10.1007/s10586-025-05417-7","title":"Deep fuzzy clustering neural network (DFC-NN): fast convergence and enhanced clustering performance","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Face and Expression Recognition","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 du Québec à Trois-Rivières","funders":"","keywords":"Cluster analysis; Adaptability; Robustness (evolution); Artificial neural network; Fuzzy clustering; Benchmark (surveying); Convergence (economics)","score_opus":0.01054450954194845,"score_gpt":0.23587270439286945,"score_spread":0.225328194850921,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414126261","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1428695,0.0012596279,0.8479234,0.00048152605,0.00028207235,0.00007787922,0.00027495832,0.0017210891,0.0051099067],"genre_scores_gemma":[0.7068576,0.0002723969,0.28575578,0.00018301471,0.000047852867,0.0000583437,0.00036843275,0.00011820849,0.0063383514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965656,0.000051039926,0.000019275412,0.00008625996,0.00013293087,0.000054006363],"domain_scores_gemma":[0.9990853,0.00018292619,0.000043332086,0.00010833409,0.00053592253,0.00004411053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009305018,0.00044389284,0.00063260546,0.00043570448,0.00050578575,0.0005776431,0.0010921203,0.0011144956,0.0016572564],"category_scores_gemma":[0.002135782,0.00026890016,0.00031013647,0.00063936715,0.00036505767,0.00086741545,0.0007400577,0.00095264363,0.0005903769],"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":[0.00050525775,0.00019888698,0.0023629146,0.00013122942,0.00008510684,0.00006890157,0.000083569284,0.3589712,0.027485432,0.007844774,0.008279733,0.593983],"study_design_scores_gemma":[0.000006318087,0.00002968009,0.00034070262,0.000003868893,0.000005836664,0.00002330139,0.00000823056,0.9930894,0.005025165,0.0010076206,0.00045120358,0.000008665493],"about_ca_topic_score_codex":0.018327313,"about_ca_topic_score_gemma":0.025381807,"teacher_disagreement_score":0.018327313,"about_ca_system_score_codex":0.001261476,"about_ca_system_score_gemma":0.0013973162,"threshold_uncertainty_score":0.036441326},"labels":[],"label_agreement":null},{"id":"W4414349631","doi":"10.1007/s10586-025-05320-1","title":"Anomaly-free federated access control policy enforcement framework for ethereum","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Access Control and Trust","field":"Social Sciences","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 Montréal","funders":"","keywords":"XACML; Access control; Scalability; Middleware (distributed applications); Markup language; Mandatory access control; Architecture; Enforcement; XML","score_opus":0.026534328698210045,"score_gpt":0.38971164329150854,"score_spread":0.3631773145932985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414349631","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009516495,0.00013978075,0.9795603,0.00048490474,0.00006960285,0.0001374025,0.00018670796,0.008295113,0.0016096261],"genre_scores_gemma":[0.583498,0.00015278498,0.41022292,0.00035916409,0.00006859528,0.00015939027,0.0006407544,0.00050730095,0.0043910127],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949778,0.0009994087,0.00038111702,0.00082915236,0.0022112436,0.00060130167],"domain_scores_gemma":[0.993715,0.0015706846,0.00049802253,0.0023116234,0.0015560436,0.00034865725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006696551,0.0005959967,0.0012454357,0.0017807276,0.001441389,0.0035988567,0.003971723,0.0017611688,0.0034072935],"category_scores_gemma":[0.012014275,0.00046809978,0.0011310736,0.0011305511,0.0016259296,0.0053497707,0.0050150906,0.0030334971,0.0007026879],"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":[0.0011274951,0.00069300947,0.0048435763,0.00027520175,0.00032958234,0.0007824408,0.00065259635,0.23827623,0.011845109,0.38804087,0.021564418,0.33156946],"study_design_scores_gemma":[0.00003412579,0.000035818957,0.00019595273,0.00002163647,0.00003300203,0.00010868026,0.00005186084,0.92364013,0.005271769,0.06471382,0.0058636325,0.00002953223],"about_ca_topic_score_codex":0.008685526,"about_ca_topic_score_gemma":0.010372849,"teacher_disagreement_score":0.008685526,"about_ca_system_score_codex":0.002421841,"about_ca_system_score_gemma":0.00449188,"threshold_uncertainty_score":0.035415173},"labels":[],"label_agreement":null},{"id":"W4414597492","doi":"10.1007/s10586-025-05629-x","title":"Time-and-Traffic-aware collaborative task offloading with service caching-replacement in cloud-assisted mobile edge computing","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Mobile edge computing; Scalability; Edge computing; Computation offloading; Adaptability; Cloud computing; Cloudlet; Service (business); Knapsack problem; Reinforcement learning","score_opus":0.0070809388047821786,"score_gpt":0.24427559248083067,"score_spread":0.2371946536760485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414597492","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14117074,0.0006002168,0.8532434,0.00026721397,0.00010953248,0.00006393831,0.000054223852,0.00074622076,0.0037444842],"genre_scores_gemma":[0.980892,0.00006346015,0.018222569,0.00005868881,0.000014351456,0.000020710606,0.000021233553,0.000018389783,0.0006886547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996332,0.0000614045,0.00001767271,0.00009188698,0.00006997262,0.00012596743],"domain_scores_gemma":[0.9993556,0.00024790017,0.00007685061,0.00008971706,0.00014764362,0.00008228804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004462809,0.00051904656,0.00083266717,0.00032618194,0.00066745153,0.0007872851,0.0014452772,0.0004998594,0.0007911997],"category_scores_gemma":[0.0011962468,0.00021844567,0.00032403247,0.00051612663,0.00058799365,0.00068475225,0.0007554219,0.0005012978,0.00014594424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014837923,0.00010795261,0.0014069313,0.00004099041,0.00002774071,0.00014426176,0.000055788387,0.941947,0.0069858595,0.0057266126,0.0011751625,0.0422333],"study_design_scores_gemma":[0.0000020005366,0.000008407278,0.000052400916,9.645976e-7,0.000002552715,0.000008374149,0.0000045652596,0.99882585,0.00040079057,0.00059997814,0.0000922319,0.0000018732893],"about_ca_topic_score_codex":0.0112949675,"about_ca_topic_score_gemma":0.010632538,"teacher_disagreement_score":0.0112949675,"about_ca_system_score_codex":0.0009958835,"about_ca_system_score_gemma":0.0012599132,"threshold_uncertainty_score":0.022458434},"labels":[],"label_agreement":null},{"id":"W4414601380","doi":"10.1007/s10586-025-05346-5","title":"Improving traffic flow forecasting with variational mode decomposition and deep learning-based prediction models","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Mode (computer interface); Traffic flow (computer networking); Convolutional neural network; Reliability (semiconductor); Nonlinear system; Decomposition; Artificial neural network; Deep learning","score_opus":0.0064795339482189174,"score_gpt":0.21306880498074066,"score_spread":0.20658927103252173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414601380","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061097674,0.00022532523,0.9362173,0.00025041425,0.0001033625,0.000016428152,0.00014252425,0.0006974538,0.001249589],"genre_scores_gemma":[0.861704,0.0002174329,0.13501003,0.000095527386,0.00008236209,0.000043227614,0.00047767474,0.00013372168,0.002236064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980885,0.000052701587,0.000007830419,0.000050796774,0.000047862493,0.000031906075],"domain_scores_gemma":[0.9993647,0.00031697343,0.000042598047,0.00008027309,0.00016157136,0.000033918946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065403525,0.0006969417,0.000924445,0.0005137322,0.00028173093,0.0006039517,0.0010658894,0.00060778565,0.0013351109],"category_scores_gemma":[0.0025108508,0.00046538113,0.0005245346,0.00077730307,0.00029964832,0.0013839805,0.00060013845,0.0015018607,0.0003177456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072112445,0.00007511356,0.0009780189,0.000018843706,0.000039041755,0.000013553586,0.000020007323,0.93325716,0.002234579,0.0039931773,0.001657154,0.057641257],"study_design_scores_gemma":[4.46115e-7,9.788045e-7,0.000020591462,2.5290905e-7,4.87547e-7,2.9987385e-7,4.5634644e-7,0.9995111,0.000059109203,0.0003896705,0.000016124997,4.085287e-7],"about_ca_topic_score_codex":0.022165705,"about_ca_topic_score_gemma":0.01885357,"teacher_disagreement_score":0.022165705,"about_ca_system_score_codex":0.00066534337,"about_ca_system_score_gemma":0.0009860285,"threshold_uncertainty_score":0.044073403},"labels":[],"label_agreement":null},{"id":"W4414633451","doi":"10.1007/s10586-025-05507-6","title":"Lattice-based cryptographic technique to preserve data confidentiality in edge computing","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Cryptography and Data Security","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Enhanced Data Rates for GSM Evolution; Digital signature; Cryptography; Edge device; Scheme (mathematics); Confidentiality; Edge computing; Public-key cryptography; Signature (topology)","score_opus":0.032135735017686275,"score_gpt":0.3170454700273678,"score_spread":0.28490973500968153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414633451","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048131395,0.00083646324,0.93610287,0.0008842791,0.00044215427,0.00013473324,0.00016124442,0.00069378514,0.012613125],"genre_scores_gemma":[0.76471794,0.00062626024,0.22719622,0.00035785572,0.0002129443,0.000120741235,0.00016929864,0.000092923845,0.006505701],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982242,0.00048814146,0.000108376444,0.0001719721,0.00075204275,0.00025520413],"domain_scores_gemma":[0.99754095,0.0005730174,0.00021218066,0.0008400436,0.00064236345,0.00019152279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013532282,0.00036853552,0.00078623067,0.0011056557,0.0022030177,0.0019552435,0.0015274254,0.00074725243,0.0032400708],"category_scores_gemma":[0.0033878027,0.00024331753,0.0006742548,0.0018493861,0.00169721,0.0030046755,0.0022554714,0.0025545862,0.0010902757],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011004885,0.00028942252,0.00085582875,0.0002788663,0.000087193526,0.0002870894,0.00038309104,0.043163527,0.0641213,0.7671012,0.009120156,0.113211885],"study_design_scores_gemma":[0.00020093138,0.00053670583,0.00039868618,0.00008079588,0.000085438835,0.001016736,0.00035649305,0.5661567,0.08998421,0.31517065,0.025831323,0.00018143914],"about_ca_topic_score_codex":0.00073989085,"about_ca_topic_score_gemma":0.001021276,"teacher_disagreement_score":0.0032400708,"about_ca_system_score_codex":0.0010235714,"about_ca_system_score_gemma":0.0021711984,"threshold_uncertainty_score":0.010839164},"labels":[],"label_agreement":null},{"id":"W4415219996","doi":"10.1007/s10586-025-05652-y","title":"Mitigating insider threats: a trust-based security framework for energy grid plants with dynamic authentication and role-based training","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Smart Grid Security and Resilience","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":"Sherbrooke O.E.M (Canada); Université de Sherbrooke","funders":"","keywords":"Bridging (networking); Robustness (evolution); Insider threat; Vulnerability (computing); Authentication (law); Grid; Competence (human resources); Data integrity; Computer security model","score_opus":0.007805518327385761,"score_gpt":0.23519782674197715,"score_spread":0.22739230841459138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415219996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0155519275,0.00012455093,0.97559255,0.0006852478,0.00008880582,0.00015308052,0.000040961215,0.0020683517,0.005694584],"genre_scores_gemma":[0.80091727,0.00014997102,0.19240674,0.00019043643,0.000057883746,0.00013112879,0.00008499918,0.00012377737,0.005937879],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794513,0.00054737704,0.0001247236,0.00032984596,0.00065386033,0.00039918482],"domain_scores_gemma":[0.99767286,0.0004274283,0.00028411997,0.0007476746,0.00049175636,0.00037612833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029527564,0.00069255556,0.0007109102,0.0007013291,0.0015039673,0.002708834,0.002991846,0.0017646378,0.0034478698],"category_scores_gemma":[0.0045482204,0.00036325105,0.0008834961,0.00036107437,0.0019792365,0.004950569,0.004531894,0.002595007,0.0011114473],"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":[0.00087244384,0.0008143142,0.0031221716,0.00029075844,0.00016502336,0.00092646515,0.0018402912,0.2436019,0.033121586,0.4983734,0.011788246,0.20508344],"study_design_scores_gemma":[0.000033330525,0.00018570285,0.0003303852,0.000044305263,0.000049417795,0.0002426579,0.00027006108,0.89524037,0.008656609,0.08438385,0.010503381,0.00005991987],"about_ca_topic_score_codex":0.0029851967,"about_ca_topic_score_gemma":0.0029871487,"teacher_disagreement_score":0.0034478698,"about_ca_system_score_codex":0.0010816132,"about_ca_system_score_gemma":0.0026566288,"threshold_uncertainty_score":0.015615821},"labels":[],"label_agreement":null},{"id":"W7083674219","doi":"10.1007/s10586-025-05335-8","title":"Privacy preserved api access control policy- based ransomware attack detection in cloud vms using sl2stm","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Biofuel production and bioconversion","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":"Hatch (Canada)","funders":"","keywords":"Cloud computing; Upload; Access control; Data access; Feature selection; Gateway (web page); Encryption; Feature (linguistics)","score_opus":0.026678553876173804,"score_gpt":0.28852872785955475,"score_spread":0.26185017398338095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7083674219","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5759566,0.0015075388,0.38539132,0.00065372407,0.00042542163,0.0003689436,0.0005167922,0.027679488,0.007500142],"genre_scores_gemma":[0.9750329,0.00006336912,0.02374671,0.00008616787,0.000019737108,0.00002625945,0.00018961317,0.000086628956,0.0007485363],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977514,0.0004031633,0.0001608793,0.0004495354,0.0006947941,0.00054019876],"domain_scores_gemma":[0.9978891,0.00038492578,0.00022294011,0.0009888347,0.00037223136,0.00014203136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011687313,0.0006947953,0.00092649873,0.00076042203,0.00065526844,0.00171037,0.001199913,0.0006280066,0.0018461804],"category_scores_gemma":[0.0041656904,0.00024118336,0.0004819579,0.00048222626,0.0005232996,0.0019985493,0.0016513225,0.00083714025,0.0006660104],"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":[0.005094308,0.0014441912,0.048978914,0.0004957994,0.00040061024,0.0017079407,0.00062180276,0.09700915,0.13957642,0.016231392,0.020898497,0.66754097],"study_design_scores_gemma":[0.000043119446,0.0002883296,0.0032683045,0.000018866256,0.00004466275,0.00036659848,0.00014479621,0.95624375,0.034811106,0.0027875556,0.0019513584,0.000031495638],"about_ca_topic_score_codex":0.002022164,"about_ca_topic_score_gemma":0.0023310203,"teacher_disagreement_score":0.002022164,"about_ca_system_score_codex":0.0005029313,"about_ca_system_score_gemma":0.0016288831,"threshold_uncertainty_score":0.0061808825},"labels":[],"label_agreement":null},{"id":"W86735328","doi":"","title":"Overlap of computation and communication on shared-memory networks-of-workstations","year":2001,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":9,"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":"Computer science; Asynchronous communication; Parallel computing; Latency (audio); Computation; Distributed memory; Shared memory; Workstation; Multiprocessing; Compiler; Transformation (genetics); CAS latency; Uniform memory access; Data access; Distributed shared memory; Memory model; Memory management; Operating system; Algorithm; Computer network; Semiconductor memory; Memory controller","score_opus":0.020143973224367326,"score_gpt":0.28078552645919475,"score_spread":0.2606415532348274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W86735328","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.547052,0.0015458027,0.42819023,0.00060200255,0.00020007332,0.000083813524,0.00014534891,0.00070666865,0.021474084],"genre_scores_gemma":[0.96549547,0.00038846504,0.030152049,0.000040606228,0.00008791312,0.00009810916,0.000118688426,0.000098976285,0.0035197877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755955,0.0007686478,0.00011215936,0.0002703266,0.0007282733,0.0005610782],"domain_scores_gemma":[0.99303234,0.003922241,0.00036167522,0.001329869,0.00087992585,0.00047401554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014797152,0.00047660654,0.0010706808,0.0008739512,0.001528467,0.00240093,0.0021437511,0.0008403527,0.003845142],"category_scores_gemma":[0.011521452,0.00051645783,0.00047176558,0.0018579218,0.0015339631,0.0033903278,0.003587545,0.0007640375,0.0004840407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031269554,0.00033135977,0.009169552,0.0003491549,0.00015761926,0.0005830523,0.0012850456,0.42805704,0.019193128,0.41984034,0.00591821,0.11198843],"study_design_scores_gemma":[0.000056006233,0.000095100244,0.001529668,0.000020833195,0.000050609095,0.000112832815,0.00026806342,0.8543859,0.0068458505,0.13213597,0.0044801184,0.000018964478],"about_ca_topic_score_codex":0.0018593639,"about_ca_topic_score_gemma":0.0020882108,"teacher_disagreement_score":0.003845142,"about_ca_system_score_codex":0.0010338736,"about_ca_system_score_gemma":0.0014222946,"threshold_uncertainty_score":0.012863338},"labels":[],"label_agreement":null}]}