{"meta":{"query_hash":"3a7975832627","filters":{"topic":"Advanced Data and IoT Technologies"},"cohort_total":59,"direct_labels_cover":0,"predictions_cover":59,"exported":59,"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/3a7975832627","api":"https://metacan.xera.ac/api/v1/cohort?topic=Advanced+Data+and+IoT+Technologies"},"results":[{"id":"W112066245","doi":"","title":"Guest editorial: Fourth quarter 2008 IEEE communications surveys and tutorials.","year":2008,"lang":"en","type":"editorial","venue":"IEEE Communications Surveys & Tutorials","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":1,"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":"Computer science; Quarter (Canadian coin); Telecommunications; Computer network","score_opus":0.037455240379661345,"score_gpt":0.29844532697598714,"score_spread":0.2609900865963258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W112066245","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006488022,0.009510107,0.0061739367,0.00023726728,0.9712001,0.0013128372,0.0072641224,0.0030539439,0.0011828089],"genre_scores_gemma":[0.0025056407,0.121615484,0.005503207,0.000009619317,0.85984886,0.0008263707,0.00882878,0.0004295212,0.00043254448],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9851467,0.00807007,0.0026561418,0.0012086461,0.0016052017,0.0013132559],"domain_scores_gemma":[0.9663507,0.014362729,0.00091314054,0.016247686,0.0017410592,0.00038468698],"candidate_categories":["metaepi_narrow","sts","open_science","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.011114831,0.0015819799,0.0024617584,0.000776672,0.0015424053,0.00051867525,0.009035597,0.0037452832,0.000018906358],"category_scores_gemma":[0.005964745,0.0017473482,0.00038825785,0.0011876954,0.002224535,0.001300804,0.0016119294,0.004255638,0.00036058913],"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":[0.000009157305,0.0001940444,0.000046683846,0.00011582343,0.00038294034,0.000004587702,0.00028138753,0.0001090634,0.00081124686,0.00007754999,0.99592334,0.0020441948],"study_design_scores_gemma":[0.0011676758,0.0001222609,0.00010908998,0.00024365872,0.00021620213,0.000009147441,0.000081351856,0.00016974815,0.0003480182,0.00038137738,0.9954401,0.0017113393],"about_ca_topic_score_codex":0.0014989047,"about_ca_topic_score_gemma":0.0026625542,"teacher_disagreement_score":0.11210538,"about_ca_system_score_codex":0.0007105664,"about_ca_system_score_gemma":0.00073743076,"threshold_uncertainty_score":0.99975747},"labels":[],"label_agreement":null},{"id":"W2259740644","doi":"10.6084/m9.figshare.911326.v2","title":"IEEE ISTAS13 Symposium","year":2014,"lang":"en","type":"article","venue":"Figshare","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.014117494970913047,"score_gpt":0.20617522521633125,"score_spread":0.1920577302454182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2259740644","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028227102,0.0011923179,0.00585026,0.0003289811,0.0012023317,0.00054273335,0.6300793,0.018371668,0.33960968],"genre_scores_gemma":[0.9076804,0.00003229778,0.0030097715,0.00021633475,0.00039793548,0.00020297871,0.08771495,0.00009029151,0.0006550384],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996842,0.0000021630804,0.000056307716,0.00007971313,0.000044883116,0.00013272038],"domain_scores_gemma":[0.9997047,0.000025600408,0.000007731288,0.00022814234,0.000011380521,0.000022430153],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0000064218198,0.00007096113,0.00006216258,0.000026322112,0.000021766626,0.000015650328,0.00015767728,0.000056807774,0.012039591],"category_scores_gemma":[0.00010740556,0.00006808795,0.000018139399,0.00006191238,0.0000024786011,0.000109928864,0.000027963335,0.000083234736,0.003766534],"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":[2.9560613e-7,0.0000017631498,0.0000020140958,0.000068125286,0.0000034995785,0.0000015051982,0.000009131662,0.002081006,0.0020443362,0.00006335841,0.98394984,0.01177513],"study_design_scores_gemma":[0.000050690363,0.00001017615,0.000051952575,0.00011544106,0.0000010378224,0.0000019472532,0.000006183829,0.006710991,0.02307875,0.00014377624,0.9697197,0.000109342494],"about_ca_topic_score_codex":2.0139483e-7,"about_ca_topic_score_gemma":8.349282e-7,"teacher_disagreement_score":0.9048577,"about_ca_system_score_codex":0.0000134693855,"about_ca_system_score_gemma":0.0000017735447,"threshold_uncertainty_score":0.99700916},"labels":[],"label_agreement":null},{"id":"W2884280087","doi":"10.1109/msmc.2018.2831418","title":"IEEE SMC 2017 in Banff, Alberta, Canada [Conference Reports]","year":2018,"lang":"en","type":"article","venue":"IEEE Systems Man and Cybernetics Magazine","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geography; History; Political science","score_opus":0.011788520648927152,"score_gpt":0.20847918463405554,"score_spread":0.19669066398512838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884280087","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90667117,0.0017007677,0.0028805253,0.0001265336,0.005012885,0.0005134997,0.00005226185,0.00039931916,0.08264305],"genre_scores_gemma":[0.99398357,0.00038347792,0.00017515963,0.00002467513,0.0001884397,0.000018979348,0.000012708658,0.000030750463,0.005182216],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99876297,0.000013624488,0.0003966622,0.0002985173,0.00016518836,0.00036304604],"domain_scores_gemma":[0.99920803,0.000036037596,0.00007374753,0.00053418207,0.000058176138,0.00008983755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008810393,0.00022699394,0.00029362008,0.00007349166,0.000048697075,0.000055046643,0.00018326374,0.00012433372,0.00001847223],"category_scores_gemma":[0.00002777654,0.00021987464,0.000016561218,0.0001429042,0.000115900904,0.00011327602,0.000028289222,0.00018558755,0.00004797864],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005193932,0.00011157611,0.014808247,0.001550701,0.00023824596,0.0022119044,0.0013069793,0.013792338,0.044866968,0.0065857465,0.892411,0.02206437],"study_design_scores_gemma":[0.0008767082,0.00020502676,0.01534074,0.0005284621,0.000037958976,0.0005525196,0.00034835318,0.049768627,0.008521166,0.0008463024,0.9216406,0.0013335838],"about_ca_topic_score_codex":0.10593563,"about_ca_topic_score_gemma":0.7263569,"teacher_disagreement_score":0.6204213,"about_ca_system_score_codex":0.000090708556,"about_ca_system_score_gemma":0.0000682583,"threshold_uncertainty_score":0.90001804},"labels":[],"label_agreement":null},{"id":"W2889277780","doi":"10.1109/mssc.2018.2844669","title":"IEEE SSCS Montreal Chapter Hosts the ReSMiQ Annual Symposium in May 2018 [Chapters]","year":2018,"lang":"en","type":"article","venue":"IEEE Solid-State Circuits Magazine","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Library science; Engineering; Engineering ethics; Political science; Computer science","score_opus":0.0138909915937341,"score_gpt":0.24551639399117392,"score_spread":0.2316254023974398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889277780","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8539401,0.0014869445,0.016996693,0.003035128,0.008484778,0.0020942,0.0020684642,0.004935057,0.106958605],"genre_scores_gemma":[0.99577665,0.00091351336,0.00010644131,0.00026042308,0.00055762666,0.000048912752,0.000028557784,0.00011333191,0.002194524],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977805,0.000029274712,0.00051158335,0.0005092506,0.00028698548,0.00088239915],"domain_scores_gemma":[0.9986189,0.00008613725,0.00008725917,0.0009754698,0.00011473867,0.00011750735],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022092996,0.00045261453,0.00039971698,0.00028526524,0.00014033922,0.00006558815,0.0007236514,0.00020125383,0.000101682046],"category_scores_gemma":[0.000037823447,0.00037099587,0.0000847458,0.00038685274,0.00048284,0.0005950092,0.000094917916,0.00048712752,0.0018058358],"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":[0.00022258975,0.00031921404,0.0015296177,0.0003380509,0.0004972281,0.0009827608,0.012047578,0.044134907,0.33927315,0.001448354,0.19152147,0.4076851],"study_design_scores_gemma":[0.005541329,0.0014342869,0.028770367,0.0005785904,0.00015254744,0.00036209886,0.00094498356,0.036683884,0.17654583,0.008084976,0.73682994,0.0040711495],"about_ca_topic_score_codex":0.00005444401,"about_ca_topic_score_gemma":0.0006137576,"teacher_disagreement_score":0.5453085,"about_ca_system_score_codex":0.00011873099,"about_ca_system_score_gemma":0.000019130655,"threshold_uncertainty_score":0.9998742},"labels":[],"label_agreement":null},{"id":"W2981687660","doi":"10.3390/electronics8111220","title":"An Efficient Encryption Algorithm for the Security of Sensitive Private Information in Cyber-Physical Systems","year":2019,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Hunan Normal University","keywords":"Encryption; Computer science; Information security; Computer security; Private information retrieval; Algorithm","score_opus":0.0031432729448480643,"score_gpt":0.2141315631248541,"score_spread":0.21098829018000603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981687660","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72764325,0.00029850088,0.2713851,0.000006691562,0.000094841795,0.00039905353,0.000031105534,0.00011011679,0.000031340922],"genre_scores_gemma":[0.99898845,0.00012385262,0.0007868726,0.0000029996168,0.000017888391,0.000023702234,0.000049297305,0.0000059760855,9.495997e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995859,0.0000073738393,0.00011630904,0.000055729553,0.00007333057,0.00016134308],"domain_scores_gemma":[0.9997034,0.000053695152,0.00003232094,0.00017407032,0.000028690552,0.000007824087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009341462,0.000064045205,0.000097690776,0.000042978874,0.000015982625,0.000010484586,0.0000870552,0.00004666823,2.4540145e-7],"category_scores_gemma":[0.000011818029,0.000049610735,0.000019328685,0.00011297964,0.000013779099,0.00021411758,0.000012857181,0.00012859589,0.0000057404236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013679411,0.00003674079,0.00002459112,0.00008292747,0.000024000881,1.553612e-7,0.0010685149,0.8598414,0.01129601,0.028241213,0.000031916883,0.09933883],"study_design_scores_gemma":[0.00017293647,0.00010428861,0.0001397953,0.0000107181895,0.0000040886675,0.0000010119086,0.0003058296,0.97429043,0.021531781,0.0004634547,0.0029145123,0.0000611265],"about_ca_topic_score_codex":0.0000029753255,"about_ca_topic_score_gemma":0.0000026002613,"teacher_disagreement_score":0.27134523,"about_ca_system_score_codex":0.00009413186,"about_ca_system_score_gemma":0.000010263302,"threshold_uncertainty_score":0.2023067},"labels":[],"label_agreement":null},{"id":"W3004542403","doi":"10.1109/map.2019.2960715","title":"IEEE AP-S Workshop Held in Cairo, Egypt [Meeting Reports]","year":2020,"lang":"en","type":"article","venue":"IEEE Antennas and Propagation Magazine","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Engineering; Telecommunications; Electrical engineering; Library science","score_opus":0.021517458724067986,"score_gpt":0.23573708239823257,"score_spread":0.21421962367416458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004542403","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84031934,0.0033433829,0.14407958,0.0039723576,0.0010498629,0.00078274315,0.00003070886,0.0022173673,0.0042046276],"genre_scores_gemma":[0.99663717,0.0008030691,0.0019820242,0.00021774594,0.00015150082,0.000022922346,0.000022396502,0.000029430634,0.00013374185],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990249,0.00001134677,0.00034596378,0.00027015764,0.00011148536,0.00023614557],"domain_scores_gemma":[0.999619,0.000033316966,0.00006065212,0.00018533433,0.00003576793,0.00006590724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000111538924,0.00016658618,0.00020735357,0.00008706844,0.000046948688,0.000042573403,0.0000855139,0.000102421,0.000015511565],"category_scores_gemma":[0.00013840235,0.00015545232,0.000026146228,0.0003634858,0.000048191545,0.000325942,0.000028182329,0.00025615597,0.000033680994],"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":[0.00009196984,0.000054584765,0.008472279,0.00069129927,0.000054442055,0.0008707934,0.0013579244,0.02487971,0.7878584,0.00026541267,0.025796218,0.14960693],"study_design_scores_gemma":[0.0023093123,0.00047023728,0.009280088,0.0010529417,0.00006932727,0.00035809114,0.0017066259,0.6210987,0.24013564,0.0028653685,0.11831957,0.0023340606],"about_ca_topic_score_codex":0.000004604029,"about_ca_topic_score_gemma":0.000033938442,"teacher_disagreement_score":0.596219,"about_ca_system_score_codex":0.000021276895,"about_ca_system_score_gemma":0.000008552827,"threshold_uncertainty_score":0.63391614},"labels":[],"label_agreement":null},{"id":"W3207004240","doi":"10.1109/mias.2021.3103373","title":"IEEE IAS 2021 Award Winners [Awards]","year":2021,"lang":"en","type":"article","venue":"IEEE Industry Applications Magazine","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Business; Engineering; Management; Accounting; Economics","score_opus":0.01642159422273484,"score_gpt":0.2574324643352319,"score_spread":0.24101087011249708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207004240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15069571,0.0026005246,0.7478559,0.006511538,0.0026808495,0.0014268017,0.0010730281,0.006168046,0.08098761],"genre_scores_gemma":[0.9356674,0.0017195824,0.041403886,0.00051145453,0.0021713846,0.0019203124,0.00073446473,0.00023921957,0.015632328],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998829,0.000011243952,0.00029050352,0.0003584411,0.00015985996,0.00035096364],"domain_scores_gemma":[0.9987231,0.00004562337,0.000041143063,0.0009820713,0.000105666535,0.00010242311],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000056673558,0.00022533312,0.00021792701,0.00009862053,0.0001044144,0.00004575514,0.00035504418,0.00048379603,0.00025724893],"category_scores_gemma":[0.000033701144,0.00025155058,0.000061564875,0.0009062563,0.00010327549,0.00023401395,0.00005285502,0.0008690757,0.0013319962],"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":[0.000005509081,0.0001856647,0.00056752894,0.00011615339,0.00016825016,0.000094070274,0.00004796267,0.05741159,0.28781083,0.0031601803,0.44215024,0.20828204],"study_design_scores_gemma":[0.00023407619,0.000010444131,0.000321573,0.000023318202,0.000027214372,0.00005378454,0.00014909767,0.0014146622,0.15774679,0.0012155123,0.8384531,0.00035041664],"about_ca_topic_score_codex":0.0000016311633,"about_ca_topic_score_gemma":0.000006853492,"teacher_disagreement_score":0.78497165,"about_ca_system_score_codex":0.00010258467,"about_ca_system_score_gemma":0.000059141443,"threshold_uncertainty_score":0.9999937},"labels":[],"label_agreement":null},{"id":"W4224215955","doi":"10.1109/vr51125.2022.00011","title":"IEEE VR 2022 Paper Reviewers for Conference Papers","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"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":"McGill University","keywords":"Computer science","score_opus":0.05112280136756653,"score_gpt":0.2892783632593225,"score_spread":0.23815556189175596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224215955","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81340224,0.0077518797,0.05697539,0.004911342,0.0146127,0.0060504735,0.012920516,0.00514792,0.078227535],"genre_scores_gemma":[0.98873764,0.0074742683,0.00025664174,0.00059932313,0.0000734488,0.0005545713,0.00016573246,0.000059172384,0.0020791746],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99756986,0.00012396861,0.00055346446,0.0007386907,0.00042529008,0.00058871147],"domain_scores_gemma":[0.9986021,0.00024211836,0.00013317977,0.0007800591,0.0000932959,0.00014926594],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00044736036,0.00047603104,0.00060733955,0.00014012001,0.0004006151,0.00013129186,0.00079680944,0.00017101785,0.0015882922],"category_scores_gemma":[0.00018185635,0.00046144915,0.00011625474,0.0002687029,0.00026476677,0.0004519771,0.0002634214,0.0008697927,0.00003730409],"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":[0.0009202484,0.0003540771,0.00011373045,0.0009071911,0.00051659654,0.000047004047,0.0037964669,0.01692397,0.16428164,0.067464605,0.090992734,0.65368176],"study_design_scores_gemma":[0.0011724475,0.0018635665,0.0001139166,0.00029583622,0.00011542504,0.00002025031,0.009417211,0.019475508,0.02375716,0.0033885187,0.9389483,0.0014318743],"about_ca_topic_score_codex":0.00004359945,"about_ca_topic_score_gemma":0.0001551456,"teacher_disagreement_score":0.8479555,"about_ca_system_score_codex":0.00015904442,"about_ca_system_score_gemma":0.00006768888,"threshold_uncertainty_score":0.9997837},"labels":[],"label_agreement":null},{"id":"W4225114873","doi":"10.1142/s0218126622502073","title":"Learning-Based Health Prediction Method for Airborne DME Receiver with Signal Processing Techniques in 6G Networks","year":2022,"lang":"en","type":"article","venue":"Journal of Circuits Systems and Computers","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Robustness (evolution); Radar; Computer science; Path loss; Real-time computing; Wireless; Fault (geology); Electronic engineering; Ground truth; Artificial intelligence; Engineering; Simulation; Telecommunications","score_opus":0.012039073780167768,"score_gpt":0.2411096009915888,"score_spread":0.22907052721142102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225114873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036876989,0.0021569435,0.9935007,0.000066989065,0.00018068189,0.0002369765,0.0000068280665,0.00015276753,0.00001041862],"genre_scores_gemma":[0.9845492,0.00007464473,0.015165485,0.000037712518,0.00010869605,0.000032386506,0.0000074131026,0.000020651318,0.0000037658835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991697,0.00006510953,0.0003473027,0.00010376374,0.0001455143,0.0001686097],"domain_scores_gemma":[0.99955356,0.00006534239,0.00023361835,0.000055289707,0.000051382907,0.00004081403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005842986,0.0001010571,0.00027747918,0.00021726776,0.00012237551,0.000038762755,0.00010741473,0.000041002357,5.181406e-7],"category_scores_gemma":[0.000003728217,0.00008670349,0.000027807468,0.00020524088,0.000015268002,0.00018154629,0.000016426795,0.00038475575,1.4311797e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000163489,0.000010614353,0.00030289148,0.00015991408,0.000013974003,0.0000058668825,0.00010064355,0.75664186,0.00006878348,0.000024110437,0.00032536557,0.2423296],"study_design_scores_gemma":[0.00056697574,0.0013540449,0.0004507677,0.00050819124,0.000008919828,0.00019190196,0.0005750262,0.9863698,0.00006145212,0.00003467482,0.009770924,0.00010735344],"about_ca_topic_score_codex":0.000007042621,"about_ca_topic_score_gemma":0.0000013954119,"teacher_disagreement_score":0.98086154,"about_ca_system_score_codex":0.00016072112,"about_ca_system_score_gemma":0.000051291216,"threshold_uncertainty_score":0.3535666},"labels":[],"label_agreement":null},{"id":"W4229636704","doi":"10.1109/surv.2014.042914.00000","title":"Editorial: Second Quarter 2014, IEEE Communications Surveys & Tutorials","year":2014,"lang":"en","type":"editorial","venue":"IEEE Communications Surveys & Tutorials","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Quarter (Canadian coin); Telecommunications; IEEE 802.11u; Computer network; IEEE 802.11; Wireless; Wireless lan","score_opus":0.042795404791173275,"score_gpt":0.31822795423895905,"score_spread":0.2754325494477858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229636704","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005103059,0.0041338033,0.034085687,0.00021298762,0.9413256,0.0015523796,0.011356393,0.0039481865,0.0033339318],"genre_scores_gemma":[0.001286926,0.02029929,0.011534569,0.000022589911,0.93704486,0.0014270374,0.02518049,0.0006523589,0.0025518744],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.97885597,0.012159495,0.0037437393,0.0014942569,0.0020310564,0.0017154873],"domain_scores_gemma":[0.9434828,0.023284448,0.0014680438,0.0287839,0.0025263433,0.000454444],"candidate_categories":["metaresearch","metaepi_narrow","sts","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.022239298,0.0020015037,0.0032764697,0.0009594243,0.001431114,0.0009029115,0.015924918,0.005105447,0.00034140726],"category_scores_gemma":[0.00946725,0.0022130073,0.0007281189,0.0012136998,0.0017467892,0.0013736892,0.0019130395,0.0054525477,0.004882426],"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":[0.000013377655,0.00022225245,0.000017671362,0.00021815764,0.0005381359,5.9050495e-7,0.00022303594,0.00022232582,0.0035632774,0.00021488655,0.99294484,0.0018214609],"study_design_scores_gemma":[0.0011472588,0.000068508074,0.00002911933,0.00025891606,0.0002879113,0.0000026436737,0.000042508254,0.00006131137,0.00039333286,0.0016385427,0.99386734,0.0022026259],"about_ca_topic_score_codex":0.0010258814,"about_ca_topic_score_gemma":0.013058345,"teacher_disagreement_score":0.035373148,"about_ca_system_score_codex":0.0011142943,"about_ca_system_score_gemma":0.000821208,"threshold_uncertainty_score":0.99986887},"labels":[],"label_agreement":null},{"id":"W4240424490","doi":"10.1109/ccece32609.2014","title":"2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE)","year":2014,"lang":"en","type":"paratext","venue":"","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":122,"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":"Computer science; Electrical engineering; Engineering","score_opus":0.01013931956886014,"score_gpt":0.20681656021156825,"score_spread":0.1966772406427081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240424490","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010400519,0.001758637,0.83263755,0.0001986168,0.0055217906,0.0005258804,0.00036285495,0.0018811191,0.1560735],"genre_scores_gemma":[0.86094147,0.018577328,0.048423223,0.0016391916,0.004276349,0.00019291573,0.002114303,0.0008399813,0.06299526],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99884605,0.000006367695,0.00018882353,0.00033797874,0.000102195794,0.0005185517],"domain_scores_gemma":[0.9993128,0.000058646878,0.000020747646,0.00038262436,0.0000260441,0.0001991288],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000035643756,0.0003858539,0.00040363058,0.00036131966,0.00004315911,0.00007731107,0.00029899544,0.00050052174,0.00041199528],"category_scores_gemma":[0.000010497314,0.00035999418,0.000035083256,0.000103328566,0.000032827364,0.00007503308,0.000035356537,0.00069643965,0.0027001298],"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":[0.0000012484514,0.0000024984688,0.0000026946286,0.00007788544,0.0000361976,0.0000064449846,0.0000065082227,0.019603048,0.00008222274,0.0023486847,0.9653329,0.012499629],"study_design_scores_gemma":[0.0001283842,0.00008968136,0.000037792222,0.00008558058,0.000009702438,0.000011818765,0.0000022172276,0.39858744,0.00069212634,0.000032440206,0.59974617,0.00057663],"about_ca_topic_score_codex":0.001747033,"about_ca_topic_score_gemma":0.002441013,"teacher_disagreement_score":0.85990137,"about_ca_system_score_codex":0.00014386962,"about_ca_system_score_gemma":0.000046214274,"threshold_uncertainty_score":0.9998852},"labels":[],"label_agreement":null},{"id":"W4240852817","doi":"10.1109/tmtt.2019.2891977","title":"Guest Editorial","year":2019,"lang":"en","type":"editorial","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Telecommunications; Computer science; Engineering; Electrical engineering","score_opus":0.0044481293880596715,"score_gpt":0.22804847871886946,"score_spread":0.2236003493308098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240852817","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000039444873,0.00028672817,0.21887149,0.000003970985,0.7760851,0.00028690085,0.00076396903,0.0026811466,0.0009812431],"genre_scores_gemma":[0.0024204724,0.0055284835,0.0016165407,0.000012125264,0.9890385,0.00012746094,0.00012839837,0.00015571612,0.0009722939],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986333,0.000061903214,0.0002957571,0.00042559835,0.00025758988,0.00032580114],"domain_scores_gemma":[0.9984087,0.00077717286,0.00005748142,0.00062293926,0.00007937522,0.000054358337],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00033912685,0.0004932145,0.00046709107,0.00029837713,0.000120318015,0.00006707513,0.00035330167,0.0017007065,0.000023428469],"category_scores_gemma":[0.000038556584,0.00047882754,0.00012485348,0.00013023571,0.00019619946,0.00023011088,0.0000047617104,0.001853993,0.00007172797],"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":[0.00009929032,0.000026701398,1.26675745e-8,0.00014887801,0.00007638942,0.0000036274782,0.000025526777,0.000019873929,0.047425214,0.00015242775,0.934451,0.017571077],"study_design_scores_gemma":[0.00010556357,0.00011659653,4.316618e-9,0.0001216876,0.000047886875,0.0000021551741,0.000017133802,8.992322e-7,0.413468,0.0028415993,0.5829659,0.00031253728],"about_ca_topic_score_codex":0.0000035689702,"about_ca_topic_score_gemma":0.0000051632533,"teacher_disagreement_score":0.3660428,"about_ca_system_score_codex":0.00011232325,"about_ca_system_score_gemma":0.00003979626,"threshold_uncertainty_score":0.99976635},"labels":[],"label_agreement":null},{"id":"W4247541456","doi":"10.1109/iris.2017.8250086","title":"IEEE IRIS2017 table of contents","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Data and IoT Technologies","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":"York University","funders":"","keywords":"Computer science; Table (database); Database","score_opus":0.03265100993284144,"score_gpt":0.262970993137926,"score_spread":0.23031998320508457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247541456","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5477275,0.00048410063,0.13313839,0.00016332588,0.0015124049,0.00020540183,0.00009250557,0.0016944853,0.31498185],"genre_scores_gemma":[0.9956987,0.00011825963,0.0031400127,0.0000043667796,0.000010654431,0.0000022829029,0.0000014408315,0.000006020473,0.0010182428],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99977094,4.832806e-7,0.00006190711,0.00004656843,0.0000345084,0.000085621585],"domain_scores_gemma":[0.9994796,0.00000511955,0.000020803729,0.00047025093,0.0000130524995,0.000011195867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000137607985,0.00004263858,0.00007871315,0.000015838592,0.000044959463,0.000013041158,0.00026858645,0.00003483696,0.000029041192],"category_scores_gemma":[0.000043034375,0.00003549849,0.000011810321,0.000009178529,0.000048246056,0.00022517139,0.00003672809,0.000038886115,0.000033873774],"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":[0.000013257257,0.000048311846,0.018070804,0.0001473006,0.00012305312,0.000015213665,0.00005057526,0.0028934968,0.52907354,0.012608937,0.28810748,0.14884806],"study_design_scores_gemma":[0.00040525195,0.00002954399,0.009663128,0.000029213119,0.0000066006273,0.0000018720084,0.000069998394,0.0057341554,0.9069703,0.0019066628,0.07500952,0.0001737594],"about_ca_topic_score_codex":0.000021838185,"about_ca_topic_score_gemma":0.000007237879,"teacher_disagreement_score":0.4479712,"about_ca_system_score_codex":0.0000049098235,"about_ca_system_score_gemma":0.000001180829,"threshold_uncertainty_score":0.14475864},"labels":[],"label_agreement":null},{"id":"W4254158391","doi":"10.1109/thms.2017.2671618","title":"2017 IEEE International Conference on Systems, Man, and Cybernetics, October 5–8, 2017, Banff Center, Banff, Canada","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"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":"Center (category theory); Cybernetics; Library science; Geography; Computer science; Artificial intelligence; Chemistry","score_opus":0.061342363293128456,"score_gpt":0.30358191912133903,"score_spread":0.24223955582821058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254158391","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27221125,0.0045933872,0.48948765,0.001366721,0.08937301,0.004339143,0.01741652,0.0042219693,0.11699035],"genre_scores_gemma":[0.9961454,0.0007904584,0.00005654279,0.00002276101,0.00017698926,0.00009150345,0.000065236454,0.000057893325,0.0025931625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982513,0.000036857222,0.00046534694,0.00045569823,0.0004036635,0.00038710173],"domain_scores_gemma":[0.99839735,0.00005655222,0.00017244866,0.0011357431,0.000098706594,0.00013922104],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011506528,0.00041254697,0.00042257414,0.00018723618,0.00065668335,0.00037260063,0.00081213634,0.0001826931,0.000045414497],"category_scores_gemma":[0.000010901354,0.0003933667,0.00006264146,0.000037098573,0.00014357295,0.00038594005,0.000008361251,0.0005525941,0.000045534198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031616123,0.0009675199,0.0014698973,0.002618037,0.0027106255,0.0007877658,0.0007166203,0.6895703,0.027824104,0.028171059,0.22201386,0.022834018],"study_design_scores_gemma":[0.010464402,0.0009765846,0.0046675266,0.004769246,0.00037828143,0.00083996204,0.0021341452,0.5953169,0.017520357,0.00032291815,0.3573024,0.0053073084],"about_ca_topic_score_codex":0.08692812,"about_ca_topic_score_gemma":0.121601865,"teacher_disagreement_score":0.72393423,"about_ca_system_score_codex":0.00028854195,"about_ca_system_score_gemma":0.00004379626,"threshold_uncertainty_score":0.9998518},"labels":[],"label_agreement":null},{"id":"W4283329507","doi":"","title":"Multimedia Technologies: A Comprehensive Review","year":2022,"lang":"en","type":"review","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Multimedia","score_opus":0.03272323224465063,"score_gpt":0.26982197629341775,"score_spread":0.23709874404876713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283329507","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[5.652778e-7,0.9758964,0.009865419,0.00041425065,0.0001832333,0.00084832066,0.00020783403,0.003634213,0.008949731],"genre_scores_gemma":[0.0000018681956,0.9701224,0.027260061,0.000030698062,0.0000066966522,0.00052167213,0.001207514,0.00010117037,0.0007479268],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99675786,0.0012337653,0.0006970696,0.00059547607,0.0003023965,0.00041344223],"domain_scores_gemma":[0.9946899,0.0019462984,0.0003094037,0.002684831,0.00030292216,0.000066646215],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001043446,0.0005237865,0.0012829297,0.00029725512,0.00022904326,0.000070537935,0.0020264566,0.0003342445,0.00038565404],"category_scores_gemma":[0.001976314,0.0005029183,0.00036890656,0.0011350648,0.000248159,0.00015904657,0.0011344047,0.0011249271,0.00018750857],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.826084e-7,0.000038597314,3.0055674e-7,0.013910336,0.00006676491,0.000007748152,0.00007580332,0.0000023276318,0.0000034089,0.00154774,0.007841177,0.97650564],"study_design_scores_gemma":[0.000087208435,1.9311155e-7,3.3285926e-7,0.031785987,0.00014526685,0.000033237062,0.00006385978,0.00038969668,0.00009198426,0.0001861552,0.9667658,0.00045025226],"about_ca_topic_score_codex":0.000018579369,"about_ca_topic_score_gemma":0.000020833662,"teacher_disagreement_score":0.9760554,"about_ca_system_score_codex":0.00022255842,"about_ca_system_score_gemma":0.00009670276,"threshold_uncertainty_score":0.99974227},"labels":[],"label_agreement":null},{"id":"W4313376734","doi":"10.5281/zenodo.7497361","title":"[Live-Streams] Toronto New Year's Eve 2023 Fireworks Live Broadcast Free","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Fireworks; STREAMS; Live streaming; Art; Geography; Archaeology; Computer science; Multimedia; Computer network","score_opus":0.020195306552067478,"score_gpt":0.21838878658135383,"score_spread":0.19819348002928636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313376734","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07589443,0.010858036,0.046418607,0.0026930661,0.002135639,0.0028439371,0.0066273464,0.03357773,0.8189512],"genre_scores_gemma":[0.95991135,0.0030379277,0.0052697593,0.00031885898,0.0009049173,8.5351985e-7,0.008015318,0.006194243,0.01634676],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987765,0.00006949237,0.00017889828,0.00030519837,0.00030072295,0.0003692122],"domain_scores_gemma":[0.9990276,0.000019495814,0.00004006016,0.000700445,0.00008618655,0.00012620025],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00015321518,0.00015130054,0.00013339875,0.000085701016,0.0011462861,0.00022718548,0.0013920753,0.00006143184,0.045856833],"category_scores_gemma":[0.00029610723,0.00017728476,0.000043821165,0.0003161058,0.00006542796,0.00036883474,0.002410462,0.00040163958,0.005406586],"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":[0.000021863372,0.000031999418,0.0000036032018,0.000018518405,0.000036658326,0.000016656564,0.0008410434,0.002169123,0.0007371988,0.0010533829,0.7916669,0.20340303],"study_design_scores_gemma":[0.00035680324,0.00013962864,0.000165087,0.000012186999,0.0000084868025,0.000040289968,0.002502418,0.0015815158,0.00012979319,0.0004573457,0.9943997,0.00020672889],"about_ca_topic_score_codex":0.0001028772,"about_ca_topic_score_gemma":0.0000040413765,"teacher_disagreement_score":0.88401693,"about_ca_system_score_codex":0.00041728409,"about_ca_system_score_gemma":0.000003336696,"threshold_uncertainty_score":0.9953678},"labels":[],"label_agreement":null},{"id":"W4313377056","doi":"10.5281/zenodo.7497352","title":"[Live-Streams] Montreal New Year's Eve 2023 Fireworks Live Broadcast Free","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Fireworks; STREAMS; Geography; Computer science; Archaeology; Computer network","score_opus":0.019293495366329582,"score_gpt":0.20977671338672382,"score_spread":0.19048321802039425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313377056","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1631368,0.0056448043,0.046986546,0.003716157,0.0018288955,0.0030250608,0.009042991,0.036666293,0.72995245],"genre_scores_gemma":[0.9727286,0.001451004,0.002850392,0.0001959193,0.00060032646,5.874434e-7,0.0068584247,0.0043925797,0.010922182],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987486,0.00007243651,0.00018211182,0.0003119927,0.00030303243,0.000381838],"domain_scores_gemma":[0.9990464,0.000022296372,0.000042231724,0.0006740419,0.00008560511,0.00012941999],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00014473517,0.00015519868,0.000138901,0.0001668107,0.0011769775,0.00022964456,0.0013519184,0.00006294641,0.021732682],"category_scores_gemma":[0.0003069823,0.0001803065,0.000045936784,0.0004922727,0.00007587128,0.00025639826,0.002320615,0.0005202703,0.006336846],"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":[0.00002542884,0.000033838445,0.0000046870255,0.00001637654,0.000037497546,0.00002715129,0.0006990214,0.0045339908,0.00059561495,0.00057433156,0.7479284,0.24552369],"study_design_scores_gemma":[0.00046491955,0.00014412453,0.00037043862,0.000012767354,0.000010565079,0.000055505887,0.0015991693,0.0031157935,0.00015152557,0.0008509696,0.99300295,0.00022128128],"about_ca_topic_score_codex":0.000075850505,"about_ca_topic_score_gemma":0.0000018601813,"teacher_disagreement_score":0.80959177,"about_ca_system_score_codex":0.0002211666,"about_ca_system_score_gemma":0.000003361834,"threshold_uncertainty_score":0.99443686},"labels":[],"label_agreement":null},{"id":"W4313377178","doi":"10.5281/zenodo.7497363","title":"[Live-Streams] Montreal New Year's Eve 2023 Fireworks Live Broadcast Free","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Fireworks; STREAMS; Computer science; Geography; Archaeology; Computer network","score_opus":0.019293495366329582,"score_gpt":0.20977671338672382,"score_spread":0.19048321802039425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313377178","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1631368,0.0056448043,0.046986546,0.003716157,0.0018288955,0.0030250608,0.009042991,0.036666293,0.72995245],"genre_scores_gemma":[0.9727286,0.001451004,0.002850392,0.0001959193,0.00060032646,5.874434e-7,0.0068584247,0.0043925797,0.010922182],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987486,0.00007243651,0.00018211182,0.0003119927,0.00030303243,0.000381838],"domain_scores_gemma":[0.9990464,0.000022296372,0.000042231724,0.0006740419,0.00008560511,0.00012941999],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00014473517,0.00015519868,0.000138901,0.0001668107,0.0011769775,0.00022964456,0.0013519184,0.00006294641,0.021732682],"category_scores_gemma":[0.0003069823,0.0001803065,0.000045936784,0.0004922727,0.00007587128,0.00025639826,0.002320615,0.0005202703,0.006336846],"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":[0.00002542884,0.000033838445,0.0000046870255,0.00001637654,0.000037497546,0.00002715129,0.0006990214,0.0045339908,0.00059561495,0.00057433156,0.7479284,0.24552369],"study_design_scores_gemma":[0.00046491955,0.00014412453,0.00037043862,0.000012767354,0.000010565079,0.000055505887,0.0015991693,0.0031157935,0.00015152557,0.0008509696,0.99300295,0.00022128128],"about_ca_topic_score_codex":0.000075850505,"about_ca_topic_score_gemma":0.0000018601813,"teacher_disagreement_score":0.80959177,"about_ca_system_score_codex":0.0002211666,"about_ca_system_score_gemma":0.000003361834,"threshold_uncertainty_score":0.99443686},"labels":[],"label_agreement":null},{"id":"W4361982554","doi":"10.1109/iccsse55346.2022.10079734","title":"Preface: (ICCSSE 2022)","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"China; Schedule; Engineering management; Library science; Engineering; Computer science; Political science; Operations research; Engineering ethics; Law","score_opus":0.0059156824920577715,"score_gpt":0.18655314489395353,"score_spread":0.18063746240189577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361982554","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54397553,0.004994959,0.17519963,0.00093241216,0.0025132445,0.0005548817,0.00025946525,0.0212512,0.25031868],"genre_scores_gemma":[0.99392897,0.00009087859,0.003027426,0.000057832844,0.00001388743,0.00005722398,0.000020435766,0.000014567046,0.0027887742],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99972415,0.0000023435352,0.000048221238,0.00006307543,0.00006232663,0.00009988181],"domain_scores_gemma":[0.9998021,0.000007075042,0.0000034143843,0.00017412288,0.000002294282,0.000010960476],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00002132505,0.000042953317,0.00004136383,0.000033087228,0.00005588703,0.000004387279,0.00013895358,0.000012271501,0.0013096499],"category_scores_gemma":[0.0000063594634,0.000041796222,0.000011829065,0.00011814831,0.000008305016,0.00007094047,0.0001344809,0.00012093305,0.000053556312],"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":[0.000007854648,0.000045554305,0.00045450035,0.000039330185,0.00004724642,0.00003672749,0.00016613887,0.18291326,0.060513258,0.06759272,0.51067287,0.17751053],"study_design_scores_gemma":[0.000109466055,0.00003174213,0.00014484083,6.606703e-7,0.0000021200524,0.00000899575,0.00058970525,0.011732242,0.014633379,0.002875538,0.96973044,0.00014086918],"about_ca_topic_score_codex":0.0000013578988,"about_ca_topic_score_gemma":0.0000012116942,"teacher_disagreement_score":0.45905757,"about_ca_system_score_codex":0.00002965999,"about_ca_system_score_gemma":0.00000209514,"threshold_uncertainty_score":0.9996033},"labels":[],"label_agreement":null},{"id":"W4381416012","doi":"10.1109/mssc.2023.3269335","title":"IEEE SSCS Toronto Chapter Holds First In-Person Wireline Workshop [Chapters]","year":2023,"lang":"en","type":"article","venue":"IEEE Solid-State Circuits Magazine","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Event (particle physics); Wireline; Face (sociological concept); Coronavirus disease 2019 (COVID-19); Telecommunications; Media studies; History; Library science; Engineering; Computer science; Sociology; Medicine; Social science","score_opus":0.03218629737159266,"score_gpt":0.2646636539960191,"score_spread":0.23247735662442642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381416012","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9218704,0.0045241187,0.03141825,0.0012292365,0.0076740147,0.001443516,0.0006608633,0.012362495,0.01881707],"genre_scores_gemma":[0.9901438,0.0073709646,0.00018618915,0.00012599681,0.00024158314,0.00008012727,0.000104413346,0.0001627363,0.0015842034],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99762887,0.000011784586,0.0005011685,0.0005696606,0.00030473762,0.000983752],"domain_scores_gemma":[0.998824,0.000101227066,0.000069161055,0.00079301203,0.00005864692,0.00015398068],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001923101,0.0004945193,0.00050904317,0.00031645648,0.000102013415,0.0000640003,0.0005294342,0.00023777969,0.0001348463],"category_scores_gemma":[0.00006179989,0.00052263174,0.00011049772,0.00061138807,0.000119313576,0.0007759418,0.00006241784,0.00041549475,0.0013280998],"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":[0.00006976197,0.00016897719,0.0008813392,0.0006682948,0.00028759803,0.0015841606,0.005099481,0.35001177,0.13950127,0.00092649483,0.07507461,0.42572623],"study_design_scores_gemma":[0.009516111,0.00075562496,0.016555218,0.0018953843,0.00017824046,0.00022464292,0.004728845,0.33564082,0.0766893,0.0051036435,0.54094285,0.0077693323],"about_ca_topic_score_codex":0.000044652188,"about_ca_topic_score_gemma":0.0021266623,"teacher_disagreement_score":0.46586823,"about_ca_system_score_codex":0.00026475266,"about_ca_system_score_gemma":0.000012805456,"threshold_uncertainty_score":0.99972254},"labels":[],"label_agreement":null},{"id":"W4386174015","doi":"10.1016/j.compeleceng.2023.108925","title":"SCHEISB: Design of a high efficiency IoMT security model based on sharded chains using bio-inspired optimizations","year":2023,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Advanced Data and IoT Technologies","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":"Brandon University","funders":"","keywords":"Computer science; Energy consumption; Quality of service; Efficient energy use; Network packet; Context (archaeology); The Internet; Process (computing); Distributed computing; Computer network; Computer security; Engineering","score_opus":0.01934030956955426,"score_gpt":0.22235071107470092,"score_spread":0.20301040150514665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386174015","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02577471,0.00007938799,0.9709686,0.000025104571,0.00014685225,0.00023053506,0.00002108123,0.0027395247,0.000014195522],"genre_scores_gemma":[0.83736193,0.000042204145,0.16244334,0.000016545962,0.000027791724,0.000017473481,0.000034830853,0.00005389426,0.0000019737274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998723,0.000011320369,0.00029055765,0.00028332023,0.00020181878,0.0004900373],"domain_scores_gemma":[0.9992845,0.00018335415,0.000034939672,0.00037624582,0.00004024992,0.00008073167],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009833062,0.0002533588,0.00032630385,0.0006768442,0.00006740445,0.000023855957,0.0003511829,0.00014570488,0.0000015456196],"category_scores_gemma":[0.00014173753,0.00027452043,0.00007301781,0.0020450738,0.00002821505,0.00011747916,0.000070853384,0.0002808299,0.0000053489002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061633673,0.000029379305,0.0000022575366,0.000044435645,0.000018509983,0.000007081094,0.000022571372,0.9876057,0.009271354,0.0012362845,0.00018376691,0.0015724961],"study_design_scores_gemma":[0.00034023452,0.00007972168,0.000018010565,0.000070705806,0.000013800995,0.0000016792793,0.0000019641627,0.9874863,0.01156454,0.00012604316,0.000024088535,0.00027289573],"about_ca_topic_score_codex":0.0000022889578,"about_ca_topic_score_gemma":5.8516868e-8,"teacher_disagreement_score":0.8115872,"about_ca_system_score_codex":0.00014915768,"about_ca_system_score_gemma":0.000035094927,"threshold_uncertainty_score":0.9999707},"labels":[],"label_agreement":null},{"id":"W4388040705","doi":"10.1109/pimrc56721.2023.10293919","title":"Online Traffic Prediction in Multi-RAT Heterogeneous Network: A User-Cybertwin Asynchronous Learning Approach","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Research and Development; National Natural Science Foundation of China; Peng Cheng Laboratory","keywords":"Computer science; Asynchronous communication; Nonlinear system; Machine learning; Traffic generation model; Noise (video); Data mining; Gaussian; Scheme (mathematics); Artificial intelligence; Algorithm; Real-time computing; Computer network","score_opus":0.02417833602680881,"score_gpt":0.24159279178546128,"score_spread":0.21741445575865248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388040705","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8447422,0.0003616246,0.14600934,0.000020635132,0.00030861335,0.00026759095,0.00002045441,0.007942323,0.00032724612],"genre_scores_gemma":[0.9766086,0.00041313499,0.021831715,0.000011417354,0.000083870036,0.000051247825,0.00044258803,0.00005400278,0.0005034723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989488,0.000017438131,0.00023662369,0.00025007184,0.00009309361,0.00045397904],"domain_scores_gemma":[0.99967915,0.00003304649,0.000017994498,0.00022255325,0.000009451446,0.000037829202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008840372,0.00017484423,0.00018801136,0.00017178743,0.000058481735,0.000022033348,0.00016373173,0.00016312067,0.000017467275],"category_scores_gemma":[0.000039152317,0.0001678438,0.00003942745,0.000642351,0.000027595435,0.00017322267,0.00008111579,0.00036790787,0.00007940209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030478861,0.00004077496,0.000869674,0.000023626035,0.0000127569665,0.000016669499,0.000066649045,0.96623987,0.0002920064,0.000015639995,0.00095567084,0.03146363],"study_design_scores_gemma":[0.00038587154,0.000044527547,0.0018197894,0.000019579158,0.000004635641,0.000014595789,0.00026894288,0.9919706,0.00023044663,0.000011038764,0.0050562746,0.00017371347],"about_ca_topic_score_codex":0.0000077235445,"about_ca_topic_score_gemma":0.00013848409,"teacher_disagreement_score":0.13186638,"about_ca_system_score_codex":0.00008491952,"about_ca_system_score_gemma":0.000006621679,"threshold_uncertainty_score":0.6844471},"labels":[],"label_agreement":null},{"id":"W4389076451","doi":"10.1109/mnet.2023.3335255","title":"Optimizing Mobile-Edge AI-Generated Everything (AIGX) Services by Prompt Engineering: Fundamental, Framework, and Case Study","year":2023,"lang":"en","type":"article","venue":"IEEE Network","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Generative grammar; Mobile edge computing; Edge device; Coding (social sciences); Mobile device; Task (project management); Artificial intelligence; Multimedia; Distributed computing; World Wide Web; Cloud computing; Systems engineering","score_opus":0.013736704933633185,"score_gpt":0.2538851889630422,"score_spread":0.240148484029409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389076451","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9846407,0.0026973553,0.006306622,0.000017225904,0.0020932467,0.00057615695,0.000034137156,0.0036136874,0.00002083521],"genre_scores_gemma":[0.99488324,0.0003967903,0.0036764508,0.000055986497,0.0005970056,0.00021166014,0.000049805658,0.00009267079,0.000036373873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863404,0.000016589593,0.00027605795,0.0003688795,0.00014154946,0.00056286575],"domain_scores_gemma":[0.99935526,0.00010914204,0.000035547233,0.00038646147,0.00002163359,0.00009195927],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014505754,0.00030961478,0.00028822923,0.00010722061,0.00018177691,0.00014797265,0.00021180337,0.00019308788,0.0000050950193],"category_scores_gemma":[0.000009395677,0.00031895473,0.00003084323,0.0007805175,0.000029019362,0.0003846659,0.00014195616,0.0004944011,0.00002576107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006842599,0.000032507458,0.0009850205,0.0001258755,0.00011440366,0.0007711067,0.0011730212,0.97804487,0.0023956364,0.000007764493,0.013924414,0.0024185411],"study_design_scores_gemma":[0.0010885366,0.0004688448,0.00025732274,0.00050581904,0.000119914235,0.00055826636,0.010729326,0.9392828,0.0040236847,0.00031823484,0.041058928,0.0015883717],"about_ca_topic_score_codex":0.000027324195,"about_ca_topic_score_gemma":0.000017055736,"teacher_disagreement_score":0.03876211,"about_ca_system_score_codex":0.000053665888,"about_ca_system_score_gemma":0.0000052935484,"threshold_uncertainty_score":0.99992627},"labels":[],"label_agreement":null},{"id":"W4392449971","doi":"10.1109/mcas.2024.3349728","title":"IEEE Sections Congress and CASS Workshop Ottawa","year":2024,"lang":"en","type":"article","venue":"IEEE Circuits and Systems Magazine","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Telecommunications; Electrical engineering; Engineering","score_opus":0.02249614817674891,"score_gpt":0.2489595804378522,"score_spread":0.2264634322611033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392449971","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7074675,0.12479754,0.11421278,0.0003079855,0.027746249,0.0010528284,0.00058749237,0.0070168925,0.016810736],"genre_scores_gemma":[0.9958511,0.00139556,0.00002459749,0.000008701445,0.0002835677,0.000040788367,0.000008509634,0.00003687768,0.002350301],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99918944,0.00001124765,0.00021812572,0.0002556335,0.00009073578,0.00023481589],"domain_scores_gemma":[0.99957466,0.0000895362,0.00001605676,0.0002219982,0.000026525253,0.00007124992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000089321176,0.00017613686,0.0002246183,0.00014533749,0.00007926008,0.00021131283,0.00008000814,0.00013728572,0.0000052138175],"category_scores_gemma":[0.000018815717,0.00015411027,0.000023389348,0.00022187478,0.00007552941,0.0002721309,0.000015854843,0.0002220894,0.00004845001],"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":[0.000005779157,0.00005345958,0.0012719341,0.006900081,0.00073535467,0.0009942737,0.001441119,0.040638976,0.18030119,0.017782355,0.48560166,0.2642738],"study_design_scores_gemma":[0.00050871365,0.00007820468,0.0008991186,0.0010067796,0.000089871595,0.0012371417,0.00075251266,0.14222124,0.0025989998,0.00049816945,0.84924656,0.00086265727],"about_ca_topic_score_codex":0.00000929266,"about_ca_topic_score_gemma":0.000037104943,"teacher_disagreement_score":0.36364493,"about_ca_system_score_codex":0.000031226155,"about_ca_system_score_gemma":0.0000074284267,"threshold_uncertainty_score":0.6284434},"labels":[],"label_agreement":null},{"id":"W4394147377","doi":"10.6084/m9.figshare.911326.v1","title":"IEEE ISTAS13 Symposium","year":2014,"lang":"en","type":"dataset","venue":"Figshare","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.01723961006802295,"score_gpt":0.22817174294200168,"score_spread":0.21093213287397872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394147377","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.445526e-8,0.00036131786,0.000010100885,0.000008658496,0.00036599778,0.00011988432,0.9974286,0.0010499569,0.00065543695],"genre_scores_gemma":[0.0000019149,0.00013980817,0.00008749294,0.00006026972,0.0003094098,0.00016883829,0.9991132,0.000042438056,0.00007660971],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991733,0.0000055603045,0.00016594357,0.00023766875,0.00012973686,0.0002878274],"domain_scores_gemma":[0.99891704,0.000053822583,0.00004508046,0.00090899423,0.000025911373,0.00004913215],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000010391159,0.0002968165,0.0002647645,0.00010709909,0.0000367454,0.000047828555,0.0006976109,0.00046863052,0.05937237],"category_scores_gemma":[0.00019952127,0.00029430477,0.00006393281,0.000104181614,0.0000062511594,0.00010570101,0.00013042567,0.00049859134,0.02151228],"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":[4.331292e-7,0.0000029524356,8.363973e-9,0.0007108244,0.000016009482,0.000014856572,7.5939965e-7,0.00041374858,0.00001279618,3.5641864e-7,0.99817264,0.00065463706],"study_design_scores_gemma":[0.00005372095,0.000015227339,5.070207e-7,0.00086776976,0.0000101981395,0.0000059522563,0.0000017910888,0.00014336198,0.00043198722,0.000012856288,0.9981034,0.00035322216],"about_ca_topic_score_codex":0.000001995325,"about_ca_topic_score_gemma":0.000009777844,"teacher_disagreement_score":0.037860088,"about_ca_system_score_codex":0.00005166899,"about_ca_system_score_gemma":0.000011999327,"threshold_uncertainty_score":0.9999509},"labels":[],"label_agreement":null},{"id":"W4394510410","doi":"10.6084/m9.figshare.911326","title":"2013 IEEE ISTAS13 Symposium","year":2014,"lang":"en","type":"dataset","venue":"Figshare","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.017169069459685885,"score_gpt":0.22784219144865372,"score_spread":0.21067312198896784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394510410","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[7.280826e-8,0.0006129073,0.0000132932555,0.000019720364,0.0004322113,0.00014804148,0.9973138,0.0010355024,0.0004244738],"genre_scores_gemma":[0.0000018450087,0.00036530336,0.00009196848,0.000063873376,0.00034192373,0.000207304,0.9987649,0.000048872593,0.000114035625],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990592,0.0000067063265,0.00018991453,0.00026954245,0.000145048,0.00032960408],"domain_scores_gemma":[0.99877965,0.00005880032,0.000054846438,0.0010154924,0.000032594042,0.000058583173],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0000118168455,0.00033765915,0.00029800605,0.00012171095,0.000041864576,0.000055627403,0.00078017643,0.0005283434,0.07268524],"category_scores_gemma":[0.00015809842,0.00033293193,0.00007161681,0.0001088754,0.00000761381,0.00013671255,0.00015386018,0.00055223564,0.03231406],"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":[4.969393e-7,0.000003562028,9.612105e-9,0.0007113332,0.00001882537,0.000013724564,8.669e-7,0.0008871929,0.000012058057,3.0528236e-7,0.9978869,0.0004647505],"study_design_scores_gemma":[0.00006137562,0.00001720307,5.738834e-7,0.0008997049,0.00001173879,0.000006320613,0.0000020875148,0.0002838779,0.0002998549,0.000013230264,0.9980032,0.0004008366],"about_ca_topic_score_codex":0.0000063029543,"about_ca_topic_score_gemma":0.000023757892,"teacher_disagreement_score":0.040371187,"about_ca_system_score_codex":0.00005432541,"about_ca_system_score_gemma":0.000012895587,"threshold_uncertainty_score":0.99991226},"labels":[],"label_agreement":null},{"id":"W4394674214","doi":"10.1109/lnet.2024.3386974","title":"Secure Private Blockchain-Based Instant Messaging Platform for Social Media Services","year":2024,"lang":"en","type":"article","venue":"IEEE Networking Letters","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Blockchain; Instant messaging; Internet privacy; Social media; Computer security; Instant; Computer science; World Wide Web; Business","score_opus":0.01793941004294834,"score_gpt":0.23163200685418084,"score_spread":0.2136925968112325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394674214","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87595385,0.0024933775,0.107951246,0.0019283232,0.0048239944,0.00040389333,0.00011511806,0.0060273977,0.00030281013],"genre_scores_gemma":[0.99531174,0.00002917658,0.002484535,0.00084660295,0.0011332886,0.00006127791,0.000047910533,0.000083339204,0.000002119149],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888545,0.0000047555936,0.0002110865,0.00025958483,0.00015184702,0.0004872974],"domain_scores_gemma":[0.9995428,0.00021206049,0.000028030128,0.00017517465,0.000008653318,0.000033307697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000107626685,0.00023224432,0.00020032786,0.00013969839,0.00014223834,0.00010477587,0.0002727259,0.0001314286,0.0000028675533],"category_scores_gemma":[0.0000039224537,0.00022526488,0.00008985836,0.00029685788,0.00004989678,0.00008697596,0.00002838783,0.000303576,0.0000065885833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000663104,0.000016947366,0.000176605,0.002721574,0.00042509765,0.0002215036,0.0028639366,0.6228039,0.16239694,0.0043806764,0.030487703,0.17343876],"study_design_scores_gemma":[0.00053904817,0.000017497898,0.000045002074,0.00074121624,0.000071463786,0.0000057744423,0.00019337688,0.49640098,0.022772409,0.0017737232,0.47672603,0.0007134837],"about_ca_topic_score_codex":0.0000013323508,"about_ca_topic_score_gemma":0.000017644828,"teacher_disagreement_score":0.4462383,"about_ca_system_score_codex":0.000104326486,"about_ca_system_score_gemma":0.000009178283,"threshold_uncertainty_score":0.91860354},"labels":[],"label_agreement":null},{"id":"W4394911616","doi":"10.1109/ispcem60569.2023.00005","title":"Preface ISPCEM 2023","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.013521757727708543,"score_gpt":0.2258246737094629,"score_spread":0.21230291598175435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394911616","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3808962,0.00075370714,0.17519122,0.0010447516,0.0018084773,0.00037505652,0.000101069425,0.068406,0.3714235],"genre_scores_gemma":[0.96957064,0.00086895004,0.0069829854,0.000037664304,0.000058346784,0.000030294033,0.00005409347,0.00003762134,0.022359403],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997325,7.329206e-7,0.0000447868,0.000059061735,0.00003649162,0.0001264356],"domain_scores_gemma":[0.9998115,0.00001276524,0.0000021394178,0.00015761379,0.0000034237169,0.000012562415],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000016200302,0.00004450332,0.00004178889,0.00004525573,0.000014603279,0.000007718445,0.00009184889,0.00003352754,0.00010598824],"category_scores_gemma":[0.000016287415,0.000038467562,0.0000105011,0.00024955833,0.000010089928,0.00010526133,0.000043637043,0.00005007941,0.0026814218],"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":[0.0000011292015,0.0000037507066,0.00032863568,0.000040986208,0.000021423653,0.00001579797,0.000038253045,0.029733798,0.023150055,0.010282982,0.85284084,0.08354235],"study_design_scores_gemma":[0.00016221608,0.000021640179,0.0024377275,0.000011275153,0.0000034102231,0.000002874756,0.00068673305,0.06405725,0.068035685,0.0069993758,0.85729426,0.00028755076],"about_ca_topic_score_codex":0.0000011894948,"about_ca_topic_score_gemma":0.000003454073,"teacher_disagreement_score":0.5886744,"about_ca_system_score_codex":0.000007773895,"about_ca_system_score_gemma":0.0000010851339,"threshold_uncertainty_score":0.9980951},"labels":[],"label_agreement":null},{"id":"W4401747345","doi":"10.1109/comst.2024.3430588","title":"Editorial: Third Quarter 2024 IEEE Communications Surveys and Tutorials","year":2024,"lang":"en","type":"editorial","venue":"IEEE Communications Surveys & Tutorials","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Telecommunications; Computer science; Geography; Archaeology","score_opus":0.03290008681825537,"score_gpt":0.31339131397826286,"score_spread":0.2804912271600075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401747345","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000024648585,0.016581228,0.0050332644,0.00036827402,0.9595084,0.0012667591,0.009458408,0.0037791054,0.0039798715],"genre_scores_gemma":[0.0015020318,0.06383255,0.0035603628,0.000010731357,0.921756,0.0010978996,0.0058120177,0.00046157918,0.001966825],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98646253,0.00694487,0.0026040846,0.0012993077,0.0015099652,0.0011792197],"domain_scores_gemma":[0.9673651,0.015472046,0.0005856149,0.014983529,0.001270663,0.000323086],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.01687358,0.0015313454,0.0022880367,0.00086933386,0.0009209702,0.0011411839,0.008476047,0.0037697924,0.000036812064],"category_scores_gemma":[0.004969925,0.0016029722,0.00042505044,0.0013966884,0.0015851824,0.0011083519,0.002159399,0.0051800758,0.00092327513],"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":[0.0000064562737,0.00011273934,0.000008578693,0.00032564646,0.00056164165,0.0000030127496,0.00030122543,0.00006730278,0.0014190486,0.00047142405,0.9935018,0.0032211626],"study_design_scores_gemma":[0.00061069406,0.00009980323,0.000017403665,0.0005057884,0.00042183077,0.000003497208,0.000156471,0.00028775693,0.0003021654,0.0033717584,0.99266016,0.0015626455],"about_ca_topic_score_codex":0.00088355807,"about_ca_topic_score_gemma":0.0020137993,"teacher_disagreement_score":0.047251325,"about_ca_system_score_codex":0.0007535493,"about_ca_system_score_gemma":0.00066242344,"threshold_uncertainty_score":0.99989575},"labels":[],"label_agreement":null},{"id":"W4401990622","doi":"10.1109/icmew63481.2024.10645390","title":"Optimizing Quality and Energy Efficiency in Webrtc with ML-Powered Adaptive FEC","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"WebRTC; Computer science; Forward error correction; Efficient energy use; Quality (philosophy); Energy (signal processing); Reliability engineering; Computer network; Telecommunications; Electrical engineering; Engineering; Physics; Statistics; Mathematics; Decoding methods","score_opus":0.019113005860237665,"score_gpt":0.25302540153301023,"score_spread":0.23391239567277256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401990622","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1120634,0.0073868358,0.8471613,0.00013039346,0.00016929074,0.00007749199,0.000019746381,0.00276527,0.030226292],"genre_scores_gemma":[0.98089373,0.00036254845,0.018553454,0.000014533617,0.0000081219705,0.000010971513,0.0000038512812,0.000014966013,0.00013783816],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948543,0.000006008609,0.00011148409,0.00017541029,0.00006008422,0.00016156583],"domain_scores_gemma":[0.99977636,0.000052889358,0.0000054904167,0.00013804449,0.00000638389,0.000020822012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000054404474,0.00010178529,0.000115166025,0.00010125415,0.00001708277,0.000029924466,0.000070859074,0.00005310211,0.000013859947],"category_scores_gemma":[0.0000125791275,0.00007617483,0.000010105076,0.00024177013,0.000047837748,0.00020571137,0.000044544766,0.0001105394,0.000003889602],"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":[0.00008243853,0.0000729748,0.0006080449,0.0003647898,0.00013335295,0.00025400595,0.0015403583,0.13353473,0.019901505,0.46923935,0.0013803862,0.37288803],"study_design_scores_gemma":[0.0012630862,0.0005255011,0.0032440608,0.0006304581,0.000028266557,0.00005246551,0.0065860385,0.8993927,0.04280041,0.007957996,0.035961207,0.0015577874],"about_ca_topic_score_codex":0.00006299943,"about_ca_topic_score_gemma":0.00016270245,"teacher_disagreement_score":0.8688303,"about_ca_system_score_codex":0.000036097732,"about_ca_system_score_gemma":0.000007835784,"threshold_uncertainty_score":0.31063193},"labels":[],"label_agreement":null},{"id":"W4402308721","doi":"10.1016/j.eswa.2024.125304","title":"Normal wiggly hesitant fuzzy modelling approach for 6G frameworks based blockchain technology","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Data and IoT Technologies","field":"Engineering","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":"University of Alberta","funders":"","keywords":"Blockchain; Computer science; Fuzzy logic; Distributed computing; Artificial intelligence; Computer security","score_opus":0.012057560235207968,"score_gpt":0.23441391064296097,"score_spread":0.222356350407753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402308721","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002892551,0.010204452,0.9829022,0.00022175691,0.00010803759,0.0013877716,0.00009537654,0.0039014544,0.0008896664],"genre_scores_gemma":[0.6857359,0.00005037828,0.30324978,0.000021413833,0.00016767623,0.010543363,0.000098500976,0.000075514385,0.000057499725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888194,0.00000504756,0.00026563025,0.00038696916,0.00011733692,0.00034309685],"domain_scores_gemma":[0.99914914,0.000078454024,0.000028068182,0.0006403265,0.000052316947,0.000051672556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000749172,0.00022623826,0.00023237352,0.00028203233,0.0001520022,0.00007341046,0.0003204209,0.000417868,0.0000015126419],"category_scores_gemma":[0.000006162564,0.00018871324,0.00004525427,0.0006625822,0.00007603626,0.000091465656,0.000026642754,0.00039279088,0.00001577422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008277112,0.000029717801,0.000007191217,0.00031887647,0.0000596851,0.0000020479577,0.00008393032,0.9229053,0.0020684772,0.06792452,0.0016539919,0.0049379827],"study_design_scores_gemma":[0.00012013517,0.000038243088,1.9777333e-7,0.00010411725,0.000012946326,0.000019047086,0.0006251937,0.9114586,0.0016764803,0.0009424067,0.08475567,0.00024696416],"about_ca_topic_score_codex":0.000008106584,"about_ca_topic_score_gemma":0.0000010227037,"teacher_disagreement_score":0.6854466,"about_ca_system_score_codex":0.00008952382,"about_ca_system_score_gemma":0.00002864342,"threshold_uncertainty_score":0.76955026},"labels":[],"label_agreement":null},{"id":"W4402390081","doi":"10.1109/icmiii62623.2024.00133","title":"Methods for Computer Network Security Management Assisted by Artificial Intelligence Models","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Network security; Artificial intelligence; Computer security","score_opus":0.04598551110853366,"score_gpt":0.34219093716379256,"score_spread":0.2962054260552589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402390081","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003388812,0.0014315245,0.9925609,0.0000760724,0.000740974,0.00021563497,0.000022465163,0.0021458403,0.0027726605],"genre_scores_gemma":[0.02704426,0.00024244754,0.97231597,0.000042428837,0.00010966606,0.00006941998,0.00004611738,0.00002337973,0.00010632167],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936944,0.000008258114,0.00016615263,0.00019306017,0.000043350035,0.00021971304],"domain_scores_gemma":[0.9996798,0.00009221306,0.0000057900684,0.00019016754,0.000009661444,0.00002236392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014195411,0.000112183625,0.00011172551,0.000042274754,0.00003470287,0.00006630785,0.00016080636,0.00006745842,0.000018488976],"category_scores_gemma":[0.0000027531003,0.00009893053,0.000045126562,0.00019598397,0.00002209291,0.00017451045,0.00008592059,0.00010119542,0.000014719163],"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":[0.000001733625,0.000004606334,5.037637e-8,0.000079702564,0.000040037517,0.0000013509923,0.00001451943,0.115820676,0.00006027441,0.1682947,0.030762171,0.6849202],"study_design_scores_gemma":[0.000008581729,0.000010981663,2.175371e-7,0.000016731627,0.000009439311,7.490424e-7,0.000023567278,0.6673284,0.0023424628,0.24622694,0.083941914,0.000090022186],"about_ca_topic_score_codex":9.2451455e-7,"about_ca_topic_score_gemma":0.0000016570557,"teacher_disagreement_score":0.6848302,"about_ca_system_score_codex":0.00003229087,"about_ca_system_score_gemma":0.0000015773657,"threshold_uncertainty_score":0.403427},"labels":[],"label_agreement":null},{"id":"W4403420770","doi":"10.1109/ieeedata.2024.3480012","title":"Descriptor: Comprehensive IEEE Research Data Collections (CIRDC)","year":2024,"lang":"en","type":"article","venue":"IEEE data descriptions.","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Data science; Information retrieval","score_opus":0.41164760109566845,"score_gpt":0.40846955602552326,"score_spread":0.0031780450701451857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403420770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023089664,0.05227101,0.718615,0.0016018252,0.039676003,0.0026755035,0.1238536,0.0247126,0.013504803],"genre_scores_gemma":[0.81630373,0.03178284,0.08866753,0.00032284964,0.0036590057,0.0005668476,0.048218433,0.00068481563,0.009793964],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99747,0.00007603839,0.00038430784,0.00096001435,0.00044571565,0.0006638792],"domain_scores_gemma":[0.9943066,0.0002696772,0.000016322168,0.0051346403,0.000148852,0.00012386516],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00044260535,0.00027462884,0.0002552779,0.0007592146,0.0005742293,0.0006919444,0.0031568059,0.00020508115,0.00012228325],"category_scores_gemma":[0.00020388408,0.0002800303,0.000035980556,0.0023133226,0.00036626367,0.003015709,0.00092279346,0.0010829602,0.001252487],"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":[0.000004627472,0.000029919276,0.0000060534735,0.00011291335,0.000098512544,0.000049770697,0.00005068728,0.0006207628,0.030248472,0.0006961383,0.95908564,0.008996527],"study_design_scores_gemma":[0.00012243562,0.00002854274,0.000026857304,0.00012294696,0.00004162623,0.00007301246,0.0005528322,0.12642702,0.0020135066,0.0012561501,0.8690418,0.00029327776],"about_ca_topic_score_codex":0.00013962251,"about_ca_topic_score_gemma":0.00026713655,"teacher_disagreement_score":0.7932141,"about_ca_system_score_codex":0.0002442042,"about_ca_system_score_gemma":0.000120943914,"threshold_uncertainty_score":0.9999652},"labels":[],"label_agreement":null},{"id":"W4404608690","doi":"10.1109/comst.2024.3464708","title":"Editorial: Fourth Quarter 2024 IEEE Communications Surveys and Tutorials","year":2024,"lang":"en","type":"editorial","venue":"IEEE Communications Surveys & Tutorials","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Telecommunications; Computer science; Geography; Archaeology","score_opus":0.034120085087223716,"score_gpt":0.31255696038768255,"score_spread":0.27843687530045885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404608690","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002419426,0.014169864,0.008391317,0.00035911446,0.9600955,0.0012695213,0.009801876,0.0034755461,0.0024130503],"genre_scores_gemma":[0.0017355748,0.060152087,0.004950801,0.000009520177,0.9239074,0.001081721,0.005973393,0.0005149869,0.0016744881],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98672295,0.0067982976,0.0025260951,0.0012839412,0.0014830276,0.0011857017],"domain_scores_gemma":[0.9681179,0.0150327645,0.00058440905,0.014682088,0.0012512554,0.00033157563],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.015913868,0.0015207967,0.0022500511,0.00088257575,0.00089718343,0.0011107724,0.008158947,0.0035055254,0.000050521565],"category_scores_gemma":[0.0048411936,0.0016012234,0.00042073266,0.001373022,0.0015019696,0.0010772918,0.0020716453,0.0048913476,0.00081552734],"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":[0.0000063285497,0.000111246314,0.000008582112,0.00031267008,0.00057186495,0.000003165202,0.0002662469,0.00010091706,0.001098611,0.00040342202,0.99212843,0.0049884967],"study_design_scores_gemma":[0.0006528537,0.000104236846,0.00001591656,0.000489716,0.0004268396,0.0000036350211,0.00014453602,0.00042904512,0.0002404709,0.0030620478,0.9928717,0.0015590071],"about_ca_topic_score_codex":0.00087885006,"about_ca_topic_score_gemma":0.0021954784,"teacher_disagreement_score":0.045982223,"about_ca_system_score_codex":0.0007439063,"about_ca_system_score_gemma":0.00066223094,"threshold_uncertainty_score":0.99996245},"labels":[],"label_agreement":null},{"id":"W4407574656","doi":"10.1109/comst.2025.3534456","title":"Editorial First Bi-Monthly 2025 IEEE Communications Surveys and Tutorials","year":2025,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Advanced Data and IoT 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":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Telecommunications; Remote sensing; Geography","score_opus":0.039551919004827824,"score_gpt":0.3077113537480863,"score_spread":0.26815943474325843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407574656","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013828336,0.03258682,0.13846697,0.0055370177,0.75147235,0.0048837354,0.006490454,0.010173772,0.036560528],"genre_scores_gemma":[0.9139368,0.03729931,0.017567186,0.00007546438,0.026879866,0.001246885,0.001648101,0.0002010246,0.0011453714],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9942084,0.003048722,0.0012499422,0.0005089026,0.0003729609,0.0006110986],"domain_scores_gemma":[0.98516184,0.005644313,0.00022889928,0.008288648,0.0005382598,0.0001380311],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0076791206,0.0005372097,0.0008913195,0.00046865424,0.0011461551,0.0003029523,0.0043600644,0.0006425525,0.000018918729],"category_scores_gemma":[0.0024751262,0.0005865437,0.00014630423,0.0012867852,0.0009796057,0.00078251766,0.0011540472,0.00091076875,0.00010375581],"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":[0.000022138318,0.0004564273,0.002730143,0.0002172933,0.0006962894,0.0000016614946,0.0005140954,0.0022755302,0.016601795,0.014861327,0.92835873,0.03326454],"study_design_scores_gemma":[0.0010169899,0.000045792767,0.003297852,0.00017003065,0.000117252785,0.0000011321066,0.00014513741,0.0012275412,0.0031639312,0.0042769187,0.985853,0.0006843869],"about_ca_topic_score_codex":0.0008830951,"about_ca_topic_score_gemma":0.0032575764,"teacher_disagreement_score":0.90010846,"about_ca_system_score_codex":0.00031448816,"about_ca_system_score_gemma":0.00019917282,"threshold_uncertainty_score":0.9996586},"labels":[],"label_agreement":null},{"id":"W4409791114","doi":"10.61091/jcmcc127a-441","title":"Research on Service Quality Improvement of Takeaway Platform Based on Artificial Intelligence","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Service quality; Service (business); Quality (philosophy); Artificial intelligence; Business; Marketing","score_opus":0.06333412274218035,"score_gpt":0.3535337788141994,"score_spread":0.29019965607201903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409791114","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93258375,0.00011599683,0.05116315,0.0002720181,0.012015267,0.00042454028,0.00001000201,0.00012364978,0.003291632],"genre_scores_gemma":[0.9972052,0.000024718842,0.0024538238,0.0000268644,0.0002638966,0.0000026174116,0.0000014100244,0.00002009188,0.0000014007007],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974975,0.00004681393,0.0012210562,0.00017242257,0.00074415683,0.00031803848],"domain_scores_gemma":[0.9969808,0.0015643988,0.00037939462,0.0003770777,0.00062354753,0.00007480454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022392883,0.00022593493,0.00061158714,0.000499829,0.0001724039,0.000084663574,0.0005163258,0.00018984576,0.0000036787578],"category_scores_gemma":[0.000615502,0.00019791492,0.00009997863,0.0007667437,0.000096254385,0.00011684982,0.00016181296,0.00084587396,0.0000020751008],"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":[0.00019104933,0.0004894025,0.000008924324,0.0005798725,0.000059459016,0.000004236048,0.00015167086,0.007841874,0.0034012732,0.962409,0.000119881566,0.024743363],"study_design_scores_gemma":[0.0009828707,0.0013662184,0.000034530745,0.0009949922,0.00002589586,0.0000016025733,0.0012810646,0.0645424,0.08313953,0.8471376,0.00028694028,0.00020638829],"about_ca_topic_score_codex":0.000006494363,"about_ca_topic_score_gemma":5.8682195e-7,"teacher_disagreement_score":0.11527143,"about_ca_system_score_codex":0.00015958157,"about_ca_system_score_gemma":0.000099884106,"threshold_uncertainty_score":0.80707365},"labels":[],"label_agreement":null},{"id":"W4409991136","doi":"10.1109/comst.2025.3560017","title":"Editorial Second Bi-Monthly 2025 IEEE Communications Surveys & Tutorials","year":2025,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Advanced Data and IoT Technologies","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":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Telecommunications; Data science","score_opus":0.05155535350439422,"score_gpt":0.3251082360560775,"score_spread":0.2735528825516833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409991136","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011588575,0.015036436,0.12489484,0.002036045,0.7639263,0.0037416734,0.008369217,0.009228935,0.061177954],"genre_scores_gemma":[0.84174806,0.020894637,0.048825085,0.00021091099,0.068272404,0.0027333517,0.0075495495,0.0004802249,0.009285743],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9919437,0.0043741595,0.0017260498,0.00061845954,0.0005140382,0.0008235506],"domain_scores_gemma":[0.98002285,0.005860739,0.00033140046,0.012808847,0.00080992404,0.00016622225],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.008922148,0.00068218814,0.0011264263,0.0006337822,0.0010721554,0.00034763987,0.0071523907,0.00085621997,0.00011732197],"category_scores_gemma":[0.0027986497,0.00075959146,0.00027036868,0.0018393623,0.00094940356,0.00096903497,0.0012365894,0.0013380746,0.00050343823],"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":[0.00001538924,0.00031667584,0.00056237326,0.000100595425,0.00047479925,7.0097343e-7,0.00025072752,0.0013877469,0.03449557,0.0064505627,0.94492024,0.01102464],"study_design_scores_gemma":[0.0008937172,0.000030280555,0.0011492625,0.00011777486,0.000100670324,9.55522e-7,0.000096129406,0.00035633225,0.0055218763,0.0047025033,0.9863068,0.00072373624],"about_ca_topic_score_codex":0.00060401193,"about_ca_topic_score_gemma":0.0059552966,"teacher_disagreement_score":0.8301595,"about_ca_system_score_codex":0.0005216042,"about_ca_system_score_gemma":0.00037794074,"threshold_uncertainty_score":0.9994855},"labels":[],"label_agreement":null},{"id":"W4411215333","doi":"10.1007/978-981-96-6468-9_25","title":"Optimizing Remote Medical Services with AloT: Integration of Large Language Models and 6G Edge Computing","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Advanced Data and IoT 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":"École de Technologie Supérieure; York University","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Artificial intelligence","score_opus":0.018780148097281516,"score_gpt":0.2868294432917256,"score_spread":0.2680492951944441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411215333","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00063861057,0.0013569051,0.9463303,0.00009691774,0.00005472524,0.00016156302,0.00002891866,0.00014927148,0.05118276],"genre_scores_gemma":[0.4098306,0.012529314,0.57688725,0.00031018717,0.000018322238,0.000004278035,0.00027623633,0.000013881813,0.00012990535],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922055,0.000005867895,0.00035700458,0.00010396431,0.0002001867,0.00011240655],"domain_scores_gemma":[0.99901307,0.00009428047,0.00010355683,0.0006435517,0.00011305936,0.000032511096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033831835,0.00012621078,0.00018287,0.00047311655,0.00013660765,0.00007727075,0.00074881327,0.000111918605,0.0000014960798],"category_scores_gemma":[0.000015553756,0.00011117072,0.00001100415,0.00019511189,0.0003729572,0.0022685113,0.00084895035,0.00031582927,8.0519413e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027722922,0.000005238369,0.0000075492862,0.00027461658,0.000010226482,3.7899613e-7,0.0052083847,0.009939187,0.000014269572,0.19017515,0.00003784225,0.7943244],"study_design_scores_gemma":[0.0001551862,0.000014725195,0.000037103615,0.00096710265,0.000004460786,0.0000064485107,0.00029237173,0.99407774,0.00004292726,0.001131392,0.0031582443,0.00011229354],"about_ca_topic_score_codex":0.000010177369,"about_ca_topic_score_gemma":0.00003831777,"teacher_disagreement_score":0.98413855,"about_ca_system_score_codex":0.00003655165,"about_ca_system_score_gemma":0.00004855047,"threshold_uncertainty_score":0.45334104},"labels":[],"label_agreement":null},{"id":"W4411614591","doi":"10.5220/0013449000003979","title":"A Hybrid-Based Transfer Learning Approach for IoT Device Identification","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ; University of Regina","funders":"","keywords":"Computer science; Identification (biology); Transfer of learning; Internet of Things; Artificial intelligence; Embedded system","score_opus":0.015463394880649317,"score_gpt":0.24800331365179662,"score_spread":0.2325399187711473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411614591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016565496,0.00012703455,0.9779598,0.00006943543,0.000057087873,0.00020156697,0.000007194028,0.0012470263,0.0037653362],"genre_scores_gemma":[0.94817084,0.000009993814,0.05095062,0.000040314175,0.000007772269,0.00012426983,0.00009450567,0.000011362689,0.00059029664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996074,0.000003489026,0.000113606104,0.00012381541,0.000034895085,0.0001167841],"domain_scores_gemma":[0.9997818,0.000036801986,0.0000039920174,0.00014766838,0.000020544341,0.000009180109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005667648,0.00007075083,0.000076464574,0.00008551938,0.000050682305,0.000020949416,0.00011356465,0.000039297847,0.000005108394],"category_scores_gemma":[0.000042005027,0.0000679375,0.00003050565,0.00012119384,0.000014211094,0.000062159794,0.0000064994606,0.000085709886,0.000004759529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013844723,0.000030437881,0.00012746506,0.00046764553,0.000040103827,3.3249475e-7,0.000021322769,0.7597798,0.070619605,0.02238344,0.0027951945,0.14372079],"study_design_scores_gemma":[0.00025071861,0.000009304054,0.00012004427,0.000009827771,0.000013894189,2.3033867e-7,0.000091678674,0.69916594,0.27332205,0.00069494447,0.026219815,0.00010154404],"about_ca_topic_score_codex":0.0000013405648,"about_ca_topic_score_gemma":0.000001648826,"teacher_disagreement_score":0.93160534,"about_ca_system_score_codex":0.0000241358,"about_ca_system_score_gemma":0.000008979544,"threshold_uncertainty_score":0.2770411},"labels":[],"label_agreement":null},{"id":"W4413155537","doi":"10.1109/comst.2025.3592430","title":"Editorial Fourth Bi-Monthly 2025 IEEE Communications Surveys and Tutorials","year":2025,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Advanced Data and IoT Technologies","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":"Memorial University of Newfoundland","funders":"","keywords":"Telecommunications; Computer science; Geography; Environmental science; Data science","score_opus":0.041959174577831285,"score_gpt":0.31365609507215586,"score_spread":0.2716969204943246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413155537","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014224282,0.025787318,0.22632109,0.003740222,0.6760698,0.0044021676,0.005573556,0.009205139,0.03467643],"genre_scores_gemma":[0.8961124,0.034492865,0.032759357,0.00009515178,0.03176328,0.0012501113,0.0018698166,0.00023295941,0.0014240686],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9931965,0.0040017697,0.0012629164,0.00051417813,0.0003937424,0.0006309082],"domain_scores_gemma":[0.9856972,0.0050583417,0.00023954696,0.008304426,0.0005573314,0.00014314114],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0083972085,0.00054330664,0.000914425,0.00049251085,0.00093438645,0.0003020026,0.0043144044,0.0006336373,0.000016732201],"category_scores_gemma":[0.0023389503,0.0005897553,0.00015067919,0.0012812723,0.000947621,0.0007996203,0.0011278763,0.00096515083,0.00009425715],"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":[0.000027483637,0.0005132111,0.0024281193,0.00020020027,0.00088483037,0.0000020800305,0.00053306815,0.0027234678,0.03754131,0.021396002,0.86241835,0.07133185],"study_design_scores_gemma":[0.0012366046,0.00005748758,0.0028899286,0.00016564698,0.00014538495,0.0000014369037,0.00018664318,0.0018580105,0.0044400548,0.006976618,0.98123914,0.00080307334],"about_ca_topic_score_codex":0.00069905695,"about_ca_topic_score_gemma":0.0016685615,"teacher_disagreement_score":0.8818881,"about_ca_system_score_codex":0.00028964807,"about_ca_system_score_gemma":0.00023737409,"threshold_uncertainty_score":0.99965537},"labels":[],"label_agreement":null},{"id":"W4417130747","doi":"10.1109/comst.2025.3641591","title":"Mixture of Experts for Decentralized Generative AI and Reinforcement Learning in Wireless Networks: A Comprehensive Survey","year":2025,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Wireless network; Wireless; Key (lock); Generative grammar; Adaptability; Resource (disambiguation); Generative model","score_opus":0.04074737434104819,"score_gpt":0.3233127668672785,"score_spread":0.2825653925262303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417130747","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13295595,0.007303128,0.85692084,0.00017316984,0.0011266666,0.001020696,0.00010478734,0.00029426045,0.00010050169],"genre_scores_gemma":[0.987341,0.008630218,0.0033099323,0.000029580291,0.000023505403,0.00021192769,0.00039881986,0.000017182661,0.00003786809],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984074,0.00067632954,0.0004823197,0.00015585693,0.000066366185,0.00021172206],"domain_scores_gemma":[0.9971132,0.0017377734,0.000088707566,0.00081090373,0.00022547653,0.000023937415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008792112,0.0001635361,0.00041803802,0.00014458761,0.0001237003,0.000028445067,0.0004538525,0.00014779762,0.0000018332233],"category_scores_gemma":[0.00040907474,0.00016870673,0.00003604286,0.00039748353,0.0001705723,0.00015293539,0.00015829981,0.00020116166,2.587132e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016853116,0.00016489842,0.039816175,0.00028226798,0.00051207194,0.0000010853528,0.001595391,0.8201952,0.06302779,0.010396211,0.0124048665,0.05143553],"study_design_scores_gemma":[0.0070667523,0.00023943391,0.08230022,0.00094376196,0.00010672288,0.00000215318,0.0014099508,0.76307595,0.06805078,0.0022648508,0.07309769,0.0014417624],"about_ca_topic_score_codex":0.00022048927,"about_ca_topic_score_gemma":0.0010133686,"teacher_disagreement_score":0.854385,"about_ca_system_score_codex":0.00007779289,"about_ca_system_score_gemma":0.000037762522,"threshold_uncertainty_score":0.68796605},"labels":[],"label_agreement":null},{"id":"W4417282062","doi":"10.1109/pimrc62392.2025.11275385","title":"Data-Driven Spectrum Demand Prediction: A Spatio-Temporal Framework with Transfer Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada; Carleton University","funders":"","keywords":"Exploit; Key (lock); Generalizability theory; Spectrum management; Wireless; Crowdsourcing; Feature (linguistics); Reliability (semiconductor); Spectral efficiency","score_opus":0.014567724230464936,"score_gpt":0.24587565010595103,"score_spread":0.2313079258754861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417282062","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015436967,0.0012899016,0.972708,0.0011310446,0.0006041559,0.0006119742,0.0003847233,0.0020701133,0.005763132],"genre_scores_gemma":[0.9406583,0.0015773564,0.05509088,0.00006715301,0.00018185191,0.00004163622,0.00081825623,0.00006032857,0.0015042522],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997749,0.00003832253,0.0005205324,0.0008191925,0.00027352205,0.0005994499],"domain_scores_gemma":[0.9981446,0.00014470781,0.00003495774,0.0015532145,0.000038532584,0.00008393499],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015584136,0.00046356162,0.00048204893,0.0002739278,0.00031410466,0.00016343153,0.0007085845,0.0004625417,0.00070115365],"category_scores_gemma":[0.0001171469,0.0004103502,0.000055850167,0.0009736799,0.00021321191,0.001088934,0.0003564773,0.0014575113,0.000058884234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005653414,0.00020717471,0.07170925,0.0011595916,0.0015988455,0.00016916714,0.00062251976,0.7988613,0.0001502403,0.08933614,0.010076177,0.02554422],"study_design_scores_gemma":[0.0020176147,0.00080970296,0.004689696,0.002068535,0.00050985126,0.000040737526,0.0019705852,0.63312995,0.0031171362,0.008493506,0.34202468,0.0011279697],"about_ca_topic_score_codex":0.000028815522,"about_ca_topic_score_gemma":0.00029029817,"teacher_disagreement_score":0.9252213,"about_ca_system_score_codex":0.00010808771,"about_ca_system_score_gemma":0.00010328806,"threshold_uncertainty_score":0.99983484},"labels":[],"label_agreement":null},{"id":"W6889787835","doi":"10.26226/morressier.5cb58cf5c668520010b56627","title":"NORMAL BASELINE CT PERFUSION PREDICTS SMALLER INFARCT VOLUMES AND BETTER FUNCTIONAL OUTCOME WITH INTRAVENOUS THROMBOLYSIS IN CLINICAL LACUNAR SYNDROME","year":2017,"lang":"en","type":"other","venue":"BiblioBoard Library Catalog (Open Research Library)","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Thrombolysis; Perfusion; Modified Rankin Scale; Infarction; Perfusion scanning; Lacunar infarction","score_opus":0.06239767309178043,"score_gpt":0.3191444314854503,"score_spread":0.2567467583936699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6889787835","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1679992,0.018974682,0.0010557515,0.0090774195,0.0018023526,0.007924029,0.008031911,0.0064418865,0.7786928],"genre_scores_gemma":[0.08626466,0.0838621,0.054010984,0.0011880877,0.0021944519,0.0011892855,0.026257258,0.0047758287,0.7402573],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9953309,0.00035826446,0.0008988033,0.0013074251,0.0008929293,0.001211665],"domain_scores_gemma":[0.99679947,0.00045140518,0.00022037675,0.002072765,0.000029354262,0.00042662228],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00080616755,0.0007809423,0.0013686644,0.007592167,0.00027052083,0.0013506198,0.0029945928,0.00072151975,0.0059555075],"category_scores_gemma":[0.00019494677,0.0006345062,0.00014202575,0.0031342993,0.0012478724,0.008139902,0.004443708,0.0027152551,0.0003750666],"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":[0.00017019278,0.00014047459,0.14612797,0.00031340268,0.00020188758,0.0014374322,0.000010661989,0.000025838843,0.000009407494,0.00009279893,0.8206303,0.030839635],"study_design_scores_gemma":[0.0016777451,0.00064014207,0.12001871,0.00084027945,0.000036870337,0.00015410394,0.00003644876,0.00055034284,0.000089164816,0.00025145232,0.87481964,0.0008851146],"about_ca_topic_score_codex":0.00024394946,"about_ca_topic_score_gemma":0.00015243184,"teacher_disagreement_score":0.08173452,"about_ca_system_score_codex":0.000030501302,"about_ca_system_score_gemma":0.00033134455,"threshold_uncertainty_score":0.99968606},"labels":[],"label_agreement":null},{"id":"W6890365784","doi":"10.34943/244cb413-06bb-4885-be2b-5db5f1dadee4","title":"Barkley Canyon Axis Oxygen Sensor Deployed 2021-08-21","year":2021,"lang":"en","type":"dataset","venue":"Ocean Networks Canada Society","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Canyon; Benthic zone; Oxygen; Sediment; Oxygen sensor","score_opus":0.0049588963859232855,"score_gpt":0.18250585759331517,"score_spread":0.17754696120739188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6890365784","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010489504,0.0079179965,0.0016226071,0.00013115648,0.0023085787,0.00025629293,0.987176,0.00045254643,0.000029898936],"genre_scores_gemma":[0.00030011032,0.016873747,0.0013502042,0.0005849839,0.0009679444,0.000016919226,0.9795577,0.00014165357,0.00020674657],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99720895,0.000036931327,0.0005048555,0.00070468656,0.0004959081,0.0010486857],"domain_scores_gemma":[0.9979093,0.00013721413,0.000120590754,0.0014853814,0.00009131044,0.00025620506],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010911358,0.00075418985,0.0007717405,0.000037848644,0.00023835142,0.0000986985,0.0008302805,0.0009855168,0.00017329313],"category_scores_gemma":[0.000063654006,0.00081499957,0.00029946523,0.0005620884,0.000110696965,0.00013536896,0.00031712174,0.001754034,0.000003708479],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021632798,0.000007877479,0.00000892355,0.00017586481,0.00032701105,0.00016383576,0.000006996792,0.05208748,0.0000046987325,0.0000013858769,0.9456863,0.0015274723],"study_design_scores_gemma":[0.00020724125,0.000011791578,0.000026674958,0.00013013738,0.00011479585,0.000019670822,0.00034724138,0.011180433,0.000026086422,0.0000064497463,0.98708594,0.00084353465],"about_ca_topic_score_codex":0.10706094,"about_ca_topic_score_gemma":0.5051305,"teacher_disagreement_score":0.39806962,"about_ca_system_score_codex":0.0010386114,"about_ca_system_score_gemma":0.0005624614,"threshold_uncertainty_score":0.99943006},"labels":[],"label_agreement":null},{"id":"W6901690359","doi":"10.6068/dp14ba83aab8c32","title":"Trend 1974 - 2006. Statistics Canada. CANSIM: Labor - Nonwage Benefits | Country: Canada | Table: Registered pension plans (RPPs) and members, by class of employees eligible for the plan, sector, type of plan and contributory status | Variable: All employees, Plans, Defined benefit registered pension plans | Units: # %, 1974-2006. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-142.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Descriptive statistics; Payroll; Pension; Census; Wages and salaries; Socioeconomic status; Social security; Summary statistics; Economic statistics; Official statistics","score_opus":0.03857648817839217,"score_gpt":0.2493760633315656,"score_spread":0.21079957515317344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901690359","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000022418093,0.01489865,0.00004788418,0.000007135845,0.00071348884,0.0010500463,0.98276526,0.00020585199,0.00028926646],"genre_scores_gemma":[0.000100586454,0.012723481,0.00041022286,0.000115202376,0.00009959402,0.000025996671,0.9855518,0.00029719903,0.0006759252],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954784,0.00012060959,0.0012742871,0.0011633137,0.00089502597,0.0010683927],"domain_scores_gemma":[0.9936609,0.0017897995,0.00087591435,0.0030862805,0.00016750567,0.0004195551],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004218009,0.0010013261,0.0015876279,0.00016525136,0.00016660696,0.00014193931,0.0016380576,0.0007639764,0.00015595509],"category_scores_gemma":[0.0002693905,0.0008559031,6.733071e-7,0.00031766397,0.0003382245,0.000297059,0.0006044314,0.00076204946,0.0000020522493],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053334556,0.000049002596,0.00012368857,0.0013782613,0.00055070076,0.00012008855,0.0000073025035,0.00036810478,0.00006837461,0.0007449795,0.9959376,0.00011854929],"study_design_scores_gemma":[0.0019446757,0.00033248952,0.000016724292,0.00021785949,0.00062583725,0.000105340594,0.0002541655,0.002616345,0.000002434717,0.000001291519,0.9929375,0.0009453019],"about_ca_topic_score_codex":0.9932278,"about_ca_topic_score_gemma":0.99770814,"teacher_disagreement_score":0.004480356,"about_ca_system_score_codex":0.00020781664,"about_ca_system_score_gemma":0.0024967403,"threshold_uncertainty_score":0.9993892},"labels":[],"label_agreement":null},{"id":"W6927452309","doi":"10.3389/fevo.2022.1066680.s004","title":"Table_4_Behavioral strategies of prehistoric and historic children from dental microwear texture analysis.pdf","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Prehistory; Deciduous teeth; Texture (cosmology); Anterior teeth; Dentition; Assemblage (archaeology); Middle Stone Age; Posterior teeth","score_opus":0.00934649817082665,"score_gpt":0.21699205624786075,"score_spread":0.2076455580770341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6927452309","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028494105,0.01211445,0.0000025001646,6.086576e-7,0.00015171744,0.00017862233,0.9870092,0.00021481847,0.000043093332],"genre_scores_gemma":[0.0005084081,0.00029811985,0.00010777674,0.0000032992396,0.000061518636,0.00011871187,0.9988152,0.00003135113,0.00005566171],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988449,0.000014795395,0.0002864107,0.0003863654,0.00023202738,0.00023546917],"domain_scores_gemma":[0.9990583,0.000033917153,0.00015288267,0.0006807847,0.0000241242,0.000049969458],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000012411604,0.00035892386,0.00055857503,0.0004130083,0.000075550386,0.000044787477,0.00063129084,0.00034679947,0.39144543],"category_scores_gemma":[0.00004218737,0.000388981,0.00013808857,0.000602084,0.000018603285,0.00025361133,0.00039076406,0.0006599548,0.00014417044],"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":[0.0000026200635,0.000021039004,0.00004607215,0.00014372876,0.0002658489,0.000017807479,0.000011570418,0.0004557356,0.00006492234,1.9769034e-7,0.99842215,0.0005483398],"study_design_scores_gemma":[0.00012124725,0.00003596378,0.0016369348,0.00010052802,0.0005126446,0.0000054606244,0.00010940449,0.000018602927,0.00006724156,0.000006241903,0.99702555,0.00036018182],"about_ca_topic_score_codex":0.0003252386,"about_ca_topic_score_gemma":0.00024107458,"teacher_disagreement_score":0.39130124,"about_ca_system_score_codex":0.0002889164,"about_ca_system_score_gemma":0.000040183702,"threshold_uncertainty_score":0.99985623},"labels":[],"label_agreement":null},{"id":"W6977150956","doi":"10.6084/m9.figshare.26684916.v1","title":"Additional file 5 of Protocol for the ONLOOP trial: pragmatic randomized trial evaluating a province-wide system of personalized reminders for evidence-based surveillance tests in adult survivors of childhood cancer in Ontario","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ottawa Hospital; University of Ottawa; University of Toronto; SickKids Foundation; Trillium Health Centre; Women's College Hospital","funders":"","keywords":"Protocol (science); Randomized controlled trial; Childhood cancer; Informed consent; MEDLINE; Clinical trial","score_opus":0.06439165678420233,"score_gpt":0.3248311322942653,"score_spread":0.26043947551006297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977150956","genre_codex":"dataset","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00034541162,0.00010335319,0.00009887593,0.00002324778,0.0000581898,0.14824677,0.85099363,0.000101249556,0.000029269828],"genre_scores_gemma":[0.006626462,9.859003e-7,0.0029341578,0.0000042837137,0.000042479733,0.90420085,0.08611638,0.000035269088,0.000039129096],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.9982807,0.00011014205,0.00088808074,0.00023110394,0.00028480482,0.00020514794],"domain_scores_gemma":[0.9730196,0.02618433,0.0003401778,0.00023240423,0.00020751421,0.000015966883],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005411914,0.0001855584,0.0007165444,0.00016296639,0.000022954362,0.00001568253,0.00026736813,0.00012179738,0.08133943],"category_scores_gemma":[0.040343214,0.00013421329,0.00022545201,0.00029958648,0.000034761626,0.00016802471,0.000029266408,0.00018448844,0.0000016722864],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.48664963,0.00014282287,0.000012219555,0.020151006,0.0001960943,0.0000019775944,0.00051357405,0.013370066,0.000038072387,0.00003970867,0.47282702,0.0060578166],"study_design_scores_gemma":[0.7000396,0.0006551862,0.00018710174,0.095051534,0.000035480392,6.840239e-7,0.00029395355,0.19144277,0.00042560388,0.00007719025,0.011504945,0.0002859197],"about_ca_topic_score_codex":0.0007366217,"about_ca_topic_score_gemma":0.03299551,"teacher_disagreement_score":0.76487726,"about_ca_system_score_codex":0.0002921585,"about_ca_system_score_gemma":0.0011685656,"threshold_uncertainty_score":0.9846498},"labels":[],"label_agreement":null},{"id":"W6984886420","doi":"","title":"","year":2024,"lang":"en","type":"other","venue":"Directory of Open access Books (OAPEN Foundation)","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México; Natural Sciences and Engineering Research Council of Canada; Secretaría de Estado de Ciencia, Tecnología e Innovación; Mitacs; Instituto Politécnico Nacional","keywords":"Sustainability; Transformative learning; Sustainable development; Resource (disambiguation); Fossil fuel","score_opus":0.06825939769797826,"score_gpt":0.3951550587631018,"score_spread":0.3268956610651236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6984886420","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022297465,0.0027750318,0.0039530424,0.00007568873,0.0025590244,0.0020479008,0.000062130486,0.0055436837,0.98276055],"genre_scores_gemma":[0.01140665,0.0046536126,0.007914634,0.00013501188,0.00047421266,0.0011902198,0.0018125916,0.0038574568,0.9685556],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99691147,0.000047139907,0.0009438137,0.0010271273,0.00049856975,0.0005719027],"domain_scores_gemma":[0.9974637,0.000097393815,0.0004259715,0.0018024961,0.00008128716,0.00012917422],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00026030018,0.0009238826,0.0013527994,0.0013546065,0.00008717176,0.002491684,0.0075737643,0.000351985,0.0071997144],"category_scores_gemma":[0.00012683883,0.00094468857,0.00019545086,0.0006756157,0.0003953963,0.0043560374,0.003838688,0.00069037825,0.0020606422],"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":[0.000041064235,0.0000783563,0.00041388243,0.002890088,0.0013465439,0.000045324447,0.00008594031,0.00040783468,0.0014425402,0.038398962,0.52661866,0.42823082],"study_design_scores_gemma":[0.0004103776,0.000025733516,0.0003119791,0.0014053644,0.00012764144,0.000005387297,0.000060313556,0.00018499637,0.0038231155,0.006508721,0.9860832,0.0010531604],"about_ca_topic_score_codex":0.000848296,"about_ca_topic_score_gemma":0.001248176,"teacher_disagreement_score":0.45946458,"about_ca_system_score_codex":0.00022439251,"about_ca_system_score_gemma":0.00011878425,"threshold_uncertainty_score":0.99930036},"labels":[],"label_agreement":null},{"id":"W7039851352","doi":"","title":"MRI measures of brain injury in children with Multiple Sclerosis","year":2014,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Multiple sclerosis; Magnetic resonance imaging; Disease; White matter; Central nervous system; Lesion; Population","score_opus":0.017360353068247705,"score_gpt":0.21663334527273576,"score_spread":0.19927299220448805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039851352","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9914035,0.00037406848,0.000009417084,0.00000477166,0.00028086826,0.0006400951,0.0017367315,0.0008799194,0.0046705925],"genre_scores_gemma":[0.9950632,0.0006495097,0.0024197872,0.000031564006,0.000019403926,0.00014183126,0.0012476369,0.00023780263,0.0001892611],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99736,0.000085283624,0.00073487475,0.00069210585,0.00053060055,0.00059714966],"domain_scores_gemma":[0.99839246,0.0001357195,0.00026861086,0.0009440641,0.00014460272,0.000114511284],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036881544,0.00070666126,0.0008637803,0.0006858386,0.00018611689,0.000029394076,0.0008019255,0.00082935917,0.000023736291],"category_scores_gemma":[0.00067566376,0.00069451483,0.00015114625,0.00070413825,0.00008145647,0.00062001107,0.00008989545,0.00139075,0.000043713244],"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":[0.00044297831,0.00023571482,0.008355894,0.0008374123,0.00074029935,0.00001059414,0.000015933152,0.0067394804,0.24736014,0.010856341,0.00008094949,0.7243243],"study_design_scores_gemma":[0.0025715085,0.00046774538,0.103178695,0.0022446827,0.00019446704,0.000014608642,0.00026730762,0.00024821644,0.86404043,0.00524124,0.0189872,0.0025439064],"about_ca_topic_score_codex":0.00020397875,"about_ca_topic_score_gemma":0.0020868143,"teacher_disagreement_score":0.72178036,"about_ca_system_score_codex":0.00028395047,"about_ca_system_score_gemma":0.000017425506,"threshold_uncertainty_score":0.9995506},"labels":[],"label_agreement":null},{"id":"W7071593181","doi":"","title":"Spatial and Channel Attention-based 3D Object Classification Research for 3D Point Clouds","year":2023,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Point cloud; Deep learning; Point (geometry); Object (grammar); Artificial neural network; Transfer of learning; Realization (probability); Cognitive neuroscience of visual object recognition","score_opus":0.036949910796669524,"score_gpt":0.26679864287647626,"score_spread":0.22984873207980672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7071593181","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9885723,0.00015981213,0.0078108427,0.00057905086,0.00064963836,0.0008468002,0.00038019056,0.0008212195,0.00018015361],"genre_scores_gemma":[0.84680045,0.0014703857,0.020084582,0.0000061919463,0.00018019778,0.000028578084,0.01208484,0.00020482561,0.119139925],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.99904686,0.00002387305,0.000108708926,0.0003157405,0.00021582839,0.00028898485],"domain_scores_gemma":[0.9992767,0.00010474849,0.00007085714,0.00030610638,0.0001969565,0.000044640212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002052968,0.00017477486,0.000257862,0.0004979728,0.000221306,0.000020177205,0.00025408616,0.00035512104,0.000013718749],"category_scores_gemma":[0.0000564226,0.00021280846,0.000073710944,0.00025782528,0.00010479525,0.000158409,0.00004426959,0.00029556802,0.000027846994],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003341695,0.00050969067,0.0018196118,0.023959734,0.0016462526,0.00015519593,0.1437261,0.010907441,0.30056265,0.0031794785,0.073238164,0.436954],"study_design_scores_gemma":[0.0036530204,0.0009334379,0.04558846,0.0014126268,0.00035863463,0.0000021642588,0.48254317,0.44311613,0.012732116,0.00270068,0.005323625,0.0016359187],"about_ca_topic_score_codex":0.003932002,"about_ca_topic_score_gemma":0.019418234,"teacher_disagreement_score":0.43531805,"about_ca_system_score_codex":0.00010302375,"about_ca_system_score_gemma":0.00003967596,"threshold_uncertainty_score":0.99847484},"labels":[],"label_agreement":null},{"id":"W7099320066","doi":"","title":"Bird Management, Population and Habitat Assessment Branch. The principal authors were Kathy","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Waterfowl; Wildlife; Population; Habitat; Principal (computer security); Service (business); Wildlife conservation; Tiger","score_opus":0.00874656905223879,"score_gpt":0.25565354690311787,"score_spread":0.2469069778508791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099320066","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7037032,0.0005953105,0.2588286,0.0022911727,0.00045755014,0.0006813527,0.000008201546,0.008292963,0.025141587],"genre_scores_gemma":[0.9856191,0.00020265035,0.013876491,0.000075173455,0.000017369479,0.000008074491,0.000005506471,0.000014986799,0.00018059001],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9995561,0.0000049953987,0.000105883846,0.00011350462,0.00007899147,0.00014051548],"domain_scores_gemma":[0.99973094,0.0000109566145,0.000012963225,0.00022114857,0.000004595234,0.000019408739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005739164,0.00009155928,0.000072733004,0.000048474725,0.00006770654,0.000036239497,0.00010726387,0.00004399031,0.000008790385],"category_scores_gemma":[0.000004844504,0.000061809405,0.0000155454,0.000092194496,0.000016749245,0.00019688848,0.000040436622,0.000103271166,0.0000113252945],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005285404,0.000027797294,0.028140262,0.000060473518,0.000053045776,0.00000856824,0.00007400966,0.007372342,0.0010620005,0.28013605,0.006175573,0.6768846],"study_design_scores_gemma":[0.00025879862,0.000054474956,0.9368399,0.000018353785,0.000018375515,0.000005146464,0.00027675377,0.014958194,0.00022994509,0.027050104,0.02007394,0.00021603944],"about_ca_topic_score_codex":0.000005492866,"about_ca_topic_score_gemma":0.000041091327,"teacher_disagreement_score":0.90869963,"about_ca_system_score_codex":0.000026490006,"about_ca_system_score_gemma":8.0163574e-7,"threshold_uncertainty_score":0.25205144},"labels":[],"label_agreement":null},{"id":"W7115910094","doi":"10.1109/comst.2025.3637470","title":"Editorial Sixth Bi-Monthly 2025 IEEE Communications Surveys and Tutorials","year":2025,"lang":"","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Advanced Data and IoT Technologies","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":"Memorial University of Newfoundland","funders":"","keywords":"Key (lock); Work (physics); Data collection","score_opus":0.04692354357072607,"score_gpt":0.32652824785177903,"score_spread":0.27960470428105294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115910094","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004816743,0.059508633,0.1036304,0.0050458,0.78446645,0.00569945,0.011120093,0.0044085756,0.02130386],"genre_scores_gemma":[0.76847297,0.13114995,0.02397358,0.00012829668,0.06768346,0.0017316794,0.0032072514,0.0004487489,0.0032040721],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9750477,0.016946068,0.0038111613,0.0015149193,0.0010075511,0.0016725739],"domain_scores_gemma":[0.96129745,0.015199641,0.00094666233,0.02043654,0.0017213041,0.00039840201],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["metaepi_narrow","sts","research_integrity"],"category_scores_codex":[0.023765631,0.0015463185,0.0025541314,0.0012305432,0.0028221987,0.0010630655,0.011033991,0.0020974393,0.0000685017],"category_scores_gemma":[0.0081952615,0.0018022163,0.0004543148,0.0033688475,0.0032253666,0.001873399,0.004264061,0.0029012894,0.00028997823],"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":[0.00010557656,0.0019216337,0.002652174,0.0007090067,0.002869639,0.0000056468784,0.0013929263,0.0026838204,0.04354053,0.039505944,0.8010956,0.10351751],"study_design_scores_gemma":[0.0028106894,0.00018701387,0.003542862,0.00075466465,0.00068175734,0.000002971147,0.0005405766,0.0023010734,0.0050387583,0.009495764,0.9726872,0.0019566938],"about_ca_topic_score_codex":0.0030039875,"about_ca_topic_score_gemma":0.0033099535,"teacher_disagreement_score":0.7636562,"about_ca_system_score_codex":0.0009932332,"about_ca_system_score_gemma":0.0011245582,"threshold_uncertainty_score":0.99997395},"labels":[],"label_agreement":null},{"id":"W7117465056","doi":"10.1109/mcas.2025.3614512","title":"WiCAS Events Report—IEEE ISCAS 2025 [CASS Conference Highlights]","year":2025,"lang":"","type":"article","venue":"IEEE Circuits and Systems Magazine","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Simon Fraser University","funders":"","keywords":"Milestone; Excellence; Session (web analytics); Empowerment; Corporate governance; Center of excellence","score_opus":0.03167813794614033,"score_gpt":0.27417146212409693,"score_spread":0.2424933241779566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117465056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19520654,0.068196714,0.55908924,0.0021511784,0.054903865,0.004694835,0.0012009066,0.003324716,0.11123198],"genre_scores_gemma":[0.95701224,0.0058778524,0.00009604096,0.000048859863,0.0004354413,0.00012184875,0.000063082516,0.00007613544,0.036268488],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9952585,0.00010860643,0.0017710163,0.0013019142,0.00049304444,0.0010669173],"domain_scores_gemma":[0.9969753,0.0002010267,0.00043437348,0.0017670707,0.00036759643,0.0002546067],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00052216306,0.00090159476,0.0014326549,0.0005460222,0.00035898187,0.00035348703,0.0007178172,0.00077167124,0.000074999916],"category_scores_gemma":[0.00021627855,0.0008634648,0.00017211415,0.0008931587,0.00027611287,0.00070186734,0.00018367519,0.00075626513,0.0003137236],"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":[0.00011070215,0.00077927625,0.0070107905,0.01829094,0.0037403828,0.008654,0.0009862181,0.013765481,0.19861983,0.29367563,0.32413486,0.1302319],"study_design_scores_gemma":[0.0036574285,0.00038331602,0.008254343,0.007045399,0.0007334989,0.0019228649,0.0008869521,0.038650822,0.010480906,0.0047133537,0.9202772,0.0029939504],"about_ca_topic_score_codex":0.00007800983,"about_ca_topic_score_gemma":0.000036514964,"teacher_disagreement_score":0.7618057,"about_ca_system_score_codex":0.00024101419,"about_ca_system_score_gemma":0.00020158052,"threshold_uncertainty_score":0.9993816},"labels":[],"label_agreement":null},{"id":"W7117736983","doi":"10.1109/mwc.2025.3620164","title":"Guest Editorial: Special Issue on 6G Local Area Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Wireless Communications","topic":"Advanced Data and IoT Technologies","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 Ottawa","funders":"","keywords":"Intelligent Network; Local area network; Key (lock); Power (physics); Radio networks; Cellular radio","score_opus":0.014777052492403011,"score_gpt":0.26497699377712064,"score_spread":0.2501999412847176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117736983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00368291,0.0023843725,0.4997232,0.00354687,0.29872495,0.00097004586,0.00022880216,0.0071249837,0.18361387],"genre_scores_gemma":[0.92496455,0.004948355,0.001749967,0.00015306588,0.06724398,0.000173399,0.00022387497,0.00005207683,0.00049072836],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99930936,0.000024835295,0.00021813712,0.00013877414,0.000096170355,0.00021272407],"domain_scores_gemma":[0.9976707,0.00022328956,0.000026786232,0.0019960366,0.000050788498,0.000032365813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006005845,0.00015097679,0.00017630712,0.00011041862,0.0002095178,0.00003718545,0.0011985991,0.00020318071,0.000013428381],"category_scores_gemma":[0.000031942924,0.00015925961,0.000042409658,0.0003684904,0.0002268473,0.00012860305,0.00019274706,0.0005559484,0.00009678579],"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":[0.000006460075,0.000058772614,0.00003327875,0.000010674196,0.000034131484,7.667787e-7,0.000038035538,0.04735766,0.00018225292,0.007927677,0.88039315,0.06395713],"study_design_scores_gemma":[0.00016063587,0.000014323842,0.000059950205,0.000078118195,0.0000118449,4.424144e-7,0.00013849189,0.04710626,0.0013330986,0.000528495,0.95041484,0.00015349337],"about_ca_topic_score_codex":0.000011548162,"about_ca_topic_score_gemma":0.00010774455,"teacher_disagreement_score":0.92128164,"about_ca_system_score_codex":0.00010643473,"about_ca_system_score_gemma":0.000017050852,"threshold_uncertainty_score":0.64944184},"labels":[],"label_agreement":null},{"id":"W7118630692","doi":"10.5281/zenodo.18168574","title":"Dynamic Pricing Models in Telecom: Implementation of Real Time, Dynamic Pricing Strategies through Artificial Intelligence","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Marriott International (Canada)","funders":"","keywords":"Dynamic pricing; Revenue management; Software deployment; Pricing strategies; Reinforcement learning; Key (lock); Process (computing); Revenue; Big data","score_opus":0.0317254492106625,"score_gpt":0.28837461787753277,"score_spread":0.25664916866687026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118630692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4412937,0.00018750476,0.5340323,0.00013172963,0.00008602068,0.0004256263,0.00018796395,0.0017117745,0.021943409],"genre_scores_gemma":[0.9916552,0.0006865736,0.006403047,0.0000051127104,0.000008471205,7.385117e-8,0.0008281686,0.00038997782,0.000023402627],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988745,0.000063458385,0.00035531825,0.0002501046,0.00017752267,0.00027909194],"domain_scores_gemma":[0.99942553,0.000019012574,0.00006341727,0.0002941462,0.0001707032,0.000027207292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001791385,0.00011982224,0.00015611206,0.00017631859,0.00026576014,0.00017751183,0.00038929284,0.00005745642,0.00083160325],"category_scores_gemma":[0.000079503465,0.00013946967,0.000025688942,0.00072758144,0.00007182215,0.00073012186,0.00039659426,0.000206158,0.00021683973],"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":[0.000020505491,0.00007501583,0.000002846061,0.00022008468,0.00004387898,0.000027890168,0.0030341255,0.18512118,0.330905,0.026789952,0.00066832517,0.4530912],"study_design_scores_gemma":[0.00044746127,0.0003177445,0.0011635146,0.0002224961,0.000031521882,0.00009670889,0.033368032,0.82215405,0.081722796,0.04872806,0.011013753,0.0007338604],"about_ca_topic_score_codex":0.000028779274,"about_ca_topic_score_gemma":0.000011880341,"teacher_disagreement_score":0.63703287,"about_ca_system_score_codex":0.00021080312,"about_ca_system_score_gemma":0.000007212993,"threshold_uncertainty_score":0.91054726},"labels":[],"label_agreement":null},{"id":"W7118803419","doi":"10.5281/zenodo.18168573","title":"Dynamic Pricing Models in Telecom: Implementation of Real Time, Dynamic Pricing Strategies through Artificial Intelligence","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Marriott International (Canada)","funders":"","keywords":"Dynamic pricing; Revenue management; Software deployment; Pricing strategies; Reinforcement learning; Key (lock); Process (computing); Revenue; Big data","score_opus":0.0317254492106625,"score_gpt":0.28837461787753277,"score_spread":0.25664916866687026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118803419","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4412937,0.00018750476,0.5340323,0.00013172963,0.00008602068,0.0004256263,0.00018796395,0.0017117745,0.021943409],"genre_scores_gemma":[0.9916552,0.0006865736,0.006403047,0.0000051127104,0.000008471205,7.385117e-8,0.0008281686,0.00038997782,0.000023402627],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988745,0.000063458385,0.00035531825,0.0002501046,0.00017752267,0.00027909194],"domain_scores_gemma":[0.99942553,0.000019012574,0.00006341727,0.0002941462,0.0001707032,0.000027207292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001791385,0.00011982224,0.00015611206,0.00017631859,0.00026576014,0.00017751183,0.00038929284,0.00005745642,0.00083160325],"category_scores_gemma":[0.000079503465,0.00013946967,0.000025688942,0.00072758144,0.00007182215,0.00073012186,0.00039659426,0.000206158,0.00021683973],"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":[0.000020505491,0.00007501583,0.000002846061,0.00022008468,0.00004387898,0.000027890168,0.0030341255,0.18512118,0.330905,0.026789952,0.00066832517,0.4530912],"study_design_scores_gemma":[0.00044746127,0.0003177445,0.0011635146,0.0002224961,0.000031521882,0.00009670889,0.033368032,0.82215405,0.081722796,0.04872806,0.011013753,0.0007338604],"about_ca_topic_score_codex":0.000028779274,"about_ca_topic_score_gemma":0.000011880341,"teacher_disagreement_score":0.63703287,"about_ca_system_score_codex":0.00021080312,"about_ca_system_score_gemma":0.000007212993,"threshold_uncertainty_score":0.91054726},"labels":[],"label_agreement":null},{"id":"W7126261737","doi":"10.18280/isi.301206","title":"Intelligent 6G IoT Configuration Optimisation Using Multi-Algorithm Machine Learning Classification","year":2025,"lang":"","type":"article","venue":"Ingénierie des systèmes d information","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Internet of Things; Feature (linguistics); Key (lock); Power (physics); Feature extraction","score_opus":0.032844751891876585,"score_gpt":0.27311181551409497,"score_spread":0.2402670636222184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126261737","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018566627,0.001708019,0.9735882,0.00006211423,0.0014592397,0.0010232403,0.00011570042,0.0011472654,0.0023295505],"genre_scores_gemma":[0.86474985,0.0016692451,0.1314995,0.00006245865,0.00008390752,0.000098354525,0.0015767991,0.000045127323,0.00021474155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968187,0.00011848483,0.0017506953,0.00034963794,0.0003606224,0.00060185225],"domain_scores_gemma":[0.9979661,0.00012093535,0.00069623307,0.0005504712,0.0005757607,0.00009049048],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005686953,0.00057486224,0.0005020829,0.0011587712,0.0008480928,0.000636352,0.00039447803,0.0006238156,0.000082556435],"category_scores_gemma":[0.0009354241,0.0006591333,0.00013987407,0.0013335132,0.00027402048,0.0044352976,0.00016132729,0.0007963567,0.00015331294],"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":[0.000031062358,0.00004100723,0.00043437857,0.00081340654,0.00012828966,0.0000011204295,0.0024111615,0.248596,0.005947165,0.0037781463,0.000048164293,0.7377701],"study_design_scores_gemma":[0.000675064,0.00008153335,0.0015224948,0.0007671088,0.00011060291,0.0000137105435,0.0050228247,0.9557995,0.023765178,0.000883326,0.010800737,0.00055791903],"about_ca_topic_score_codex":0.00017641652,"about_ca_topic_score_gemma":0.000012417813,"teacher_disagreement_score":0.84618324,"about_ca_system_score_codex":0.00203251,"about_ca_system_score_gemma":0.00017532201,"threshold_uncertainty_score":0.999586},"labels":[],"label_agreement":null},{"id":"W7140398077","doi":"10.1109/etcom66606.2025.11436773","title":"Self-Supervised Learning for Network Traffic Analysis in 5G Environments","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Traffic analysis; Feature (linguistics); Perspective (graphical); Domain (mathematical analysis); Key (lock)","score_opus":0.006925239283522394,"score_gpt":0.22437405485776665,"score_spread":0.21744881557424425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7140398077","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11980764,0.0046183015,0.8704287,0.0001994352,0.00038135034,0.000841873,0.0000139458925,0.0010295367,0.0026792584],"genre_scores_gemma":[0.92867965,0.0036428964,0.065538324,0.000045601144,0.00004376623,0.0001085345,0.00009488124,0.000032374148,0.0018139953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980471,0.000033289743,0.00056021084,0.0005262912,0.00011253754,0.0007205851],"domain_scores_gemma":[0.9991734,0.00023219113,0.000048240727,0.0004937379,0.00000763289,0.00004482135],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021355346,0.0003368029,0.0005996747,0.0005862581,0.0001804833,0.000053016844,0.0003838221,0.0003299322,0.00013243698],"category_scores_gemma":[0.00008770753,0.00036711557,0.00024745447,0.0023416162,0.000042100644,0.00025466405,0.00012626391,0.00045213653,0.00002529775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017248907,0.000059621038,0.003554718,0.00011317271,0.00096828846,0.0000024648712,0.00011765774,0.88805073,0.00009204729,0.0006065738,0.00026007646,0.10615742],"study_design_scores_gemma":[0.0008043827,0.000053718457,0.0027468174,0.000044688022,0.0006502858,1.2140214e-7,0.00050619844,0.9264665,0.00027227402,0.00020780943,0.067918845,0.00032834345],"about_ca_topic_score_codex":0.000006539702,"about_ca_topic_score_gemma":0.00007907479,"teacher_disagreement_score":0.808872,"about_ca_system_score_codex":0.000235023,"about_ca_system_score_gemma":0.000017285087,"threshold_uncertainty_score":0.9998781},"labels":[],"label_agreement":null},{"id":"W7164321798","doi":"10.1109/iciics67880.2026.11483469","title":"HG-SSA-ChurnNet: A Hybrid Gradient-Guided Salp Swarm Optimized Deep Learning Framework for Telecom Analytics","year":2005,"lang":"","type":"article","venue":"","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Deep learning; Analytics; Artificial neural network; Deep neural networks; Big data; Key (lock)","score_opus":0.021857945808368202,"score_gpt":0.27532277775021097,"score_spread":0.25346483194184277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7164321798","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01241726,0.005265701,0.97431266,0.0010826966,0.0009008991,0.0010988779,0.00007858879,0.0026523347,0.002191004],"genre_scores_gemma":[0.2851361,0.0051384238,0.707054,0.00022804069,0.00055209023,0.000094181974,0.00014697705,0.00021383885,0.0014363249],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952113,0.00004815138,0.0014022584,0.0010752834,0.0003889433,0.0018740348],"domain_scores_gemma":[0.9969052,0.0009537352,0.0003180161,0.0012777301,0.0002108528,0.0003344413],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039504512,0.0009959114,0.0012208796,0.00055192254,0.00051823823,0.00026851674,0.001066094,0.00066453585,0.00076669495],"category_scores_gemma":[0.0022389488,0.0010362822,0.00054076867,0.0007858004,0.00023491893,0.00080998486,0.0003776514,0.0017451678,0.00027815488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000874474,0.00013989981,0.00012456733,0.0001726507,0.00045838213,0.000019625464,0.00020856241,0.83249265,0.00030126394,0.0145973405,0.005300216,0.14609742],"study_design_scores_gemma":[0.0021784282,0.00031924204,0.000030061052,0.00018242603,0.0002820774,0.00004904705,0.0008207483,0.79607254,0.017405033,0.009188932,0.17220388,0.0012675823],"about_ca_topic_score_codex":0.0000139384565,"about_ca_topic_score_gemma":0.000021070486,"teacher_disagreement_score":0.27271885,"about_ca_system_score_codex":0.00049114216,"about_ca_system_score_gemma":0.00005003956,"threshold_uncertainty_score":0.99920875},"labels":[],"label_agreement":null}]}