{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":59,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":59,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"3a7975832627","filters":{"topic":"Advanced Data and IoT Technologies"}},"results":[{"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Computer science; Electrical engineering; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01013931956886014,"gpt":0.2068165602115682,"spread":0.1966772406427081,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00003564376,0.0003858539,0.0004036306,0.0003613197,0.00004315911,0.00007731107,0.0002989954,0.0005005217,0.0004119953],"category_scores_gemma":[0.00001049731,0.0003599942,0.00003508326,0.0001033286,0.00003282736,0.00007503308,0.00003535654,0.0006964396,0.00270013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001438696,"about_ca_system_score_gemma":0.00004621427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001747033,"about_ca_topic_score_gemma":0.002441013,"domain_scores_codex":[0.9988461,0.000006367695,0.0001888235,0.0003379787,0.0001021958,0.0005185517],"domain_scores_gemma":[0.9993128,0.00005864688,0.00002074765,0.0003826244,0.0000260441,0.0001991288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001248451,0.000002498469,0.000002694629,0.00007788544,0.0000361976,0.000006444985,0.000006508223,0.01960305,0.00008222274,0.002348685,0.9653329,0.01249963],"study_design_scores_gemma":[0.0001283842,0.00008968136,0.00003779222,0.00008558058,0.000009702438,0.00001181877,0.000002217228,0.3985874,0.0006921263,0.00003244021,0.5997462,0.00057663],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001040052,0.001758637,0.8326375,0.0001986168,0.005521791,0.0005258804,0.0003628549,0.001881119,0.1560735],"genre_scores_gemma":[0.8609415,0.01857733,0.04842322,0.001639192,0.004276349,0.0001929157,0.002114303,0.0008399813,0.06299526],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8599014,"threshold_uncertainty_score":0.9998852,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.01373670493363319,"gpt":0.2538851889630422,"spread":0.240148484029409,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001450575,0.0003096148,0.0002882292,0.0001072206,0.0001817769,0.0001479726,0.0002118034,0.0001930879,0.000005095019],"category_scores_gemma":[0.000009395677,0.0003189547,0.00003084323,0.0007805175,0.00002901936,0.0003846659,0.0001419562,0.0004944011,0.00002576107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005366589,"about_ca_system_score_gemma":0.000005293548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002732419,"about_ca_topic_score_gemma":0.00001705574,"domain_scores_codex":[0.998634,0.00001658959,0.000276058,0.0003688795,0.0001415495,0.0005628658],"domain_scores_gemma":[0.9993553,0.000109142,0.00003554723,0.0003864615,0.00002163359,0.00009195927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006842599,0.00003250746,0.0009850205,0.0001258755,0.0001144037,0.0007711067,0.001173021,0.9780449,0.002395636,0.000007764493,0.01392441,0.002418541],"study_design_scores_gemma":[0.001088537,0.0004688448,0.0002573227,0.000505819,0.0001199142,0.0005582664,0.01072933,0.9392828,0.004023685,0.0003182348,0.04105893,0.001588372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846407,0.002697355,0.006306622,0.0000172259,0.002093247,0.0005761569,0.00003413716,0.003613687,0.00002083521],"genre_scores_gemma":[0.9948832,0.0003967903,0.003676451,0.0000559865,0.0005970056,0.0002116601,0.00004980566,0.00009267079,0.00003637387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03876211,"threshold_uncertainty_score":0.9999263,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Blockchain; Computer science; Fuzzy logic; Distributed computing; Artificial intelligence; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.01205756023520797,"gpt":0.234413910642961,"spread":0.222356350407753,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000749172,0.0002262383,0.0002323735,0.0002820323,0.0001520022,0.00007341046,0.0003204209,0.000417868,0.000001512642],"category_scores_gemma":[0.000006162564,0.0001887132,0.00004525427,0.0006625822,0.00007603626,0.00009146566,0.00002664275,0.0003927909,0.00001577422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008952382,"about_ca_system_score_gemma":0.00002864342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008106584,"about_ca_topic_score_gemma":0.000001022704,"domain_scores_codex":[0.9988819,0.00000504756,0.0002656303,0.0003869692,0.0001173369,0.0003430968],"domain_scores_gemma":[0.9991491,0.00007845402,0.00002806818,0.0006403265,0.00005231695,0.00005167256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008277112,0.0000297178,0.000007191217,0.0003188765,0.0000596851,0.000002047958,0.00008393032,0.9229053,0.002068477,0.06792452,0.001653992,0.004937983],"study_design_scores_gemma":[0.0001201352,0.00003824309,1.977733e-7,0.0001041173,0.00001294633,0.00001904709,0.0006251937,0.9114586,0.00167648,0.0009424067,0.08475567,0.0002469642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002892551,0.01020445,0.9829022,0.0002217569,0.0001080376,0.001387772,0.00009537654,0.003901454,0.0008896664],"genre_scores_gemma":[0.6857359,0.00005037828,0.3032498,0.00002141383,0.0001676762,0.01054336,0.00009850098,0.00007551438,0.00005749972],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6854466,"threshold_uncertainty_score":0.7695503,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brandon University","funders":"Hunan Normal University","keywords":"Encryption; Computer science; Information security; Computer security; Private information retrieval; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.003143272944848064,"gpt":0.2141315631248541,"spread":0.210988290180006,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009341462,0.00006404521,0.00009769078,0.00004297887,0.00001598262,0.00001048459,0.0000870552,0.00004666823,2.454015e-7],"category_scores_gemma":[0.00001181803,0.00004961074,0.00001932869,0.0001129796,0.0000137791,0.0002141176,0.00001285718,0.0001285959,0.000005740424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009413186,"about_ca_system_score_gemma":0.0000102633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002975326,"about_ca_topic_score_gemma":0.000002600261,"domain_scores_codex":[0.9995859,0.000007373839,0.000116309,0.00005572955,0.00007333057,0.0001613431],"domain_scores_gemma":[0.9997034,0.00005369515,0.00003232094,0.0001740703,0.00002869055,0.000007824087],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001367941,0.00003674079,0.00002459112,0.00008292747,0.00002400088,1.553612e-7,0.001068515,0.8598414,0.01129601,0.02824121,0.00003191688,0.09933883],"study_design_scores_gemma":[0.0001729365,0.0001042886,0.0001397953,0.00001071819,0.000004088668,0.000001011909,0.0003058296,0.9742904,0.02153178,0.0004634547,0.002914512,0.0000611265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7276433,0.0002985009,0.2713851,0.000006691562,0.00009484179,0.0003990535,0.00003110553,0.0001101168,0.00003134092],"genre_scores_gemma":[0.9989884,0.0001238526,0.0007868726,0.000002999617,0.00001788839,0.00002370223,0.0000492973,0.000005976086,9.495997e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2713452,"threshold_uncertainty_score":0.2023067,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.02417833602680881,"gpt":0.2415927917854613,"spread":0.2174144557586525,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008840372,0.0001748442,0.0001880114,0.0001717874,0.00005848174,0.00002203335,0.0001637317,0.0001631207,0.00001746728],"category_scores_gemma":[0.00003915232,0.0001678438,0.00003942745,0.000642351,0.00002759544,0.0001732227,0.00008111579,0.0003679079,0.00007940209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008491952,"about_ca_system_score_gemma":0.000006621679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007723545,"about_ca_topic_score_gemma":0.0001384841,"domain_scores_codex":[0.9989488,0.00001743813,0.0002366237,0.0002500718,0.00009309361,0.000453979],"domain_scores_gemma":[0.9996791,0.00003304649,0.0000179945,0.0002225533,0.000009451446,0.0000378292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003047886,0.00004077496,0.000869674,0.00002362603,0.00001275697,0.0000166695,0.00006664905,0.9662399,0.0002920064,0.00001563999,0.0009556708,0.03146363],"study_design_scores_gemma":[0.0003858715,0.00004452755,0.001819789,0.00001957916,0.000004635641,0.00001459579,0.0002689429,0.9919706,0.0002304466,0.00001103876,0.005056275,0.0001737135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8447422,0.0003616246,0.1460093,0.00002063513,0.0003086134,0.0002675909,0.00002045441,0.007942323,0.0003272461],"genre_scores_gemma":[0.9766086,0.000413135,0.02183172,0.00001141735,0.00008387004,0.00005124783,0.000442588,0.00005400278,0.0005034723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1318664,"threshold_uncertainty_score":0.6844471,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Data science; Information retrieval","retraction":null,"screen_n_in":null,"score":{"opus":0.4116476010956684,"gpt":0.4084695560255233,"spread":0.003178045070145186,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004426053,0.0002746288,0.0002552779,0.0007592146,0.0005742293,0.0006919444,0.003156806,0.0002050811,0.0001222833],"category_scores_gemma":[0.0002038841,0.0002800303,0.00003598056,0.002313323,0.0003662637,0.003015709,0.0009227935,0.00108296,0.001252487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002442042,"about_ca_system_score_gemma":0.0001209439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001396225,"about_ca_topic_score_gemma":0.0002671366,"domain_scores_codex":[0.99747,0.00007603839,0.0003843078,0.0009600144,0.0004457156,0.0006638792],"domain_scores_gemma":[0.9943066,0.0002696772,0.00001632217,0.00513464,0.000148852,0.0001238652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004627472,0.00002991928,0.000006053473,0.0001129134,0.00009851254,0.0000497707,0.00005068728,0.0006207628,0.03024847,0.0006961383,0.9590856,0.008996527],"study_design_scores_gemma":[0.0001224356,0.00002854274,0.0000268573,0.000122947,0.00004162623,0.00007301246,0.0005528322,0.126427,0.002013507,0.00125615,0.8690418,0.0002932778],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02308966,0.05227101,0.718615,0.001601825,0.039676,0.002675503,0.1238536,0.0247126,0.0135048],"genre_scores_gemma":[0.8163037,0.03178284,0.08866753,0.0003228496,0.003659006,0.0005668476,0.04821843,0.0006848156,0.009793964],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7932141,"threshold_uncertainty_score":0.9999652,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.01793941004294834,"gpt":0.2316320068541808,"spread":0.2136925968112325,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001076267,0.0002322443,0.0002003279,0.0001396984,0.0001422383,0.0001047759,0.0002727259,0.0001314286,0.000002867553],"category_scores_gemma":[0.000003922454,0.0002252649,0.00008985836,0.0002968579,0.00004989678,0.00008697596,0.00002838783,0.000303576,0.000006588583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001043265,"about_ca_system_score_gemma":0.000009178283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001332351,"about_ca_topic_score_gemma":0.00001764483,"domain_scores_codex":[0.9988855,0.000004755594,0.0002110865,0.0002595848,0.000151847,0.0004872974],"domain_scores_gemma":[0.9995428,0.0002120605,0.00002803013,0.0001751746,0.000008653318,0.0000333077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000663104,0.00001694737,0.000176605,0.002721574,0.0004250976,0.0002215036,0.002863937,0.6228039,0.1623969,0.004380676,0.0304877,0.1734388],"study_design_scores_gemma":[0.0005390482,0.0000174979,0.00004500207,0.0007412162,0.00007146379,0.000005774442,0.0001933769,0.496401,0.02277241,0.001773723,0.476726,0.0007134837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8759539,0.002493378,0.1079512,0.001928323,0.004823994,0.0004038933,0.0001151181,0.006027398,0.0003028101],"genre_scores_gemma":[0.9953117,0.00002917658,0.002484535,0.000846603,0.001133289,0.00006127791,0.00004791053,0.0000833392,0.000002119149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4462383,"threshold_uncertainty_score":0.9186035,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.01934030956955426,"gpt":0.2223507110747009,"spread":0.2030104015051467,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009833062,0.0002533588,0.0003263039,0.0006768442,0.00006740445,0.00002385596,0.0003511829,0.0001457049,0.00000154562],"category_scores_gemma":[0.0001417375,0.0002745204,0.00007301781,0.002045074,0.00002821505,0.0001174792,0.00007085338,0.0002808299,0.0000053489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001491577,"about_ca_system_score_gemma":0.00003509493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002288958,"about_ca_topic_score_gemma":5.851687e-8,"domain_scores_codex":[0.998723,0.00001132037,0.0002905577,0.0002833202,0.0002018188,0.0004900373],"domain_scores_gemma":[0.9992845,0.0001833541,0.00003493967,0.0003762458,0.00004024992,0.00008073167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006163367,0.00002937931,0.000002257537,0.00004443564,0.00001850998,0.000007081094,0.00002257137,0.9876057,0.009271354,0.001236285,0.0001837669,0.001572496],"study_design_scores_gemma":[0.0003402345,0.00007972168,0.00001801057,0.00007070581,0.00001380099,0.000001679279,0.000001964163,0.9874863,0.01156454,0.0001260432,0.00002408853,0.0002728957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02577471,0.00007938799,0.9709686,0.00002510457,0.0001468523,0.0002305351,0.00002108123,0.002739525,0.00001419552],"genre_scores_gemma":[0.8373619,0.00004220414,0.1624433,0.00001654596,0.00002779172,0.00001747348,0.00003483085,0.00005389426,0.000001973727],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8115872,"threshold_uncertainty_score":0.9999707,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Wireless network; Wireless; Key (lock); Generative grammar; Adaptability; Resource (disambiguation); Generative model","retraction":null,"screen_n_in":null,"score":{"opus":0.04074737434104819,"gpt":0.3233127668672785,"spread":0.2825653925262303,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008792112,0.0001635361,0.000418038,0.0001445876,0.0001237003,0.00002844507,0.0004538525,0.0001477976,0.000001833223],"category_scores_gemma":[0.0004090747,0.0001687067,0.00003604286,0.0003974835,0.0001705723,0.0001529354,0.0001582998,0.0002011617,2.587132e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007779289,"about_ca_system_score_gemma":0.00003776252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002204893,"about_ca_topic_score_gemma":0.001013369,"domain_scores_codex":[0.9984074,0.0006763295,0.0004823197,0.0001558569,0.00006636618,0.0002117221],"domain_scores_gemma":[0.9971132,0.001737773,0.00008870757,0.0008109037,0.0002254765,0.00002393741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001685312,0.0001648984,0.03981617,0.000282268,0.0005120719,0.000001085353,0.001595391,0.8201952,0.06302779,0.01039621,0.01240487,0.05143553],"study_design_scores_gemma":[0.007066752,0.0002394339,0.08230022,0.000943762,0.0001067229,0.00000215318,0.001409951,0.7630759,0.06805078,0.002264851,0.07309769,0.001441762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.132956,0.007303128,0.8569208,0.0001731698,0.001126667,0.001020696,0.0001047873,0.0002942604,0.0001005017],"genre_scores_gemma":[0.987341,0.008630218,0.003309932,0.00002958029,0.0000235054,0.0002119277,0.0003988199,0.00001718266,0.00003786809],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.854385,"threshold_uncertainty_score":0.687966,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Computer science; Quarter (Canadian coin); Telecommunications; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.03745524037966134,"gpt":0.2984453269759871,"spread":0.2609900865963258,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","open_science","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.01111483,0.00158198,0.002461758,0.000776672,0.001542405,0.0005186753,0.009035597,0.003745283,0.00001890636],"category_scores_gemma":[0.005964745,0.001747348,0.0003882578,0.001187695,0.002224535,0.001300804,0.001611929,0.004255638,0.0003605891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007105664,"about_ca_system_score_gemma":0.0007374308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001498905,"about_ca_topic_score_gemma":0.002662554,"domain_scores_codex":[0.9851467,0.00807007,0.002656142,0.001208646,0.001605202,0.001313256],"domain_scores_gemma":[0.9663507,0.01436273,0.0009131405,0.01624769,0.001741059,0.000384687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009157305,0.0001940444,0.00004668385,0.0001158234,0.0003829403,0.000004587702,0.0002813875,0.0001090634,0.0008112469,0.00007754999,0.9959233,0.002044195],"study_design_scores_gemma":[0.001167676,0.0001222609,0.00010909,0.0002436587,0.0002162021,0.000009147441,0.00008135186,0.0001697481,0.0003480182,0.0003813774,0.9954401,0.001711339],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00006488022,0.009510107,0.006173937,0.0002372673,0.9712001,0.001312837,0.007264122,0.003053944,0.001182809],"genre_scores_gemma":[0.002505641,0.1216155,0.005503207,0.000009619317,0.8598489,0.0008263707,0.00882878,0.0004295212,0.0004325445],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.1121054,"threshold_uncertainty_score":0.9997575,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Geography; History; Political science","retraction":null,"screen_n_in":null,"score":{"opus":0.01178852064892715,"gpt":0.2084791846340555,"spread":0.1966906639851284,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008810393,0.0002269939,0.0002936201,0.00007349166,0.00004869707,0.00005504664,0.0001832637,0.0001243337,0.00001847223],"category_scores_gemma":[0.00002777654,0.0002198746,0.00001656122,0.0001429042,0.0001159009,0.000113276,0.00002828922,0.0001855875,0.00004797864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009070856,"about_ca_system_score_gemma":0.0000682583,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1059356,"about_ca_topic_score_gemma":0.7263569,"domain_scores_codex":[0.998763,0.00001362449,0.0003966622,0.0002985173,0.0001651884,0.000363046],"domain_scores_gemma":[0.999208,0.0000360376,0.00007374753,0.0005341821,0.00005817614,0.00008983755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005193932,0.0001115761,0.01480825,0.001550701,0.000238246,0.002211904,0.001306979,0.01379234,0.04486697,0.006585747,0.892411,0.02206437],"study_design_scores_gemma":[0.0008767082,0.0002050268,0.01534074,0.0005284621,0.00003795898,0.0005525196,0.0003483532,0.04976863,0.008521166,0.0008463024,0.9216406,0.001333584],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9066712,0.001700768,0.002880525,0.0001265336,0.005012885,0.0005134997,0.00005226185,0.0003993192,0.08264305],"genre_scores_gemma":[0.9939836,0.0003834779,0.0001751596,0.00002467513,0.0001884397,0.00001897935,0.00001270866,0.00003075046,0.005182216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6204213,"threshold_uncertainty_score":0.900018,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.01203907378016777,"gpt":0.2411096009915888,"spread":0.229070527211421,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005842986,0.0001010571,0.0002774792,0.0002172678,0.0001223755,0.00003876276,0.0001074147,0.00004100236,5.181406e-7],"category_scores_gemma":[0.000003728217,0.00008670349,0.00002780747,0.0002052409,0.000015268,0.0001815463,0.00001642679,0.0003847558,1.43118e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001607211,"about_ca_system_score_gemma":0.00005129122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007042621,"about_ca_topic_score_gemma":0.000001395412,"domain_scores_codex":[0.9991697,0.00006510953,0.0003473027,0.0001037637,0.0001455143,0.0001686097],"domain_scores_gemma":[0.9995536,0.00006534239,0.0002336184,0.00005528971,0.00005138291,0.00004081403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000163489,0.00001061435,0.0003028915,0.0001599141,0.000013974,0.000005866882,0.0001006435,0.7566419,0.00006878348,0.00002411044,0.0003253656,0.2423296],"study_design_scores_gemma":[0.0005669757,0.001354045,0.0004507677,0.0005081912,0.000008919828,0.000191902,0.0005750262,0.9863698,0.00006145212,0.00003467482,0.009770924,0.0001073534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003687699,0.002156944,0.9935007,0.00006698907,0.0001806819,0.0002369765,0.000006828066,0.0001527675,0.00001041862],"genre_scores_gemma":[0.9845492,0.00007464473,0.01516549,0.00003771252,0.000108696,0.00003238651,0.000007413103,0.00002065132,0.000003765884],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9808615,"threshold_uncertainty_score":0.3535666,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.01911300586023766,"gpt":0.2530254015330102,"spread":0.2339123956727726,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005440447,0.0001017853,0.000115166,0.0001012541,0.00001708277,0.00002992447,0.00007085907,0.00005310211,0.00001385995],"category_scores_gemma":[0.00001257913,0.00007617483,0.00001010508,0.0002417701,0.00004783775,0.0002057114,0.00004454477,0.0001105394,0.000003889602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003609773,"about_ca_system_score_gemma":0.000007835784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006299943,"about_ca_topic_score_gemma":0.0001627024,"domain_scores_codex":[0.9994854,0.000006008609,0.0001114841,0.0001754103,0.00006008422,0.0001615658],"domain_scores_gemma":[0.9997764,0.00005288936,0.000005490417,0.0001380445,0.00000638389,0.00002082201],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008243853,0.0000729748,0.0006080449,0.0003647898,0.000133353,0.000254006,0.001540358,0.1335347,0.0199015,0.4692394,0.001380386,0.372888],"study_design_scores_gemma":[0.001263086,0.0005255011,0.003244061,0.0006304581,0.00002826656,0.00005246551,0.006586039,0.8993927,0.04280041,0.007957996,0.03596121,0.001557787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1120634,0.007386836,0.8471613,0.0001303935,0.0001692907,0.00007749199,0.00001974638,0.00276527,0.03022629],"genre_scores_gemma":[0.9808937,0.0003625485,0.01855345,0.00001453362,0.00000812197,0.00001097151,0.000003851281,0.00001496601,0.0001378382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8688303,"threshold_uncertainty_score":0.3106319,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Multimedia","retraction":null,"screen_n_in":null,"score":{"opus":0.03272323224465063,"gpt":0.2698219762934178,"spread":0.2370987440487671,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001043446,0.0005237865,0.00128293,0.0002972551,0.0002290433,0.00007053794,0.002026457,0.0003342445,0.000385654],"category_scores_gemma":[0.001976314,0.0005029183,0.0003689066,0.001135065,0.000248159,0.0001590466,0.001134405,0.001124927,0.0001875086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002225584,"about_ca_system_score_gemma":0.00009670276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001857937,"about_ca_topic_score_gemma":0.00002083366,"domain_scores_codex":[0.9967579,0.001233765,0.0006970696,0.0005954761,0.0003023965,0.0004134422],"domain_scores_gemma":[0.9946899,0.001946298,0.0003094037,0.002684831,0.0003029222,0.00006664621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[1.826084e-7,0.00003859731,3.005567e-7,0.01391034,0.00006676491,0.000007748152,0.00007580332,0.000002327632,0.0000034089,0.00154774,0.007841177,0.9765056],"study_design_scores_gemma":[0.00008720843,1.931115e-7,3.328593e-7,0.03178599,0.0001452669,0.00003323706,0.00006385978,0.0003896967,0.00009198426,0.0001861552,0.9667658,0.0004502523],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[5.652778e-7,0.9758964,0.009865419,0.0004142507,0.0001832333,0.0008483207,0.000207834,0.003634213,0.008949731],"genre_scores_gemma":[0.000001868196,0.9701224,0.02726006,0.00003069806,0.000006696652,0.0005216721,0.001207514,0.0001011704,0.0007479268],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9760554,"threshold_uncertainty_score":0.9997423,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; York University","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.01878014809728152,"gpt":0.2868294432917256,"spread":0.2680492951944441,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003383184,0.0001262108,0.00018287,0.0004731166,0.0001366076,0.00007727075,0.0007488133,0.0001119186,0.00000149608],"category_scores_gemma":[0.00001555376,0.0001111707,0.00001100415,0.0001951119,0.0003729572,0.002268511,0.0008489504,0.0003158293,8.051941e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003655165,"about_ca_system_score_gemma":0.00004855047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001017737,"about_ca_topic_score_gemma":0.00003831777,"domain_scores_codex":[0.9992206,0.000005867895,0.0003570046,0.0001039643,0.0002001867,0.0001124066],"domain_scores_gemma":[0.9990131,0.00009428047,0.0001035568,0.0006435517,0.0001130594,0.0000325111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002772292,0.000005238369,0.000007549286,0.0002746166,0.00001022648,3.789961e-7,0.005208385,0.009939187,0.00001426957,0.1901751,0.00003784225,0.7943244],"study_design_scores_gemma":[0.0001551862,0.00001472519,0.00003710361,0.0009671026,0.000004460786,0.000006448511,0.0002923717,0.9940777,0.00004292726,0.001131392,0.003158244,0.0001122935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006386106,0.001356905,0.9463303,0.00009691774,0.00005472524,0.000161563,0.00002891866,0.0001492715,0.05118276],"genre_scores_gemma":[0.4098306,0.01252931,0.5768872,0.0003101872,0.00001832224,0.000004278035,0.0002762363,0.00001388181,0.0001299054],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9841385,"threshold_uncertainty_score":0.453341,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Computer science; Network security; Artificial intelligence; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.04598551110853366,"gpt":0.3421909371637926,"spread":0.2962054260552589,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001419541,0.0001121836,0.0001117255,0.00004227475,0.00003470287,0.00006630785,0.0001608064,0.00006745842,0.00001848898],"category_scores_gemma":[0.0000027531,0.00009893053,0.00004512656,0.000195984,0.00002209291,0.0001745105,0.00008592059,0.0001011954,0.00001471916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003229087,"about_ca_system_score_gemma":0.000001577366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.245146e-7,"about_ca_topic_score_gemma":0.000001657056,"domain_scores_codex":[0.9993694,0.000008258114,0.0001661526,0.0001930602,0.00004335004,0.000219713],"domain_scores_gemma":[0.9996798,0.00009221306,0.000005790068,0.0001901675,0.000009661444,0.00002236392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001733625,0.000004606334,5.037637e-8,0.00007970256,0.00004003752,0.000001350992,0.00001451943,0.1158207,0.00006027441,0.1682947,0.03076217,0.6849202],"study_design_scores_gemma":[0.000008581729,0.00001098166,2.175371e-7,0.00001673163,0.000009439311,7.490424e-7,0.00002356728,0.6673284,0.002342463,0.2462269,0.08394191,0.00009002219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003388812,0.001431525,0.9925609,0.0000760724,0.000740974,0.000215635,0.00002246516,0.00214584,0.00277266],"genre_scores_gemma":[0.02704426,0.0002424475,0.972316,0.00004242884,0.0001096661,0.00006941998,0.00004611738,0.00002337973,0.0001063217],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6848302,"threshold_uncertainty_score":0.403427,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Telecommunications; Remote sensing; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.03955191900482782,"gpt":0.3077113537480863,"spread":0.2681594347432584,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.007679121,0.0005372097,0.0008913195,0.0004686542,0.001146155,0.0003029523,0.004360064,0.0006425525,0.00001891873],"category_scores_gemma":[0.002475126,0.0005865437,0.0001463042,0.001286785,0.0009796057,0.0007825177,0.001154047,0.0009107688,0.0001037558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003144882,"about_ca_system_score_gemma":0.0001991728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008830951,"about_ca_topic_score_gemma":0.003257576,"domain_scores_codex":[0.9942084,0.003048722,0.001249942,0.0005089026,0.0003729609,0.0006110986],"domain_scores_gemma":[0.9851618,0.005644313,0.0002288993,0.008288648,0.0005382598,0.0001380311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002213832,0.0004564273,0.002730143,0.0002172933,0.0006962894,0.000001661495,0.0005140954,0.00227553,0.0166018,0.01486133,0.9283587,0.03326454],"study_design_scores_gemma":[0.00101699,0.00004579277,0.003297852,0.0001700306,0.0001172528,0.000001132107,0.0001451374,0.001227541,0.003163931,0.004276919,0.985853,0.0006843869],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.01382834,0.03258682,0.138467,0.005537018,0.7514724,0.004883735,0.006490454,0.01017377,0.03656053],"genre_scores_gemma":[0.9139368,0.03729931,0.01756719,0.00007546438,0.02687987,0.001246885,0.001648101,0.0002010246,0.001145371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9001085,"threshold_uncertainty_score":0.9996586,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Canyon; Benthic zone; Oxygen; Sediment; Oxygen sensor","retraction":null,"screen_n_in":null,"score":{"opus":0.004958896385923285,"gpt":0.1825058575933152,"spread":0.1775469612073919,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001091136,0.0007541899,0.0007717405,0.00003784864,0.0002383514,0.0000986985,0.0008302805,0.0009855168,0.0001732931],"category_scores_gemma":[0.00006365401,0.0008149996,0.0002994652,0.0005620884,0.000110697,0.000135369,0.0003171217,0.001754034,0.000003708479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001038611,"about_ca_system_score_gemma":0.0005624614,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1070609,"about_ca_topic_score_gemma":0.5051305,"domain_scores_codex":[0.997209,0.00003693133,0.0005048555,0.0007046866,0.0004959081,0.001048686],"domain_scores_gemma":[0.9979093,0.0001372141,0.0001205908,0.001485381,0.00009131044,0.0002562051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000216328,0.000007877479,0.00000892355,0.0001758648,0.000327011,0.0001638358,0.000006996792,0.05208748,0.000004698732,0.000001385877,0.9456863,0.001527472],"study_design_scores_gemma":[0.0002072412,0.00001179158,0.00002667496,0.0001301374,0.0001147958,0.00001967082,0.0003472414,0.01118043,0.00002608642,0.000006449746,0.9870859,0.0008435347],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000104895,0.007917996,0.001622607,0.0001311565,0.002308579,0.0002562929,0.987176,0.0004525464,0.00002989894],"genre_scores_gemma":[0.0003001103,0.01687375,0.001350204,0.0005849839,0.0009679444,0.00001691923,0.9795577,0.0001416536,0.0002067466],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3980696,"threshold_uncertainty_score":0.9994301,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Fireworks; STREAMS; Geography; Computer science; Archaeology; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.01929349536632958,"gpt":0.2097767133867238,"spread":0.1904832180203943,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001447352,0.0001551987,0.000138901,0.0001668107,0.001176978,0.0002296446,0.001351918,0.00006294641,0.02173268],"category_scores_gemma":[0.0003069823,0.0001803065,0.00004593678,0.0004922727,0.00007587128,0.0002563983,0.002320615,0.0005202703,0.006336846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002211666,"about_ca_system_score_gemma":0.000003361834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000758505,"about_ca_topic_score_gemma":0.000001860181,"domain_scores_codex":[0.9987486,0.00007243651,0.0001821118,0.0003119927,0.0003030324,0.000381838],"domain_scores_gemma":[0.9990464,0.00002229637,0.00004223172,0.0006740419,0.00008560511,0.00012942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002542884,0.00003383845,0.000004687025,0.00001637654,0.00003749755,0.00002715129,0.0006990214,0.004533991,0.000595615,0.0005743316,0.7479284,0.2455237],"study_design_scores_gemma":[0.0004649195,0.0001441245,0.0003704386,0.00001276735,0.00001056508,0.00005550589,0.001599169,0.003115793,0.0001515256,0.0008509696,0.993003,0.0002212813],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1631368,0.005644804,0.04698655,0.003716157,0.001828896,0.003025061,0.009042991,0.03666629,0.7299525],"genre_scores_gemma":[0.9727286,0.001451004,0.002850392,0.0001959193,0.0006003265,5.874434e-7,0.006858425,0.00439258,0.01092218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8095918,"threshold_uncertainty_score":0.9944369,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Thrombolysis; Perfusion; Modified Rankin Scale; Infarction; Perfusion scanning; Lacunar infarction","retraction":null,"screen_n_in":null,"score":{"opus":0.06239767309178043,"gpt":0.3191444314854503,"spread":0.2567467583936699,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008061675,0.0007809423,0.001368664,0.007592167,0.0002705208,0.00135062,0.002994593,0.0007215198,0.005955508],"category_scores_gemma":[0.0001949468,0.0006345062,0.0001420257,0.003134299,0.001247872,0.008139902,0.004443708,0.002715255,0.0003750666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000305013,"about_ca_system_score_gemma":0.0003313445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002439495,"about_ca_topic_score_gemma":0.0001524318,"domain_scores_codex":[0.9953309,0.0003582645,0.0008988033,0.001307425,0.0008929293,0.001211665],"domain_scores_gemma":[0.9967995,0.0004514052,0.0002203768,0.002072765,0.00002935426,0.0004266223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001701928,0.0001404746,0.146128,0.0003134027,0.0002018876,0.001437432,0.00001066199,0.00002583884,0.000009407494,0.00009279893,0.8206303,0.03083964],"study_design_scores_gemma":[0.001677745,0.0006401421,0.1200187,0.0008402794,0.00003687034,0.0001541039,0.00003644876,0.0005503428,0.00008916482,0.0002514523,0.8748196,0.0008851146],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.1679992,0.01897468,0.001055751,0.00907742,0.001802353,0.007924029,0.008031911,0.006441887,0.7786928],"genre_scores_gemma":[0.08626466,0.0838621,0.05401098,0.001188088,0.002194452,0.001189286,0.02625726,0.004775829,0.7402573],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.08173452,"threshold_uncertainty_score":0.9996861,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Fireworks; STREAMS; Live streaming; Art; Geography; Archaeology; Computer science; Multimedia; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.02019530655206748,"gpt":0.2183887865813538,"spread":0.1981934800292864,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001532152,0.0001513005,0.0001333987,0.00008570102,0.001146286,0.0002271855,0.001392075,0.00006143184,0.04585683],"category_scores_gemma":[0.0002961072,0.0001772848,0.00004382116,0.0003161058,0.00006542796,0.0003688347,0.002410462,0.0004016396,0.005406586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004172841,"about_ca_system_score_gemma":0.000003336696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001028772,"about_ca_topic_score_gemma":0.000004041377,"domain_scores_codex":[0.9987765,0.00006949237,0.0001788983,0.0003051984,0.000300723,0.0003692122],"domain_scores_gemma":[0.9990276,0.00001949581,0.00004006016,0.000700445,0.00008618655,0.0001262002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002186337,0.00003199942,0.000003603202,0.00001851841,0.00003665833,0.00001665656,0.0008410434,0.002169123,0.0007371988,0.001053383,0.7916669,0.203403],"study_design_scores_gemma":[0.0003568032,0.0001396286,0.000165087,0.000012187,0.000008486802,0.00004028997,0.002502418,0.001581516,0.0001297932,0.0004573457,0.9943997,0.0002067289],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.07589443,0.01085804,0.04641861,0.002693066,0.002135639,0.002843937,0.006627346,0.03357773,0.8189512],"genre_scores_gemma":[0.9599113,0.003037928,0.005269759,0.000318859,0.0009049173,8.535199e-7,0.008015318,0.006194243,0.01634676],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8840169,"threshold_uncertainty_score":0.9953678,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Prehistory; Deciduous teeth; Texture (cosmology); Anterior teeth; Dentition; Assemblage (archaeology); Middle Stone Age; Posterior teeth","retraction":null,"screen_n_in":null,"score":{"opus":0.00934649817082665,"gpt":0.2169920562478607,"spread":0.2076455580770341,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000124116,0.0003589239,0.000558575,0.0004130083,0.00007555039,0.00004478748,0.0006312908,0.0003467995,0.3914454],"category_scores_gemma":[0.00004218737,0.000388981,0.0001380886,0.000602084,0.00001860328,0.0002536113,0.0003907641,0.0006599548,0.0001441704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002889164,"about_ca_system_score_gemma":0.0000401837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003252386,"about_ca_topic_score_gemma":0.0002410746,"domain_scores_codex":[0.9988449,0.0000147954,0.0002864107,0.0003863654,0.0002320274,0.0002354692],"domain_scores_gemma":[0.9990583,0.00003391715,0.0001528827,0.0006807847,0.0000241242,0.00004996946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002620063,0.000021039,0.00004607215,0.0001437288,0.0002658489,0.00001780748,0.00001157042,0.0004557356,0.00006492234,1.976903e-7,0.9984221,0.0005483398],"study_design_scores_gemma":[0.0001212472,0.00003596378,0.001636935,0.000100528,0.0005126446,0.000005460624,0.0001094045,0.00001860293,0.00006724156,0.000006241903,0.9970255,0.0003601818],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002849411,0.01211445,0.000002500165,6.086576e-7,0.0001517174,0.0001786223,0.9870092,0.0002148185,0.00004309333],"genre_scores_gemma":[0.0005084081,0.0002981198,0.0001077767,0.00000329924,0.00006151864,0.0001187119,0.9988152,0.00003135113,0.00005566171],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3913012,"threshold_uncertainty_score":0.9998562,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.01411749497091305,"gpt":0.2061752252163312,"spread":0.1920577302454182,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00000642182,0.00007096113,0.00006216258,0.00002632211,0.00002176663,0.00001565033,0.0001576773,0.00005680777,0.01203959],"category_scores_gemma":[0.0001074056,0.00006808795,0.0000181394,0.00006191238,0.000002478601,0.0001099289,0.00002796334,0.00008323474,0.003766534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001346939,"about_ca_system_score_gemma":0.000001773545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.013948e-7,"about_ca_topic_score_gemma":8.349282e-7,"domain_scores_codex":[0.9996842,0.00000216308,0.00005630772,0.00007971313,0.00004488312,0.0001327204],"domain_scores_gemma":[0.9997047,0.00002560041,0.000007731288,0.0002281423,0.00001138052,0.00002243015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[2.956061e-7,0.00000176315,0.000002014096,0.00006812529,0.000003499578,0.000001505198,0.000009131662,0.002081006,0.002044336,0.00006335841,0.9839498,0.01177513],"study_design_scores_gemma":[0.00005069036,0.00001017615,0.00005195257,0.0001154411,0.000001037822,0.000001947253,0.000006183829,0.006710991,0.02307875,0.0001437762,0.9697197,0.0001093425],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.00282271,0.001192318,0.00585026,0.0003289811,0.001202332,0.0005427334,0.6300793,0.01837167,0.3396097],"genre_scores_gemma":[0.9076804,0.00003229778,0.003009771,0.0002163348,0.0003979355,0.0002029787,0.08771495,0.00009029151,0.0006550384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9048577,"threshold_uncertainty_score":0.9970092,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.01723961006802295,"gpt":0.2281717429420017,"spread":0.2109321328739787,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00001039116,0.0002968165,0.0002647645,0.0001070991,0.0000367454,0.00004782855,0.0006976109,0.0004686305,0.05937237],"category_scores_gemma":[0.0001995213,0.0002943048,0.00006393281,0.0001041816,0.000006251159,0.000105701,0.0001304257,0.0004985913,0.02151228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005166899,"about_ca_system_score_gemma":0.00001199933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001995325,"about_ca_topic_score_gemma":0.000009777844,"domain_scores_codex":[0.9991733,0.000005560305,0.0001659436,0.0002376687,0.0001297369,0.0002878274],"domain_scores_gemma":[0.998917,0.00005382258,0.00004508046,0.0009089942,0.00002591137,0.00004913215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[4.331292e-7,0.000002952436,8.363973e-9,0.0007108244,0.00001600948,0.00001485657,7.593997e-7,0.0004137486,0.00001279618,3.564186e-7,0.9981726,0.0006546371],"study_design_scores_gemma":[0.00005372095,0.00001522734,5.070207e-7,0.0008677698,0.00001019814,0.000005952256,0.000001791089,0.000143362,0.0004319872,0.00001285629,0.9981034,0.0003532222],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[6.445526e-8,0.0003613179,0.00001010089,0.000008658496,0.0003659978,0.0001198843,0.9974286,0.001049957,0.0006554369],"genre_scores_gemma":[0.0000019149,0.0001398082,0.00008749294,0.00006026972,0.0003094098,0.0001688383,0.9991132,0.00004243806,0.00007660971],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03786009,"threshold_uncertainty_score":0.9999509,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Fireworks; STREAMS; Computer science; Geography; Archaeology; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.01929349536632958,"gpt":0.2097767133867238,"spread":0.1904832180203943,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001447352,0.0001551987,0.000138901,0.0001668107,0.001176978,0.0002296446,0.001351918,0.00006294641,0.02173268],"category_scores_gemma":[0.0003069823,0.0001803065,0.00004593678,0.0004922727,0.00007587128,0.0002563983,0.002320615,0.0005202703,0.006336846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002211666,"about_ca_system_score_gemma":0.000003361834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000758505,"about_ca_topic_score_gemma":0.000001860181,"domain_scores_codex":[0.9987486,0.00007243651,0.0001821118,0.0003119927,0.0003030324,0.000381838],"domain_scores_gemma":[0.9990464,0.00002229637,0.00004223172,0.0006740419,0.00008560511,0.00012942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002542884,0.00003383845,0.000004687025,0.00001637654,0.00003749755,0.00002715129,0.0006990214,0.004533991,0.000595615,0.0005743316,0.7479284,0.2455237],"study_design_scores_gemma":[0.0004649195,0.0001441245,0.0003704386,0.00001276735,0.00001056508,0.00005550589,0.001599169,0.003115793,0.0001515256,0.0008509696,0.993003,0.0002212813],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1631368,0.005644804,0.04698655,0.003716157,0.001828896,0.003025061,0.009042991,0.03666629,0.7299525],"genre_scores_gemma":[0.9727286,0.001451004,0.002850392,0.0001959193,0.0006003265,5.874434e-7,0.006858425,0.00439258,0.01092218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8095918,"threshold_uncertainty_score":0.9944369,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.01716906945968589,"gpt":0.2278421914486537,"spread":0.2106731219889678,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00001181685,0.0003376591,0.0002980061,0.000121711,0.00004186458,0.0000556274,0.0007801764,0.0005283434,0.07268524],"category_scores_gemma":[0.0001580984,0.0003329319,0.00007161681,0.0001088754,0.00000761381,0.0001367126,0.0001538602,0.0005522356,0.03231406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005432541,"about_ca_system_score_gemma":0.00001289559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006302954,"about_ca_topic_score_gemma":0.00002375789,"domain_scores_codex":[0.9990592,0.000006706327,0.0001899145,0.0002695424,0.000145048,0.0003296041],"domain_scores_gemma":[0.9987797,0.00005880032,0.00005484644,0.001015492,0.00003259404,0.00005858317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[4.969393e-7,0.000003562028,9.612105e-9,0.0007113332,0.00001882537,0.00001372456,8.669e-7,0.0008871929,0.00001205806,3.052824e-7,0.9978869,0.0004647505],"study_design_scores_gemma":[0.00006137562,0.00001720307,5.738834e-7,0.0008997049,0.00001173879,0.000006320613,0.000002087515,0.0002838779,0.0002998549,0.00001323026,0.9980032,0.0004008366],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[7.280826e-8,0.0006129073,0.00001329326,0.00001972036,0.0004322113,0.0001480415,0.9973138,0.001035502,0.0004244738],"genre_scores_gemma":[0.000001845009,0.0003653034,0.00009196848,0.00006387338,0.0003419237,0.000207304,0.9987649,0.00004887259,0.0001140356],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04037119,"threshold_uncertainty_score":0.9999123,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.03218629737159266,"gpt":0.2646636539960191,"spread":0.2324773566244264,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001923101,0.0004945193,0.0005090432,0.0003164565,0.0001020134,0.0000640003,0.0005294342,0.0002377797,0.0001348463],"category_scores_gemma":[0.00006179989,0.0005226317,0.0001104977,0.0006113881,0.0001193136,0.0007759418,0.00006241784,0.0004154948,0.0013281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002647527,"about_ca_system_score_gemma":0.00001280546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004465219,"about_ca_topic_score_gemma":0.002126662,"domain_scores_codex":[0.9976289,0.00001178459,0.0005011685,0.0005696606,0.0003047376,0.000983752],"domain_scores_gemma":[0.998824,0.0001012271,0.00006916105,0.000793012,0.00005864692,0.0001539807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006976197,0.0001689772,0.0008813392,0.0006682948,0.000287598,0.001584161,0.005099481,0.3500118,0.1395013,0.0009264948,0.07507461,0.4257262],"study_design_scores_gemma":[0.009516111,0.000755625,0.01655522,0.001895384,0.0001782405,0.0002246429,0.004728845,0.3356408,0.0766893,0.005103644,0.5409428,0.007769332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9218704,0.004524119,0.03141825,0.001229236,0.007674015,0.001443516,0.0006608633,0.0123625,0.01881707],"genre_scores_gemma":[0.9901438,0.007370965,0.0001861892,0.0001259968,0.0002415831,0.00008012727,0.0001044133,0.0001627363,0.001584203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4658682,"threshold_uncertainty_score":0.9997225,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Computer science; Telecommunications; Electrical engineering; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02249614817674891,"gpt":0.2489595804378522,"spread":0.2264634322611033,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008932118,0.0001761369,0.0002246183,0.0001453375,0.00007926008,0.0002113128,0.00008000814,0.0001372857,0.000005213818],"category_scores_gemma":[0.00001881572,0.0001541103,0.00002338935,0.0002218748,0.00007552941,0.0002721309,0.00001585484,0.0002220894,0.00004845001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003122615,"about_ca_system_score_gemma":0.000007428427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000929266,"about_ca_topic_score_gemma":0.00003710494,"domain_scores_codex":[0.9991894,0.00001124765,0.0002181257,0.0002556335,0.00009073578,0.0002348159],"domain_scores_gemma":[0.9995747,0.0000895362,0.00001605676,0.0002219982,0.00002652525,0.00007124992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005779157,0.00005345958,0.001271934,0.006900081,0.0007353547,0.0009942737,0.001441119,0.04063898,0.1803012,0.01778235,0.4856017,0.2642738],"study_design_scores_gemma":[0.0005087137,0.00007820468,0.0008991186,0.00100678,0.0000898716,0.001237142,0.0007525127,0.1422212,0.002599,0.0004981695,0.8492466,0.0008626573],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7074675,0.1247975,0.1142128,0.0003079855,0.02774625,0.001052828,0.0005874924,0.007016893,0.01681074],"genre_scores_gemma":[0.9958511,0.00139556,0.00002459749,0.000008701445,0.0002835677,0.00004078837,0.000008509634,0.00003687768,0.002350301],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3636449,"threshold_uncertainty_score":0.6284434,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.06439165678420233,"gpt":0.3248311322942653,"spread":0.260439475510063,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005411914,0.0001855584,0.0007165444,0.0001629664,0.00002295436,0.00001568253,0.0002673681,0.0001217974,0.08133943],"category_scores_gemma":[0.04034321,0.0001342133,0.000225452,0.0002995865,0.00003476163,0.0001680247,0.00002926641,0.0001844884,0.000001672286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002921585,"about_ca_system_score_gemma":0.001168566,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007366217,"about_ca_topic_score_gemma":0.03299551,"domain_scores_codex":[0.9982807,0.000110142,0.0008880807,0.0002311039,0.0002848048,0.0002051479],"domain_scores_gemma":[0.9730196,0.02618433,0.0003401778,0.0002324042,0.0002075142,0.00001596688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","study_design_scores_codex":[0.4866496,0.0001428229,0.00001221956,0.02015101,0.0001960943,0.000001977594,0.0005135741,0.01337007,0.00003807239,0.00003970867,0.472827,0.006057817],"study_design_scores_gemma":[0.7000396,0.0006551862,0.0001871017,0.09505153,0.00003548039,6.840239e-7,0.0002939535,0.1914428,0.0004256039,0.00007719025,0.01150495,0.0002859197],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","genre_codex":"dataset","genre_gemma":"protocol","genre_scores_codex":[0.0003454116,0.0001033532,0.00009887593,0.00002324778,0.0000581898,0.1482468,0.8509936,0.0001012496,0.00002926983],"genre_scores_gemma":[0.006626462,9.859003e-7,0.002934158,0.000004283714,0.00004247973,0.9042009,0.08611638,0.00003526909,0.0000391291],"genre_candidate":"protocol","genre_consensus":null,"teacher_disagreement_score":0.7648773,"threshold_uncertainty_score":0.9846498,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Engineering; Telecommunications; Electrical engineering; Library science","retraction":null,"screen_n_in":null,"score":{"opus":0.02151745872406799,"gpt":0.2357370823982326,"spread":0.2142196236741646,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001115389,0.0001665862,0.0002073536,0.00008706844,0.00004694869,0.0000425734,0.0000855139,0.000102421,0.00001551156],"category_scores_gemma":[0.0001384023,0.0001554523,0.00002614623,0.0003634858,0.00004819154,0.000325942,0.00002818233,0.000256156,0.00003368099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000212769,"about_ca_system_score_gemma":0.000008552827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004604029,"about_ca_topic_score_gemma":0.00003393844,"domain_scores_codex":[0.9990249,0.00001134677,0.0003459638,0.0002701576,0.0001114854,0.0002361456],"domain_scores_gemma":[0.999619,0.00003331697,0.00006065212,0.0001853343,0.00003576793,0.00006590724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009196984,0.00005458477,0.008472279,0.0006912993,0.00005444205,0.0008707934,0.001357924,0.02487971,0.7878584,0.0002654127,0.02579622,0.1496069],"study_design_scores_gemma":[0.002309312,0.0004702373,0.009280088,0.001052942,0.00006932727,0.0003580911,0.001706626,0.6210987,0.2401356,0.002865369,0.1183196,0.002334061],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8403193,0.003343383,0.1440796,0.003972358,0.001049863,0.0007827432,0.00003070886,0.002217367,0.004204628],"genre_scores_gemma":[0.9966372,0.0008030691,0.001982024,0.0002177459,0.0001515008,0.00002292235,0.0000223965,0.00002943063,0.0001337419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.596219,"threshold_uncertainty_score":0.6339161,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Internet of Things; Feature (linguistics); Key (lock); Power (physics); Feature extraction","retraction":null,"screen_n_in":null,"score":{"opus":0.03284475189187658,"gpt":0.273111815514095,"spread":0.2402670636222184,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005686953,0.0005748622,0.0005020829,0.001158771,0.0008480928,0.000636352,0.000394478,0.0006238156,0.00008255643],"category_scores_gemma":[0.0009354241,0.0006591333,0.0001398741,0.001333513,0.0002740205,0.004435298,0.0001613273,0.0007963567,0.0001533129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00203251,"about_ca_system_score_gemma":0.000175322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001764165,"about_ca_topic_score_gemma":0.00001241781,"domain_scores_codex":[0.9968187,0.0001184848,0.001750695,0.0003496379,0.0003606224,0.0006018523],"domain_scores_gemma":[0.9979661,0.0001209354,0.0006962331,0.0005504712,0.0005757607,0.00009049048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003106236,0.00004100723,0.0004343786,0.0008134065,0.0001282897,0.000001120429,0.002411162,0.248596,0.005947165,0.003778146,0.00004816429,0.7377701],"study_design_scores_gemma":[0.000675064,0.00008153335,0.001522495,0.0007671088,0.0001106029,0.00001371054,0.005022825,0.9557995,0.02376518,0.000883326,0.01080074,0.000557919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01856663,0.001708019,0.9735882,0.00006211423,0.00145924,0.00102324,0.0001157004,0.001147265,0.00232955],"genre_scores_gemma":[0.8647498,0.001669245,0.1314995,0.00006245865,0.00008390752,0.00009835453,0.001576799,0.00004512732,0.0002147416],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8461832,"threshold_uncertainty_score":0.999586,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.03694991079666952,"gpt":0.2667986428764763,"spread":0.2298487320798067,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002052968,0.0001747749,0.000257862,0.0004979728,0.000221306,0.00002017721,0.0002540862,0.000355121,0.00001371875],"category_scores_gemma":[0.0000564226,0.0002128085,0.00007371094,0.0002578253,0.0001047952,0.000158409,0.00004426959,0.000295568,0.00002784699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001030237,"about_ca_system_score_gemma":0.00003967596,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003932002,"about_ca_topic_score_gemma":0.01941823,"domain_scores_codex":[0.9990469,0.00002387305,0.0001087089,0.0003157405,0.0002158284,0.0002889849],"domain_scores_gemma":[0.9992767,0.0001047485,0.00007085714,0.0003061064,0.0001969565,0.00004464021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.003341695,0.0005096907,0.001819612,0.02395973,0.001646253,0.0001551959,0.1437261,0.01090744,0.3005626,0.003179478,0.07323816,0.436954],"study_design_scores_gemma":[0.00365302,0.0009334379,0.04558846,0.001412627,0.0003586346,0.000002164259,0.4825432,0.4431161,0.01273212,0.00270068,0.005323625,0.001635919],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885723,0.0001598121,0.007810843,0.0005790509,0.0006496384,0.0008468002,0.0003801906,0.0008212195,0.0001801536],"genre_scores_gemma":[0.8468004,0.001470386,0.02008458,0.000006191946,0.0001801978,0.00002857808,0.01208484,0.0002048256,0.1191399],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4353181,"threshold_uncertainty_score":0.9984748,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Computer science; Service quality; Service (business); Quality (philosophy); Artificial intelligence; Business; Marketing","retraction":null,"screen_n_in":null,"score":{"opus":0.06333412274218035,"gpt":0.3535337788141994,"spread":0.290199656072019,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002239288,0.0002259349,0.0006115871,0.000499829,0.0001724039,0.00008466357,0.0005163258,0.0001898458,0.000003678758],"category_scores_gemma":[0.000615502,0.0001979149,0.00009997863,0.0007667437,0.00009625439,0.0001168498,0.000161813,0.000845874,0.000002075101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001595816,"about_ca_system_score_gemma":0.00009988411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006494363,"about_ca_topic_score_gemma":5.86822e-7,"domain_scores_codex":[0.9974975,0.00004681393,0.001221056,0.0001724226,0.0007441568,0.0003180385],"domain_scores_gemma":[0.9969808,0.001564399,0.0003793946,0.0003770777,0.0006235475,0.00007480454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001910493,0.0004894025,0.000008924324,0.0005798725,0.00005945902,0.000004236048,0.0001516709,0.007841874,0.003401273,0.962409,0.0001198816,0.02474336],"study_design_scores_gemma":[0.0009828707,0.001366218,0.00003453075,0.0009949922,0.00002589586,0.000001602573,0.001281065,0.0645424,0.08313953,0.8471376,0.0002869403,0.0002063883],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9325837,0.0001159968,0.05116315,0.0002720181,0.01201527,0.0004245403,0.00001000201,0.0001236498,0.003291632],"genre_scores_gemma":[0.9972052,0.00002471884,0.002453824,0.0000268644,0.0002638966,0.000002617412,0.000001410024,0.00002009188,0.000001400701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1152714,"threshold_uncertainty_score":0.8070737,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Multiple sclerosis; Magnetic resonance imaging; Disease; White matter; Central nervous system; Lesion; Population","retraction":null,"screen_n_in":null,"score":{"opus":0.0173603530682477,"gpt":0.2166333452727358,"spread":0.199272992204488,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003688154,0.0007066613,0.0008637803,0.0006858386,0.0001861169,0.00002939408,0.0008019255,0.0008293592,0.00002373629],"category_scores_gemma":[0.0006756638,0.0006945148,0.0001511462,0.0007041382,0.00008145647,0.0006200111,0.00008989545,0.00139075,0.00004371324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002839505,"about_ca_system_score_gemma":0.00001742551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002039788,"about_ca_topic_score_gemma":0.002086814,"domain_scores_codex":[0.99736,0.00008528362,0.0007348747,0.0006921059,0.0005306005,0.0005971497],"domain_scores_gemma":[0.9983925,0.0001357195,0.0002686109,0.0009440641,0.0001446027,0.0001145113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004429783,0.0002357148,0.008355894,0.0008374123,0.0007402994,0.00001059414,0.00001593315,0.00673948,0.2473601,0.01085634,0.00008094949,0.7243243],"study_design_scores_gemma":[0.002571509,0.0004677454,0.1031787,0.002244683,0.000194467,0.00001460864,0.0002673076,0.0002482164,0.8640404,0.00524124,0.0189872,0.002543906],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914035,0.0003740685,0.000009417084,0.00000477166,0.0002808683,0.0006400951,0.001736732,0.0008799194,0.004670592],"genre_scores_gemma":[0.9950632,0.0006495097,0.002419787,0.00003156401,0.00001940393,0.0001418313,0.001247637,0.0002378026,0.0001892611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7217804,"threshold_uncertainty_score":0.9995506,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.01546339488064932,"gpt":0.2480033136517966,"spread":0.2325399187711473,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005667648,0.00007075083,0.00007646457,0.00008551938,0.00005068231,0.00002094942,0.0001135647,0.00003929785,0.000005108394],"category_scores_gemma":[0.00004200503,0.0000679375,0.00003050565,0.0001211938,0.00001421109,0.00006215979,0.000006499461,0.00008570989,0.000004759529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000241358,"about_ca_system_score_gemma":0.000008979544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001340565,"about_ca_topic_score_gemma":0.000001648826,"domain_scores_codex":[0.9996074,0.000003489026,0.0001136061,0.0001238154,0.00003489509,0.0001167841],"domain_scores_gemma":[0.9997818,0.00003680199,0.000003992017,0.0001476684,0.00002054434,0.000009180109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001384472,0.00003043788,0.0001274651,0.0004676455,0.00004010383,3.324947e-7,0.00002132277,0.7597798,0.07061961,0.02238344,0.002795195,0.1437208],"study_design_scores_gemma":[0.0002507186,0.000009304054,0.0001200443,0.000009827771,0.00001389419,2.303387e-7,0.00009167867,0.6991659,0.273322,0.0006949445,0.02621982,0.000101544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0165655,0.0001270346,0.9779598,0.00006943543,0.00005708787,0.000201567,0.000007194028,0.001247026,0.003765336],"genre_scores_gemma":[0.9481708,0.000009993814,0.05095062,0.00004031417,0.000007772269,0.0001242698,0.00009450567,0.00001136269,0.0005902966],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9316053,"threshold_uncertainty_score":0.2770411,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.01456772423046494,"gpt":0.245875650105951,"spread":0.2313079258754861,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001558414,0.0004635616,0.0004820489,0.0002739278,0.0003141047,0.0001634315,0.0007085845,0.0004625417,0.0007011537],"category_scores_gemma":[0.0001171469,0.0004103502,0.00005585017,0.0009736799,0.0002132119,0.001088934,0.0003564773,0.001457511,0.00005888423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001080877,"about_ca_system_score_gemma":0.0001032881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002881552,"about_ca_topic_score_gemma":0.0002902982,"domain_scores_codex":[0.997749,0.00003832253,0.0005205324,0.0008191925,0.000273522,0.0005994499],"domain_scores_gemma":[0.9981446,0.0001447078,0.00003495774,0.001553215,0.00003853258,0.00008393499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005653414,0.0002071747,0.07170925,0.001159592,0.001598845,0.0001691671,0.0006225198,0.7988613,0.0001502403,0.08933614,0.01007618,0.02554422],"study_design_scores_gemma":[0.002017615,0.000809703,0.004689696,0.002068535,0.0005098513,0.00004073753,0.001970585,0.63313,0.003117136,0.008493506,0.3420247,0.00112797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01543697,0.001289902,0.972708,0.001131045,0.0006041559,0.0006119742,0.0003847233,0.002070113,0.005763132],"genre_scores_gemma":[0.9406583,0.001577356,0.05509088,0.00006715301,0.0001818519,0.00004163622,0.0008182562,0.00006032857,0.001504252],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9252213,"threshold_uncertainty_score":0.9998348,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Computer science; Table (database); Database","retraction":null,"screen_n_in":null,"score":{"opus":0.03265100993284144,"gpt":0.262970993137926,"spread":0.2303199832050846,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000137608,0.00004263858,0.00007871315,0.00001583859,0.00004495946,0.00001304116,0.0002685864,0.00003483696,0.00002904119],"category_scores_gemma":[0.00004303438,0.00003549849,0.00001181032,0.000009178529,0.00004824606,0.0002251714,0.00003672809,0.00003888612,0.00003387377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004909823,"about_ca_system_score_gemma":0.000001180829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002183818,"about_ca_topic_score_gemma":0.000007237879,"domain_scores_codex":[0.9997709,4.832806e-7,0.00006190711,0.00004656843,0.0000345084,0.00008562158],"domain_scores_gemma":[0.9994796,0.00000511955,0.00002080373,0.0004702509,0.0000130525,0.00001119587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001325726,0.00004831185,0.0180708,0.0001473006,0.0001230531,0.00001521367,0.00005057526,0.002893497,0.5290735,0.01260894,0.2881075,0.1488481],"study_design_scores_gemma":[0.000405252,0.00002954399,0.009663128,0.00002921312,0.000006600627,0.000001872008,0.00006999839,0.005734155,0.9069703,0.001906663,0.07500952,0.0001737594],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5477275,0.0004841006,0.1331384,0.0001633259,0.001512405,0.0002054018,0.00009250557,0.001694485,0.3149818],"genre_scores_gemma":[0.9956987,0.0001182596,0.003140013,0.00000436678,0.00001065443,0.000002282903,0.000001440832,0.000006020473,0.001018243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4479712,"threshold_uncertainty_score":0.1447586,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.005915682492057771,"gpt":0.1865531448939535,"spread":0.1806374624018958,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00002132505,0.00004295332,0.00004136383,0.00003308723,0.00005588703,0.000004387279,0.0001389536,0.0000122715,0.00130965],"category_scores_gemma":[0.000006359463,0.00004179622,0.00001182907,0.0001181483,0.000008305016,0.00007094047,0.0001344809,0.0001209331,0.00005355631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002965999,"about_ca_system_score_gemma":0.00000209514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001357899,"about_ca_topic_score_gemma":0.000001211694,"domain_scores_codex":[0.9997241,0.000002343535,0.00004822124,0.00006307543,0.00006232663,0.00009988181],"domain_scores_gemma":[0.9998021,0.000007075042,0.000003414384,0.0001741229,0.000002294282,0.00001096048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000007854648,0.0000455543,0.0004545003,0.00003933019,0.00004724642,0.00003672749,0.0001661389,0.1829133,0.06051326,0.06759272,0.5106729,0.1775105],"study_design_scores_gemma":[0.0001094661,0.00003174213,0.0001448408,6.606703e-7,0.000002120052,0.00000899575,0.0005897052,0.01173224,0.01463338,0.002875538,0.9697304,0.0001408692],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5439755,0.004994959,0.1751996,0.0009324122,0.002513245,0.0005548817,0.0002594652,0.0212512,0.2503187],"genre_scores_gemma":[0.993929,0.00009087859,0.003027426,0.00005783284,0.00001388743,0.00005722398,0.00002043577,0.00001456705,0.002788774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4590576,"threshold_uncertainty_score":0.9996033,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.01352175772770854,"gpt":0.2258246737094629,"spread":0.2123029159817544,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000162003,0.00004450332,0.00004178889,0.00004525573,0.00001460328,0.000007718445,0.00009184889,0.00003352754,0.0001059882],"category_scores_gemma":[0.00001628741,0.00003846756,0.0000105011,0.0002495583,0.00001008993,0.0001052613,0.00004363704,0.00005007941,0.002681422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007773895,"about_ca_system_score_gemma":0.000001085134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001189495,"about_ca_topic_score_gemma":0.000003454073,"domain_scores_codex":[0.9997325,7.329206e-7,0.0000447868,0.00005906173,0.00003649162,0.0001264356],"domain_scores_gemma":[0.9998115,0.00001276524,0.000002139418,0.0001576138,0.000003423717,0.00001256241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001129201,0.000003750707,0.0003286357,0.00004098621,0.00002142365,0.00001579797,0.00003825305,0.0297338,0.02315005,0.01028298,0.8528408,0.08354235],"study_design_scores_gemma":[0.0001622161,0.00002164018,0.002437728,0.00001127515,0.000003410223,0.000002874756,0.0006867331,0.06405725,0.06803568,0.006999376,0.8572943,0.0002875508],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3808962,0.0007537071,0.1751912,0.001044752,0.001808477,0.0003750565,0.0001010694,0.068406,0.3714235],"genre_scores_gemma":[0.9695706,0.00086895,0.006982985,0.0000376643,0.00005834678,0.00003029403,0.00005409347,0.00003762134,0.0223594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5886744,"threshold_uncertainty_score":0.9980951,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Waterfowl; Wildlife; Population; Habitat; Principal (computer security); Service (business); Wildlife conservation; Tiger","retraction":null,"screen_n_in":null,"score":{"opus":0.00874656905223879,"gpt":0.2556535469031179,"spread":0.2469069778508791,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005739164,0.00009155928,0.000072733,0.00004847473,0.00006770654,0.0000362395,0.0001072639,0.00004399031,0.000008790385],"category_scores_gemma":[0.000004844504,0.00006180941,0.0000155454,0.0000921945,0.00001674925,0.0001968885,0.00004043662,0.0001032712,0.00001132529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002649001,"about_ca_system_score_gemma":8.016357e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005492866,"about_ca_topic_score_gemma":0.00004109133,"domain_scores_codex":[0.9995561,0.000004995399,0.0001058838,0.0001135046,0.00007899147,0.0001405155],"domain_scores_gemma":[0.9997309,0.00001095661,0.00001296323,0.0002211486,0.000004595234,0.00001940874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000005285404,0.00002779729,0.02814026,0.00006047352,0.00005304578,0.00000856824,0.00007400966,0.007372342,0.001062001,0.280136,0.006175573,0.6768846],"study_design_scores_gemma":[0.0002587986,0.00005447496,0.9368399,0.00001835378,0.00001837551,0.000005146464,0.0002767538,0.01495819,0.0002299451,0.0270501,0.02007394,0.0002160394],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7037032,0.0005953105,0.2588286,0.002291173,0.0004575501,0.0006813527,0.000008201546,0.008292963,0.02514159],"genre_scores_gemma":[0.9856191,0.0002026503,0.01387649,0.00007517346,0.00001736948,0.000008074491,0.000005506471,0.0000149868,0.00018059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9086996,"threshold_uncertainty_score":0.2520514,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"","keywords":"Traffic analysis; Feature (linguistics); Perspective (graphical); Domain (mathematical analysis); Key (lock)","retraction":null,"screen_n_in":null,"score":{"opus":0.006925239283522394,"gpt":0.2243740548577667,"spread":0.2174488155742443,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002135535,0.0003368029,0.0005996747,0.0005862581,0.0001804833,0.00005301684,0.0003838221,0.0003299322,0.000132437],"category_scores_gemma":[0.00008770753,0.0003671156,0.0002474545,0.002341616,0.00004210064,0.000254664,0.0001262639,0.0004521365,0.00002529775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000235023,"about_ca_system_score_gemma":0.00001728509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006539702,"about_ca_topic_score_gemma":0.00007907479,"domain_scores_codex":[0.9980471,0.00003328974,0.0005602108,0.0005262912,0.0001125375,0.0007205851],"domain_scores_gemma":[0.9991734,0.0002321911,0.00004824073,0.0004937379,0.00000763289,0.00004482135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001724891,0.00005962104,0.003554718,0.0001131727,0.0009682885,0.000002464871,0.0001176577,0.8880507,0.00009204729,0.0006065738,0.0002600765,0.1061574],"study_design_scores_gemma":[0.0008043827,0.00005371846,0.002746817,0.00004468802,0.0006502858,1.214021e-7,0.0005061984,0.9264665,0.000272274,0.0002078094,0.06791884,0.0003283434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1198076,0.004618302,0.8704287,0.0001994352,0.0003813503,0.000841873,0.00001394589,0.001029537,0.002679258],"genre_scores_gemma":[0.9286796,0.003642896,0.06553832,0.00004560114,0.00004376623,0.0001085345,0.00009488124,0.00003237415,0.001813995],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.808872,"threshold_uncertainty_score":0.9998781,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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)","retraction":null,"screen_n_in":null,"score":{"opus":0.0218579458083682,"gpt":0.275322777750211,"spread":0.2534648319418428,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003950451,0.0009959114,0.00122088,0.0005519225,0.0005182382,0.0002685167,0.001066094,0.0006645359,0.000766695],"category_scores_gemma":[0.002238949,0.001036282,0.0005407687,0.0007858004,0.0002349189,0.0008099849,0.0003776514,0.001745168,0.0002781549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004911422,"about_ca_system_score_gemma":0.00005003956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001393846,"about_ca_topic_score_gemma":0.00002107049,"domain_scores_codex":[0.9952113,0.00004815138,0.001402258,0.001075283,0.0003889433,0.001874035],"domain_scores_gemma":[0.9969052,0.0009537352,0.0003180161,0.00127773,0.0002108528,0.0003344413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000874474,0.0001398998,0.0001245673,0.0001726507,0.0004583821,0.00001962546,0.0002085624,0.8324926,0.0003012639,0.01459734,0.005300216,0.1460974],"study_design_scores_gemma":[0.002178428,0.000319242,0.00003006105,0.000182426,0.0002820774,0.00004904705,0.0008207483,0.7960725,0.01740503,0.009188932,0.1722039,0.001267582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01241726,0.005265701,0.9743127,0.001082697,0.0009008991,0.001098878,0.00007858879,0.002652335,0.002191004],"genre_scores_gemma":[0.2851361,0.005138424,0.707054,0.0002280407,0.0005520902,0.00009418197,0.0001469771,0.0002138389,0.001436325],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2727188,"threshold_uncertainty_score":0.9992087,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.06825939769797826,"gpt":0.3951550587631018,"spread":0.3268956610651236,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002603002,0.0009238826,0.001352799,0.001354606,0.00008717176,0.002491684,0.007573764,0.000351985,0.007199714],"category_scores_gemma":[0.0001268388,0.0009446886,0.0001954509,0.0006756157,0.0003953963,0.004356037,0.003838688,0.0006903782,0.002060642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002243925,"about_ca_system_score_gemma":0.0001187842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000848296,"about_ca_topic_score_gemma":0.001248176,"domain_scores_codex":[0.9969115,0.00004713991,0.0009438137,0.001027127,0.0004985697,0.0005719027],"domain_scores_gemma":[0.9974637,0.00009739381,0.0004259715,0.001802496,0.00008128716,0.0001291742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004106424,0.0000783563,0.0004138824,0.002890088,0.001346544,0.00004532445,0.00008594031,0.0004078347,0.00144254,0.03839896,0.5266187,0.4282308],"study_design_scores_gemma":[0.0004103776,0.00002573352,0.0003119791,0.001405364,0.0001276414,0.000005387297,0.00006031356,0.0001849964,0.003823115,0.006508721,0.9860832,0.00105316],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0002229746,0.002775032,0.003953042,0.00007568873,0.002559024,0.002047901,0.00006213049,0.005543684,0.9827605],"genre_scores_gemma":[0.01140665,0.004653613,0.007914634,0.0001350119,0.0004742127,0.00119022,0.001812592,0.003857457,0.9685556],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.4594646,"threshold_uncertainty_score":0.9993004,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"","keywords":"Telecommunications; Computer science; Engineering; Electrical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.004448129388059672,"gpt":0.2280484787188695,"spread":0.2236003493308098,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0003391269,0.0004932145,0.0004670911,0.0002983771,0.000120318,0.00006707513,0.0003533017,0.001700707,0.00002342847],"category_scores_gemma":[0.00003855658,0.0004788275,0.0001248535,0.0001302357,0.0001961995,0.0002301109,0.00000476171,0.001853993,0.00007172797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001123233,"about_ca_system_score_gemma":0.00003979626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000356897,"about_ca_topic_score_gemma":0.000005163253,"domain_scores_codex":[0.9986333,0.00006190321,0.0002957571,0.0004255984,0.0002575899,0.0003258011],"domain_scores_gemma":[0.9984087,0.0007771729,0.00005748142,0.0006229393,0.00007937522,0.00005435834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009929032,0.0000267014,1.266757e-8,0.000148878,0.00007638942,0.000003627478,0.00002552678,0.00001987393,0.04742521,0.0001524277,0.934451,0.01757108],"study_design_scores_gemma":[0.0001055636,0.0001165965,4.316618e-9,0.0001216876,0.00004788688,0.000002155174,0.0000171338,8.992322e-7,0.413468,0.002841599,0.5829659,0.0003125373],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00003944487,0.0002867282,0.2188715,0.000003970985,0.7760851,0.0002869008,0.000763969,0.002681147,0.0009812431],"genre_scores_gemma":[0.002420472,0.005528484,0.001616541,0.00001212526,0.9890385,0.0001274609,0.0001283984,0.0001557161,0.0009722939],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.3660428,"threshold_uncertainty_score":0.9997663,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Library science; Engineering; Engineering ethics; Political science; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.0138909915937341,"gpt":0.2455163939911739,"spread":0.2316254023974398,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022093,0.0004526145,0.000399717,0.0002852652,0.0001403392,0.00006558815,0.0007236514,0.0002012538,0.000101682],"category_scores_gemma":[0.00003782345,0.0003709959,0.0000847458,0.0003868527,0.00048284,0.0005950092,0.00009491792,0.0004871275,0.001805836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000118731,"about_ca_system_score_gemma":0.00001913066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005444401,"about_ca_topic_score_gemma":0.0006137576,"domain_scores_codex":[0.9977805,0.00002927471,0.0005115833,0.0005092506,0.0002869855,0.0008823992],"domain_scores_gemma":[0.9986189,0.00008613725,0.00008725917,0.0009754698,0.0001147387,0.0001175073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002225897,0.000319214,0.001529618,0.0003380509,0.0004972281,0.0009827608,0.01204758,0.04413491,0.3392732,0.001448354,0.1915215,0.4076851],"study_design_scores_gemma":[0.005541329,0.001434287,0.02877037,0.0005785904,0.0001525474,0.0003620989,0.0009449836,0.03668388,0.1765458,0.008084976,0.7368299,0.00407115],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8539401,0.001486945,0.01699669,0.003035128,0.008484778,0.0020942,0.002068464,0.004935057,0.1069586],"genre_scores_gemma":[0.9957767,0.0009135134,0.0001064413,0.0002604231,0.0005576267,0.00004891275,0.00002855778,0.0001133319,0.002194524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5453085,"threshold_uncertainty_score":0.9998742,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Center (category theory); Cybernetics; Library science; Geography; Computer science; Artificial intelligence; Chemistry","retraction":null,"screen_n_in":null,"score":{"opus":0.06134236329312846,"gpt":0.303581919121339,"spread":0.2422395558282106,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001150653,0.000412547,0.0004225741,0.0001872362,0.0006566833,0.0003726006,0.0008121363,0.0001826931,0.0000454145],"category_scores_gemma":[0.00001090135,0.0003933667,0.00006264146,0.00003709857,0.000143573,0.00038594,0.000008361251,0.0005525941,0.0000455342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000288542,"about_ca_system_score_gemma":0.00004379626,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.08692812,"about_ca_topic_score_gemma":0.1216019,"domain_scores_codex":[0.9982513,0.00003685722,0.0004653469,0.0004556982,0.0004036635,0.0003871017],"domain_scores_gemma":[0.9983974,0.00005655222,0.0001724487,0.001135743,0.00009870659,0.000139221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003161612,0.0009675199,0.001469897,0.002618037,0.002710626,0.0007877658,0.0007166203,0.6895703,0.0278241,0.02817106,0.2220139,0.02283402],"study_design_scores_gemma":[0.0104644,0.0009765846,0.004667527,0.004769246,0.0003782814,0.000839962,0.002134145,0.5953169,0.01752036,0.0003229181,0.3573024,0.005307308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2722113,0.004593387,0.4894876,0.001366721,0.08937301,0.004339143,0.01741652,0.004221969,0.1169904],"genre_scores_gemma":[0.9961454,0.0007904584,0.00005654279,0.00002276101,0.0001769893,0.00009150345,0.00006523645,0.00005789332,0.002593162],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7239342,"threshold_uncertainty_score":0.9998518,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Business; Engineering; Management; Accounting; Economics","retraction":null,"screen_n_in":null,"score":{"opus":0.01642159422273484,"gpt":0.2574324643352319,"spread":0.2410108701124971,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005667356,0.0002253331,0.000217927,0.00009862053,0.0001044144,0.00004575514,0.0003550442,0.000483796,0.0002572489],"category_scores_gemma":[0.00003370114,0.0002515506,0.00006156488,0.0009062563,0.0001032755,0.0002340139,0.00005285502,0.0008690757,0.001331996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001025847,"about_ca_system_score_gemma":0.00005914144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001631163,"about_ca_topic_score_gemma":0.000006853492,"domain_scores_codex":[0.998829,0.00001124395,0.0002905035,0.0003584411,0.00015986,0.0003509636],"domain_scores_gemma":[0.9987231,0.00004562337,0.00004114306,0.0009820713,0.0001056665,0.0001024231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005509081,0.0001856647,0.0005675289,0.0001161534,0.0001682502,0.00009407027,0.00004796267,0.05741159,0.2878108,0.00316018,0.4421502,0.208282],"study_design_scores_gemma":[0.0002340762,0.00001044413,0.000321573,0.0000233182,0.00002721437,0.00005378454,0.0001490977,0.001414662,0.1577468,0.001215512,0.8384531,0.0003504166],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1506957,0.002600525,0.7478559,0.006511538,0.00268085,0.001426802,0.001073028,0.006168046,0.08098761],"genre_scores_gemma":[0.9356674,0.001719582,0.04140389,0.0005114545,0.002171385,0.001920312,0.0007344647,0.0002392196,0.01563233],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7849717,"threshold_uncertainty_score":0.9999937,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.03857648817839217,"gpt":0.2493760633315656,"spread":0.2107995751531734,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004218009,0.001001326,0.001587628,0.0001652514,0.000166607,0.0001419393,0.001638058,0.0007639764,0.0001559551],"category_scores_gemma":[0.0002693905,0.0008559031,6.733071e-7,0.000317664,0.0003382245,0.000297059,0.0006044314,0.0007620495,0.000002052249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002078166,"about_ca_system_score_gemma":0.00249674,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9932278,"about_ca_topic_score_gemma":0.9977081,"domain_scores_codex":[0.9954784,0.0001206096,0.001274287,0.001163314,0.000895026,0.001068393],"domain_scores_gemma":[0.9936609,0.0017898,0.0008759144,0.003086281,0.0001675057,0.0004195551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005333456,0.0000490026,0.0001236886,0.001378261,0.0005507008,0.0001200886,0.000007302503,0.0003681048,0.00006837461,0.0007449795,0.9959376,0.0001185493],"study_design_scores_gemma":[0.001944676,0.0003324895,0.00001672429,0.0002178595,0.0006258372,0.0001053406,0.0002541655,0.002616345,0.000002434717,0.000001291519,0.9929375,0.0009453019],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002241809,0.01489865,0.00004788418,0.000007135845,0.0007134888,0.001050046,0.9827653,0.000205852,0.0002892665],"genre_scores_gemma":[0.0001005865,0.01272348,0.0004102229,0.0001152024,0.00009959402,0.00002599667,0.9855518,0.000297199,0.0006759252],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.004480356,"threshold_uncertainty_score":0.9993892,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4229636704","doi":"10.1109/surv.2014.042914.00000","title":"Editorial: Second Quarter 2014, IEEE Communications Surveys &amp;amp; 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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Computer science; Quarter (Canadian coin); Telecommunications; IEEE 802.11u; Computer network; IEEE 802.11; Wireless; Wireless lan","retraction":null,"screen_n_in":null,"score":{"opus":0.04279540479117327,"gpt":0.3182279542389591,"spread":0.2754325494477858,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.0222393,0.002001504,0.00327647,0.0009594243,0.001431114,0.0009029115,0.01592492,0.005105447,0.0003414073],"category_scores_gemma":[0.00946725,0.002213007,0.0007281189,0.0012137,0.001746789,0.001373689,0.001913039,0.005452548,0.004882426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001114294,"about_ca_system_score_gemma":0.000821208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001025881,"about_ca_topic_score_gemma":0.01305834,"domain_scores_codex":[0.978856,0.01215949,0.003743739,0.001494257,0.002031056,0.001715487],"domain_scores_gemma":[0.9434828,0.02328445,0.001468044,0.0287839,0.002526343,0.000454444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001337765,0.0002222524,0.00001767136,0.0002181576,0.0005381359,5.90505e-7,0.0002230359,0.0002223258,0.003563277,0.0002148865,0.9929448,0.001821461],"study_design_scores_gemma":[0.001147259,0.00006850807,0.00002911933,0.0002589161,0.0002879113,0.000002643674,0.00004250825,0.00006131137,0.0003933329,0.001638543,0.9938673,0.002202626],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00005103059,0.004133803,0.03408569,0.0002129876,0.9413256,0.00155238,0.01135639,0.003948187,0.003333932],"genre_scores_gemma":[0.001286926,0.02029929,0.01153457,0.00002258991,0.9370449,0.001427037,0.02518049,0.0006523589,0.002551874],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.03537315,"threshold_uncertainty_score":0.9998689,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4409991136","doi":"10.1109/comst.2025.3560017","title":"Editorial Second Bi-Monthly 2025 IEEE Communications Surveys &amp; 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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Telecommunications; Data science","retraction":null,"screen_n_in":null,"score":{"opus":0.05155535350439422,"gpt":0.3251082360560775,"spread":0.2735528825516833,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.008922148,0.0006821881,0.001126426,0.0006337822,0.001072155,0.0003476399,0.007152391,0.00085622,0.000117322],"category_scores_gemma":[0.00279865,0.0007595915,0.0002703687,0.001839362,0.0009494036,0.000969035,0.001236589,0.001338075,0.0005034382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005216042,"about_ca_system_score_gemma":0.0003779407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006040119,"about_ca_topic_score_gemma":0.005955297,"domain_scores_codex":[0.9919437,0.00437416,0.00172605,0.0006184595,0.0005140382,0.0008235506],"domain_scores_gemma":[0.9800228,0.005860739,0.0003314005,0.01280885,0.000809924,0.0001662222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001538924,0.0003166758,0.0005623733,0.0001005954,0.0004747992,7.009734e-7,0.0002507275,0.001387747,0.03449557,0.006450563,0.9449202,0.01102464],"study_design_scores_gemma":[0.0008937172,0.00003028055,0.001149263,0.0001177749,0.0001006703,9.55522e-7,0.00009612941,0.0003563323,0.005521876,0.004702503,0.9863068,0.0007237362],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.01158858,0.01503644,0.1248948,0.002036045,0.7639263,0.003741673,0.008369217,0.009228935,0.06117795],"genre_scores_gemma":[0.8417481,0.02089464,0.04882509,0.000210911,0.0682724,0.002733352,0.007549549,0.0004802249,0.009285743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8301595,"threshold_uncertainty_score":0.9994855,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}