{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":21,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":21,"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":"faadf53d7bbb","filters":{"venue":"CAAI Transactions on Intelligence Technology"}},"results":[{"id":"W4321615196","doi":"10.1049/cit2.12180","title":"Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey","year":2023,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":162,"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); University of Northern British Columbia","funders":"","keywords":"Leverage (statistics); Deep learning; Computer science; Artificial intelligence; Handwriting; Machine learning; Data science","retraction":null,"screen_n_in":null,"score":{"opus":0.03906646309947973,"gpt":0.286993582203467,"spread":0.2479271191039873,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002195947,0.0002230543,0.000223746,0.0008628829,0.0005585317,0.0001104146,0.0008447187,0.0004881231,0.00001898928],"category_scores_gemma":[0.00003221659,0.0002314424,0.00006947009,0.003054574,0.0002901684,0.0001478382,0.00005145767,0.001237804,0.0001671563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005016819,"about_ca_system_score_gemma":0.0000342616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000343259,"about_ca_topic_score_gemma":0.00003425899,"domain_scores_codex":[0.998371,0.00009152709,0.0003189952,0.0006864202,0.0001675842,0.0003644848],"domain_scores_gemma":[0.9983862,0.0004454818,0.00009490871,0.0008012485,0.0001744086,0.00009774775],"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":[0.000006770777,0.0000617054,0.00005545134,0.00001142973,0.0000233021,0.00000285135,0.0001266472,0.0459911,0.0001192273,0.07538699,0.00005510756,0.8781594],"study_design_scores_gemma":[0.00009845511,0.0003365689,0.0008794403,0.00003289401,0.00001607782,0.0001161571,0.0005560979,0.6322657,0.0116494,0.3376838,0.01578866,0.0005766771],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001454508,0.0003160512,0.9925642,0.00159206,0.00006105869,0.000540131,0.00001676415,0.003263955,0.0001912746],"genre_scores_gemma":[0.9632361,0.001466216,0.03353238,0.0002076438,0.00001420988,0.001232843,0.00001611262,0.00002514297,0.0002693569],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9617816,"threshold_uncertainty_score":0.9437945,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3211570179","doi":"10.1049/cit2.12065","title":"A transformer generative adversarial network for multi‐track music generation","year":2021,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Music Technology and Sound Studies","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Transformer; Generative adversarial network; Computer science; Artificial neural network; Artificial intelligence; Speech recognition; Deep learning; Engineering; Electrical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.06836119008236424,"gpt":0.2980363492522372,"spread":0.2296751591698729,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001810794,0.0002617772,0.0003229108,0.0002842949,0.0008060988,0.00005517791,0.0006154997,0.0005177308,0.00009911248],"category_scores_gemma":[0.00005663524,0.0002630718,0.000187114,0.001220545,0.0003155427,0.0002346831,0.00001233738,0.0004922637,0.00005160409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000926327,"about_ca_system_score_gemma":0.0001603564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005559815,"about_ca_topic_score_gemma":0.0004867276,"domain_scores_codex":[0.99815,0.00005070763,0.0003963309,0.0007501703,0.0001385546,0.0005142262],"domain_scores_gemma":[0.9987844,0.0001113146,0.00007709174,0.0006765074,0.0002981763,0.00005251536],"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":[0.00003811202,0.0004877158,0.0000179243,0.00002141295,0.0002803493,0.00003854314,0.00185496,0.01365744,0.006979953,0.4759043,0.002295888,0.4984235],"study_design_scores_gemma":[0.00106566,0.0008547711,0.00002385924,0.00005382723,0.0001197461,0.0001847858,0.001249159,0.2379985,0.6203374,0.09542579,0.0417311,0.0009554377],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002935198,0.0005184141,0.9890348,0.004193815,0.001880384,0.0004552547,0.00002217647,0.000732943,0.0002269881],"genre_scores_gemma":[0.5724609,0.0003449443,0.4242334,0.0009598412,0.0002029029,0.0006956149,0.00001368059,0.00002261371,0.001066028],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6133574,"threshold_uncertainty_score":0.9999822,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4391754563","doi":"10.1049/cit2.12293","title":"ECG‐TransCovNet: A hybrid transformer model for accurate arrhythmia detection using Electrocardiogram signals","year":2024,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Information Technology Research Centre; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; National Research Foundation of Korea; Institute for Information and Communications Technology Promotion; Princess Nourah Bint Abdulrahman University; National Research Foundation","keywords":"Deep learning; Convolutional neural network; Transformer; Artificial intelligence; Computer science; Cardiac arrhythmia; Benchmark (surveying); Feature extraction; Pattern recognition (psychology); Artificial neural network; Machine learning; Engineering; Atrial fibrillation; Cardiology; Medicine; Voltage","retraction":null,"screen_n_in":null,"score":{"opus":0.04164747743931271,"gpt":0.3402372615712562,"spread":0.2985897841319435,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001939184,0.0002984521,0.0004392414,0.001361204,0.0002643692,0.00005156012,0.0001301754,0.0003150127,0.00004157535],"category_scores_gemma":[0.00001870697,0.000278702,0.0005807285,0.001359314,0.000158586,0.0001453773,9.545703e-7,0.0007171917,0.00003660505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002246078,"about_ca_system_score_gemma":0.0001623158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000204156,"about_ca_topic_score_gemma":0.00001655756,"domain_scores_codex":[0.9982322,0.00001778751,0.0004437015,0.0005974697,0.0001937808,0.0005149872],"domain_scores_gemma":[0.9992542,0.00009102622,0.00003948839,0.0003531013,0.0001574186,0.0001047734],"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":[0.0002091439,0.0000790833,0.000006018628,0.0002106415,0.0005871142,0.00003953184,0.000132018,0.1254551,0.1720855,0.00008827538,0.000006959317,0.7011005],"study_design_scores_gemma":[0.00009415994,0.0003507171,3.444476e-7,0.0001574214,0.0005288686,0.0002365864,0.0001224655,0.5394741,0.4561297,0.002364167,0.0003882406,0.0001532362],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05224183,0.001013069,0.9437497,0.001120853,0.0003103686,0.0005650539,0.00004308187,0.000899709,0.00005628306],"genre_scores_gemma":[0.989413,0.0007228723,0.008777579,0.00006561243,0.0001393373,0.0003277963,0.000007827097,0.00006828732,0.0004777523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9371711,"threshold_uncertainty_score":0.9999665,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4320733972","doi":"10.1049/cit2.12164","title":"Privacy‐preserving remote sensing images recognition based on limited visual cryptography","year":2023,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brandon University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Encryption; Visual cryptography; Block (permutation group theory); Cryptography; Artificial intelligence; Computer vision; Secret sharing; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.03087608101628661,"gpt":0.2935827629035605,"spread":0.2627066818872739,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002939794,0.0003098148,0.0002523899,0.003438879,0.0004957016,0.0001033011,0.0009847811,0.000343784,0.00001804871],"category_scores_gemma":[0.00007698815,0.0003175735,0.0001787528,0.005569919,0.0002628472,0.0003510108,0.00003917188,0.0007254815,0.0001197561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005814393,"about_ca_system_score_gemma":0.00003233692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001252716,"about_ca_topic_score_gemma":0.000003023782,"domain_scores_codex":[0.9978796,0.0001010373,0.0003866938,0.0007713082,0.0002889761,0.0005723644],"domain_scores_gemma":[0.9983032,0.0003105855,0.0001215649,0.001017956,0.0001779078,0.00006883884],"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":[0.00003643365,0.00006712912,0.000008819007,0.00002118071,0.00002070616,0.00005630918,0.00009170169,0.002422998,0.004867586,0.0005882969,0.00007670191,0.9917421],"study_design_scores_gemma":[0.0001058165,0.0006143016,0.00003689612,0.0002887645,0.00001351234,0.00002560866,0.00008878542,0.2987995,0.5531965,0.1455224,0.0009086812,0.0003993374],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005889969,0.00002381278,0.984024,0.002399987,0.0003444242,0.0003219661,0.00001046251,0.006510246,0.0004751626],"genre_scores_gemma":[0.7580562,0.0001754942,0.2413406,0.0003274123,0.00001482584,0.00001856139,0.00001024826,0.00003004669,0.00002668272],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9913428,"threshold_uncertainty_score":0.9999276,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4224260662","doi":"10.1049/cit2.12094","title":"Medical data publishing based on average distribution and clustering","year":2022,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Nipissing University","funders":"National Natural Science Foundation of China","keywords":"Cluster analysis; Computer science; Distribution (mathematics); Data mining; Environmental science; Mathematics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.04751004956674833,"gpt":0.2947991708321599,"spread":0.2472891212654116,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001042082,0.0002103634,0.0002068984,0.0006641155,0.0007428694,0.0002534126,0.03122278,0.0003116729,0.0002546862],"category_scores_gemma":[0.006888988,0.0002265118,0.00003570442,0.001756825,0.0003446793,0.001030004,0.01296838,0.001935523,0.00001956063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002769924,"about_ca_system_score_gemma":0.0001506727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003428236,"about_ca_topic_score_gemma":0.00002795399,"domain_scores_codex":[0.9973049,0.0001091627,0.0003540904,0.001074831,0.0007030136,0.0004540314],"domain_scores_gemma":[0.9900832,0.0003890315,0.0001018431,0.009278297,0.00005007364,0.00009754854],"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":[0.00003070743,0.0002893426,0.00005832231,0.00002256906,0.00002568834,0.0001340827,0.00003051001,0.008316108,0.00005320273,0.01269578,0.01151611,0.9668276],"study_design_scores_gemma":[0.0001322305,0.0002695714,0.00001283802,0.00003817231,0.000005741798,0.0001366272,0.0001200111,0.944311,0.002930309,0.04411884,0.007701424,0.0002231855],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006791899,0.00006389947,0.9008727,0.09536134,0.0005584403,0.0001877269,0.0003301528,0.001782227,0.0001643163],"genre_scores_gemma":[0.957297,0.00009300011,0.04158195,0.0007122172,0.00001303627,0.0001357456,0.0001321402,0.00001646851,0.00001848345],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9666044,"threshold_uncertainty_score":0.9950145,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4224306551","doi":"10.1049/cit2.12084","title":"Age estimation from facial images based on Gabor feature fusion and the CIASO‐SA algorithm","year":2022,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Face recognition and analysis","field":"Computer Science","cited_by":11,"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":"Pattern recognition (psychology); Artificial intelligence; Histogram; Computer science; Feature (linguistics); Simulated annealing; Feature selection; Algorithm; Chaotic; Support vector machine; Face (sociological concept); Facial recognition system; Computer vision; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.009570455776964385,"gpt":0.2404836509447373,"spread":0.2309131951677729,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002016015,0.0001649202,0.0001987939,0.0005428246,0.0007929843,0.00008898792,0.0006118174,0.0001233576,0.0003296434],"category_scores_gemma":[0.00003207695,0.000131462,0.0001102743,0.001221156,0.0002856021,0.0001148342,0.00002787142,0.0006745024,0.00005079575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007456903,"about_ca_system_score_gemma":0.00004047191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005121564,"about_ca_topic_score_gemma":0.00001700654,"domain_scores_codex":[0.9987543,0.0001300039,0.0001867546,0.0004500128,0.0002805395,0.0001984475],"domain_scores_gemma":[0.999106,0.0002339612,0.00007953621,0.0004837443,0.00005359162,0.00004320244],"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":[0.00002692966,0.00009806245,0.000002492895,0.000002045593,0.00002029591,0.0000175537,0.0001941928,0.01766862,0.0002528905,0.001656654,0.0001293976,0.9799309],"study_design_scores_gemma":[0.0003826366,0.0001832827,0.00002724188,0.00001706197,0.00004075749,0.00001589581,0.0006405266,0.9541487,0.02420227,0.01830689,0.001837417,0.0001973545],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00115547,0.0001034324,0.982311,0.01531433,0.0002709171,0.0002176892,0.0001009745,0.0003533603,0.0001727991],"genre_scores_gemma":[0.9380602,0.0001518317,0.0599267,0.001179273,0.00001685897,0.0002596696,0.00002801402,0.00001255835,0.0003649354],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9797335,"threshold_uncertainty_score":0.6099074,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392756246","doi":"10.1049/cit2.12310","title":"Multi‐granularity feature enhancement network for maritime ship detection","year":2024,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":10,"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":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Granularity; Feature (linguistics); Computer science; Artificial intelligence; Benchmark (surveying); Convolutional neural network; Pattern recognition (psychology); Data mining; Remote sensing; Computer vision; Geography; Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.02069290628437447,"gpt":0.2924868966955325,"spread":0.271793990411158,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003902232,0.0003019276,0.00024438,0.0006293697,0.0003692332,0.0001993195,0.001004406,0.0004498819,0.00006804864],"category_scores_gemma":[0.00002992,0.0003080211,0.0001836078,0.001637683,0.0001809218,0.0004285598,0.0000260973,0.0007852514,0.0001450957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002650521,"about_ca_system_score_gemma":0.00006964458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000115035,"about_ca_topic_score_gemma":0.00007296329,"domain_scores_codex":[0.9979368,0.000041798,0.0003371822,0.0008680474,0.0002104266,0.0006057572],"domain_scores_gemma":[0.998749,0.000154016,0.00005599007,0.0008293643,0.0001473194,0.00006437783],"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":[0.00002183085,0.0001562993,0.000002722565,0.00008476098,0.00006228613,0.0000182339,0.000115626,0.0003892274,0.01711696,0.01955392,0.0007131905,0.9617649],"study_design_scores_gemma":[0.00006128092,0.0005216337,0.000004031319,0.0001476986,0.00002870996,0.00004268501,0.00003300106,0.1474051,0.7982666,0.03780971,0.01535041,0.000329087],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001321717,0.0008747546,0.9899404,0.002830184,0.001553047,0.0009661742,0.00001327829,0.003521872,0.0001680976],"genre_scores_gemma":[0.5801519,0.0002126104,0.4167963,0.0002130612,0.00006734797,0.001117318,0.000004814517,0.00002946373,0.001407152],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9614359,"threshold_uncertainty_score":0.9999372,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4317662918","doi":"10.1049/cit2.12182","title":"Multi‐granularity re‐ranking for visible‐infrared person re‐identification","year":2023,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"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":"National Natural Science Foundation of China","keywords":"Granularity; Modality (human–computer interaction); Computer science; Identification (biology); Encoder; Artificial intelligence; Ranking (information retrieval); Pattern recognition (psychology); Autoencoder; Feature (linguistics); Reciprocal; Similarity (geometry); Computer vision; Deep learning","retraction":null,"screen_n_in":null,"score":{"opus":0.09731236392277717,"gpt":0.3574756507002333,"spread":0.2601632867774561,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001134044,0.0002335868,0.000285573,0.001228468,0.0005810561,0.0001362965,0.001229295,0.0003768051,0.00003362388],"category_scores_gemma":[0.000226923,0.0002505534,0.0001949664,0.003041886,0.0001740683,0.0003677006,0.00001495898,0.0004736226,0.0002470351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001004515,"about_ca_system_score_gemma":0.0000665032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001995953,"about_ca_topic_score_gemma":0.00006497542,"domain_scores_codex":[0.9978635,0.00009858316,0.000432854,0.0008303149,0.0002402671,0.0005344462],"domain_scores_gemma":[0.9979556,0.0004156666,0.0001427271,0.00117411,0.0002456217,0.0000663125],"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":[0.0000365438,0.000204155,0.0002496987,0.00008859124,0.00007725958,0.00002056267,0.001445665,0.006128452,0.0147695,0.01371778,0.0004552739,0.9628065],"study_design_scores_gemma":[0.0005676876,0.0004710968,0.001601104,0.0001539533,0.00004866694,0.00003870415,0.002111793,0.416472,0.4836918,0.08777532,0.00619459,0.0008732775],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002906169,0.0001028151,0.989454,0.003603203,0.001096954,0.0005300892,0.00002112686,0.002120551,0.0001651055],"genre_scores_gemma":[0.8320247,0.0001821468,0.1660143,0.0001366324,0.00003031685,0.0004037008,0.00001188391,0.00002927351,0.00116706],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9619333,"threshold_uncertainty_score":0.9999947,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4316673537","doi":"10.1049/cit2.12175","title":"Predicting recurrence in osteosarcoma via a quantitative histological image classifier derived from tumour nuclear morphological features","year":2023,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Sarcoma Diagnosis and Treatment","field":"Medicine","cited_by":8,"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":"Natural Science Foundation of Zhejiang Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Osteosarcoma; Classifier (UML); Artificial intelligence; Feature extraction; Medicine; Pathology; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.04853993164704506,"gpt":0.3198876288026712,"spread":0.2713476971556262,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001325441,0.0002918089,0.0005130371,0.0007639863,0.0001782357,0.00001888237,0.0002621954,0.000490953,0.0005325891],"category_scores_gemma":[0.0002526221,0.0002403939,0.0001673208,0.001380404,0.0005967198,0.00007640526,0.0000210232,0.001033806,0.0006750717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002605926,"about_ca_system_score_gemma":0.00004621402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000180242,"about_ca_topic_score_gemma":0.0001379251,"domain_scores_codex":[0.9980248,0.00009251236,0.0004463999,0.0007212894,0.0002212441,0.0004937067],"domain_scores_gemma":[0.9987717,0.0005151601,0.00009790131,0.0004098512,0.00009016031,0.0001152221],"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":[0.002736473,0.00720993,0.09549106,0.0001027535,0.0006650227,0.02019444,0.003456056,0.0006159278,0.2286858,0.008056039,0.0009424946,0.631844],"study_design_scores_gemma":[0.00387678,0.008786532,0.5421975,0.001358113,0.0006607822,0.00284542,0.02388731,0.02014529,0.359261,0.0341088,0.001191599,0.001680957],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9699752,0.0006045132,0.02045726,0.00679179,0.000252531,0.0005842574,0.00005938474,0.0008815445,0.0003934978],"genre_scores_gemma":[0.989832,0.000515849,0.008997214,0.0002170784,0.00001632673,0.0003046236,0.00002979568,0.00003006519,0.00005701237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6301631,"threshold_uncertainty_score":0.9802979,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4316663034","doi":"10.1049/cit2.12156","title":"Kernel extreme learning machine‐based general solution to forward kinematics of parallel robots","year":2023,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Forward kinematics; Kinematics; Inverse kinematics; Extreme learning machine; Computer science; Artificial intelligence; Support vector machine; Parallel manipulator; Kernel (algebra); Robot; Algorithm; Pattern recognition (psychology); Mathematics; Artificial neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.03675459530519202,"gpt":0.2921534207295232,"spread":0.2553988254243312,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003328274,0.0002145924,0.0002899111,0.00123476,0.000256054,0.00003274043,0.0009346482,0.0002202814,0.00006164605],"category_scores_gemma":[0.0001466399,0.0002132033,0.0001300601,0.002486577,0.0001064724,0.0001075667,0.00003443165,0.0005045008,0.0003864541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006230844,"about_ca_system_score_gemma":0.00006040946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008915547,"about_ca_topic_score_gemma":0.00003635231,"domain_scores_codex":[0.9982992,0.00006758609,0.00042978,0.0004731436,0.0002731353,0.0004571481],"domain_scores_gemma":[0.9988407,0.0001547493,0.0001248682,0.0006626457,0.0001193553,0.00009768383],"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":[0.00001144444,0.00007449387,0.0001068669,0.00002818716,0.00001683493,0.000006886175,0.000258137,0.8088888,0.002051129,0.006974357,0.000094188,0.1814887],"study_design_scores_gemma":[0.0001064722,0.0004331023,0.0001334389,0.00007132744,0.00001295104,0.00001422164,0.00008584626,0.9756351,0.0177897,0.004388502,0.001097817,0.0002314867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01088095,0.00003533341,0.9810093,0.005929044,0.0002991834,0.0002107409,0.000003700686,0.001400997,0.0002307677],"genre_scores_gemma":[0.8546301,0.00003819718,0.1438733,0.0001325408,0.00001527818,0.00008480417,0.000004722305,0.00002151529,0.001199553],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8437491,"threshold_uncertainty_score":0.8694179,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4226136518","doi":"10.1049/cit2.12090","title":"A weighted block cooperative sparse representation algorithm based on visual saliency dictionary","year":2022,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Sparse approximation; Neural coding; Pattern recognition (psychology); Salient; Computer science; Artificial intelligence; Block (permutation group theory); Coding (social sciences); K-SVD; Representation (politics); Residual; Algorithm; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02023162553009298,"gpt":0.2976008848444051,"spread":0.2773692593143121,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002314113,0.0002538043,0.0002211884,0.001411773,0.001206178,0.00006173712,0.0007419248,0.0001552761,0.0005983988],"category_scores_gemma":[0.00002270557,0.0002691017,0.0001463041,0.003477579,0.0001845995,0.0002749994,0.00003465783,0.0008330073,0.0002127255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000317485,"about_ca_system_score_gemma":0.0001257923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002489557,"about_ca_topic_score_gemma":0.000006543686,"domain_scores_codex":[0.9975292,0.0002377179,0.0004491504,0.000853672,0.0005681798,0.0003620686],"domain_scores_gemma":[0.998827,0.0001218413,0.0001361438,0.0006471309,0.0001782406,0.00008961152],"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":[0.0001364685,0.002643008,0.00005818316,0.000008034738,0.0000641602,0.00009410369,0.0005068624,0.1606046,0.002760506,0.02332107,0.000280467,0.8095226],"study_design_scores_gemma":[0.0002911676,0.002813503,0.00006490856,0.00001546458,0.00001636714,0.0001318634,0.0008958586,0.9313676,0.05743746,0.004566202,0.002064906,0.0003346751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006899463,0.00002294939,0.9866611,0.002881545,0.001310491,0.0004529981,0.00002506259,0.001132712,0.0006136526],"genre_scores_gemma":[0.9893971,0.00002426752,0.008529569,0.0006633584,0.00002489626,0.0006552014,0.00001332668,0.00002127329,0.0006710058],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9824976,"threshold_uncertainty_score":0.9999761,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4224251933","doi":"10.1049/cit2.12092","title":"A robust sparse representation algorithm based on adaptive joint dictionary","year":2022,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Jiangsu Province","keywords":"Sparse approximation; K-SVD; Robustness (evolution); Computer science; Artificial intelligence; Pattern recognition (psychology); Subspace topology; Facial recognition system; Neural coding; Face (sociological concept); Dictionary learning; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.05506130975710653,"gpt":0.2473443290674456,"spread":0.1922830193103391,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000837855,0.0002103153,0.0001978479,0.0009308623,0.0003482211,0.00001312461,0.0002467193,0.0001404618,0.0005108019],"category_scores_gemma":[0.00001040756,0.0002447416,0.0001094847,0.0009659231,0.0001289681,0.00006510651,0.00001011658,0.0008334294,0.00005433836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002981892,"about_ca_system_score_gemma":0.0000285136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002158377,"about_ca_topic_score_gemma":0.000003895942,"domain_scores_codex":[0.9987955,0.00005085433,0.0002728135,0.0003702302,0.000245327,0.0002652873],"domain_scores_gemma":[0.9992291,0.000082659,0.00004652032,0.0005343238,0.00006169839,0.00004570269],"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":[0.00002699317,0.0001179134,0.000002679361,0.000001960582,0.00002757368,0.00003737663,0.00004834785,0.826552,0.0008820844,0.0006482761,0.0005993226,0.1710555],"study_design_scores_gemma":[0.00008536173,0.0005493172,0.00001494043,0.0000307769,0.00002197286,0.00006115663,0.0007527728,0.8212522,0.1699175,0.005697966,0.001383643,0.000232379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00167338,0.00007536609,0.9926148,0.0004409506,0.0005697377,0.0003159133,0.0000508787,0.002722328,0.001536624],"genre_scores_gemma":[0.9787263,0.00006738061,0.02039557,0.000164426,0.00002482126,0.0004663075,0.00001372276,0.00004774103,0.00009373002],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9770529,"threshold_uncertainty_score":0.9980274,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4417038151","doi":"10.1049/cit2.70090","title":"Multi‐Objective Optimisation Framework for Heterogeneous Federated Learning","year":2025,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"National Research Foundation of Korea","keywords":"Federated learning; Selection (genetic algorithm); Key (lock); Computation; Subnetwork; Sizing; Scheme (mathematics); Heterogeneous network","retraction":null,"screen_n_in":null,"score":{"opus":0.03560905398971009,"gpt":0.3241529245288061,"spread":0.288543870539096,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0002381058,0.0002779731,0.0002827616,0.001125984,0.0006852454,0.0001477368,0.008903536,0.0007282056,0.00002395839],"category_scores_gemma":[0.00685141,0.0003000942,0.0001166753,0.00214306,0.000302364,0.0002866579,0.001068715,0.001080621,0.00005135746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002917295,"about_ca_system_score_gemma":0.000117872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001218549,"about_ca_topic_score_gemma":0.00001594032,"domain_scores_codex":[0.9979779,0.00005962006,0.0004016327,0.0009086972,0.0001440958,0.0005080855],"domain_scores_gemma":[0.9957411,0.0005921535,0.0001285216,0.003238969,0.0002583318,0.00004095498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005740883,0.0003185955,0.00005031241,0.00004615459,0.0001835148,0.00001286164,0.0001252671,0.02793531,0.002235685,0.05601599,0.000594308,0.9124246],"study_design_scores_gemma":[0.0001034532,0.0002167177,0.000004833348,0.00008410696,0.00001330049,0.00001514827,0.0002122364,0.3612472,0.2715702,0.3656948,0.0006372872,0.0002007821],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001137702,0.0001875472,0.9785795,0.01498529,0.000709464,0.0006465164,0.00001392246,0.003634707,0.0001053478],"genre_scores_gemma":[0.4688417,0.0001113831,0.5303858,0.0001410083,0.000005255026,0.0003739084,0.000003768778,0.00001356693,0.0001236159],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9122238,"threshold_uncertainty_score":0.9999451,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4322761487","doi":"10.1049/cit2.12205","title":"Guest Editorial: Special issue on machine learning and deep learning algorithms for complex networks","year":2023,"lang":"en","type":"editorial","venue":"CAAI Transactions on Intelligence Technology","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Machine learning; Deep learning; Feature (linguistics); Artificial neural network; Deep neural networks; Range (aeronautics); Node (physics); Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01676770270441815,"gpt":0.3111845275297921,"spread":0.294416824825374,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0004832915,0.0007728141,0.001078952,0.001086438,0.0009313775,0.0001524017,0.0006940144,0.001445798,0.0006615493],"category_scores_gemma":[0.0001719583,0.000842066,0.0004027165,0.001120045,0.0003966439,0.00007459885,0.00004920816,0.004833627,0.0001321313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001824722,"about_ca_system_score_gemma":0.00007114879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001794548,"about_ca_topic_score_gemma":0.0001334018,"domain_scores_codex":[0.9965181,0.0001316074,0.0007938331,0.001208161,0.0005069944,0.000841274],"domain_scores_gemma":[0.9961405,0.002323819,0.0004419246,0.0005314854,0.0004258407,0.0001363756],"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":[0.00009444584,0.0001225956,0.00001562305,0.00002734595,0.0003225909,0.000003307007,0.00005183318,0.01178876,0.000006891869,0.0004288889,0.6471866,0.3399511],"study_design_scores_gemma":[0.0002167265,0.0008857723,3.674944e-7,0.0001405313,0.0002480481,6.910398e-7,0.0002816509,0.04500392,0.0003207716,0.003094573,0.9491456,0.0006614138],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.000001474701,0.00007385414,0.4629408,0.0001103137,0.5350929,0.0004322552,0.0001159062,0.0008574445,0.0003750776],"genre_scores_gemma":[0.001403134,0.0008555574,0.001849523,0.000006414909,0.9915247,0.0006447228,0.001108959,0.0002205928,0.00238642],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.4610913,"threshold_uncertainty_score":0.9998505,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3155584919","doi":"10.1049/cit2.12034","title":"Constrained tolerance rough set in incomplete information systems","year":2021,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":3,"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":"Rough set; Relation (database); Set (abstract data type); Matching (statistics); Mathematics; Object (grammar); Mathematical proof; Data mining; Class (philosophy); Degree (music); Computer science; Null (SQL); Theoretical computer science; Artificial intelligence; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.02308101591460387,"gpt":0.2545243065697755,"spread":0.2314432906551717,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000166119,0.0001784863,0.0002585899,0.0005362631,0.0001421801,0.0001329301,0.0007193504,0.0002497185,0.00004451891],"category_scores_gemma":[0.00003431752,0.0001795471,0.00006167051,0.001952922,0.000162569,0.0006708212,0.00002080848,0.0004729802,0.0002232637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001158164,"about_ca_system_score_gemma":0.0001373644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005203618,"about_ca_topic_score_gemma":0.00004418449,"domain_scores_codex":[0.9985329,0.00005629676,0.00051017,0.000353461,0.0001877011,0.0003595084],"domain_scores_gemma":[0.998902,0.00008686499,0.00009533938,0.0007022366,0.0001606138,0.00005293056],"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":[0.00001465121,0.0001681627,0.000109578,0.00006116387,0.00002701244,0.0001381593,0.001090892,0.1149391,0.0002465198,0.421644,0.0001063424,0.4614545],"study_design_scores_gemma":[0.0009015107,0.0006696161,0.000283824,0.0004054588,0.0000197873,0.001452629,0.004629672,0.8391542,0.03547928,0.06806161,0.04764988,0.00129247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002813788,0.0002129281,0.9897393,0.002341154,0.0007447914,0.0002233497,0.00002606929,0.000399494,0.003499134],"genre_scores_gemma":[0.9820811,0.0001619432,0.01720545,0.0003705474,0.00001126467,0.0001027667,0.000008936914,0.000005830592,0.00005219384],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9792673,"threshold_uncertainty_score":0.7321717,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4323859699","doi":"10.1049/cit2.12199","title":"A privacy‐preserving method for publishing data with multiple sensitive attributes","year":2023,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Nipissing University","funders":"","keywords":"Data publishing; Computer science; Information privacy; Private information retrieval; Publishing; Adversary; Process (computing); Privacy protection; Information sensitivity; Mode (computer interface); Data mining; Value (mathematics); Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.1038296706118148,"gpt":0.3463898258585442,"spread":0.2425601552467294,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001379036,0.0003793668,0.0004311506,0.001675878,0.0005854451,0.0005029828,0.04274373,0.0005528887,0.00001384247],"category_scores_gemma":[0.02310617,0.0003515897,0.00008136199,0.00550104,0.0003514838,0.003733097,0.01164171,0.001092264,0.00008972292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001626808,"about_ca_system_score_gemma":0.0001691883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008480214,"about_ca_topic_score_gemma":0.0001424995,"domain_scores_codex":[0.9962745,0.00009718483,0.0004918114,0.001739782,0.000412915,0.0009838362],"domain_scores_gemma":[0.9807599,0.002524895,0.0001989369,0.0160079,0.0004057983,0.0001025417],"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":[0.0001033124,0.0002317765,0.0001893859,0.0001187682,0.0003251305,0.00012942,0.0003541214,0.004350225,0.002605893,0.01848235,0.05594244,0.9171672],"study_design_scores_gemma":[0.0002287747,0.000265591,0.00002743009,0.0001156339,0.0000264351,0.00008895861,0.0007207759,0.7203099,0.1159788,0.1554614,0.006377244,0.0003990758],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005132651,0.0000487064,0.8997413,0.09114525,0.0003534743,0.0007873268,0.0005301124,0.006803998,0.00007656254],"genre_scores_gemma":[0.1713722,0.0001099135,0.8276474,0.000195147,0.00002553007,0.0003794742,0.0001283911,0.00004661958,0.0000953421],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9167681,"threshold_uncertainty_score":0.9998936,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4404775376","doi":"10.1049/cit2.12375","title":"Feature pyramid attention network for audio‐visual scene classification","year":2024,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Music and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province","keywords":"Pyramid (geometry); Audio visual; Computer science; Artificial intelligence; Feature (linguistics); Computer vision; Pattern recognition (psychology); Multimedia; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02904321832120359,"gpt":0.3099807059072142,"spread":0.2809374875860106,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002097058,0.0001888086,0.0001701956,0.0004327654,0.000378387,0.0002028348,0.0005878887,0.0003402439,0.00002546874],"category_scores_gemma":[0.00001734891,0.0001795163,0.0001284831,0.00164525,0.0001738996,0.0003586518,0.00001053053,0.0004690548,0.0001278572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001154998,"about_ca_system_score_gemma":0.00010497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001355946,"about_ca_topic_score_gemma":0.000005248657,"domain_scores_codex":[0.9985728,0.00001941316,0.0002267914,0.0006394206,0.0001530021,0.0003885289],"domain_scores_gemma":[0.9992529,0.00009999143,0.00006258783,0.0004120895,0.0001202547,0.00005213194],"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":[0.000007947274,0.0000494692,0.000009396251,0.00006514183,0.00002943621,0.000004549944,0.00005381081,0.001313283,0.002602192,0.08903898,0.002475998,0.9043498],"study_design_scores_gemma":[0.0001176702,0.0004532228,0.0000914261,0.00048203,0.00006103895,0.0001179165,0.0002212652,0.778997,0.05900413,0.09972665,0.06020654,0.000521132],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008617608,0.0008443329,0.9735088,0.02128129,0.001525127,0.000304017,0.000005730527,0.001434908,0.0002339748],"genre_scores_gemma":[0.9180292,0.000183137,0.07920733,0.0003484352,0.0001645581,0.0002586929,0.000008752665,0.0000250936,0.001774835],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9171674,"threshold_uncertainty_score":0.7320461,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4416295184","doi":"10.1049/cit2.70080","title":"A Temporal Correlation Networks Based on Interactive Modelling for Remote Sensing Images Change Detection","year":2025,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Remote-Sensing Image Classification","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":"St. Francis Xavier University","funders":"","keywords":"Change detection; Feature (linguistics); Upsampling; Noise (video); Correlation; Object detection; Pattern recognition (psychology)","retraction":null,"screen_n_in":null,"score":{"opus":0.02835156841000454,"gpt":0.2687146611304996,"spread":0.2403630927204951,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001481876,0.0002621296,0.0002304983,0.001194287,0.000236948,0.00004255017,0.0001365807,0.0004199264,0.000005797296],"category_scores_gemma":[0.00005222017,0.0003030318,0.0001143794,0.001094429,0.0001178923,0.0001709009,0.000002533343,0.000684677,0.00001845015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004573131,"about_ca_system_score_gemma":0.00002243678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003093667,"about_ca_topic_score_gemma":0.00002988881,"domain_scores_codex":[0.9987785,0.00003354034,0.0003664448,0.0004099423,0.00009846216,0.0003130746],"domain_scores_gemma":[0.9989662,0.000287006,0.00007858218,0.0004481059,0.0001907314,0.00002935284],"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":[0.00005568321,0.00001305101,7.282583e-7,0.0000192597,0.00001621906,0.000001109414,0.00003480993,0.5431476,0.001508273,0.00002533197,0.000005549932,0.4551724],"study_design_scores_gemma":[0.0001206452,0.0001153472,0.000004899111,0.0002544185,0.0000405087,0.000006402906,0.0001735649,0.8412145,0.1558268,0.001747239,0.0003070464,0.0001886854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001235849,0.00008798058,0.9942912,0.0005218497,0.001327778,0.0007856479,0.000008847111,0.001325827,0.0004149499],"genre_scores_gemma":[0.9644976,0.00006643146,0.03511131,0.00009503409,0.00003738228,0.00002710628,0.00001431726,0.00005159717,0.00009926208],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9632617,"threshold_uncertainty_score":0.9999422,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4393018768","doi":"10.1049/cit2.12300","title":"GP‐FMLNet: A feature matrix learning network enhanced by glyph and phonetic information for Chinese sentiment analysis","year":2024,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","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":"Glyph (data visualization); Computer science; Feature (linguistics); Artificial intelligence; Sentiment analysis; Natural language processing; Pattern recognition (psychology); Visualization; Linguistics","retraction":null,"screen_n_in":null,"score":{"opus":0.004678158519829099,"gpt":0.269678636448601,"spread":0.2650004779287718,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000233166,0.0002126904,0.0002866611,0.001041486,0.0003070212,0.0002946881,0.0003705919,0.0002084189,0.00007490387],"category_scores_gemma":[0.00001679839,0.0001894484,0.000207543,0.00374173,0.00007611255,0.0004530781,0.00001874306,0.0003759872,0.00005784657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005918026,"about_ca_system_score_gemma":0.00002576221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007517031,"about_ca_topic_score_gemma":0.000007423109,"domain_scores_codex":[0.9986852,0.0000298968,0.0003415882,0.0004376342,0.0001795571,0.0003260635],"domain_scores_gemma":[0.9992797,0.0001573644,0.00009150718,0.0003319131,0.00007941368,0.00006011832],"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":[0.00003788294,0.00009421723,0.0002938698,0.0001026709,0.00172591,0.000003284777,0.002358706,0.2035319,0.002741104,0.01085629,0.001726201,0.776528],"study_design_scores_gemma":[0.0001271772,0.0002633506,0.00004134092,0.0000673819,0.0003528632,0.000009603184,0.000524777,0.9600836,0.0229583,0.003513993,0.01171688,0.000340724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008990522,0.001511267,0.9863641,0.001974564,0.0003544412,0.0002426144,0.000008267227,0.0004905171,0.000063663],"genre_scores_gemma":[0.9737523,0.0004193966,0.02450007,0.00008726057,0.00002925866,0.000135624,0.00002937657,0.00001080078,0.001035962],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9647617,"threshold_uncertainty_score":0.7725483,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4378575626","doi":"10.1049/cit2.12235","title":"A multiple sensitive attributes data publishing method with guaranteed information utility","year":2023,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Nipissing University","funders":"Fundamental Research Funds for the Central Universities","keywords":"Data publishing; Publishing; Computer science; Information loss; Heuristic; Data mining; Scheme (mathematics); Information retrieval; Anonymity; Information sensitivity; Computer security; Artificial intelligence; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.08471274562177526,"gpt":0.3220306600910973,"spread":0.237317914469322,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.001324543,0.0003058976,0.0003341993,0.0016931,0.0004271361,0.0004801806,0.02593103,0.0004990149,0.00001975943],"category_scores_gemma":[0.01352027,0.0002780739,0.00005284706,0.006253504,0.0004309249,0.006043635,0.006239996,0.001211677,0.0003255997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000127489,"about_ca_system_score_gemma":0.0001530962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001293193,"about_ca_topic_score_gemma":0.0001079736,"domain_scores_codex":[0.9972667,0.0001102749,0.0005281892,0.000953905,0.0004536491,0.0006872591],"domain_scores_gemma":[0.9860849,0.0009388945,0.0002039653,0.01235364,0.000346262,0.00007237653],"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":[0.00006138904,0.0001069273,0.000264771,0.00004257101,0.000124122,0.00005437693,0.0003012563,0.001230992,0.0002736062,0.007262349,0.01521978,0.9750578],"study_design_scores_gemma":[0.0002045933,0.0001802577,0.0001398275,0.0000793307,0.00001925193,0.0001503266,0.001477213,0.8497215,0.07219602,0.07012989,0.005340531,0.0003612792],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001216939,0.00001907514,0.9487413,0.04140413,0.0003111953,0.0004443187,0.0005393744,0.007122689,0.0002009564],"genre_scores_gemma":[0.5556206,0.00006986492,0.4437777,0.0002405102,0.000008457148,0.00009352397,0.0001593478,0.00001478365,0.00001520867],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9746966,"threshold_uncertainty_score":0.9999672,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4389302254","doi":"10.1049/cit2.12274","title":"Guest Editorial: Special issue on explainable AI empowered for indoor positioning and indoor navigation","year":2023,"lang":"en","type":"editorial","venue":"CAAI Transactions on Intelligence Technology","topic":"Indoor and Outdoor Localization 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":"Nanyang Technological University; Department of Science and Technology, Ministry of Science and Technology, India; Ministry of Education, India; Tamilnadu State Council For Science And Technology","keywords":"Computer science; Aeronautics; Architectural engineering; Positioning technology; Engineering; Real-time computing","retraction":null,"screen_n_in":null,"score":{"opus":0.00783755641009503,"gpt":0.2682503812444514,"spread":0.2604128248343563,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0002759491,0.0006743324,0.0006991498,0.001710119,0.0005394428,0.0001581166,0.0006118022,0.003867968,0.00007107934],"category_scores_gemma":[0.0006196343,0.000760726,0.0001706706,0.001243276,0.00037267,0.0002221749,0.00001938386,0.002428279,0.0002893684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005120566,"about_ca_system_score_gemma":0.0001199831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002371711,"about_ca_topic_score_gemma":0.00004001128,"domain_scores_codex":[0.9971449,0.00002991115,0.0007550636,0.0008110967,0.0005295618,0.0007295095],"domain_scores_gemma":[0.997708,0.0008935464,0.0001541462,0.0006478373,0.0005060681,0.00009042216],"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":[0.0001124653,0.00005718633,9.080504e-7,0.0002903513,0.0001167468,0.00001330482,0.0001775764,0.004966476,0.000131862,0.0006019001,0.9707879,0.02274327],"study_design_scores_gemma":[0.0003930341,0.000729453,2.166388e-7,0.0004992374,0.00009374377,0.000005108504,0.0007850727,0.001200432,0.06771341,0.004625213,0.9232514,0.000703661],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00005790521,0.00007874351,0.1545755,0.0003430913,0.8392003,0.0009050948,0.0007496523,0.003840637,0.0002490048],"genre_scores_gemma":[0.004415762,0.001025263,0.0005415464,0.00002663296,0.990661,0.001409124,0.0007843433,0.0002871481,0.0008491544],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.154034,"threshold_uncertainty_score":0.9998732,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}