{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":144,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":144,"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","author_layer_release":"2026-06-26"},"query_hash":"c9d89d0dc078","filters":{"venue":"IEEE Transactions on Multimedia"}},"results":[{"id":"W1999316653","doi":"10.1109/tmm.2013.2244870","title":"Directive Contrast Based Multimodal Medical Image Fusion in NSCT Domain","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":472,"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":"","keywords":"Contourlet; Computer science; Image fusion; Artificial intelligence; Modalities; Contrast (vision); Medical imaging; Phase congruency; Fusion rules; Computer vision; Image (mathematics); Pattern recognition (psychology); Fuse (electrical); Modality (human–computer interaction); Wavelet transform; Engineering","authors":[{"name":"Gaurav Bhatnagar","is_ca":true},{"name":"Q. M. Jonathan Wu","is_ca":true},{"name":"Zheng Liu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.004383614316694057,"gpt":0.2282344904893442,"spread":0.2238508761726501,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005550967,0.0003942509,0.0004458153,0.0007584987,0.0001605172,0.000594733,0.0003651118,0.0005856826,0.001212769],"category_scores_gemma":[0.001422924,0.0001367901,0.0005259198,0.0006853525,0.0003641984,0.0008678805,0.0007147197,0.0004859042,0.00051792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002368829,"about_ca_system_score_gemma":0.0003022792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005067796,"about_ca_topic_score_gemma":0.0004696437,"domain_scores_codex":[0.9997372,0.00005689141,0.00001536158,0.00004066094,0.0001328687,0.0000170034],"domain_scores_gemma":[0.9997265,0.00008318524,0.00003912222,0.00004623787,0.00009113225,0.00001384988],"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":[0.0006212316,0.0001098208,0.001059887,0.0003179574,0.0001124774,0.0007261891,0.0002493752,0.1007265,0.3540399,0.02046978,0.003800558,0.5177664],"study_design_scores_gemma":[0.00002190653,0.0001809954,0.001798096,0.0000257653,0.00006462477,0.00122016,0.00006548938,0.842657,0.1391193,0.00811593,0.006688718,0.00004204853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02256168,0.0002705317,0.9748127,0.0001464729,0.0000388138,0.00004503403,0.00007624164,0.0003181644,0.001730413],"genre_scores_gemma":[0.354598,0.000939793,0.6407675,0.0002172698,0.0001062349,0.0001112668,0.000499943,0.0001066044,0.002653476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001212769,"threshold_uncertainty_score":0.004057109,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2908941882","doi":"10.1109/tmm.2019.2893549","title":"Hybrid Deep-Learning-Based Anomaly Detection Scheme for Suspicious Flow Detection in SDN: A Social Multimedia Perspective","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":269,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Fundação para a Ciência e a Tecnologia; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Computer science; Anomaly detection; Quality of service; Scalability; Multimedia; Computer network; Machine learning; Artificial intelligence; Database","authors":[{"name":"Sahil Garg","is_ca":true},{"name":"Kuljeet Kaur","is_ca":true},{"name":"Neeraj Kumar","is_ca":false},{"name":"Joel J. P. C. Rodrigues","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008978700472931145,"gpt":0.235699129488738,"spread":0.2267204290158069,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001391763,0.0007913389,0.0009533632,0.0008516349,0.0005092769,0.0006780812,0.001878551,0.0009626467,0.000660738],"category_scores_gemma":[0.003102633,0.0002374863,0.0004851151,0.0007004521,0.0006168169,0.001678252,0.001297115,0.001381656,0.0001962667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001264913,"about_ca_system_score_gemma":0.001339716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006957082,"about_ca_topic_score_gemma":0.006653585,"domain_scores_codex":[0.9992175,0.0001513823,0.0000490771,0.0001780445,0.0002469726,0.0001570779],"domain_scores_gemma":[0.9987962,0.0003309986,0.0001391765,0.0001283999,0.0004870926,0.0001180608],"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":[0.0006396474,0.000583623,0.013033,0.00009759873,0.000179468,0.0003171642,0.0002005041,0.4569166,0.01690124,0.007581653,0.005198831,0.4983507],"study_design_scores_gemma":[0.000002993892,0.00002200717,0.0002313849,0.00000169315,0.000005332713,0.00001505763,0.000006541944,0.9973531,0.001302354,0.0008976339,0.0001581292,0.000003797857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1390895,0.0005986798,0.8556088,0.0007206517,0.0001289642,0.00008355175,0.0001476582,0.002116549,0.001505641],"genre_scores_gemma":[0.9017016,0.000200426,0.09517228,0.0002823605,0.00005915177,0.00006702,0.0003246973,0.00005560829,0.002136882],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006957082,"threshold_uncertainty_score":0.01383317,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2053157787","doi":"10.1109/tmm.2013.2247583","title":"QoE-Driven Cache Management for HTTP Adaptive Bit Rate Streaming Over Wireless Networks","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":213,"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":"Nanyang Technological University; State University of New York; Cisco Systems","keywords":"Computer science; Cache; Wireless network; Wireless; Scalability; Optimization problem; Computer network; Quality of experience; Algorithm; Quality of service; Operating system","authors":[{"name":"Weiwen Zhang","is_ca":false},{"name":"Yonggang Wen","is_ca":false},{"name":"Zhenzhong Chen","is_ca":false},{"name":"Ashish Khisti","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02577135084674648,"gpt":0.277268989288357,"spread":0.2514976384416105,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001342308,0.0006166212,0.0009409336,0.0003627287,0.0003737151,0.0008628269,0.0009697141,0.0008186591,0.0007214933],"category_scores_gemma":[0.005357787,0.0003266758,0.0003372736,0.0005115984,0.0006342657,0.001545981,0.0006181028,0.0007254063,0.00008058965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001138365,"about_ca_system_score_gemma":0.001165401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004751422,"about_ca_topic_score_gemma":0.003618385,"domain_scores_codex":[0.9993756,0.0002714536,0.0000243952,0.00008491439,0.0001609464,0.00008260529],"domain_scores_gemma":[0.9979802,0.001433488,0.0001839674,0.00005290505,0.0002911757,0.00005830544],"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":[0.00008748064,0.00007538843,0.0007504924,0.00009565392,0.00002378534,0.0001023103,0.00005985755,0.9655147,0.005999498,0.008521863,0.0005748414,0.01819404],"study_design_scores_gemma":[0.00000426572,0.00001922374,0.00007499432,0.00000211657,0.00000322337,0.000009987348,0.00001050375,0.9983212,0.0003935284,0.001092215,0.00006609521,0.000002671627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08440325,0.0007492497,0.9124737,0.000436296,0.00003585628,0.00009367903,0.00004484527,0.0001165831,0.001646521],"genre_scores_gemma":[0.9336049,0.0005859063,0.06453589,0.00008652145,0.00005536037,0.00008730482,0.00004589164,0.00003530331,0.0009629407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004751422,"threshold_uncertainty_score":0.009447515,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2109729353","doi":"10.1109/tmm.2003.819747","title":"Toward Robust Logo Watermarking Using Multiresolution Image Fusion Principles","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":200,"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":"Computer science; Watermark; Digital watermarking; Artificial intelligence; Computer vision; Robustness (evolution); Logo (programming language); Image fusion; Pattern recognition (psychology); Image (mathematics); Multiresolution analysis; Feature extraction; Wavelet transform; Wavelet; Discrete wavelet transform","authors":[{"name":"Deepa Kundur","is_ca":true},{"name":"Dimitrios Hatzinakos","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05404327102636397,"gpt":0.2709136055886803,"spread":0.2168703345623164,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007768691,0.00062765,0.0007658956,0.0007401038,0.0002719064,0.0007529538,0.0005582484,0.000949688,0.0006421373],"category_scores_gemma":[0.001437261,0.0003391179,0.000736456,0.0004448716,0.0008505892,0.002282232,0.001274488,0.0009814659,0.0004356833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002588977,"about_ca_system_score_gemma":0.0002339948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001560795,"about_ca_topic_score_gemma":0.0001601469,"domain_scores_codex":[0.9995814,0.0000684293,0.00001729441,0.00006768783,0.0002333595,0.00003194009],"domain_scores_gemma":[0.9995164,0.0001789685,0.00010284,0.0001008087,0.00008310695,0.000017985],"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":[0.0002419747,0.0001074567,0.0005894799,0.0002461941,0.00007915562,0.0003314532,0.0002643153,0.1039371,0.5151615,0.06346322,0.0008836105,0.3146945],"study_design_scores_gemma":[0.00003166894,0.0002776819,0.0003957717,0.00002853039,0.00003960993,0.0005722973,0.00005151761,0.8084126,0.1595978,0.02301196,0.007521322,0.00005916581],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01239057,0.0004152345,0.9860109,0.0001264017,0.00001931802,0.00001711832,0.000006857658,0.0002044763,0.0008092202],"genre_scores_gemma":[0.2698694,0.001189627,0.7267992,0.0001321897,0.0001036655,0.00004769621,0.00004013494,0.00008210258,0.001736052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.000949688,"threshold_uncertainty_score":0.004108548,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2891286892","doi":"10.1109/tmm.2018.2870521","title":"Content Popularity Prediction Towards Location-Aware Mobile Edge Caching","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":197,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Backhaul (telecommunications); Cache; Algorithm; Exploit; Regret; Data mining; Machine learning; Base station; Computer network","authors":[{"name":"Peng Yang","is_ca":false},{"name":"Ning Zhang","is_ca":false},{"name":"Shan Zhang","is_ca":false},{"name":"Li Yu","is_ca":false},{"name":"Junshan Zhang","is_ca":false},{"name":"Xuemin Shen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03979363620046616,"gpt":0.2607314566692937,"spread":0.2209378204688275,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088124,0.0006225007,0.0009925857,0.0007291121,0.0003674205,0.001014276,0.001479782,0.0008177189,0.0007315981],"category_scores_gemma":[0.006168875,0.000354504,0.0003342977,0.001252595,0.0004267099,0.001176582,0.0007905376,0.0007602445,0.0004380544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009054409,"about_ca_system_score_gemma":0.001087882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009420457,"about_ca_topic_score_gemma":0.00803992,"domain_scores_codex":[0.9995047,0.0001528794,0.00002038906,0.000127237,0.0001146546,0.00008023726],"domain_scores_gemma":[0.9971606,0.001530766,0.0003578484,0.0002679661,0.0005795283,0.0001034901],"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":[0.0002903121,0.0001069471,0.005495819,0.00007131379,0.00004430766,0.0001831494,0.00006473335,0.903057,0.003789521,0.005954582,0.003713943,0.07722826],"study_design_scores_gemma":[0.000001890243,0.000004657943,0.0001188771,0.000001624849,0.000002188017,0.000007941941,0.000003888737,0.9988981,0.0002678336,0.0006096816,0.00008157433,0.000001674769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1151971,0.0008433337,0.879298,0.0004962802,0.00006846437,0.00006265184,0.0002744193,0.001219613,0.00254015],"genre_scores_gemma":[0.9309589,0.0003671718,0.06658846,0.0001241041,0.0000983173,0.00005093756,0.000293315,0.00005166555,0.001467159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009420457,"threshold_uncertainty_score":0.01873124,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2137394636","doi":"10.1109/tmm.2005.843357","title":"Comments on \"An SVD-based watermarking scheme for protecting rightful Ownership\"","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":189,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Watermark; Digital watermarking; Singular value decomposition; Computer science; Image (mathematics); Artificial intelligence; Scheme (mathematics); Detector; Singular value; Value (mathematics); Algorithm; Pattern recognition (psychology); Computer vision; Mathematics; Machine learning; Telecommunications","authors":[{"name":"Xiao–Ping Zhang","is_ca":true},{"name":"Kan Li","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03136254584442519,"gpt":0.2897016967318364,"spread":0.2583391508874112,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006793957,0.001291533,0.0007819359,0.000811168,0.00357244,0.002355212,0.004875723,0.02697186,0.005848095],"category_scores_gemma":[0.0341107,0.0005861321,0.001780927,0.0008201982,0.005456308,0.004214767,0.002199917,0.0201458,0.005214822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003970078,"about_ca_system_score_gemma":0.003862954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01169701,"about_ca_topic_score_gemma":0.01013825,"domain_scores_codex":[0.9907257,0.001727625,0.0008499579,0.001179447,0.004723195,0.0007940743],"domain_scores_gemma":[0.9756017,0.01137825,0.001632236,0.001097804,0.009731847,0.0005580777],"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":[0.0001825399,0.00003804115,0.0005617677,0.0003536378,0.00003911419,0.001137206,0.0008087006,0.0008971356,0.003157909,0.08715305,0.8930508,0.01262006],"study_design_scores_gemma":[0.00009893338,0.0002148619,0.001732238,0.0004733268,0.00009644805,0.000882316,0.0006612478,0.002087398,0.00506685,0.03020728,0.9582481,0.0002310286],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.002490027,0.002543359,0.01047903,0.9235054,0.04400737,0.0001081414,0.000401674,0.0002878001,0.01617721],"genre_scores_gemma":[0.0386578,0.003427494,0.007636385,0.8987477,0.02807574,0.000286766,0.0001178381,0.0001215875,0.02292862],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.02697186,"threshold_uncertainty_score":0.03593034,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2903559293","doi":"10.1109/tmm.2018.2883866","title":"EVM-CNN: Real-Time Contactless Heart Rate Estimation From Facial Video","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":181,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Convolutional neural network; Benchmark (surveying); Artificial intelligence; Consistency (knowledge bases); Ground truth; Estimation; Pattern recognition (psychology); Computer vision; Speech recognition","authors":[{"name":"Ying Qiu","is_ca":true},{"name":"Yang Liu","is_ca":true},{"name":"Juan Arteaga-Falconi","is_ca":true},{"name":"Haiwei Dong","is_ca":true},{"name":"Abdulmotaleb El Saddik","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01339889546878373,"gpt":0.2421667185512783,"spread":0.2287678230824946,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004110874,0.0009794361,0.0005965602,0.0005015114,0.0001348149,0.0003591277,0.001090971,0.0005749398,0.002590324],"category_scores_gemma":[0.001068638,0.0003071346,0.0004021958,0.0003808556,0.0001206418,0.0005409304,0.0006549054,0.0005844674,0.001156406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003168913,"about_ca_system_score_gemma":0.0003885601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007154018,"about_ca_topic_score_gemma":0.01163283,"domain_scores_codex":[0.9997892,0.00002135192,0.000009254506,0.00007800233,0.00006574635,0.00003641073],"domain_scores_gemma":[0.9998782,0.00002469134,0.00001498888,0.00003311406,0.00003854531,0.00001048478],"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":[0.0004373675,0.0002163861,0.003634724,0.0001691367,0.0002111156,0.0002482294,0.00003587848,0.03846812,0.04672373,0.0007822151,0.01770013,0.8913729],"study_design_scores_gemma":[0.00002701189,0.0001309877,0.006090082,0.00002803167,0.0000515477,0.0003111014,0.00001606159,0.9635801,0.0246451,0.0006421939,0.004453819,0.00002407939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1493461,0.00424839,0.8186815,0.0004222107,0.0009357333,0.0003130814,0.00445348,0.0133223,0.008277123],"genre_scores_gemma":[0.6979468,0.002010795,0.2716897,0.0005183917,0.0002634357,0.0002697691,0.00846999,0.0003507152,0.01848047],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007154018,"threshold_uncertainty_score":0.01422477,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1930223417","doi":"10.1109/tmm.2015.2482228","title":"Deep Multimodal Learning for Affective Analysis and Retrieval","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":155,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Modalities; Social media; Upload; Representation (politics); Multimodal learning; Artificial intelligence; Emotion classification; Feature learning; Multimodality; Information retrieval; Automatic summarization; Natural language processing; Machine learning; World Wide Web","authors":[{"name":"Lei Pang","is_ca":false},{"name":"Shiai Zhu","is_ca":true},{"name":"Chong‐Wah Ngo","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02515312700733929,"gpt":0.2826347932597046,"spread":0.2574816662523653,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006730882,0.0009776065,0.0007968089,0.0007319768,0.0003032569,0.0008682192,0.001115021,0.001109663,0.006524211],"category_scores_gemma":[0.002177099,0.0003747465,0.0009621768,0.0006816185,0.0004170392,0.001338329,0.001138432,0.001645646,0.002333327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001067686,"about_ca_system_score_gemma":0.0005326145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004763507,"about_ca_topic_score_gemma":0.005869602,"domain_scores_codex":[0.9996704,0.00009546856,0.00001541377,0.00009182242,0.00005746215,0.00006952172],"domain_scores_gemma":[0.9997131,0.0001231762,0.00002856685,0.00004309198,0.00007104952,0.00002093159],"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":[0.0004714043,0.000294555,0.001199952,0.000344168,0.0002410723,0.0001864293,0.0001884819,0.1833394,0.02487327,0.01463244,0.02354128,0.7506876],"study_design_scores_gemma":[0.000009355235,0.00003439012,0.0003706567,0.00001862357,0.00002527723,0.0000295514,0.00002899943,0.9843126,0.002672591,0.01064494,0.001842621,0.0000104911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03024559,0.005126139,0.9527043,0.001085291,0.0001908643,0.00008879975,0.0006512976,0.004039088,0.005868631],"genre_scores_gemma":[0.7954228,0.002934275,0.1764014,0.001204501,0.0003592447,0.0003198353,0.002580319,0.0004072527,0.02037026],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006524211,"threshold_uncertainty_score":0.02182561,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3008774220","doi":"10.1109/tmm.2020.2976573","title":"Automated Colorization of a Grayscale Image With Seed Points Propagation","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":155,"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":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Grayscale; Artificial intelligence; Computer science; Pixel; Computer vision; RGB color model; Similarity (geometry); Pattern recognition (psychology); Image (mathematics); Color image; Artificial neural network; Image processing","authors":[{"name":"Shaohua Wan","is_ca":false},{"name":"Xia Yu","is_ca":false},{"name":"Lianyong Qi","is_ca":false},{"name":"Yee‐Hong Yang","is_ca":true},{"name":"Mohammed Atiquzzaman","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01125100075100221,"gpt":0.2371668884619035,"spread":0.2259158877109013,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005800712,0.001010766,0.0007641901,0.001147766,0.0003468514,0.0007994179,0.00123768,0.0006251736,0.002039795],"category_scores_gemma":[0.001180689,0.0004841374,0.0006780386,0.0007516877,0.000598681,0.001168663,0.0007891024,0.0008707005,0.00075624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007501363,"about_ca_system_score_gemma":0.0009114562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004884548,"about_ca_topic_score_gemma":0.006434409,"domain_scores_codex":[0.9995939,0.00005064071,0.00001779702,0.0001235608,0.0001673524,0.00004689597],"domain_scores_gemma":[0.9994888,0.0001125164,0.00006096033,0.0001307945,0.0001843812,0.00002251393],"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":[0.0002614449,0.0001072452,0.001042073,0.0001311262,0.00006246162,0.0001153694,0.000113207,0.1096734,0.1694279,0.005675265,0.002160479,0.7112299],"study_design_scores_gemma":[0.00001693005,0.00004501013,0.0005812345,0.000008244747,0.00001835779,0.00009254241,0.00001369625,0.9286566,0.06675407,0.002069062,0.001727082,0.00001718304],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01139353,0.00009644768,0.9865019,0.0000429587,0.00001878241,0.00003820997,0.00001836355,0.001338052,0.0005518945],"genre_scores_gemma":[0.1357909,0.0001738262,0.8614168,0.00008758101,0.00002379363,0.00006208126,0.0001193957,0.0002821752,0.002043316],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004884548,"threshold_uncertainty_score":0.009712219,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2126552487","doi":"10.1109/tmm.2012.2189550","title":"Kernel Cross-Modal Factor Analysis for Information Fusion With Application to Bimodal Emotion Recognition","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":155,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Kernel (algebra); Artificial intelligence; Kernel principal component analysis; Pattern recognition (psychology); Tree kernel; Kernel embedding of distributions; Kernel method; Canonical correlation; Polynomial kernel; Radial basis function kernel; Domain (mathematical analysis); Support vector machine; Machine learning; Mathematics","authors":[{"name":"Yongjin Wang","is_ca":false},{"name":"Ling Guan","is_ca":true},{"name":"A.N. Venetsanopoulos","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.020322360921014,"gpt":0.2726646795590411,"spread":0.2523423186380271,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002896997,0.001253475,0.001071203,0.001434268,0.0004233658,0.001201283,0.0009097171,0.0008691458,0.002208557],"category_scores_gemma":[0.007367339,0.0003365473,0.001439069,0.001666723,0.0008426837,0.001617508,0.001255816,0.001341139,0.0007351888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007019566,"about_ca_system_score_gemma":0.0006953985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001969876,"about_ca_topic_score_gemma":0.001322809,"domain_scores_codex":[0.9987252,0.0005152548,0.00007448321,0.0002388813,0.0003618766,0.00008410204],"domain_scores_gemma":[0.9977416,0.001286409,0.000211632,0.0002616683,0.0004430604,0.00005575327],"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":[0.0003372183,0.000174594,0.001532182,0.000408402,0.000314085,0.0001824588,0.0004061582,0.3394105,0.02841532,0.06031026,0.002539953,0.5659689],"study_design_scores_gemma":[0.000006186633,0.00004972866,0.000460433,0.00001295571,0.00002556345,0.00004801763,0.00003048966,0.9817323,0.003899373,0.01253549,0.001170911,0.00002854835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003001446,0.0002596095,0.9961988,0.00005243704,0.00001240323,0.00001755717,0.00001869051,0.0001517574,0.0002873772],"genre_scores_gemma":[0.330544,0.001077449,0.6661032,0.0001072959,0.00008747014,0.0002081633,0.0002614751,0.0001411217,0.001469732],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002896997,"threshold_uncertainty_score":0.01532096,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2050316560","doi":"10.1109/tmm.2009.2036294","title":"Impact of Network Dynamics on User's Video Quality: Analytical Framework and QoS Provision","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":125,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Jitter; Network packet; Quality of service; FIFO (computing and electronics); Throughput; Computer network; Packet loss; Queue; Quality (philosophy); Video quality; Real-time computing; Queueing theory; Operating system; Telecommunications","authors":[{"name":"Tom H. Luan","is_ca":true},{"name":"Lin Cai","is_ca":true},{"name":"Xuemin Shen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01458557399586114,"gpt":0.3024400945836951,"spread":0.287854520587834,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001344723,0.001102756,0.0006894463,0.0007867755,0.0005109388,0.00126144,0.001288573,0.001167179,0.001408921],"category_scores_gemma":[0.006466486,0.0004105524,0.0006354129,0.0006907152,0.001074331,0.001822421,0.001100789,0.001142802,0.0001786196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002314982,"about_ca_system_score_gemma":0.001477713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01456687,"about_ca_topic_score_gemma":0.006621215,"domain_scores_codex":[0.999356,0.0002446689,0.00001779088,0.00008321915,0.0001687266,0.0001297135],"domain_scores_gemma":[0.9984647,0.0009859351,0.0002011742,0.00006466496,0.000229007,0.0000545101],"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":[0.00005313632,0.00006942825,0.001699029,0.00007900801,0.00002628934,0.0003194784,0.0002208931,0.9423595,0.005361424,0.04270223,0.0006776823,0.006431875],"study_design_scores_gemma":[0.000001545951,0.000007570136,0.0001199988,0.000005624457,0.00000497262,0.00001960331,0.00002348316,0.9981064,0.0002491319,0.001362472,0.00009513129,0.00000413302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2198966,0.003055559,0.7525573,0.00176102,0.0001122571,0.0002022674,0.0002313269,0.0003333874,0.02185031],"genre_scores_gemma":[0.9824696,0.001531027,0.01381699,0.00008257819,0.00005588122,0.00008417751,0.00004122589,0.00003233815,0.001886283],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01456687,"threshold_uncertainty_score":0.02896416,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4285163934","doi":"10.1109/tmm.2022.3187856","title":"C$^{2}$DFNet: Criss-Cross Dynamic Filter Network for RGB-D Salient Object Detection","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":117,"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":"Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; RGB color model; Computer vision; Context (archaeology); Convolution (computer science); Modality (human–computer interaction); Filter (signal processing); Salient; Pattern recognition (psychology); Artificial neural network","authors":[{"name":"Miao Zhang","is_ca":false},{"name":"Shunyu Yao","is_ca":false},{"name":"Beiqi Hu","is_ca":false},{"name":"Yongri Piao","is_ca":false},{"name":"Wei Ji","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01810113609284413,"gpt":0.2926798188489161,"spread":0.2745786827560719,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003637664,0.00106289,0.0005204667,0.0008582274,0.00041984,0.0006059423,0.001660325,0.0008788108,0.008826355],"category_scores_gemma":[0.000962648,0.0003758319,0.0005899674,0.0006815337,0.0003804124,0.0009612284,0.0009815033,0.0008352686,0.002213082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001099073,"about_ca_system_score_gemma":0.001090082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0257208,"about_ca_topic_score_gemma":0.03382782,"domain_scores_codex":[0.9998139,0.00001523922,0.000006827634,0.00008164289,0.00004262549,0.00003987652],"domain_scores_gemma":[0.9998701,0.00002769818,0.00001241613,0.00002626914,0.00005000625,0.00001349905],"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":[0.0004873487,0.0002540637,0.00169868,0.000180273,0.0001309853,0.0002520306,0.00007418135,0.1330917,0.03652197,0.01491591,0.05269851,0.7596943],"study_design_scores_gemma":[0.00001834609,0.00004477831,0.0006844493,0.00001467953,0.00001937133,0.00008490715,0.00001221068,0.9748535,0.009530448,0.006616676,0.00810308,0.00001766258],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03119308,0.000666344,0.9443566,0.0004626932,0.0002677337,0.0001584345,0.002026065,0.01207942,0.008789606],"genre_scores_gemma":[0.5236976,0.0008903646,0.439715,0.0009203952,0.0001920991,0.0004637528,0.008132862,0.0009427148,0.02504528],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0257208,"threshold_uncertainty_score":0.05114222,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4226294111","doi":"10.1109/tmm.2022.3163847","title":"Spatial-Channel Enhanced Transformer for Visible-Infrared Person Re-Identification","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":110,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Six Talent Peaks Project in Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Discriminative model; Artificial intelligence; Feature learning; Pattern recognition (psychology); Embedding; Transformer; Feature extraction; Feature (linguistics); Feature vector; Computer vision; Engineering","authors":[{"name":"Jiaqi Zhao","is_ca":false},{"name":"Hanzheng Wang","is_ca":false},{"name":"Yong Zhou","is_ca":false},{"name":"Rui Yao","is_ca":false},{"name":"Silin Chen","is_ca":false},{"name":"Abdulmotaleb El Saddik","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03848727033689459,"gpt":0.2986878252806538,"spread":0.2602005549437592,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006579759,0.0008422583,0.001032183,0.0006930223,0.0003139042,0.0004295476,0.00194078,0.0008006884,0.002887211],"category_scores_gemma":[0.001630066,0.0003067082,0.0009265676,0.0008837609,0.0004462184,0.001842006,0.001198822,0.001099616,0.002062433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00037402,"about_ca_system_score_gemma":0.0005532554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002618162,"about_ca_topic_score_gemma":0.003864404,"domain_scores_codex":[0.9995158,0.00007340182,0.00001562443,0.0001759982,0.0001328118,0.00008637104],"domain_scores_gemma":[0.9995769,0.00007640397,0.00005225555,0.0001602109,0.0001089974,0.00002521278],"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":[0.0005619679,0.0002093885,0.00245714,0.0001403235,0.0001319143,0.0002925042,0.0001033166,0.06756301,0.03175579,0.004227883,0.01040387,0.8821529],"study_design_scores_gemma":[0.00002334281,0.0002215084,0.002184511,0.00001541304,0.00008429299,0.0007543739,0.00006943798,0.9462327,0.03872582,0.005945472,0.005702,0.00004104713],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04894804,0.0006660052,0.9438562,0.0001317922,0.0001377211,0.00006628809,0.0002881094,0.003003869,0.002901901],"genre_scores_gemma":[0.7599541,0.0007215546,0.2215839,0.0003826118,0.0001279957,0.0001109066,0.001803571,0.0002624003,0.0150529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002887211,"threshold_uncertainty_score":0.009658694,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2108169811","doi":"10.1109/tmm.2005.843360","title":"Performance analysis of TCP-friendly AIMD algorithms for multimedia applications","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":105,"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; Algorithm; Environmentally friendly; Computer network; Distributed computing","authors":[{"name":"Lin Cai","is_ca":true},{"name":"Xuemin Shen","is_ca":true},{"name":"Jianping Pan","is_ca":true},{"name":"J.W. Mark","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01372683371030007,"gpt":0.2554394679309254,"spread":0.2417126342206254,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004036685,0.001083206,0.0008034207,0.001622545,0.0007128841,0.001740771,0.001326626,0.0008868301,0.001439104],"category_scores_gemma":[0.01764032,0.0003971635,0.0005665479,0.0008001617,0.0006506742,0.001718116,0.001299819,0.001121241,0.0003568524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001109389,"about_ca_system_score_gemma":0.0009393069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008431543,"about_ca_topic_score_gemma":0.0006152294,"domain_scores_codex":[0.9972771,0.0009040756,0.0001662509,0.0002067275,0.001179547,0.0002663242],"domain_scores_gemma":[0.9917203,0.004992478,0.0005687162,0.0007501365,0.001772665,0.0001956452],"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":[0.001877484,0.0004092573,0.007641019,0.0006917773,0.0002893852,0.0004410936,0.0003558359,0.6270481,0.04784829,0.03626335,0.0025581,0.2745762],"study_design_scores_gemma":[0.00001872061,0.00016264,0.0005060998,0.00001051837,0.00003176834,0.0001322626,0.00001892937,0.9895946,0.00678798,0.002186272,0.000534344,0.00001593547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1129881,0.003301497,0.8768864,0.0002906562,0.0001586162,0.0002132516,0.00006917596,0.001230669,0.00486166],"genre_scores_gemma":[0.9159815,0.0009586886,0.08186582,0.0001033342,0.00009329844,0.0001057914,0.00008731207,0.00007727487,0.000727027],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004036685,"threshold_uncertainty_score":0.02134824,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2077419187","doi":"10.1109/tmm.2007.907460","title":"Network Coding in Live Peer-to-Peer Streaming","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":94,"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":"Computer science; Linear network coding; Computer network; Multicast; Testbed; Peer-to-peer; Wireless network; Multiple description coding; Distributed computing; Coding (social sciences); Erasure code; Decoding methods; Wireless; Network packet; Algorithm","authors":[{"name":"Mea Wang","is_ca":true},{"name":"Baochun Li","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.039406431935872,"gpt":0.299197082251655,"spread":0.259790650315783,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001452615,0.0003130587,0.0004185788,0.0004380811,0.0006220251,0.0007018356,0.0007937851,0.0007967094,0.001381577],"category_scores_gemma":[0.005132609,0.0002374894,0.0002166097,0.0005416204,0.001779116,0.001498995,0.0009206667,0.0008560218,0.0003030217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008521882,"about_ca_system_score_gemma":0.0008533692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002204009,"about_ca_topic_score_gemma":0.00140146,"domain_scores_codex":[0.9990481,0.0004261059,0.00003077529,0.00008555649,0.0003572846,0.00005226214],"domain_scores_gemma":[0.9975308,0.001766581,0.0001160063,0.0002176074,0.0003088775,0.00006017064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001601338,0.00006700533,0.0006285286,0.0001945122,0.00001679899,0.0002447699,0.0003720416,0.5835377,0.0225496,0.3194834,0.001335764,0.07140962],"study_design_scores_gemma":[0.00002006928,0.00005047232,0.00009532791,0.00002163174,0.000005458985,0.00008561469,0.00003815912,0.9164521,0.006548699,0.07366334,0.003003751,0.00001548759],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03440043,0.001048753,0.9574464,0.0003790354,0.0000499585,0.00005992728,0.000029695,0.0002749139,0.006310988],"genre_scores_gemma":[0.7047747,0.001451208,0.2898715,0.0001615474,0.00009052212,0.0001766864,0.00008080119,0.00009515024,0.003297978],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002204009,"threshold_uncertainty_score":0.007682204,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2293936600","doi":"10.1109/tmm.2015.2508147","title":"Multiplicative Watermark Decoder in Contourlet Domain Using the Normal Inverse Gaussian Distribution","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":87,"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":"Contourlet; Watermark; Generalized normal distribution; Digital watermarking; Computer science; Robustness (evolution); Artificial intelligence; Pattern recognition (psychology); Gaussian; Algorithm; Computer vision; Normal distribution; Mathematics; Image (mathematics); Wavelet; Statistics; Wavelet transform","authors":[{"name":"Hamidreza Sadreazami","is_ca":true},{"name":"M. Omair Ahmad","is_ca":true},{"name":"M.N.S. Swamy","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03100630872646192,"gpt":0.2782674574003173,"spread":0.2472611486738553,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005619533,0.0005183349,0.000562225,0.0004257342,0.0002506509,0.0006331272,0.0005353828,0.00079985,0.0009001614],"category_scores_gemma":[0.001564486,0.0002155941,0.0003828665,0.0005725304,0.0004451418,0.0009761536,0.0004704816,0.0006527861,0.0007799244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003984493,"about_ca_system_score_gemma":0.0007375003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001033535,"about_ca_topic_score_gemma":0.001277602,"domain_scores_codex":[0.9994823,0.0000720547,0.00002701106,0.00009642058,0.0002911596,0.00003093376],"domain_scores_gemma":[0.9994922,0.0001843959,0.00005059554,0.000060328,0.0001961546,0.00001635709],"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":[0.0006090148,0.0001337406,0.00224848,0.0003327778,0.0001259824,0.0005741079,0.0002783843,0.1570171,0.28505,0.07314514,0.002336358,0.478149],"study_design_scores_gemma":[0.00003431232,0.0001301524,0.0003526192,0.00001959093,0.0000325771,0.000528635,0.00001817419,0.9156485,0.0731251,0.005838176,0.004245655,0.00002657468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01041466,0.0002287058,0.9876343,0.00007777954,0.000033171,0.00002031467,0.00002219435,0.0002921258,0.001276713],"genre_scores_gemma":[0.4567625,0.001156712,0.5338376,0.0002408877,0.00009566519,0.0000715226,0.0001982169,0.0001113161,0.007525598],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001033535,"threshold_uncertainty_score":0.003011286,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2123033112","doi":"10.1109/tmm.2005.843364","title":"Quality metric for approximating subjective evaluation of 3-D objects","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Computer science; Metric (unit); Quality (philosophy); Artificial intelligence; Perception; Reliability (semiconductor); Texture (cosmology); Graphics; Computer vision; Image quality; Data mining; Pattern recognition (psychology); Image (mathematics); Computer graphics (images)","authors":[{"name":"Yixin Pan","is_ca":true},{"name":"Irene Cheng","is_ca":true},{"name":"Anup Basu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0923963486530244,"gpt":0.3920083632978217,"spread":0.2996120146447974,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006942816,0.0013721,0.0009111358,0.003839785,0.0004303242,0.001679431,0.001583112,0.00134693,0.002192273],"category_scores_gemma":[0.03270603,0.0003786671,0.0009133967,0.002642083,0.001028366,0.002174617,0.001323893,0.0009736745,0.0008238291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001230846,"about_ca_system_score_gemma":0.0006368291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003859616,"about_ca_topic_score_gemma":0.00275314,"domain_scores_codex":[0.9933976,0.002382261,0.0005390109,0.0007081318,0.002814886,0.0001580619],"domain_scores_gemma":[0.9859306,0.00649712,0.001538147,0.001670423,0.004127787,0.0002359411],"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":[0.001041292,0.0004071819,0.01661902,0.00147325,0.0004359903,0.0002923239,0.0008600879,0.4170845,0.1189843,0.03431881,0.006389805,0.4020934],"study_design_scores_gemma":[0.00002600276,0.0003958171,0.006957106,0.00005546498,0.00004519896,0.0002920387,0.00009637821,0.9725204,0.01066624,0.005066082,0.003785449,0.00009385176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01369882,0.0003650734,0.9842777,0.00003578274,0.00004903339,0.0001325372,0.0001982976,0.000411222,0.0008315466],"genre_scores_gemma":[0.3623106,0.0005711917,0.6342189,0.00009925834,0.00009054371,0.0004726764,0.0008134391,0.0002337346,0.001189593],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006942816,"threshold_uncertainty_score":0.03671753,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2106159865","doi":"10.1109/tmm.2003.822793","title":"Globally Optimal Uneven Error-Protected Packetization of Scalable Code Streams","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Erasure; Network packet; Algorithm; Scalability; Payload (computing); Binary logarithm; Discrete mathematics; Mathematics; Computer network","authors":[{"name":"Sorina Dumitrescu","is_ca":true},{"name":"Xiaobin Wu","is_ca":true},{"name":"Z. Wang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01642296285742713,"gpt":0.2544252547356321,"spread":0.238002291878205,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001015292,0.0007707466,0.0009241666,0.000480457,0.000392425,0.0007669741,0.001104517,0.0005520491,0.001583263],"category_scores_gemma":[0.004254486,0.0002926075,0.0004373995,0.0005848419,0.0008066238,0.001962744,0.001823083,0.001131095,0.0002806906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000796204,"about_ca_system_score_gemma":0.0008405253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001030522,"about_ca_topic_score_gemma":0.0006656218,"domain_scores_codex":[0.9993562,0.0001513812,0.00004033399,0.0001194396,0.0002358413,0.00009666877],"domain_scores_gemma":[0.99887,0.0005622035,0.0001281806,0.0002559109,0.0001378292,0.00004585544],"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":[0.0003002759,0.0000591081,0.0006382366,0.00009403429,0.00003534835,0.0001062617,0.0001652197,0.7000237,0.01418602,0.0437157,0.002024075,0.2386521],"study_design_scores_gemma":[0.00001388539,0.0000420456,0.00006464295,0.000006219531,0.00000435326,0.00003266418,0.00002709805,0.9788399,0.008332672,0.0118343,0.0007953988,0.000006780143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01682608,0.00008183649,0.9818367,0.00005792405,0.00001417047,0.00003556421,0.00002426473,0.0003904174,0.0007329856],"genre_scores_gemma":[0.451381,0.000227755,0.5453911,0.00009750985,0.00003941585,0.0001418337,0.0002274932,0.0001893145,0.002304614],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001583263,"threshold_uncertainty_score":0.005776882,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3138954746","doi":"10.1109/tmm.2021.3067205","title":"Design and Analysis of MEC- and Proactive Caching-Based $360^{\\circ }$ Mobile VR Video Streaming","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":78,"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":"National Natural Science Foundation of China","keywords":"Computer science; Video streaming; Multimedia; Mobile computing; Server; Computer network","authors":[{"name":"Cheng Ying Qi","is_ca":false},{"name":"Hangguan Shan","is_ca":false},{"name":"Weihua Zhuang","is_ca":true},{"name":"Zhaoyang Zhang","is_ca":false},{"name":"Tony Q. S. Quek","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01829156168778562,"gpt":0.2392927899908476,"spread":0.221001228303062,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000804281,0.000659526,0.0007441874,0.0004203818,0.0004986267,0.001006174,0.001664355,0.0007265228,0.00133851],"category_scores_gemma":[0.001581025,0.0004237054,0.000438277,0.0005609153,0.0005764994,0.0007489938,0.0006497967,0.0004753919,0.0002016007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001983606,"about_ca_system_score_gemma":0.001496765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009362618,"about_ca_topic_score_gemma":0.005248741,"domain_scores_codex":[0.9991702,0.0001538688,0.0000371528,0.0001782661,0.000308099,0.00015244],"domain_scores_gemma":[0.9993052,0.0001935536,0.0001031899,0.00004759202,0.0002951373,0.00005523857],"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":[0.0001608116,0.00007574599,0.001670476,0.0001832224,0.00005341884,0.0003336421,0.0001115921,0.901306,0.03410073,0.0211366,0.001412969,0.03945477],"study_design_scores_gemma":[0.000003476702,0.00004429812,0.00009125498,0.000002973747,0.000008524135,0.00002994341,0.00001102213,0.9978835,0.001215038,0.0004530256,0.0002527819,0.000004157478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0553012,0.000986993,0.9359704,0.0003319056,0.00006079095,0.0001556618,0.00006240677,0.000392392,0.006738137],"genre_scores_gemma":[0.9632121,0.0005611546,0.03436505,0.00007810652,0.00002208415,0.00007424437,0.00004055589,0.0000266584,0.001620202],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009362618,"threshold_uncertainty_score":0.0186162,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2103806271","doi":"10.1109/tmm.2010.2076799","title":"Energy-Efficient Multicasting of Scalable Video Streams Over WiMAX Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":77,"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; Multicast; Energy consumption; WiMAX; Computer network; Scalability; Quality of service; Video quality; Wireless; Real-time computing; IMT Advanced; Mobile computing; Distributed computing; Mobile technology; Mobile Web; Telecommunications","authors":[{"name":"Somsubhra Sharangi","is_ca":true},{"name":"Ramesh Krishnamurti","is_ca":true},{"name":"Mohamed Hefeeda","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.005238289156049966,"gpt":0.205590914934917,"spread":0.200352625778867,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003978232,0.0004216398,0.0004799949,0.0003625638,0.0003300857,0.0002622036,0.0004992787,0.0003760713,0.0004661572],"category_scores_gemma":[0.001153921,0.0001570803,0.0001719264,0.000433354,0.0001985954,0.0005109356,0.0004267016,0.0002535491,0.00008000046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004641292,"about_ca_system_score_gemma":0.0003339976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002223353,"about_ca_topic_score_gemma":0.002611152,"domain_scores_codex":[0.9998533,0.00004107985,0.000005806045,0.0000185322,0.00005028181,0.00003099847],"domain_scores_gemma":[0.9997532,0.0001329783,0.000044869,0.0000228117,0.00002933967,0.00001695881],"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":[0.0003284939,0.0001132254,0.001169344,0.00007528094,0.00002985054,0.0001578506,0.00007093618,0.8463807,0.02710655,0.005742741,0.001021734,0.1178034],"study_design_scores_gemma":[0.00001252516,0.00003963201,0.0001742611,0.000001721616,0.000003742129,0.00001135565,0.00001101525,0.9955444,0.002207456,0.001842506,0.0001497888,0.000001601992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4064353,0.000348786,0.5891274,0.0001909129,0.00002541847,0.00008380649,0.00008118877,0.0004098548,0.003297326],"genre_scores_gemma":[0.9280246,0.0001336679,0.07078688,0.00002005673,0.00001567597,0.00004855741,0.00006590853,0.00001857412,0.0008860935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002223353,"threshold_uncertainty_score":0.004420877,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2140834120","doi":"10.1109/tmm.2007.911226","title":"A Graphical Model for Context-Aware Visual Content Recommendation","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Recommender system; Information overload; Information retrieval; Context (archaeology); World Wide Web; The Internet; Digital library; Human–computer interaction; Multimedia","authors":[{"name":"Sabri Boutemedjet","is_ca":true},{"name":"Djemel Ziou","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06997677448712307,"gpt":0.3230960029260488,"spread":0.2531192284389258,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008746639,0.0009181088,0.0009866289,0.001768134,0.0006420938,0.002217301,0.002385323,0.002100069,0.009668623],"category_scores_gemma":[0.004273352,0.0007268359,0.001844609,0.00222592,0.0006800584,0.002490615,0.000988652,0.001638557,0.004148952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001366078,"about_ca_system_score_gemma":0.0008784014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02437397,"about_ca_topic_score_gemma":0.03259616,"domain_scores_codex":[0.9990989,0.0003057589,0.0000588299,0.0002369796,0.0002128023,0.00008671517],"domain_scores_gemma":[0.9987549,0.0005798693,0.0001047942,0.0002190579,0.0002644273,0.00007694951],"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":[0.000490409,0.0002618426,0.002594533,0.0004531017,0.0002453737,0.0006738341,0.0005322877,0.5094399,0.01023236,0.2474901,0.02184736,0.2057388],"study_design_scores_gemma":[0.00004500928,0.00004567206,0.0003389416,0.00002669111,0.00005715093,0.0001288391,0.00002797893,0.9484543,0.0006288881,0.04183654,0.008377929,0.00003194751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005137146,0.0005446702,0.984871,0.0005221429,0.00008035215,0.00009049619,0.0009667201,0.001854369,0.005933099],"genre_scores_gemma":[0.4562177,0.001741332,0.5149959,0.0005182473,0.0002242647,0.0006544722,0.002743827,0.0003966352,0.02250762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02437397,"threshold_uncertainty_score":0.04846424,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3134680050","doi":"10.1109/tmm.2021.3052419","title":"Video Frame Interpolation via Generalized Deformable Convolution","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion interpolation; Computer science; Interpolation (computer graphics); Artificial intelligence; Kernel (algebra); Computer vision; Frame (networking); Optical flow; Convolution (computer science); Motion estimation; Algorithm; Motion (physics); Mathematics; Video tracking; Video processing; Block-matching algorithm; Image (mathematics); Artificial neural network","authors":[{"name":"Zhihao Shi","is_ca":true},{"name":"Xiaohong Liu","is_ca":true},{"name":"Kangdi Shi","is_ca":true},{"name":"Linhui Dai","is_ca":true},{"name":"Jun Chen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01515195106255921,"gpt":0.2686879798880343,"spread":0.253536028825475,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070595,0.0008390961,0.0007871542,0.001136943,0.0002907831,0.0005711984,0.00133668,0.0008399928,0.003127056],"category_scores_gemma":[0.001953383,0.0003633661,0.000970533,0.001143157,0.0003883975,0.0009358503,0.001114221,0.0012441,0.001074516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006726826,"about_ca_system_score_gemma":0.000819317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006305061,"about_ca_topic_score_gemma":0.005933713,"domain_scores_codex":[0.9995731,0.00005372908,0.00002317825,0.0001189048,0.0001869037,0.00004407608],"domain_scores_gemma":[0.999612,0.00009357005,0.0000502323,0.000115756,0.00009494944,0.0000334161],"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":[0.0003574157,0.00008122047,0.0007317626,0.000144567,0.00008050413,0.0002641855,0.0001635847,0.259745,0.07196388,0.02178839,0.005474811,0.6392047],"study_design_scores_gemma":[0.00001004691,0.00002456868,0.0001321013,0.000007481136,0.000007947619,0.00008605707,0.000006894996,0.9807215,0.01309406,0.002975693,0.002922404,0.00001117404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004702721,0.00008881074,0.9936132,0.00003850351,0.0000422149,0.00002903799,0.0000602551,0.0007017355,0.0007235502],"genre_scores_gemma":[0.1244407,0.0002882625,0.8708856,0.00008521941,0.00005588585,0.00009188125,0.0004282995,0.000254439,0.00346977],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006305061,"threshold_uncertainty_score":0.01253676,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2137587644","doi":"10.1109/tmm.2003.813280","title":"Joint semantics and feature based image retrieval using relevance feedback","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":71,"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":"Relevance feedback; Computer science; Image retrieval; Relevance (law); Semantics (computer science); Information retrieval; Ranking (information retrieval); Feature (linguistics); Image (mathematics); Automatic image annotation; Artificial intelligence; Data mining; Pattern recognition (psychology)","authors":[{"name":"Ye Lü","is_ca":true},{"name":"Chunhui Hu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02672601995115716,"gpt":0.2609262115134166,"spread":0.2342001915622594,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002527705,0.001044174,0.001895979,0.002558239,0.0005537051,0.00136422,0.001690221,0.001366269,0.002047995],"category_scores_gemma":[0.008903911,0.0004637256,0.001133051,0.002356048,0.001304697,0.003868825,0.001707654,0.0007474593,0.0009268647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008802258,"about_ca_system_score_gemma":0.0008992227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002107436,"about_ca_topic_score_gemma":0.002239623,"domain_scores_codex":[0.9971188,0.0009757166,0.0001590449,0.00048568,0.001111549,0.0001491829],"domain_scores_gemma":[0.9975536,0.001049548,0.0003215223,0.0004619288,0.0005475369,0.0000658272],"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":[0.0005260646,0.0002923654,0.0009488861,0.0005875428,0.0002000826,0.0003518073,0.0002576344,0.1825611,0.04948213,0.04448553,0.005730199,0.7145768],"study_design_scores_gemma":[0.00007065284,0.0002471543,0.0005804485,0.00001670734,0.00007670543,0.0003091082,0.00004889402,0.9284229,0.01375867,0.05301927,0.003394739,0.00005483856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009953632,0.0008045598,0.9870769,0.0001515286,0.00003817213,0.00008257628,0.00004960617,0.0006571647,0.001185835],"genre_scores_gemma":[0.5856654,0.0009990993,0.4082213,0.0001801131,0.0003444091,0.0003273792,0.0003882746,0.0001570753,0.003716972],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002558239,"threshold_uncertainty_score":0.01336789,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3008273026","doi":"10.1109/tmm.2020.2974323","title":"A Multi-Stream Graph Convolutional Networks-Hidden Conditional Random Field Model for Skeleton-Based Action Recognition","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Conditional random field; Softmax function; Pattern recognition (psychology); Artificial intelligence; Convolutional neural network; Graph; Classifier (UML); Adjacency list; Action recognition; RGB color model; Algorithm; Theoretical computer science","authors":[{"name":"Kai Liu","is_ca":false},{"name":"Lei Gao","is_ca":true},{"name":"Naimul Khan","is_ca":true},{"name":"Lin Qi","is_ca":false},{"name":"Ling Guan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07359573018393849,"gpt":0.2848202832929515,"spread":0.211224553109013,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004622302,0.0008206012,0.0005568561,0.0007172871,0.0002155617,0.0004007209,0.001387398,0.0007513493,0.001974363],"category_scores_gemma":[0.0007987943,0.0003731137,0.0007642541,0.0007596348,0.0003356183,0.0007689573,0.000393696,0.001177241,0.000669436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001129147,"about_ca_system_score_gemma":0.001118503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03170625,"about_ca_topic_score_gemma":0.0388133,"domain_scores_codex":[0.9998121,0.00002930918,0.000006314397,0.00007396431,0.00004541096,0.0000329937],"domain_scores_gemma":[0.9998242,0.00005753401,0.00002195367,0.00002394208,0.00005521084,0.00001723906],"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":[0.0002404861,0.0001581164,0.001747409,0.00009438366,0.0001408901,0.0001389783,0.00003986661,0.6366763,0.01332413,0.008140732,0.008538906,0.3307599],"study_design_scores_gemma":[0.000002208533,0.00001065394,0.0001891682,0.000002748782,0.000007002084,0.00001336612,0.000001205718,0.9976417,0.0009496376,0.0008364019,0.0003418532,0.000004017892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02481873,0.0008484363,0.968586,0.0003331561,0.0001441313,0.00005457391,0.0005299335,0.002954226,0.001730721],"genre_scores_gemma":[0.652005,0.001221415,0.3309535,0.0003544103,0.00009408212,0.000157825,0.002535778,0.0002507185,0.0124273],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03170625,"threshold_uncertainty_score":0.06304342,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2086277433","doi":"10.1109/tmm.2013.2291658","title":"Adaptive Watermarking and Tree Structure Based Image Quality Estimation","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":59,"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; University of Ottawa","funders":"","keywords":"Watermark; Digital watermarking; Computer science; Artificial intelligence; Image quality; Set partitioning in hierarchical trees; Distortion (music); Tree (set theory); Additive white Gaussian noise; Gaussian noise; Pattern recognition (psychology); Image compression; Computer vision; Image processing; Mathematics; Image (mathematics); White noise","authors":[{"name":"Sha Wang","is_ca":true},{"name":"Zheng Dong","is_ca":true},{"name":"Jiying Zhao","is_ca":true},{"name":"Wa James Tam","is_ca":true},{"name":"Filippo Speranza","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01532327245440409,"gpt":0.2658326711455773,"spread":0.2505093986911733,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006855583,0.0005020983,0.0004079199,0.001347197,0.0001834696,0.0005957775,0.0005243804,0.0005495012,0.0008962046],"category_scores_gemma":[0.002855881,0.0002147044,0.0004527822,0.001063233,0.0004001365,0.001377481,0.0004850927,0.0003995369,0.0003492504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004344631,"about_ca_system_score_gemma":0.000274076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008030813,"about_ca_topic_score_gemma":0.0007774208,"domain_scores_codex":[0.9993082,0.0001115455,0.00004163265,0.0001310475,0.0003735415,0.00003396147],"domain_scores_gemma":[0.9990048,0.0002459892,0.0002779507,0.000131654,0.0003152127,0.00002441535],"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":[0.0003366121,0.00007544336,0.003303706,0.0002750282,0.00009800719,0.0001998384,0.000141875,0.1229999,0.2501511,0.01073588,0.0008680319,0.6108146],"study_design_scores_gemma":[0.00001932871,0.0002608577,0.004167128,0.00002782781,0.00004846116,0.0005279339,0.00002874429,0.9075013,0.08120164,0.003748996,0.00241482,0.0000528819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03430879,0.0006532984,0.9635731,0.00005042508,0.00003179715,0.00004911298,0.00004682726,0.0004191645,0.0008675274],"genre_scores_gemma":[0.5603672,0.00110018,0.4360954,0.00004987237,0.00007378204,0.00007175657,0.0002068237,0.00006284954,0.001972187],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001347197,"threshold_uncertainty_score":0.003625631,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2053996691","doi":"10.1109/tmm.2014.2298832","title":"Texture Modeling Using Contourlets and Finite Mixtures of Generalized Gaussian Distributions and Applications","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Contourlet; Histogram; Computer science; Gaussian; Texture (cosmology); Artificial intelligence; Range (aeronautics); Probability density function; Pattern recognition (psychology); Probability distribution; Image texture; Mixture model; Computer vision; Image (mathematics); Image processing; Mathematics; Statistics; Wavelet transform; Wavelet","authors":[{"name":"Mohand Saïd Allili","is_ca":true},{"name":"Nadia Baaziz","is_ca":true},{"name":"Marouene Mejri","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01869518243869018,"gpt":0.2405276914845451,"spread":0.2218325090458549,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001054973,0.0007490719,0.0006759514,0.001593248,0.0002225447,0.001076813,0.00113885,0.001210389,0.0007090173],"category_scores_gemma":[0.003150219,0.000580652,0.00100843,0.001914022,0.0008944836,0.001576868,0.0006899846,0.001175298,0.00039577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005622339,"about_ca_system_score_gemma":0.0004318023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001894102,"about_ca_topic_score_gemma":0.001069156,"domain_scores_codex":[0.9995432,0.0001309591,0.00002552618,0.00007689876,0.0001882076,0.00003523655],"domain_scores_gemma":[0.9989491,0.0005511808,0.0001416094,0.0001476672,0.0001683645,0.000042146],"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":[0.0001008285,0.00005557759,0.001209649,0.0001442274,0.00007120797,0.0002384505,0.0001288677,0.7081628,0.02129176,0.07163528,0.0015238,0.1954376],"study_design_scores_gemma":[0.000001702068,0.000007443606,0.0001145175,0.00000402471,0.000003970181,0.00004476603,0.00000440676,0.9925625,0.0009439845,0.005815168,0.0004911016,0.000006427692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003010765,0.0002530251,0.9962977,0.00006095183,0.00002138011,0.000007100542,0.00001345005,0.0001092963,0.0002263532],"genre_scores_gemma":[0.3854864,0.002421898,0.6092973,0.0001388585,0.000243964,0.00007791142,0.0002047661,0.0001889412,0.001939892],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001894102,"threshold_uncertainty_score":0.005579293,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2129884074","doi":"10.1109/tmm.2013.2240670","title":"CloudMoV: Cloud-Based Mobile Social TV","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Cloud computing; Mobile device; Mobile computing; Computer network; Mobile cloud computing; Exploit; Mobile Web; Quality of service; Cloudlet; Bottleneck; Mobile technology; Multimedia; Computer security; World Wide Web; Embedded system; Operating system","authors":[{"name":"Yu Wu","is_ca":false},{"name":"Zhizhong Zhang","is_ca":false},{"name":"Chuan Wu","is_ca":false},{"name":"Zongpeng Li","is_ca":true},{"name":"Francis C. M. Lau","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02319342419825297,"gpt":0.2917527802506207,"spread":0.2685593560523677,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003005734,0.0004174413,0.0003423837,0.0005705748,0.0009211655,0.00115185,0.001507409,0.0005541098,0.005719471],"category_scores_gemma":[0.000764747,0.000167307,0.00035495,0.0006356906,0.0003699866,0.000974794,0.002233643,0.0006296536,0.001348053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000597048,"about_ca_system_score_gemma":0.0008158634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006353005,"about_ca_topic_score_gemma":0.005673425,"domain_scores_codex":[0.9996073,0.00007235521,0.00001444279,0.00005806285,0.0001189908,0.0001289362],"domain_scores_gemma":[0.999549,0.00005618003,0.00003988908,0.00008654747,0.0000975058,0.0001708692],"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":[0.002946163,0.001767159,0.0150552,0.0007108174,0.0003441474,0.003156669,0.001022027,0.0494277,0.1463901,0.07528012,0.1980077,0.5058922],"study_design_scores_gemma":[0.000315423,0.0005186411,0.006134091,0.00006446312,0.00006886543,0.0007955758,0.0004308141,0.8270649,0.03107394,0.01080454,0.1226089,0.0001198351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3054116,0.00229812,0.5445758,0.002861951,0.000912804,0.001952427,0.002441043,0.05036749,0.08917876],"genre_scores_gemma":[0.9281719,0.0002803571,0.05748943,0.0005492207,0.0001922246,0.0002279668,0.001230329,0.0004374408,0.01142118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006353005,"threshold_uncertainty_score":0.01913357,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2160624927","doi":"10.1109/tmm.2008.2001364","title":"Effect of Delay and Buffering on Jitter-Free Streaming Over Random VBR Channels","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":57,"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":"Queensland Cyber Infrastructure Foundation","keywords":"Jitter; Computer science; Variable bitrate; Channel (broadcasting); Real-time computing; Computer network; Performance metric; Telecommunications; Bit rate","authors":[{"name":"Guanfeng Liang","is_ca":true},{"name":"Ben Liang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.005886804516660743,"gpt":0.211322282901157,"spread":0.2054354783844962,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002733576,0.001419468,0.0007738295,0.0009369785,0.0007256205,0.001198884,0.0009839466,0.0008606365,0.0007189158],"category_scores_gemma":[0.01776741,0.000422583,0.0003608781,0.0006399698,0.001921039,0.001596447,0.0009189389,0.000737384,0.00007520531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00139574,"about_ca_system_score_gemma":0.0007799534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002623516,"about_ca_topic_score_gemma":0.001166167,"domain_scores_codex":[0.998613,0.0005319088,0.00005012699,0.0001355682,0.0003370277,0.0003323549],"domain_scores_gemma":[0.9740943,0.02195497,0.00231935,0.0004524169,0.0007205645,0.0004583332],"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":[0.0006276373,0.00008717027,0.002192454,0.0001380585,0.00005599792,0.0007771995,0.0001368898,0.9507526,0.01813205,0.02007899,0.0001855915,0.00683529],"study_design_scores_gemma":[0.00002786289,0.000219027,0.0006851235,0.00001616633,0.00006447751,0.0001416278,0.00004760353,0.988867,0.006898033,0.002885598,0.0001213158,0.00002613301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7037302,0.002982131,0.2871415,0.0005043377,0.0001089321,0.00009037331,0.00009542302,0.0002979389,0.005049161],"genre_scores_gemma":[0.9957744,0.0004459166,0.003457898,0.0000259246,0.00002928512,0.0000124793,0.000009495145,0.00001779807,0.0002267067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002733576,"threshold_uncertainty_score":0.01445669,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2811207102","doi":"10.1109/tmm.2018.2851447","title":"A Channel-Dependent Statistical Watermark Detector for Color Images","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Watermark; Digital watermarking; Computer science; Detector; Robustness (evolution); RGB color model; Artificial intelligence; Channel (broadcasting); Computer vision; RGB color space; Color image; Pattern recognition (psychology); Image (mathematics); Image processing; Telecommunications","authors":[{"name":"Marzieh Amini","is_ca":true},{"name":"Hamidreza Sadreazami","is_ca":true},{"name":"M. Omair Ahmad","is_ca":true},{"name":"M.N.S. Swamy","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01705086314740194,"gpt":0.2761206773815466,"spread":0.2590698142341446,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007507023,0.0003939639,0.0005606036,0.0007183213,0.0002393644,0.0004667268,0.0005980956,0.0008234246,0.0009685405],"category_scores_gemma":[0.002482773,0.0002251468,0.0003972002,0.0005265046,0.0004376499,0.0009107952,0.0004577817,0.0005994621,0.0005611837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003832229,"about_ca_system_score_gemma":0.0006461802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004185939,"about_ca_topic_score_gemma":0.0007742948,"domain_scores_codex":[0.9995168,0.00008332929,0.00002163083,0.00009558798,0.0002400482,0.00004252562],"domain_scores_gemma":[0.9989319,0.0004809913,0.0001305082,0.0001251275,0.000296457,0.00003488309],"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":[0.0006470306,0.0002589737,0.002998871,0.0002670866,0.000145338,0.0002481285,0.00007254869,0.05101233,0.4811769,0.02089572,0.001593001,0.4406841],"study_design_scores_gemma":[0.00002964196,0.0003395407,0.001374808,0.00001352899,0.00006242249,0.0005669992,0.00001390444,0.8351341,0.1568983,0.002514018,0.003008875,0.00004387926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03249258,0.0004224509,0.9655886,0.0001089262,0.00007762177,0.0000563616,0.00005642501,0.0003682968,0.0008287152],"genre_scores_gemma":[0.5684907,0.0007118555,0.4269043,0.000184724,0.0000878006,0.00009016519,0.0002401404,0.00003743509,0.003252735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009685405,"threshold_uncertainty_score":0.003970146,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3095715663","doi":"10.1109/tmm.2020.3035275","title":"Environmental Sound Classification Using Local Binary Pattern and Audio Features Collaboration","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Music and Audio Processing","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Local binary patterns; Computer science; Support vector machine; Audio signal processing; Audio signal; Artificial intelligence; Pattern recognition (psychology); Mel-frequency cepstrum; Spectrogram; Feature extraction; k-nearest neighbors algorithm; Random forest; Feature vector; Sound recording and reproduction; Speech recognition; Histogram; Speech coding; Image (mathematics)","authors":[{"name":"Ohini Kafui Toffa","is_ca":true},{"name":"Max Mignotte","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02995517247449536,"gpt":0.2519905603140803,"spread":0.2220353878395849,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006959401,0.000909598,0.001188862,0.00547202,0.0004174089,0.001241606,0.0008915543,0.0009904504,0.001954694],"category_scores_gemma":[0.002228125,0.0002374192,0.001125138,0.002463824,0.000453736,0.001629114,0.001453661,0.0007184641,0.001883966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003053772,"about_ca_system_score_gemma":0.0004951712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002370106,"about_ca_topic_score_gemma":0.002680678,"domain_scores_codex":[0.9987723,0.0001371321,0.00006150106,0.000278083,0.0005848501,0.0001660404],"domain_scores_gemma":[0.9990723,0.0002438739,0.0001185835,0.0001108004,0.0003670293,0.00008729516],"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":[0.0004930277,0.0002443509,0.007791107,0.000154388,0.0001398015,0.0002373028,0.00008960784,0.01625131,0.05012216,0.001131463,0.002927162,0.9204183],"study_design_scores_gemma":[0.0001009129,0.0004758574,0.02805595,0.00007262477,0.0003284915,0.0007891303,0.0003890536,0.9099039,0.04375228,0.006376075,0.009649835,0.0001058049],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1288029,0.001041087,0.8570429,0.000263015,0.0002506434,0.0001889459,0.0006138098,0.003949463,0.007847292],"genre_scores_gemma":[0.7002018,0.0004981263,0.2907464,0.0001913143,0.0003047385,0.0001657852,0.001641552,0.0002182138,0.006032074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00547202,"threshold_uncertainty_score":0.006539166,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2328057526","doi":"10.1109/tmm.2016.2538718","title":"Delay-Optimized Video Traffic Routing in Software-Defined Interdatacenter Networks","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta; University of Toronto","funders":"University of Toronto; Amazon Web Services","keywords":"Computer science; Computer network; Software-defined networking; Cloud computing; Network packet; Schedule; Throughput; Software deployment; Overhead (engineering); Distributed computing; Real-time computing; Wireless; Operating system","authors":[{"name":"Yinan Liu","is_ca":true},{"name":"Di Niu","is_ca":true},{"name":"Baochun Li","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0141675165857604,"gpt":0.2318158340543961,"spread":0.2176483174686357,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001106953,0.0007435105,0.0004901584,0.0006042243,0.0005180933,0.0008762894,0.001231485,0.0004688361,0.0004441853],"category_scores_gemma":[0.002089842,0.000322372,0.0002249026,0.0005950338,0.0004989645,0.0008566651,0.0005869712,0.0005258043,0.0001039258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001914936,"about_ca_system_score_gemma":0.001429234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007098579,"about_ca_topic_score_gemma":0.007445899,"domain_scores_codex":[0.9992809,0.0002536324,0.000031917,0.0001242395,0.0001725746,0.0001366861],"domain_scores_gemma":[0.9991677,0.000343078,0.0001341499,0.00007927739,0.0002052209,0.00007063447],"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":[0.0001324856,0.00007225465,0.0006521557,0.00004027722,0.00001783334,0.00005279548,0.00007196232,0.9353918,0.01352881,0.006902861,0.0008784331,0.04225833],"study_design_scores_gemma":[0.000005376205,0.00002415545,0.00006852314,0.00000192007,0.000004262527,0.00001093789,0.0000148006,0.9962932,0.001762899,0.001464999,0.0003450706,0.000003834498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1805833,0.0007686312,0.8147194,0.0002871125,0.00008374399,0.0001025881,0.00007992355,0.001133891,0.002241376],"genre_scores_gemma":[0.9200597,0.0002524402,0.07848328,0.00006236153,0.00002861132,0.00005038607,0.00008435319,0.00006710417,0.0009118094],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007098579,"threshold_uncertainty_score":0.01411456,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1985444590","doi":"10.1109/tmm.2014.2306175","title":"Image Similarity Using Sparse Representation and Compression Distance","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Sparse approximation; Image compression; Similarity (geometry); Cluster analysis; Context (archaeology); Image (mathematics); Similarity measure; Representation (politics); Computer vision; Image processing","authors":[{"name":"Tanaya Guha","is_ca":true},{"name":"Rabab Ward","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03735347427742566,"gpt":0.3014806470151919,"spread":0.2641271727377663,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00116526,0.0006190522,0.001277388,0.005121546,0.0003654275,0.001657043,0.0009358431,0.001150004,0.001357543],"category_scores_gemma":[0.006469288,0.0002138031,0.0007458292,0.004293319,0.001183739,0.00300075,0.00169863,0.0009720076,0.0005635255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008181881,"about_ca_system_score_gemma":0.0004468616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009598265,"about_ca_topic_score_gemma":0.0006137436,"domain_scores_codex":[0.9978439,0.000430173,0.0001413128,0.0003423649,0.001163945,0.00007837178],"domain_scores_gemma":[0.9974266,0.00105307,0.0004208531,0.0003995562,0.0006177397,0.00008229879],"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":[0.000367011,0.0001842084,0.003984728,0.0006068454,0.0003280267,0.0002991369,0.0003911644,0.09798604,0.03595909,0.1067221,0.006525774,0.7466459],"study_design_scores_gemma":[0.00003596023,0.0004337746,0.006186223,0.00009689596,0.00009605082,0.001154622,0.0002255635,0.8774702,0.01893061,0.08625554,0.008999979,0.0001145817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02969298,0.002178533,0.9638794,0.0003581095,0.0001221994,0.0001169089,0.0001850933,0.0003706269,0.003096096],"genre_scores_gemma":[0.5998502,0.002938098,0.3914426,0.0002844072,0.0006215726,0.0003121941,0.0009525814,0.0001196403,0.003478788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005121546,"threshold_uncertainty_score":0.006162524,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2051526232","doi":"10.1109/tmm.2014.2321113","title":"Prototype-Based Modeling for &lt;newline/&gt;Facial Expression Analysis","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Facial expression; Computer science; Expression (computer science); Set (abstract data type); Artificial intelligence; Computer vision; Representation (politics); Face (sociological concept); Active appearance model; Pattern recognition (psychology); Class (philosophy); Scale-invariant feature transform; Image (mathematics)","authors":[{"name":"Mohamed Dahmane","is_ca":true},{"name":"Jean Meunier","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02348939211062919,"gpt":0.2634280411345102,"spread":0.239938649023881,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000815879,0.0009977692,0.000990155,0.000859337,0.0003476325,0.001123246,0.001874168,0.0007606416,0.006318704],"category_scores_gemma":[0.001674889,0.0003892671,0.001430721,0.0006476667,0.000327928,0.001456786,0.0007821852,0.0009726102,0.003991613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006537134,"about_ca_system_score_gemma":0.0005035301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005507446,"about_ca_topic_score_gemma":0.006828231,"domain_scores_codex":[0.9991987,0.0001611843,0.00003967985,0.0002511685,0.0002830132,0.00006629395],"domain_scores_gemma":[0.9995171,0.00007849444,0.00003568114,0.0001820874,0.0001692262,0.00001756858],"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":[0.0003671023,0.0002296589,0.001621624,0.0002279719,0.0001607293,0.0001545961,0.0001061441,0.09550557,0.05474891,0.005793951,0.01811242,0.8229712],"study_design_scores_gemma":[0.00001182908,0.0000940996,0.000942241,0.00001483643,0.00002026933,0.0001761941,0.00003849723,0.9698181,0.01802243,0.002139166,0.00870113,0.00002122235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01377617,0.0004132453,0.9779214,0.0001234371,0.0001174159,0.0001585332,0.0006478009,0.004989468,0.001852501],"genre_scores_gemma":[0.3103154,0.0009334574,0.6679623,0.0002450332,0.00009599373,0.000386975,0.006623817,0.001307612,0.01212948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006318704,"threshold_uncertainty_score":0.02113819,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3117941406","doi":"10.1109/tmm.2022.3142398","title":"STNet: Scale Tree Network With Multi-Level Auxiliator for Crowd Counting","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland; University of Guelph","funders":"University of Guelph","keywords":"Computer science; Tree (set theory); Scale (ratio); Artificial intelligence; Machine learning; Data mining; Pattern recognition (psychology)","authors":[{"name":"Mingjie Wang","is_ca":true},{"name":"Hao Cai","is_ca":true},{"name":"Xian-Feng Han","is_ca":false},{"name":"Jun Zhou","is_ca":false},{"name":"Minglun Gong","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05101680530524946,"gpt":0.2914439014463287,"spread":0.2404270961410792,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005439371,0.001310406,0.0007842238,0.0009892827,0.0005310228,0.0006768039,0.001586153,0.00106298,0.002255057],"category_scores_gemma":[0.001489349,0.0004143785,0.0006316,0.0008309726,0.0004540498,0.001627761,0.001339543,0.000993866,0.0007923529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009189948,"about_ca_system_score_gemma":0.0007727764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0090926,"about_ca_topic_score_gemma":0.01426529,"domain_scores_codex":[0.9997687,0.00003556444,0.000008913328,0.00007880435,0.00005951853,0.00004858034],"domain_scores_gemma":[0.9997308,0.00007795524,0.00003800228,0.00003264561,0.00008707082,0.00003343942],"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":[0.000365733,0.0001879714,0.003934538,0.0001498895,0.0001669698,0.0002841341,0.0002247718,0.4575092,0.01581935,0.009257995,0.02021971,0.4918797],"study_design_scores_gemma":[0.000006200367,0.00002509256,0.0003079227,0.000008050254,0.00001121985,0.00002926408,0.00001495649,0.9942147,0.001656841,0.002737155,0.0009812536,0.000007368781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06495651,0.0007449062,0.922497,0.0003997073,0.0002487423,0.0001298936,0.0005696003,0.005036633,0.005416903],"genre_scores_gemma":[0.6692653,0.0005536339,0.317624,0.0005216126,0.0001742002,0.0002298583,0.001614102,0.0003938046,0.009623476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0090926,"threshold_uncertainty_score":0.01807934,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2534681680","doi":"10.1109/tmm.2016.2618218","title":"Sound-Event Classification Using Robust Texture Features for Robot Hearing","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Music and Audio Processing","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Gottfried Wilhelm Leibniz Universität Hannover; National Research Foundation Singapore; University of Ottawa","keywords":"Spectrogram; Computer science; Local binary patterns; Artificial intelligence; Pattern recognition (psychology); Feature extraction; Noise (video); Feature (linguistics); Robustness (evolution); Speech recognition; Computer vision; Histogram; Image (mathematics)","authors":[{"name":"Jianfeng Ren","is_ca":false},{"name":"Xudong Jiang","is_ca":false},{"name":"Junsong Yuan","is_ca":false},{"name":"Nadia Magnenat‐Thalmann","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08289732554385422,"gpt":0.3032119450116715,"spread":0.2203146194678173,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003936748,0.0006800966,0.001109746,0.002478973,0.0003206102,0.0006561946,0.0008942569,0.0006309797,0.00265964],"category_scores_gemma":[0.001654017,0.0001481431,0.0006642134,0.001398067,0.0002617205,0.0007404679,0.0007939095,0.000520613,0.001472426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003779745,"about_ca_system_score_gemma":0.0004575902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003996345,"about_ca_topic_score_gemma":0.003420904,"domain_scores_codex":[0.9993143,0.00005498458,0.00004727183,0.0001469781,0.0003320384,0.0001043989],"domain_scores_gemma":[0.9994031,0.0001104072,0.00007690639,0.0001056685,0.0002541548,0.00004969989],"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":[0.001552822,0.000377756,0.005924419,0.000289776,0.00007054508,0.0004016152,0.00006051028,0.01249528,0.09908686,0.0004740149,0.008800179,0.8704662],"study_design_scores_gemma":[0.0001818966,0.0007229088,0.0509359,0.00005919415,0.0001591352,0.001085235,0.0003976329,0.8477797,0.08321872,0.00215607,0.01319197,0.000111606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5202843,0.00288485,0.4503659,0.0004132963,0.0005467924,0.0006369514,0.006164321,0.01176185,0.006941696],"genre_scores_gemma":[0.8449082,0.0007216328,0.1418351,0.0001171699,0.0001835789,0.0003070588,0.008268697,0.0001406006,0.003517764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003996345,"threshold_uncertainty_score":0.008897424,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4210825863","doi":"10.1109/tmm.2022.3149641","title":"Encoded Feature Enhancement in Watermarking Network for Distortion in Real Scenes","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Steganography and Watermarking Techniques","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":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Watermark; Digital watermarking; Robustness (evolution); Distortion (music); Artificial intelligence; Encoder; Phase distortion; Image quality; Noise (video); Feature (linguistics); Algorithm; Pattern recognition (psychology); Computer vision; Image (mathematics); Telecommunications; Bandwidth (computing)","authors":[{"name":"Han Fang","is_ca":false},{"name":"Zhaoyang Jia","is_ca":false},{"name":"Hang Zhou","is_ca":true},{"name":"Zehua Ma","is_ca":false},{"name":"Weiming Zhang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01730875617166179,"gpt":0.2646660447423372,"spread":0.2473572885706755,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000326892,0.0007245975,0.0004339548,0.000322035,0.0001857229,0.0003141281,0.0007415561,0.0006120524,0.001160886],"category_scores_gemma":[0.0008049852,0.0002096623,0.0004352707,0.0002599848,0.0004689767,0.001111944,0.0006044728,0.0006795016,0.0002351721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004508613,"about_ca_system_score_gemma":0.0003343675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001154656,"about_ca_topic_score_gemma":0.001741719,"domain_scores_codex":[0.9998314,0.00002380912,0.000008782959,0.00004154448,0.0000645563,0.0000297298],"domain_scores_gemma":[0.9998148,0.00005306037,0.0000401659,0.0000383378,0.00004143174,0.00001218782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004499428,0.0001477539,0.001441814,0.000189176,0.00008889294,0.0004305965,0.0001220218,0.4634323,0.1487195,0.01190446,0.001591131,0.3714823],"study_design_scores_gemma":[0.00001064189,0.0001197785,0.0003653276,0.000008509343,0.0000218252,0.000146869,0.00000930059,0.9630446,0.03324997,0.002006365,0.001005583,0.00001120741],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09550922,0.0006857969,0.9004373,0.0002059808,0.00007594345,0.00004509162,0.00004469887,0.0006622336,0.002333879],"genre_scores_gemma":[0.8443176,0.0005357805,0.1491518,0.000122999,0.00004771895,0.00004628247,0.000117315,0.00005698724,0.005603588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001160886,"threshold_uncertainty_score":0.003883541,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3182688976","doi":"10.1109/tmm.2021.3096088","title":"Infrared and Visible Image Fusion Based on Deep Decomposition Network and Saliency Analysis","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Image fusion; Pattern recognition (psychology); Fusion; Fuse (electrical); Merge (version control); Decomposition; Computer vision; Residual; Image texture; Autoencoder; Image (mathematics); Image processing; Deep learning; Algorithm","authors":[{"name":"Lihua Jian","is_ca":false},{"name":"Rakiba Rayhana","is_ca":true},{"name":"Ling Ma","is_ca":false},{"name":"Shaowu Wu","is_ca":false},{"name":"Zheng Liu","is_ca":true},{"name":"Huiqin Jiang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.004761814742510791,"gpt":0.2411403695029266,"spread":0.2363785547604158,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005914459,0.0009800675,0.0008616647,0.001341083,0.0002462507,0.000599605,0.0007827974,0.0005775614,0.001308186],"category_scores_gemma":[0.001011105,0.0003215435,0.0009530899,0.000769796,0.0004062095,0.001504401,0.00119081,0.000804686,0.0003181427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005435297,"about_ca_system_score_gemma":0.0004686384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002691861,"about_ca_topic_score_gemma":0.003137841,"domain_scores_codex":[0.9996992,0.00003479116,0.00001438159,0.00007547225,0.0001248058,0.00005132844],"domain_scores_gemma":[0.9997287,0.00004424581,0.00003969272,0.00003787542,0.0001154404,0.00003395565],"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":[0.0006132663,0.0002398905,0.002087167,0.0002001592,0.0002415065,0.0002431874,0.0001561757,0.1506894,0.1647434,0.007418517,0.003379221,0.6699881],"study_design_scores_gemma":[0.00001339673,0.00009515892,0.001129738,0.000009829496,0.0000474426,0.0001050551,0.00002378671,0.9714171,0.02236951,0.003747733,0.001024453,0.00001685943],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04227322,0.0003898147,0.9541795,0.0001302762,0.00006863945,0.0000600335,0.0001017407,0.0009531371,0.001843647],"genre_scores_gemma":[0.6369254,0.000500045,0.3576162,0.0001777812,0.0001081148,0.00008441261,0.0005111349,0.0001373588,0.003939525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002691861,"threshold_uncertainty_score":0.005352378,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2897585580","doi":"10.1109/tmm.2018.2875510","title":"Multimodal Learning for Human Action Recognition Via Bimodal/Multimodal Hybrid Centroid Canonical Correlation Analysis","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Canonical correlation; Centroid; Computer science; Modalities; Artificial intelligence; Pattern recognition (psychology); Discriminative model; Correlation; Feature vector; Feature (linguistics); Machine learning; Mathematics","authors":[{"name":"Nour El Din Elmadany","is_ca":true},{"name":"Yifeng He","is_ca":true},{"name":"Ling Guan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03197822218404654,"gpt":0.2916044915662906,"spread":0.259626269382244,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009382889,0.001237703,0.001277774,0.001366802,0.0003852342,0.0007903968,0.001190437,0.0006757224,0.003066512],"category_scores_gemma":[0.002686251,0.0003165314,0.001060215,0.001975147,0.0008879771,0.0009798715,0.001618123,0.001235985,0.001451525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005557906,"about_ca_system_score_gemma":0.001124144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006285624,"about_ca_topic_score_gemma":0.008481325,"domain_scores_codex":[0.9988422,0.0002664554,0.00003643332,0.0004275801,0.000288021,0.0001394008],"domain_scores_gemma":[0.9992839,0.0002123338,0.0000753577,0.0001297751,0.0002292893,0.0000693345],"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":[0.0003228271,0.0002210466,0.004706307,0.0001721078,0.0001930021,0.0002029781,0.0001922267,0.1276182,0.02296972,0.01108834,0.008918444,0.8233948],"study_design_scores_gemma":[0.000007437157,0.00007231838,0.002089052,0.0000167199,0.00002929669,0.0001273908,0.00005511969,0.9837045,0.004387823,0.007394403,0.002089182,0.00002675004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01400915,0.0005978175,0.9827574,0.0001539414,0.00005484116,0.0000469735,0.0001539413,0.0007600684,0.001465836],"genre_scores_gemma":[0.6260266,0.001249772,0.3636486,0.0004115627,0.0003195064,0.0002979661,0.001530843,0.0003065857,0.006208716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006285624,"threshold_uncertainty_score":0.01249808,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3003423830","doi":"10.1109/tmm.2020.2971171","title":"Dual Convolutional LSTM Network for Referring Image Segmentation","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Encoder; Focus (optics); Dual (grammatical number); Segmentation; Image segmentation; Intersection (aeronautics); Natural language; Object (grammar)","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.02954615709882722,"gpt":0.2871377927065734,"spread":0.2575916356077462,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003431307,0.0008920906,0.0005447398,0.0005871365,0.0002514278,0.0006159666,0.001300686,0.00150212,0.003378274],"category_scores_gemma":[0.0009974118,0.0002983618,0.0007356072,0.0008723825,0.0004561343,0.001440586,0.0007029035,0.0009512772,0.001137526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009205972,"about_ca_system_score_gemma":0.0007575347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005901092,"about_ca_topic_score_gemma":0.007851456,"domain_scores_codex":[0.999721,0.00004756208,0.00001277165,0.0001157525,0.00005087448,0.00005206564],"domain_scores_gemma":[0.9998522,0.00004583881,0.00002447452,0.00002461865,0.00004066565,0.00001229478],"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":[0.0005839942,0.0002113753,0.001095884,0.0004305826,0.0002209383,0.000819105,0.0003461066,0.258632,0.1109253,0.01488887,0.01948258,0.5923632],"study_design_scores_gemma":[0.000008179728,0.00005201324,0.0004054144,0.00001485101,0.00004526023,0.0001313924,0.00002483857,0.9763698,0.01449776,0.005424188,0.003010456,0.00001586736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04225985,0.002387228,0.9397529,0.0007478449,0.0002080608,0.00008602923,0.0007107301,0.006984315,0.006863019],"genre_scores_gemma":[0.7037888,0.001499029,0.2761419,0.001070095,0.0001690341,0.0001644145,0.002421695,0.0003906167,0.01435442],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005901092,"threshold_uncertainty_score":0.01173347,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1970862222","doi":"10.1109/tmm.2013.2283451","title":"Robust Semi-Automatic Depth Map Generation in Unconstrained Images and Video Sequences for 2D to Stereoscopic 3D Conversion","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; 2D to 3D conversion; Depth map; Stereoscopy; Rendering (computer graphics); Segmentation; Cut; Image segmentation; Image (mathematics)","authors":[{"name":"Raymond Phan","is_ca":true},{"name":"Dimitrios Androutsos","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03018609404191342,"gpt":0.2728657174709004,"spread":0.242679623428987,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000409514,0.0008506755,0.0004983765,0.001007866,0.0002656036,0.0008201032,0.001128466,0.0005403458,0.007534184],"category_scores_gemma":[0.001753933,0.000519212,0.0006466041,0.0005279391,0.0003123274,0.0007055996,0.001005222,0.000703001,0.001927324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004236516,"about_ca_system_score_gemma":0.0005070471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001511888,"about_ca_topic_score_gemma":0.002169869,"domain_scores_codex":[0.9994782,0.00006373811,0.00002261505,0.0001039223,0.0002823781,0.00004917302],"domain_scores_gemma":[0.9994807,0.0001444175,0.00005108757,0.0001377926,0.0001505018,0.00003552702],"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":[0.0003354293,0.0001400877,0.0009243565,0.0002714987,0.00006140258,0.0002542063,0.0002911617,0.05019306,0.339859,0.005972305,0.009920475,0.591777],"study_design_scores_gemma":[0.00006944022,0.0001768017,0.002267124,0.00004177276,0.00002967728,0.0007339314,0.0000949176,0.6848438,0.2844641,0.006252756,0.02091779,0.0001077897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007476408,0.00005424198,0.9855941,0.00004088145,0.00002755558,0.0001058409,0.0002253922,0.005267013,0.001208674],"genre_scores_gemma":[0.1177625,0.0001133872,0.8781359,0.00007592517,0.00003032083,0.0001951783,0.0007103636,0.001066038,0.001910449],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007534184,"threshold_uncertainty_score":0.0252043,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2124333148","doi":"10.1109/6046.845016","title":"Automatic key video object plane selection using the shape information in the MPEG-4 compressed domain","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Video tracking; Computer vision; Multiview Video Coding; Artificial intelligence; Video compression picture types; Hausdorff distance; MPEG-4; Block-matching algorithm; Decoding methods; Motion compensation; Object (grammar); Video processing; Coding (social sciences); Data compression; Algorithm; Mathematics","authors":[{"name":"B. Erol","is_ca":true},{"name":"F. Kossentini","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01288374106735909,"gpt":0.2349184134883682,"spread":0.2220346724210091,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004481058,0.0005559831,0.0007955374,0.00122142,0.0003310147,0.0008643672,0.0005483433,0.0003825664,0.0008642758],"category_scores_gemma":[0.001602384,0.0002092365,0.0003488395,0.0009475207,0.0004049037,0.001256954,0.0006882928,0.0004568421,0.0006723189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003450818,"about_ca_system_score_gemma":0.000428443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001195819,"about_ca_topic_score_gemma":0.0009793567,"domain_scores_codex":[0.9996151,0.00006480903,0.00002495308,0.00005413484,0.0002041652,0.00003692897],"domain_scores_gemma":[0.9994855,0.0001445184,0.00007143169,0.00009793368,0.0001711116,0.00002941675],"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":[0.0007611692,0.00005289743,0.001181849,0.0001188933,0.00003075684,0.000209644,0.0001704375,0.01684251,0.3166102,0.007232693,0.002234587,0.6545543],"study_design_scores_gemma":[0.00008768041,0.0002008265,0.00266395,0.00002283364,0.00005794912,0.0009459601,0.0001933166,0.5558826,0.4228685,0.006308906,0.010704,0.00006342096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03205172,0.0003529559,0.9659489,0.00004703332,0.00002891359,0.00007266034,0.00005241855,0.0007109162,0.0007344581],"genre_scores_gemma":[0.2544803,0.0007509969,0.7426608,0.00006539211,0.00005706953,0.00009679454,0.0004537065,0.000171218,0.001263658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00122142,"threshold_uncertainty_score":0.002891243,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2301300958","doi":"10.1109/tmm.2016.2522639","title":"Human Visual System-Based Saliency Detection for High Dynamic Range Content","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Telus (Canada); University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Human visual system model; Artificial intelligence; Computer vision; Salient; High dynamic range; Computer graphics; Visualization; Graphics; Range (aeronautics); Computational model; Human eye; Dynamic range; Computer graphics (images); Image (mathematics)","authors":[{"name":"Yuanyuan Dong","is_ca":true},{"name":"Mahsa T. Pourazad","is_ca":true},{"name":"Panos Nasiopoulos","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02675596534092534,"gpt":0.2841701884584154,"spread":0.2574142231174901,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003829729,0.0003275675,0.0004343093,0.0008043257,0.000172351,0.000369329,0.0004753463,0.0003131939,0.00117822],"category_scores_gemma":[0.001628514,0.0001431693,0.0004484917,0.0003398084,0.0002639287,0.0005838519,0.0003829447,0.0002728174,0.0001858878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006779265,"about_ca_system_score_gemma":0.0003109002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005638801,"about_ca_topic_score_gemma":0.004308856,"domain_scores_codex":[0.9998648,0.00003235009,0.000004492515,0.00003151421,0.00004789449,0.00001887304],"domain_scores_gemma":[0.9995839,0.0002172578,0.00005750613,0.00003761976,0.00008090684,0.00002280936],"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":[0.0007418836,0.0002309586,0.009835835,0.0005453952,0.0001959947,0.0004415275,0.000484934,0.4076603,0.1996252,0.01294503,0.003330033,0.363963],"study_design_scores_gemma":[0.000005548929,0.00007414832,0.005410648,0.000005470283,0.0000132312,0.0000877199,0.00001388735,0.9872609,0.004605218,0.002200309,0.0003129416,0.000009892432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2653514,0.001092893,0.7293321,0.0001839193,0.00006923957,0.00008981808,0.0001264215,0.0007348668,0.003019384],"genre_scores_gemma":[0.9679047,0.0001660543,0.03134261,0.00002138695,0.00002125547,0.00001467048,0.00005971578,0.00002213713,0.0004472989],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005638801,"threshold_uncertainty_score":0.01121199,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2884820794","doi":"10.1109/tmm.2018.2859590","title":"The Labeled Multiple Canonical Correlation Analysis for Information Fusion","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":42,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Discriminative model; Canonical correlation; Pattern recognition (psychology); Representation (politics); Cognitive neuroscience of visual object recognition; Object (grammar); Information fusion; Facial recognition system; Face (sociological concept)","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.02775256273819334,"gpt":0.3074821224205281,"spread":0.2797295596823348,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002802706,0.001341686,0.001037235,0.002420195,0.0007200571,0.001542789,0.001052653,0.0009431725,0.002689153],"category_scores_gemma":[0.007449452,0.0003860213,0.001321808,0.003636705,0.001438047,0.002180039,0.001702906,0.001946147,0.001079518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001145881,"about_ca_system_score_gemma":0.00192986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0036241,"about_ca_topic_score_gemma":0.002952004,"domain_scores_codex":[0.99755,0.0009967171,0.0001306553,0.0004590274,0.0007546805,0.0001089222],"domain_scores_gemma":[0.9975673,0.0008505171,0.0002112496,0.0004816268,0.0008133404,0.00007608664],"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":[0.0001846494,0.0001028256,0.00171008,0.0004464245,0.0002960393,0.0002160184,0.0002922668,0.1685537,0.01849996,0.2577391,0.01317971,0.5387792],"study_design_scores_gemma":[0.00000759882,0.00005023885,0.0005577487,0.0000371655,0.00003460336,0.00009726136,0.00003195768,0.9375702,0.006665501,0.04862896,0.006264237,0.00005454088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001509412,0.0004154446,0.996657,0.000110858,0.00004592487,0.00003069419,0.00005912891,0.0002155192,0.0009559952],"genre_scores_gemma":[0.1914846,0.001759223,0.8025101,0.0003297918,0.0003164682,0.0003996773,0.0006851805,0.000219998,0.002294888],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0036241,"threshold_uncertainty_score":0.01482224,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3005647066","doi":"10.1109/tmm.2020.2973855","title":"Mobile Streaming of Live 360-Degree Videos","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Viewport; Multicast; Unicast; Testbed; Quality of experience; Computer network; Scalability; Cellular network; Multimedia; Video quality; Mobile device; Distributed computing; Quality of service; Metric (unit); World Wide Web; Operating system","authors":[{"name":"Omar Eltobgy","is_ca":true},{"name":"Omar Arafa","is_ca":true},{"name":"Mohamed Hefeeda","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05373105398172572,"gpt":0.3002468309967883,"spread":0.2465157770150626,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002866714,0.0004053807,0.0004194889,0.0003050923,0.0003712805,0.0004168361,0.0004501624,0.0004799642,0.0009669901],"category_scores_gemma":[0.001092931,0.0001192912,0.000217142,0.0003369057,0.000216031,0.0004896101,0.0004211827,0.000403446,0.0002203009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003204706,"about_ca_system_score_gemma":0.0002972194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0033913,"about_ca_topic_score_gemma":0.002116136,"domain_scores_codex":[0.9997967,0.00003100505,0.000009699897,0.00003802661,0.00008130637,0.00004340237],"domain_scores_gemma":[0.9996013,0.0001306486,0.00003891194,0.00004784493,0.0001251191,0.00005608976],"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":[0.001983045,0.0005269838,0.006057962,0.0006206844,0.0001528999,0.001460518,0.0004548487,0.3924362,0.3162473,0.01540534,0.008510554,0.2561437],"study_design_scores_gemma":[0.0001010592,0.0005530279,0.001672418,0.00001961226,0.00002177008,0.0003024384,0.0001046179,0.9565064,0.03296299,0.002453395,0.005280918,0.00002134938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.619121,0.001589121,0.3611246,0.0005882462,0.0002556275,0.0003326121,0.0007712042,0.002499171,0.01371845],"genre_scores_gemma":[0.9677814,0.0003945737,0.02984375,0.00007051778,0.00005032662,0.00003150651,0.0003993636,0.00002292443,0.001405596],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0033913,"threshold_uncertainty_score":0.006743133,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2124636945","doi":"10.1109/tmm.2006.870738","title":"A novel fractal image watermarking","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Lethbridge; University of Alberta","funders":"","keywords":"Watermark; Digital watermarking; Fractal compression; Fractal; Mathematics; Fractal transform; Artificial intelligence; Computer vision; Algorithm; Computer science; Pattern recognition (psychology); Image processing; Image compression; Embedding; Image (mathematics); Mathematical analysis","authors":[{"name":"Hong Pi","is_ca":true},{"name":"H. Li","is_ca":true},{"name":"Hua Li","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01114733094534601,"gpt":0.2401019208685395,"spread":0.2289545899231935,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001619808,0.0003976126,0.0005500666,0.0007405099,0.0003340906,0.0004954616,0.000553684,0.0008826946,0.001424141],"category_scores_gemma":[0.0004598048,0.0001859506,0.000438846,0.0006128644,0.0004587865,0.001213871,0.0005772524,0.0004438264,0.0006994032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002453063,"about_ca_system_score_gemma":0.0002381831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001548677,"about_ca_topic_score_gemma":0.0001717236,"domain_scores_codex":[0.9997597,0.0000207999,0.00001371126,0.00005127049,0.0001295638,0.00002498071],"domain_scores_gemma":[0.9997717,0.00004154548,0.00004398593,0.00004832739,0.00006980209,0.00002466296],"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":[0.000194312,0.00008077919,0.0005249329,0.00031868,0.00004781324,0.001087815,0.0001159788,0.007635208,0.6382177,0.03576333,0.004874767,0.3111387],"study_design_scores_gemma":[0.000181156,0.0009337869,0.002548718,0.00008164102,0.0001389891,0.0154988,0.00006634824,0.3882519,0.4126101,0.01476375,0.1647262,0.0001985672],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0675774,0.004094065,0.9135099,0.0005759039,0.0007305795,0.0001504624,0.0001688107,0.001772471,0.01142043],"genre_scores_gemma":[0.4510816,0.003580352,0.5229133,0.0003759575,0.0006011435,0.0001907838,0.0004632745,0.0001088754,0.02068469],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001424141,"threshold_uncertainty_score":0.004764199,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2139467041","doi":"10.1109/tmm.2011.2127464","title":"Moving Region Segmentation From Compressed Video Using Global Motion Estimation and Markov Random Fields","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":42,"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":"Artificial intelligence; Maximum a posteriori estimation; Segmentation; Markov random field; Computer science; Motion estimation; Pattern recognition (psychology); Image segmentation; Computer vision; Markov process; Prior probability; A priori and a posteriori; Mathematics; Maximum likelihood; Bayesian probability; Statistics","authors":[{"name":"Yue-Meng Chen","is_ca":true},{"name":"Ivan V. Bajić","is_ca":true},{"name":"Parvaneh Saeedi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03457499967339993,"gpt":0.2811220196912056,"spread":0.2465470200178057,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005022769,0.000746146,0.0009135842,0.002127773,0.0002820144,0.0005258414,0.0007488368,0.0006355467,0.0006202866],"category_scores_gemma":[0.00148555,0.0003757001,0.0007435002,0.0009062833,0.0004609331,0.0009741677,0.0004462154,0.0006230675,0.0003640373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004274525,"about_ca_system_score_gemma":0.0005224035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003048383,"about_ca_topic_score_gemma":0.003615213,"domain_scores_codex":[0.9996649,0.00005886676,0.00002058534,0.00009536929,0.0001255102,0.00003476821],"domain_scores_gemma":[0.9994412,0.0002510827,0.000105038,0.00007072513,0.0001103471,0.00002154018],"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":[0.000254855,0.00008232859,0.00106148,0.0001921812,0.00007299469,0.0001869193,0.0001647517,0.1177835,0.1143252,0.006132938,0.001396818,0.7583461],"study_design_scores_gemma":[0.00001949587,0.00009487999,0.001862502,0.00002653964,0.00004127501,0.0003228792,0.00003798373,0.957583,0.03183252,0.005863672,0.00228117,0.00003405762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01031099,0.0003663911,0.9884448,0.00005197483,0.00001671234,0.00003761256,0.00003590641,0.0004318701,0.0003036647],"genre_scores_gemma":[0.1431361,0.0005160088,0.854324,0.00009696891,0.00008621599,0.00007632888,0.0003966225,0.0001682669,0.001199543],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003048383,"threshold_uncertainty_score":0.006061316,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3194484130","doi":"10.1109/tmm.2021.3102401","title":"Learning-Based Quality Assessment for Image Super-Resolution","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":41,"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":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Image quality; Feature extraction; Database; Data mining; Feature (linguistics); Pattern recognition (psychology); Artificial neural network; Quality (philosophy); Image (mathematics); Image resolution; Deep learning; Generalization","authors":[{"name":"Tiesong Zhao","is_ca":false},{"name":"Yu‐Ting Lin","is_ca":false},{"name":"Yiwen Xu","is_ca":false},{"name":"Weiling Chen","is_ca":false},{"name":"Zhou Wang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03562366763511123,"gpt":0.3555343833712776,"spread":0.3199107157361664,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00164407,0.0008949831,0.0007702032,0.001833391,0.0002118167,0.001065251,0.001002005,0.0007080805,0.001854591],"category_scores_gemma":[0.005200793,0.0002997012,0.0006259674,0.0009936052,0.0004535779,0.001836222,0.001107182,0.001053693,0.0007564707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00074581,"about_ca_system_score_gemma":0.0004916363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002791455,"about_ca_topic_score_gemma":0.004223621,"domain_scores_codex":[0.9989275,0.0001599818,0.00006669577,0.0002454022,0.0005303667,0.00006999028],"domain_scores_gemma":[0.9981092,0.0003075832,0.0003136913,0.0003197249,0.000860347,0.00008945566],"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":[0.0006913166,0.0002292682,0.008731381,0.0005965891,0.000260502,0.0001840071,0.0001308887,0.1173567,0.1044167,0.003745093,0.009631624,0.754026],"study_design_scores_gemma":[0.00002387618,0.0001962888,0.006246084,0.00004751643,0.0000745547,0.000267236,0.00004366212,0.9483719,0.03764948,0.004024987,0.003014228,0.0000402625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04698114,0.001631017,0.9464269,0.0002275657,0.00007625624,0.0001710374,0.0007255049,0.001998609,0.001761921],"genre_scores_gemma":[0.5517032,0.001530743,0.4407648,0.0002852935,0.0001219875,0.0001548155,0.002677566,0.0003574289,0.002404143],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002791455,"threshold_uncertainty_score":0.008694828,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2140334624","doi":"10.1109/tmm.2011.2129497","title":"Perceptually Guided Fast Compression of 3-D Motion Capture Data","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":41,"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; Data compression; Animation; Compression (physics); Motion capture; Computer vision; Degradation (telecommunications); Data compression ratio; Artificial intelligence; Quarter-pixel motion; Wavelet; Motion (physics); Computer graphics (images); Image compression; Image processing","authors":[{"name":"Amirhossein Firouzmanesh","is_ca":true},{"name":"Irene Cheng","is_ca":true},{"name":"Anup Basu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09553335985342506,"gpt":0.3097180811317939,"spread":0.2141847212783689,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001627307,0.0005038958,0.0002640996,0.000597133,0.0002275351,0.0003447753,0.0003452685,0.0003157299,0.001193737],"category_scores_gemma":[0.0006575575,0.0001543404,0.0002139177,0.0005215324,0.0002428848,0.0003531766,0.0003714653,0.0003033347,0.0003530161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001722357,"about_ca_system_score_gemma":0.0003437301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007432289,"about_ca_topic_score_gemma":0.001932933,"domain_scores_codex":[0.9998828,0.00001172701,0.000006687791,0.00001513706,0.00007014733,0.00001340932],"domain_scores_gemma":[0.9997409,0.00009465354,0.00003177023,0.000036562,0.00008014363,0.00001597274],"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":[0.0002561643,0.00005821467,0.0003462861,0.0001247972,0.00001678901,0.0001741872,0.00007913081,0.008624527,0.6274723,0.002063104,0.001378577,0.3594059],"study_design_scores_gemma":[0.00005644612,0.0005197941,0.005302622,0.00005736329,0.00005582821,0.001602916,0.00006874332,0.4018342,0.5749382,0.002036296,0.01346418,0.00006334701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06849204,0.0007613166,0.9276054,0.0001488005,0.0001293315,0.0001335483,0.0001216756,0.0007179639,0.00189001],"genre_scores_gemma":[0.3809994,0.001301941,0.6124902,0.0001681053,0.0001372231,0.0001740476,0.0004142492,0.0001392648,0.004175412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001193737,"threshold_uncertainty_score":0.003993452,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2091280331","doi":"10.1109/tmm.2014.2306183","title":"Self-Sorting Map: An Efficient Algorithm for Presenting Multimedia Data in Structured Layouts","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Sorting; Cluster analysis; Set (abstract data type); Dimension (graph theory); Data set; Reduction (mathematics); sort; Dimensionality reduction; Data mining; Sorting algorithm; Information retrieval; Algorithm; Theoretical computer science; Artificial intelligence","authors":[{"name":"Grant Strong","is_ca":true},{"name":"Minglun Gong","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02161517724741845,"gpt":0.2761367865054528,"spread":0.2545216092580343,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008335627,0.001596508,0.001161855,0.004708853,0.001072155,0.00235346,0.002561447,0.001178559,0.012956],"category_scores_gemma":[0.004278578,0.0007529961,0.00118167,0.004640348,0.000726038,0.004360237,0.003195125,0.001033733,0.00569051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009122029,"about_ca_system_score_gemma":0.001149654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00249358,"about_ca_topic_score_gemma":0.002892604,"domain_scores_codex":[0.9990999,0.0001527935,0.00006849699,0.0001857904,0.0004147275,0.00007843848],"domain_scores_gemma":[0.9986228,0.0003964851,0.00008541239,0.000312095,0.0004774277,0.0001058917],"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":[0.0004490993,0.0001429068,0.001028629,0.0004598614,0.00009275613,0.0001959853,0.0006330272,0.02231868,0.01743266,0.01872078,0.03530494,0.9032207],"study_design_scores_gemma":[0.0001779305,0.0003706367,0.001227703,0.0001371212,0.00007908413,0.0007358927,0.0008964444,0.7137574,0.05715907,0.0896232,0.1356374,0.0001981546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005706021,0.0002522827,0.9818071,0.0001492302,0.0001110795,0.0001659775,0.0007621789,0.009070246,0.001975955],"genre_scores_gemma":[0.03368919,0.0002935354,0.9603444,0.00008950248,0.00004830645,0.0003484539,0.001668875,0.0008208899,0.002696855],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.012956,"threshold_uncertainty_score":0.04334211,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2112336602","doi":"10.1109/tmm.2008.2008929","title":"Optimal Prefetching Scheme in P2P VoD Applications With Guided Seeks","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Peer-to-Peer Network Technologies","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Scheme (mathematics); Cache; Popularity; Peer-to-peer; Position (finance); Distributed computing; Computer network","authors":[{"name":"Yifeng He","is_ca":true},{"name":"Guobin Shen","is_ca":false},{"name":"Yongqiang Xiong","is_ca":false},{"name":"Ling Guan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02571846912565651,"gpt":0.2541340654331863,"spread":0.2284155963075298,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007241392,0.0003860128,0.0007247032,0.0004475187,0.0005641314,0.0005568322,0.001115912,0.0006804651,0.0004944486],"category_scores_gemma":[0.003660427,0.0003989939,0.0001851133,0.0006480687,0.0005086054,0.001287919,0.0008238805,0.0003925526,0.0001416963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005941562,"about_ca_system_score_gemma":0.000768152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004186488,"about_ca_topic_score_gemma":0.004885737,"domain_scores_codex":[0.9995157,0.0001406422,0.00003903099,0.00008405983,0.0001461317,0.00007446166],"domain_scores_gemma":[0.9984419,0.0007946985,0.0001359534,0.0002226672,0.0002883076,0.0001165499],"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":[0.0009118043,0.0001563422,0.006044612,0.000193667,0.00006475043,0.0006162979,0.0004939145,0.7932717,0.03032611,0.01283398,0.002473227,0.1526137],"study_design_scores_gemma":[0.00002035669,0.00007169351,0.0003585022,0.000004619208,0.000008378227,0.00006494436,0.00003843945,0.9946619,0.001920294,0.002429974,0.0004093783,0.00001148759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2660666,0.001182327,0.729559,0.0002620654,0.00004247119,0.0001258148,0.00008558414,0.001192669,0.001483492],"genre_scores_gemma":[0.9512042,0.0001480082,0.04782674,0.00002597522,0.00001546004,0.00004578217,0.00003690841,0.00002173422,0.0006752026],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004186488,"threshold_uncertainty_score":0.008324206,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}