{"meta":{"query_hash":"c9d89d0dc078","filters":{"venue":"IEEE Transactions on Multimedia"},"cohort_total":144,"direct_labels_cover":0,"predictions_cover":144,"exported":144,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/c9d89d0dc078","api":"https://metacan.xera.ac/api/v1/cohort?venue=IEEE+Transactions+on+Multimedia"},"results":[{"id":"W1521035521","doi":"10.1109/tmm.2015.2460193","title":"Cloud-Assisted Live Streaming for Crowdsourced Multimedia Content","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Jiangnan University; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; University of Mississippi","keywords":"Computer science; PlanetLab; Cloud computing; Server; Real Time Streaming Protocol; Provisioning; The Internet; Multimedia; Quality of service; Computer network; World Wide Web; Operating system","score_opus":0.1243270820741618,"score_gpt":0.3276654642542678,"score_spread":0.203338382180106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1521035521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009677606,0.000049012247,0.9852191,0.00087592297,0.002828894,0.0007735427,0.00006405292,0.00037590016,0.000135956],"genre_scores_gemma":[0.7536954,0.000009868891,0.24370167,0.0006250836,0.00024288033,0.00032283927,0.0000120676095,0.000036003792,0.001354195],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771976,0.00015458986,0.00047913348,0.00059979514,0.00051904324,0.0005276647],"domain_scores_gemma":[0.9976148,0.0007148199,0.00015135808,0.0006919035,0.00038980614,0.0004373411],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00050842395,0.00030582887,0.00034427678,0.00020195113,0.00023992585,0.00018535816,0.0006541023,0.00015500913,0.000029697374],"category_scores_gemma":[0.000071241986,0.00028963992,0.0002462312,0.00027689035,0.00009287758,0.00053672097,0.00000650499,0.00029857614,0.00027324285],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045448687,0.0021945147,0.00004626468,0.00008354273,0.0003713806,0.000058789054,0.022260759,0.0049019656,0.01632082,0.0002017992,0.005079717,0.94802594],"study_design_scores_gemma":[0.0077759502,0.0011716034,0.0006075898,0.00011313852,0.00011769161,0.000029035618,0.0025337704,0.8552262,0.12699108,0.00017411223,0.0043789903,0.0008807932],"about_ca_topic_score_codex":0.00026422055,"about_ca_topic_score_gemma":0.00013820696,"teacher_disagreement_score":0.94714516,"about_ca_system_score_codex":0.00022481049,"about_ca_system_score_gemma":0.00022106637,"threshold_uncertainty_score":0.9999556},"labels":[],"label_agreement":null},{"id":"W1924814796","doi":"10.1109/tmm.2015.2485538","title":"Guest Editorial: Deep Learning for Multimedia Computing","year":2015,"lang":"en","type":"editorial","venue":"IEEE Transactions on Multimedia","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Multimedia; Deep learning; Artificial intelligence","score_opus":0.014566186611034149,"score_gpt":0.274321100272386,"score_spread":0.25975491366135184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1924814796","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000010834556,0.000045955134,0.49576786,0.00004639153,0.50338787,0.0003352156,0.000049949693,0.0003225323,0.000043123866],"genre_scores_gemma":[0.00093919196,0.00011385886,0.04406679,0.000025972084,0.95300126,0.00013251437,0.0005431498,0.000121273166,0.0010560177],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9945723,0.0003171587,0.00096026156,0.0013402884,0.0020395396,0.00077049737],"domain_scores_gemma":[0.9926127,0.0036574847,0.0005797741,0.00092010805,0.001782109,0.000447818],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001528121,0.0007144,0.00096689916,0.00065643864,0.0006411317,0.0005179993,0.0014666399,0.0016097758,0.000015734504],"category_scores_gemma":[0.0009205489,0.00072468934,0.0005829902,0.0007365514,0.00009687333,0.00058618886,0.000014462297,0.002058327,0.0002483375],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004324389,0.00018488348,0.0000010889108,0.000054191387,0.00017968386,0.000004472639,0.00097453495,0.032023452,0.000075134696,0.0000016082373,0.9055668,0.060890872],"study_design_scores_gemma":[0.001083488,0.00017804268,5.4859885e-7,0.00006906634,0.00013712433,4.7512836e-7,0.000026018379,0.4650943,0.00027833297,0.000014000567,0.53266066,0.0004579676],"about_ca_topic_score_codex":0.00016954936,"about_ca_topic_score_gemma":0.00021147258,"teacher_disagreement_score":0.45170107,"about_ca_system_score_codex":0.0004030367,"about_ca_system_score_gemma":0.0005812109,"threshold_uncertainty_score":0.99968636},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; 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","score_opus":0.025153127007339293,"score_gpt":0.28263479325970464,"score_spread":0.25748166625236535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1930223417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024488112,0.000053934527,0.97453034,0.00013481235,0.0004072646,0.0001945174,0.0000026703049,0.00011892431,0.000069403235],"genre_scores_gemma":[0.89756805,0.000014684917,0.10198883,0.00004625956,0.000052581563,0.000022817063,0.0000063042817,0.000010597223,0.000289899],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867475,0.000098908415,0.00021136619,0.00046785304,0.00029635962,0.00025075013],"domain_scores_gemma":[0.998825,0.00045687557,0.0000885302,0.00024991223,0.0001560403,0.00022365112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042918077,0.00016483075,0.00027902797,0.00051670056,0.00022069749,0.00012844848,0.00021878387,0.00008610006,0.000019915733],"category_scores_gemma":[0.00004562674,0.00015575025,0.00023847753,0.0010257844,0.000048285947,0.00032188182,0.000003495432,0.00019363833,0.000028170643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034229437,0.0004910703,0.0033292212,0.000018400364,0.0027877653,0.000010567737,0.012541098,0.51128405,0.002580618,0.00010976652,0.00012354375,0.4663816],"study_design_scores_gemma":[0.0011290426,0.00018560191,0.0010649337,0.0000049796045,0.0003151241,0.0000015022766,0.0002497047,0.9875225,0.009158101,0.00003700822,0.00014688338,0.00018463892],"about_ca_topic_score_codex":0.00004715533,"about_ca_topic_score_gemma":0.000054624372,"teacher_disagreement_score":0.8730799,"about_ca_system_score_codex":0.00005739723,"about_ca_system_score_gemma":0.00003433425,"threshold_uncertainty_score":0.6351311},"labels":[],"label_agreement":null},{"id":"W1966283235","doi":"10.1109/tmm.2015.2389714","title":"Anchor View Allocation for Collaborative Free Viewpoint Video Streaming","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Innovation and Technology Fund; Innovation and Technology Commission - Hong Kong; Hong Kong University of Science and Technology","keywords":"Computer science; Rendering (computer graphics); Control reconfiguration; Merge (version control); Distributed computing; Computer vision; Parallel computing","score_opus":0.05276525795042175,"score_gpt":0.28858364525286434,"score_spread":0.2358183873024426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966283235","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010241499,0.00034593928,0.99254966,0.003418158,0.0011805223,0.00052092347,0.000034970853,0.00074022304,0.0001854297],"genre_scores_gemma":[0.6966329,0.00016455745,0.30161822,0.00039975732,0.000071724025,0.0006878085,0.0000040786313,0.000022750532,0.00039820455],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986454,0.00006925958,0.00028087178,0.00043962226,0.00029762296,0.00026718742],"domain_scores_gemma":[0.9982737,0.0003011564,0.00010796418,0.0008241459,0.00034853755,0.00014454979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028759937,0.00019439649,0.00022472686,0.0001993984,0.00020540624,0.00010758824,0.00088248757,0.00012663333,0.0000072588246],"category_scores_gemma":[0.000110992216,0.00017187235,0.00008670588,0.0005650841,0.000063811865,0.00043911816,0.000008702715,0.00020069515,0.00006387856],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041484076,0.00022160921,0.0000024642395,0.000024282177,0.000040769053,0.000002733914,0.0015896282,0.0043560285,0.002750671,0.0009091348,0.0076554567,0.9824057],"study_design_scores_gemma":[0.003921716,0.0010393255,0.000063716114,0.00033101495,0.000060488826,0.000011616628,0.0012211077,0.49920416,0.45211446,0.011879554,0.029356187,0.00079663587],"about_ca_topic_score_codex":0.00002197928,"about_ca_topic_score_gemma":0.000041717394,"teacher_disagreement_score":0.9816091,"about_ca_system_score_codex":0.00010815286,"about_ca_system_score_gemma":0.00016161212,"threshold_uncertainty_score":0.7008751},"labels":[],"label_agreement":null},{"id":"W1968709821","doi":"10.1109/tmm.2012.2217735","title":"Coordinate Live Streaming and Storage Sharing for Social Media Content Distribution","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; User-generated content; Social media; World Wide Web; Context (archaeology); The Internet; Overlay; Server; Multimedia; Internet privacy","score_opus":0.0565362318636394,"score_gpt":0.2591172673384834,"score_spread":0.202581035474844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968709821","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2265989,0.00008468651,0.77145475,0.00016444248,0.0012592226,0.00017534131,0.00012152446,0.00012930456,0.000011796767],"genre_scores_gemma":[0.99779725,0.00001723971,0.0017481414,0.000052391155,0.00017931875,0.00006731234,0.000018249315,0.000011313249,0.00010879713],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990008,0.000032642947,0.0001737631,0.0002801619,0.00016883652,0.00034379624],"domain_scores_gemma":[0.9991894,0.00035871673,0.00006115011,0.00016814591,0.00007458413,0.00014801715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024938583,0.00014969047,0.00016224626,0.00006996817,0.0003757162,0.00008820433,0.00020756663,0.00008261082,0.000007032856],"category_scores_gemma":[0.000022125181,0.0001478231,0.00010436932,0.000095355055,0.00005012662,0.00052056566,0.0000049980345,0.00018788043,0.000019251711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020397978,0.00088230596,0.00069234445,0.00008183438,0.00023043764,0.000009632888,0.02325862,0.0006657764,0.019996854,0.0020584946,0.0006660538,0.95125365],"study_design_scores_gemma":[0.004994715,0.00026970534,0.017728267,0.00014300614,0.00022192206,0.0000384612,0.0023045,0.9539352,0.018054023,0.00023872458,0.00094466354,0.0011268308],"about_ca_topic_score_codex":0.00007279064,"about_ca_topic_score_gemma":0.00003352544,"teacher_disagreement_score":0.9532694,"about_ca_system_score_codex":0.00009453655,"about_ca_system_score_gemma":0.000015418535,"threshold_uncertainty_score":0.60280514},"labels":[],"label_agreement":null},{"id":"W1970076768","doi":"10.1109/tmm.2014.2299515","title":"Illumination Robust Video Foreground Prediction Based on Color Recovering","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Foreground detection; Frame (networking); Optical flow; Segmentation; Pixel; Background subtraction; Opacity; Image segmentation; Pattern recognition (psychology); Image (mathematics)","score_opus":0.01567198960955278,"score_gpt":0.23206444117904998,"score_spread":0.2163924515694972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970076768","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004402787,0.0000018145295,0.99094605,0.00034580976,0.0014488017,0.00042368297,0.000012541004,0.000916685,0.0015018438],"genre_scores_gemma":[0.8166641,0.000006189519,0.18236198,0.00033939135,0.00007946095,0.00019234262,0.000006767091,0.00002086938,0.0003289192],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984578,0.0001032658,0.00026226105,0.0004723032,0.00042997528,0.0002744185],"domain_scores_gemma":[0.9988236,0.000318184,0.00009462137,0.00057359925,0.000101935584,0.00008810462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041333487,0.00020028524,0.00015143264,0.00033464486,0.00023205412,0.00011531999,0.0003893959,0.00011983006,0.000056059114],"category_scores_gemma":[0.00003409455,0.00020527386,0.000092987444,0.0003520798,0.000054737346,0.0007259778,0.000002188172,0.0002588617,0.00012345838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009940756,0.00065590994,0.000020068961,0.00004062531,0.00002781011,0.0000047891826,0.0005073016,0.21798895,0.022776455,0.00023221072,0.0013411217,0.75630534],"study_design_scores_gemma":[0.00050586567,0.00042919742,0.00027195882,0.000056302953,0.000010022496,0.0000015547881,0.0000054943544,0.79837537,0.19919847,0.000059314727,0.00093208475,0.00015434364],"about_ca_topic_score_codex":0.000026601549,"about_ca_topic_score_gemma":0.000031724456,"teacher_disagreement_score":0.8122613,"about_ca_system_score_codex":0.00024049717,"about_ca_system_score_gemma":0.00003715355,"threshold_uncertainty_score":0.8370825},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; 2D to 3D conversion; Depth map; Stereoscopy; Rendering (computer graphics); Segmentation; Cut; Image segmentation; Image (mathematics)","score_opus":0.030186094041913417,"score_gpt":0.2728657174709004,"score_spread":0.24267962342898697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970862222","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033675067,0.000029301747,0.9637006,0.0012090228,0.00053621153,0.0007099381,0.000008493457,0.000106698695,0.00002466105],"genre_scores_gemma":[0.55066425,0.0000114519335,0.44866624,0.0003860891,0.000022005068,0.00011718613,0.0000025864879,0.000007649898,0.00012252483],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893,0.00004815454,0.0002468891,0.00038077924,0.00015309991,0.00024108142],"domain_scores_gemma":[0.99932027,0.000196674,0.000050767525,0.00022064308,0.000073909745,0.00013773194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012036452,0.00015416046,0.00016386915,0.00024034837,0.00013966557,0.00014270995,0.00019552617,0.00005127771,0.000060140534],"category_scores_gemma":[0.000015258825,0.00014152848,0.000035207842,0.0002142663,0.00005689184,0.00090910535,0.000003674325,0.000116576884,0.00007635345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000782209,0.00010081365,0.0001175488,0.000069048634,0.000013309006,0.000003961137,0.0015573795,0.021619739,0.06866248,0.000009077219,0.00072880933,0.90711004],"study_design_scores_gemma":[0.00078892655,0.00010373077,0.00041766724,0.00006940365,0.000005795459,0.00000454338,0.00014783852,0.9494557,0.04870221,0.000054516826,0.00008367545,0.0001659948],"about_ca_topic_score_codex":0.00008562946,"about_ca_topic_score_gemma":0.000090237256,"teacher_disagreement_score":0.92783594,"about_ca_system_score_codex":0.000062795705,"about_ca_system_score_gemma":0.000042735526,"threshold_uncertainty_score":0.5771364},"labels":[],"label_agreement":null},{"id":"W1979954176","doi":"10.1109/tmm.2012.2198802","title":"Video Completion Using Bandlet Transform","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Motion compensation; Segmentation; Preprocessor; Object (grammar); Video tracking","score_opus":0.03934462526742338,"score_gpt":0.30354807689187746,"score_spread":0.2642034516244541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979954176","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031638418,0.000055619537,0.9939735,0.00032086394,0.001593415,0.00016989288,0.000007953385,0.00023538902,0.0004795059],"genre_scores_gemma":[0.7495403,0.000019590449,0.24990205,0.0003454852,0.00007637199,0.000010558757,0.000001175499,0.000013438373,0.00009101166],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988128,0.000045170265,0.00021596451,0.00024092234,0.00028071142,0.0004044133],"domain_scores_gemma":[0.99925685,0.000108813656,0.00004245232,0.0003318003,0.000046400095,0.00021367923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017712411,0.00016245338,0.00015454675,0.00017708578,0.00025897895,0.000051960007,0.00027653362,0.00005618663,0.00012935119],"category_scores_gemma":[0.000004049015,0.00015352493,0.000106375825,0.0003164351,0.00004961816,0.0012836701,0.0000014147616,0.00023702512,0.00023058642],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023413899,0.0004116583,0.000058172278,0.000013551912,0.000023938952,0.0000029890955,0.002414692,0.005930329,0.030637547,0.0001663116,0.00014905047,0.96016836],"study_design_scores_gemma":[0.00082532625,0.000047756526,0.0004287625,0.000038792983,0.00001700126,0.000036842048,0.000056460187,0.9146265,0.077436045,0.00011429937,0.006071123,0.00030110948],"about_ca_topic_score_codex":0.000023168246,"about_ca_topic_score_gemma":0.000004042124,"teacher_disagreement_score":0.95986724,"about_ca_system_score_codex":0.00008546392,"about_ca_system_score_gemma":0.000024342864,"threshold_uncertainty_score":0.6260565},"labels":[],"label_agreement":null},{"id":"W1980604989","doi":"10.1109/tmm.2005.846796","title":"Image retrieval based on histogram of fractal parameters","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Luminance; Histogram; Image retrieval; Artificial intelligence; Scaling; Fractal; Search engine indexing; Offset (computer science); Pattern recognition (psychology); Fractal analysis; Fractal compression; Image texture; Image processing; Fractal dimension; Mathematics; Image (mathematics); Image compression","score_opus":0.017287044320724972,"score_gpt":0.2614271735601025,"score_spread":0.2441401292393775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980604989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027357049,0.000010305987,0.9947972,0.0009150595,0.0003134892,0.0002374209,0.000022133194,0.00039164,0.0005770206],"genre_scores_gemma":[0.68131006,0.000011092955,0.31817287,0.00028118366,0.000020960944,0.000015517093,0.0000020244238,0.000012183702,0.00017408766],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99855214,0.000072269475,0.00032205603,0.0003584494,0.0004653917,0.00022968586],"domain_scores_gemma":[0.9987105,0.0002966707,0.0001270702,0.00061967573,0.00012491393,0.00012115689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021304612,0.00017767667,0.00019178378,0.00028994068,0.00009954093,0.00004584234,0.00050134695,0.00011536922,0.000094343006],"category_scores_gemma":[0.000025522204,0.00016572357,0.00019122766,0.0005118452,0.0001473468,0.00033977887,0.0000014770238,0.0002912299,0.00016468155],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029036452,0.0018284906,0.000008285768,0.000031061896,0.00003487449,0.000009320219,0.0004794211,0.0010837759,0.1524496,0.00011446438,0.00046552753,0.8432048],"study_design_scores_gemma":[0.00036714278,0.00023117855,0.00009695103,0.00001847274,0.0000094181,0.0000016669476,0.0000058884825,0.37837332,0.61983436,0.000020898287,0.0009053823,0.0001353039],"about_ca_topic_score_codex":0.00001653347,"about_ca_topic_score_gemma":0.0000023456307,"teacher_disagreement_score":0.8430695,"about_ca_system_score_codex":0.00011928788,"about_ca_system_score_gemma":0.00007852969,"threshold_uncertainty_score":0.67580116},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.03735347427742566,"score_gpt":0.30148064701519195,"score_spread":0.2641271727377663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985444590","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004934691,0.000014812186,0.9938572,0.00029147972,0.00028660067,0.00016073056,0.0000068136505,0.00026840702,0.0001792739],"genre_scores_gemma":[0.74833834,0.000032214022,0.2514211,0.00008648475,0.000023601215,0.000011452584,0.0000013778164,0.0000073427927,0.000078104604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990031,0.0001053478,0.00018184916,0.000345478,0.00021525352,0.00014893492],"domain_scores_gemma":[0.99919134,0.0001538385,0.00007600949,0.00040614925,0.00008103694,0.00009161684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016879314,0.000116919015,0.0001244468,0.00009026227,0.0002187128,0.00010625526,0.00022176196,0.000065639346,0.000014536619],"category_scores_gemma":[0.000016786978,0.00010833546,0.000046874113,0.00024165795,0.000102624515,0.0005287851,0.0000034916882,0.00016963133,0.000017985543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053482625,0.00034462748,0.0001251698,0.00004534872,0.000018717576,0.000006526389,0.0008221606,0.0003654082,0.36178216,0.0005606312,0.00015221574,0.63572353],"study_design_scores_gemma":[0.00022573397,0.000028192333,0.00070037437,0.000022521719,0.000007372818,0.0000059646104,0.0000104472465,0.7201336,0.27808133,0.00037955202,0.00029413312,0.00011076982],"about_ca_topic_score_codex":0.000037453225,"about_ca_topic_score_gemma":0.00000492419,"teacher_disagreement_score":0.7434036,"about_ca_system_score_codex":0.000033679087,"about_ca_system_score_gemma":0.000015602891,"threshold_uncertainty_score":0.4417792},"labels":[],"label_agreement":null},{"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.004383614316694057,"score_gpt":0.22823449048934416,"score_spread":0.2238508761726501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999316653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060588177,0.000030416068,0.93529487,0.00019203233,0.0005633104,0.00080547284,0.000045224697,0.0011641766,0.0013163117],"genre_scores_gemma":[0.90132457,0.00006235711,0.09773217,0.00013963589,0.00005130294,0.0005456618,0.000008510099,0.000078314464,0.000057507907],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998294,0.00008424276,0.00040268034,0.00033472912,0.00044472187,0.0004396206],"domain_scores_gemma":[0.99887586,0.00043148908,0.00003319562,0.00031181175,0.00007915233,0.0002684699],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018139045,0.00030955358,0.00031633192,0.00036984798,0.00008068348,0.000029531599,0.0002219806,0.00025740615,0.003499431],"category_scores_gemma":[0.00003560486,0.00030287766,0.00011687793,0.00033920808,0.0001474398,0.00037423012,0.0000017548412,0.00074224797,0.00047836572],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008645288,0.0006168602,0.000055946584,0.000057184,0.000038161463,0.000093508876,0.0006914373,0.01758374,0.46110332,0.0000023851646,0.001135591,0.51853544],"study_design_scores_gemma":[0.0019389918,0.00006424535,0.0014496094,0.00012412031,0.00000836297,0.0000061801707,0.000099370416,0.6025287,0.39310056,0.00008241939,0.0002199431,0.0003775001],"about_ca_topic_score_codex":0.00024654032,"about_ca_topic_score_gemma":0.00023047277,"teacher_disagreement_score":0.8407364,"about_ca_system_score_codex":0.000220354,"about_ca_system_score_gemma":0.000042470874,"threshold_uncertainty_score":0.99994236},"labels":[],"label_agreement":null},{"id":"W2013670523","doi":"10.1109/tmm.2013.2266633","title":"Visually Favorable Tone-Mapping With High Compression Performance in Bit-Depth Scalable Video Coding","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Scalability; Coding (social sciences); Encoder; Algorithm; Artificial intelligence; Speech recognition; Mathematics; Database; Statistics","score_opus":0.0158407353988739,"score_gpt":0.2545894113385192,"score_spread":0.2387486759396453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013670523","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14286645,0.000011766423,0.85466015,0.00016423049,0.00031499218,0.00060877076,0.0000015993439,0.00053611153,0.0008359296],"genre_scores_gemma":[0.8329931,0.000043280914,0.16615562,0.00015711828,0.000021768048,0.00024898906,0.0000023888756,0.000022753906,0.00035500556],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980901,0.00007302876,0.00034953284,0.0005300621,0.00043423646,0.0005230519],"domain_scores_gemma":[0.99896026,0.00012618053,0.00010520589,0.0005599456,0.00012464673,0.00012374022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023565278,0.0002647629,0.00026530097,0.00043979747,0.0002416398,0.00020005708,0.0006227179,0.000101435915,0.00011234173],"category_scores_gemma":[0.0000042719903,0.00022968368,0.000044087243,0.00078479195,0.00007995801,0.002036776,0.000009336765,0.00043012135,0.00026584032],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009391379,0.0013298221,0.0021178145,0.00019421673,0.00007314684,0.00003928705,0.0033371227,0.030204926,0.34132087,0.000107099026,0.000864791,0.620317],"study_design_scores_gemma":[0.00087391003,0.00023155015,0.0053301346,0.00058152864,0.000005192873,0.000007091789,0.000041366002,0.44364193,0.548796,0.000023962082,0.00013183852,0.00033548725],"about_ca_topic_score_codex":0.0005205882,"about_ca_topic_score_gemma":0.00007133819,"teacher_disagreement_score":0.6901266,"about_ca_system_score_codex":0.00016205401,"about_ca_system_score_gemma":0.00007365849,"threshold_uncertainty_score":0.93662286},"labels":[],"label_agreement":null},{"id":"W2039351044","doi":"10.1109/tmm.2007.906557","title":"Efficient Algorithms for Optimal Uneven Protection of Single and Multiple Scalable Code Streams Against Packet Erasures","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Erasure; Scalability; Algorithm; Network packet; Set partitioning in hierarchical trees; Code (set theory); Transmission (telecommunications); Binary erasure channel; Code rate; Decoding methods; Computer network; Channel capacity; Image compression; Channel (broadcasting)","score_opus":0.03320495448483631,"score_gpt":0.2834498373875809,"score_spread":0.2502448829027446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039351044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04528292,0.000036568083,0.9527884,0.000057911246,0.00034617577,0.00094792823,0.0001945926,0.00032445503,0.000021020813],"genre_scores_gemma":[0.5516017,0.000008335306,0.4482313,0.000019350773,0.000021008058,0.00006188556,0.000005305678,0.000015197361,0.000035910656],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984611,0.00004153675,0.0003763051,0.0004804329,0.0003050596,0.0003355619],"domain_scores_gemma":[0.9986364,0.00042355078,0.00015419835,0.00047606434,0.00016894094,0.00014080822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039636172,0.00020305761,0.00023275785,0.0002507451,0.00020128791,0.00003931948,0.00033055633,0.00012609422,0.000003850133],"category_scores_gemma":[0.000042077754,0.00019073497,0.00008217867,0.000312421,0.00012239137,0.00023129744,0.00000846991,0.00019136613,0.000003267882],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012219166,0.00061382155,0.000008192247,0.000034859368,0.000022095375,0.00000257126,0.00036133104,0.08323637,0.22327714,0.000009603951,0.00006021299,0.6922516],"study_design_scores_gemma":[0.00057861337,0.00020679168,0.00005053606,0.0000468082,0.0000061285937,0.0000031585216,0.000030078972,0.49591023,0.50273526,0.000030980977,0.00027927736,0.00012212434],"about_ca_topic_score_codex":0.000041832835,"about_ca_topic_score_gemma":0.000038608585,"teacher_disagreement_score":0.6921295,"about_ca_system_score_codex":0.00007617024,"about_ca_system_score_gemma":0.000032855492,"threshold_uncertainty_score":0.7777946},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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","score_opus":0.014585573995861138,"score_gpt":0.30244009458369514,"score_spread":0.287854520587834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050316560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055112895,0.00003998541,0.9426492,0.0010254444,0.0005884026,0.00028490566,0.0000148414765,0.0001665847,0.00011776637],"genre_scores_gemma":[0.977628,0.00004894367,0.021699226,0.00032577524,0.00017442348,0.000010759232,0.0000021133833,0.000011164197,0.000099621335],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824864,0.00014637507,0.0004026542,0.0004572956,0.00037803728,0.00036700792],"domain_scores_gemma":[0.9981177,0.0008596672,0.00012174758,0.0005683433,0.00009220888,0.00024032514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031140688,0.0002396559,0.0003561812,0.00014019482,0.0001488333,0.00007819423,0.00034591215,0.00020558508,0.000027453478],"category_scores_gemma":[0.000028109029,0.00020141296,0.00021494589,0.000501155,0.00008465003,0.00022018314,0.0000023032635,0.00048802674,0.00002473722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018597717,0.00035786777,0.00019920367,0.000004355412,0.000068263216,0.000005619479,0.00016832411,0.12754035,0.000021981501,0.0053445194,0.0002549526,0.8658486],"study_design_scores_gemma":[0.0007753817,0.0010303336,0.01357587,0.00010087307,0.000035266752,0.000004900153,0.000008941825,0.9832265,0.000040555897,0.0009256005,0.00004759376,0.00022820759],"about_ca_topic_score_codex":0.000028027775,"about_ca_topic_score_gemma":0.000023030127,"teacher_disagreement_score":0.9225151,"about_ca_system_score_codex":0.00009933936,"about_ca_system_score_gemma":0.000086926986,"threshold_uncertainty_score":0.8213382},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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)","score_opus":0.023489392110629188,"score_gpt":0.26342804113451024,"score_spread":0.23993864902388104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051526232","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009118284,0.0000058131627,0.9889069,0.00028766048,0.00058335403,0.0007006757,0.00003182165,0.00027807182,0.00008736996],"genre_scores_gemma":[0.7913052,0.0000035086916,0.2077349,0.00020841914,0.000095301206,0.00053150725,0.000019693678,0.000015482741,0.0000859883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843216,0.000090498695,0.00030829315,0.0005212934,0.0003393191,0.00030846536],"domain_scores_gemma":[0.99885833,0.00023773352,0.000079613244,0.00049578724,0.00016659021,0.00016195781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027595405,0.00020416116,0.00025699905,0.00044477804,0.00031420088,0.00009325945,0.00039072268,0.00015673779,0.00007003917],"category_scores_gemma":[0.000021347194,0.00017999382,0.0003240155,0.0005553479,0.000026205696,0.00033392262,0.0000021470655,0.00017444912,0.00009719391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018211264,0.00030083506,0.0000076847155,0.000026673712,0.000069627415,7.192886e-7,0.00028002146,0.78136975,0.042752072,0.000012427287,0.00019093379,0.17480716],"study_design_scores_gemma":[0.0009609165,0.0001386886,0.0000067747906,0.000036972353,0.00008671642,2.3735454e-7,0.0000067163164,0.8545859,0.14320298,0.00013462032,0.00064163236,0.00019785237],"about_ca_topic_score_codex":0.000017398212,"about_ca_topic_score_gemma":0.00003181759,"teacher_disagreement_score":0.7821869,"about_ca_system_score_codex":0.00003272426,"about_ca_system_score_gemma":0.00005278667,"threshold_uncertainty_score":0.7339935},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.025771350846746477,"score_gpt":0.27726898928835697,"score_spread":0.2514976384416105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053157787","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004987463,0.000017021186,0.992048,0.0004096445,0.0009887671,0.0011008832,0.000020838032,0.00019431497,0.0002330536],"genre_scores_gemma":[0.8896175,0.000044381406,0.10783047,0.00055767817,0.000105218634,0.00065826956,0.000005169711,0.000026948266,0.001154377],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826854,0.0001241854,0.0003226731,0.00053472933,0.0002635631,0.00048632437],"domain_scores_gemma":[0.99868983,0.00038241746,0.00010344683,0.0005511023,0.00011409668,0.00015913515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002254277,0.00025631618,0.00023406326,0.00016026998,0.00027745613,0.00020261874,0.0005312287,0.00010637155,0.00009653399],"category_scores_gemma":[0.00000200859,0.00024155527,0.00017234273,0.00028853002,0.000056932022,0.0007546556,0.000008274796,0.00025979718,0.00015313433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004569763,0.00055141724,0.000015966576,0.000057097808,0.00032614134,0.00001895045,0.0014419139,0.08325126,0.0012216881,0.0009843304,0.0020153164,0.91007024],"study_design_scores_gemma":[0.0010672407,0.00015039,0.0011510374,0.000055258486,0.000048022597,0.0000013369307,0.00021003914,0.9918661,0.004635842,0.00013824305,0.00037231264,0.00030416716],"about_ca_topic_score_codex":0.00022452022,"about_ca_topic_score_gemma":0.00007112516,"teacher_disagreement_score":0.9097661,"about_ca_system_score_codex":0.00012776915,"about_ca_system_score_gemma":0.000036026508,"threshold_uncertainty_score":0.98503387},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.018695182438690176,"score_gpt":0.24052769148454506,"score_spread":0.2218325090458549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053996691","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.091999054,0.00013536586,0.90704703,0.000011925868,0.0002496668,0.00025900913,0.000108165856,0.000097533586,0.00009226114],"genre_scores_gemma":[0.9973387,0.0000472649,0.00238261,0.000008438581,0.00013093633,0.000046679997,0.0000067469114,0.000020092308,0.000018522549],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930936,0.000040222123,0.00025106047,0.00015961159,0.00010239688,0.00013732564],"domain_scores_gemma":[0.9995529,0.00011712506,0.000036741152,0.00015204106,0.00004575176,0.00009544363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000113760405,0.00013804813,0.00020219343,0.00012883304,0.00016727613,0.000026524858,0.000036795715,0.00016635461,0.000013209361],"category_scores_gemma":[0.000007706822,0.0001302927,0.000051571944,0.00014539775,0.000043264885,0.00007665941,7.3636284e-7,0.00019290911,0.0000027986673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003362366,0.000045293273,0.000014751851,0.00008485747,0.00007861186,4.904889e-7,0.00030358342,0.8151796,0.087572515,0.00008827986,0.000033381395,0.096565016],"study_design_scores_gemma":[0.00066663395,0.000027836548,0.000024032915,0.000043131888,0.00005016168,0.000008369019,0.00003129994,0.97880054,0.019477068,0.000064185544,0.00067167083,0.00013506095],"about_ca_topic_score_codex":0.00009879376,"about_ca_topic_score_gemma":0.00004031553,"teacher_disagreement_score":0.90533966,"about_ca_system_score_codex":0.000025052173,"about_ca_system_score_gemma":0.000008358545,"threshold_uncertainty_score":0.5313182},"labels":[],"label_agreement":null},{"id":"W2074652467","doi":"10.1109/tmm.2012.2234729","title":"An Unsupervised Hierarchical Feature Learning Framework for One-Shot Image Recognition","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Pyramid (geometry); Pattern recognition (psychology); Cognitive neuroscience of visual object recognition; Feature (linguistics); Feature extraction; Domain (mathematical analysis); Machine learning; Feature learning; Domain knowledge; Matching (statistics)","score_opus":0.05355395215045962,"score_gpt":0.3000440028239455,"score_spread":0.2464900506734859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074652467","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01161352,0.000010350932,0.9845975,0.0016931448,0.0006329177,0.00063118705,0.000013766631,0.0005387337,0.00026889626],"genre_scores_gemma":[0.41278592,0.0000158899,0.5859828,0.00052567536,0.00011545866,0.00023335345,0.000026079471,0.00002905182,0.0002857368],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819607,0.00021716666,0.00024956945,0.0005674861,0.00033880287,0.0004308943],"domain_scores_gemma":[0.9982185,0.00074755796,0.00008364128,0.00041839606,0.00021303722,0.00031890217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002597546,0.00022981505,0.00021445422,0.0002427003,0.0004911265,0.0003824884,0.00046845124,0.00025433282,0.00048085366],"category_scores_gemma":[0.00007831112,0.00023635596,0.00015633163,0.00039861666,0.00008320543,0.0011242402,0.0000022372708,0.0010226809,0.00062607694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007403559,0.0004897717,0.00001434754,0.000028597819,0.00004891131,0.0000038071817,0.00393241,0.0050262734,0.031850018,0.00033621304,0.00019541295,0.9580002],"study_design_scores_gemma":[0.0015234029,0.0006157619,0.0012723543,0.000095378346,0.00003133818,0.000009197967,0.0004340169,0.97055537,0.0178735,0.0055443146,0.001482495,0.00056290015],"about_ca_topic_score_codex":0.000024418896,"about_ca_topic_score_gemma":0.000007457073,"teacher_disagreement_score":0.9655291,"about_ca_system_score_codex":0.000051227446,"about_ca_system_score_gemma":0.00005629447,"threshold_uncertainty_score":0.96383166},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; 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","score_opus":0.039406431935872,"score_gpt":0.29919708225165503,"score_spread":0.259790650315783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077419187","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009958086,0.00005417752,0.9851095,0.0020565419,0.0010117033,0.00026251134,0.0000024857418,0.00017676112,0.0013682259],"genre_scores_gemma":[0.9222912,0.000061054736,0.07557285,0.0007658158,0.00010916951,0.000026134594,0.0000019518097,0.000014587211,0.0011571988],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983129,0.00010165283,0.000336312,0.00036276257,0.00041842018,0.00046795988],"domain_scores_gemma":[0.9982634,0.00066985935,0.000051675834,0.00058906665,0.0002328606,0.00019312471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011259771,0.00017630271,0.00017932692,0.00028428866,0.0002862014,0.00009939686,0.0007024303,0.00008517398,0.000078128],"category_scores_gemma":[0.000043805907,0.00018576799,0.0000643086,0.0010430912,0.000034804332,0.000280579,0.000011118451,0.0004738078,0.0002730929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040519648,0.00024677932,0.00030516903,0.0000047462236,0.000022957489,0.000029418887,0.009689976,0.073201984,0.0019050023,0.0007447003,0.0016306546,0.9121781],"study_design_scores_gemma":[0.002241772,0.0003062254,0.016979959,0.00041437903,0.000021378712,0.00002069149,0.00070974906,0.9415026,0.016455581,0.00023212783,0.01995342,0.0011621657],"about_ca_topic_score_codex":0.000033862783,"about_ca_topic_score_gemma":0.0012120634,"teacher_disagreement_score":0.91233313,"about_ca_system_score_codex":0.00015568241,"about_ca_system_score_gemma":0.00004515067,"threshold_uncertainty_score":0.75753987},"labels":[],"label_agreement":null},{"id":"W2083418467","doi":"10.1109/tmm.2003.814725","title":"Optimal adaptive bandwidth monitoring for qos based retrieval","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Dynamic bandwidth allocation; Bandwidth (computing); Quality of service; Bandwidth allocation; Probabilistic logic; Computer network; Bandwidth management; Real-time computing; Distributed computing; Artificial intelligence","score_opus":0.023650907582190937,"score_gpt":0.24850896772553746,"score_spread":0.22485806014334653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083418467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027075196,0.000055106884,0.9924543,0.00021076595,0.0036368167,0.00047522428,0.000015410344,0.0002804119,0.00016442487],"genre_scores_gemma":[0.76980585,0.0000070787623,0.22944653,0.00009041637,0.00017223507,0.00010033193,6.943641e-7,0.000016766318,0.0003600753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857885,0.00009144995,0.00023074965,0.0004408261,0.00027734667,0.00038080208],"domain_scores_gemma":[0.99859095,0.0006505187,0.00006235147,0.00037096866,0.00014013016,0.00018510048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026582577,0.00020508627,0.0001956419,0.00012946386,0.0002710721,0.00007742138,0.00033061253,0.00011694742,0.00004032502],"category_scores_gemma":[0.000020806245,0.00020336971,0.00017503361,0.00031931413,0.0000478681,0.00024957102,6.529434e-7,0.00024799723,0.000058375583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004370007,0.00042698762,0.000019319352,0.00001139244,0.000105108964,0.000013039715,0.000429089,0.5226261,0.0013086488,0.0007009157,0.00041803502,0.47350436],"study_design_scores_gemma":[0.0023841166,0.00031548587,0.00003423908,0.000029090696,0.000034093606,0.000003966227,0.000033681677,0.94760823,0.04735047,0.000027752385,0.0019145956,0.00026428662],"about_ca_topic_score_codex":0.0000029246896,"about_ca_topic_score_gemma":0.0000021392593,"teacher_disagreement_score":0.76709837,"about_ca_system_score_codex":0.0000747386,"about_ca_system_score_gemma":0.00015576338,"threshold_uncertainty_score":0.82931757},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.015323272454404095,"score_gpt":0.26583267114557735,"score_spread":0.2505093986911733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086277433","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0095753865,0.0000058664573,0.98919314,0.00018543498,0.00026003143,0.0001768116,0.000018555424,0.0004585466,0.00012624079],"genre_scores_gemma":[0.5785713,0.0000029169266,0.42127776,0.00010254684,0.000013423111,0.000014349883,0.000002326956,0.0000071889735,0.000008173311],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878883,0.00016972852,0.00022123024,0.00038312268,0.0002125562,0.00022455616],"domain_scores_gemma":[0.9990896,0.0002627399,0.000084629646,0.00041203282,0.00005564805,0.00009535841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001825416,0.000190907,0.00017669128,0.00019687132,0.00024234405,0.00009294921,0.00028305096,0.00009947303,0.000007459126],"category_scores_gemma":[0.000009413006,0.00016678683,0.00007424042,0.00020016586,0.00011080962,0.00057769037,0.000003038927,0.00024185028,0.000003675995],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060862327,0.000073469775,0.00005180536,0.000027782078,0.00001919876,0.000003223048,0.0007505924,0.0027594008,0.022974072,0.00027275927,0.000016338805,0.9729905],"study_design_scores_gemma":[0.00046334387,0.000111786474,0.001285313,0.00003578779,0.0000103856255,0.000003823452,0.000006794527,0.7501602,0.24401826,0.0036051862,0.00008218261,0.00021694326],"about_ca_topic_score_codex":0.000025256591,"about_ca_topic_score_gemma":0.000027161095,"teacher_disagreement_score":0.97277355,"about_ca_system_score_codex":0.000026851474,"about_ca_system_score_gemma":0.000014186381,"threshold_uncertainty_score":0.680137},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; 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","score_opus":0.021615177247418453,"score_gpt":0.27613678650545276,"score_spread":0.2545216092580343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091280331","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006189638,0.000013107811,0.9917691,0.00013427415,0.0010564221,0.00049402006,0.000065785265,0.00025053573,0.000027135147],"genre_scores_gemma":[0.43708614,0.0000036319952,0.5625349,0.00006049519,0.00012672415,0.000041800944,0.000086198605,0.000020229872,0.000039876653],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99751204,0.00018799143,0.000548226,0.0008698563,0.0004313161,0.00045055232],"domain_scores_gemma":[0.9978244,0.00042745707,0.00017815258,0.0012706423,0.0001223045,0.00017705689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000906369,0.0002341223,0.00030023418,0.00035449053,0.00025838456,0.000194849,0.0011724198,0.00013856251,0.000013809909],"category_scores_gemma":[0.00006365464,0.00022520414,0.00009967978,0.00050390064,0.000031705542,0.00063391414,0.000015701164,0.00025498887,0.000021571705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000083492605,0.0004468478,0.0001945826,0.00003092898,0.000056640056,0.0000032388268,0.0024629575,0.10252996,0.0007512933,0.000029487615,0.00003283728,0.8934529],"study_design_scores_gemma":[0.0010444522,0.0000504348,0.0005819462,0.000018917854,0.00004101802,0.0000015424707,0.00005374597,0.99479413,0.0027704802,0.000059922444,0.0003326674,0.00025072147],"about_ca_topic_score_codex":0.000115880845,"about_ca_topic_score_gemma":0.0004252055,"teacher_disagreement_score":0.8932021,"about_ca_system_score_codex":0.000059306643,"about_ca_system_score_gemma":0.000057017965,"threshold_uncertainty_score":0.9183559},"labels":[],"label_agreement":null},{"id":"W2093210365","doi":"10.1109/tmm.2012.2225036","title":"A Robust Technique for Motion-Based Video Sequences Temporal Alignment","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Trajectory; Computer vision; Dynamic time warping; Motion (physics); Image warping; Motion estimation; Point (geometry); Probabilistic logic; Pattern recognition (psychology); Mathematics","score_opus":0.03577841793015396,"score_gpt":0.2598075192913459,"score_spread":0.22402910136119192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093210365","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003554115,0.000023968305,0.9975458,0.0006407279,0.00062163844,0.0005651766,0.000024310439,0.00016241439,0.00006056312],"genre_scores_gemma":[0.7482502,0.0000046649257,0.25086817,0.00021616399,0.00006618849,0.0004539357,0.000012109143,0.000010938464,0.000117647534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986881,0.00008369605,0.0002915994,0.00031125036,0.00030061536,0.00032476068],"domain_scores_gemma":[0.9990521,0.00020066233,0.000095287825,0.00039059363,0.000098110526,0.00016325054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050272845,0.00016909407,0.00017797979,0.00023694619,0.00024436475,0.000076151824,0.0003286507,0.00010823343,0.000055086126],"category_scores_gemma":[0.000012714454,0.00015136582,0.00019105256,0.00040567943,0.000043073924,0.0005516094,0.0000013116684,0.00011394517,0.000038981463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100486184,0.0031614185,0.002929002,0.00013199323,0.0003131035,0.000006061934,0.0023674108,0.55644584,0.06497202,0.0014455199,0.001995662,0.3661315],"study_design_scores_gemma":[0.00050241593,0.00010812279,0.00020377072,0.000026960586,0.000048181882,0.00000228138,0.000028715958,0.7726317,0.22514227,0.00013338242,0.0009112059,0.00026097984],"about_ca_topic_score_codex":0.00008835678,"about_ca_topic_score_gemma":0.000045568177,"teacher_disagreement_score":0.74789476,"about_ca_system_score_codex":0.000106834945,"about_ca_system_score_gemma":0.0000683568,"threshold_uncertainty_score":0.61725193},"labels":[],"label_agreement":null},{"id":"W2098090906","doi":"10.1109/tmm.2003.814793","title":"HMM delay prediction technique for VoIP","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Voice over IP; Network packet; Jitter; Hidden Markov model; End-to-end delay; Processing delay; Transmission delay; Real-time computing; Network delay; Queuing delay; Packet loss; RTP Control Protocol; Computer network; Algorithm; Speech recognition; The Internet; Telecommunications","score_opus":0.017469535775151996,"score_gpt":0.25370142969398285,"score_spread":0.23623189391883084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098090906","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000575647,0.000032621298,0.99678886,0.00015934001,0.0010522179,0.00048165827,0.000020678843,0.00033344975,0.00055549963],"genre_scores_gemma":[0.5229344,0.000013744937,0.47602206,0.00017150362,0.000046286463,0.00037439624,0.0000015533013,0.000016153568,0.0004199443],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989358,0.000038774804,0.000200019,0.00035393154,0.00018634563,0.00028512962],"domain_scores_gemma":[0.99927473,0.00014395469,0.000053427906,0.00032193284,0.00009063743,0.00011531466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024706736,0.00014680551,0.000121968675,0.00016576968,0.00026358035,0.00007423912,0.00027021999,0.00011834666,0.000028772223],"category_scores_gemma":[0.00002125379,0.000141512,0.00010340891,0.0003240379,0.000036127327,0.00041066448,6.3734205e-7,0.00019085621,0.000050213865],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055972872,0.00071004813,0.000045854977,0.00007838763,0.000082084094,0.000014754496,0.0010322776,0.010130268,0.29552957,0.0002739206,0.0019559339,0.6900909],"study_design_scores_gemma":[0.00054718513,0.00013632726,0.000021816419,0.00002870514,0.00001336116,0.000035714722,0.000013107865,0.021663493,0.97258276,0.0006804685,0.004115862,0.00016118107],"about_ca_topic_score_codex":0.0000041015865,"about_ca_topic_score_gemma":0.0000067453716,"teacher_disagreement_score":0.6899297,"about_ca_system_score_codex":0.000060251234,"about_ca_system_score_gemma":0.00009324358,"threshold_uncertainty_score":0.5770692},"labels":[],"label_agreement":null},{"id":"W2101655796","doi":"10.1109/tmm.2006.870722","title":"Adaptive online transmission of 3-D TexMesh using scale-space and visual perception analysis","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Bandwidth (computing); Computer vision; Artificial intelligence; Visualization; Human visual system model; Feature (linguistics); Image texture; Feature vector; Algorithm; Pattern recognition (psychology); Image processing; Image (mathematics)","score_opus":0.018518045162630607,"score_gpt":0.29610813700013633,"score_spread":0.2775900918375057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101655796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13099727,0.00002826339,0.86854225,0.000045538905,0.00007337126,0.00013716653,0.00001765783,0.00014489185,0.0000136105955],"genre_scores_gemma":[0.8071559,0.000055136716,0.1926999,0.000021779697,0.000021465905,0.000004202582,0.0000064431515,0.000008686662,0.000026462412],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988215,0.000078800986,0.00030567864,0.00036446034,0.00027352272,0.00015605931],"domain_scores_gemma":[0.99940884,0.00007826903,0.000104103434,0.00019609803,0.00013312716,0.00007954371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012782576,0.00016251057,0.00024776367,0.000744229,0.00013649919,0.00004625006,0.00016913684,0.00010164424,0.000018193248],"category_scores_gemma":[6.951487e-7,0.0001570464,0.00017009005,0.0013072292,0.00008542164,0.00026922332,0.0000031337463,0.00013519278,8.5643535e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012885754,0.0033155608,0.0015700859,0.00007581051,0.0004556784,0.000008428617,0.00359737,0.051133607,0.10422662,0.0019349434,0.00006973959,0.8334833],"study_design_scores_gemma":[0.00027733028,0.00015496652,0.00580052,0.000030668456,0.0001250276,0.0000028437162,0.000037913465,0.9702091,0.022997519,0.00017973766,0.000024063756,0.00016028059],"about_ca_topic_score_codex":0.00044282447,"about_ca_topic_score_gemma":0.000093332055,"teacher_disagreement_score":0.91907555,"about_ca_system_score_codex":0.000032852226,"about_ca_system_score_gemma":0.00003054181,"threshold_uncertainty_score":0.6404166},"labels":[],"label_agreement":null},{"id":"W2103398904","doi":"10.1109/tmm.2006.886291","title":"Perceptually Optimized 3-D Transmission Over Wireless Networks","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Packet loss; Bandwidth (computing); Transmission (telecommunications); Image quality; Quality (philosophy); Wireless; Perception; Network packet; Artificial intelligence; Computer network; Computer vision; Image (mathematics); Telecommunications","score_opus":0.019415731957409817,"score_gpt":0.29403415077483047,"score_spread":0.27461841881742066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103398904","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004113958,0.000039659502,0.99312276,0.00041305769,0.0011038864,0.00029138997,0.0000035199453,0.0003434503,0.0005683181],"genre_scores_gemma":[0.7879963,0.000114540904,0.21001652,0.0009648205,0.00011917025,0.000023658096,0.000003539054,0.000027322205,0.0007341524],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978789,0.00011739357,0.00044464742,0.0005095857,0.0005051924,0.0005442823],"domain_scores_gemma":[0.99856305,0.00041411663,0.0000771705,0.00057088636,0.00008283098,0.0002919516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006710849,0.0002704624,0.0002687221,0.00020628887,0.0002819167,0.00012424108,0.0006083072,0.00018956848,0.00029043478],"category_scores_gemma":[0.0000026206906,0.00024002732,0.00022524578,0.00043160282,0.000080086684,0.0005750188,0.000002933635,0.0004921907,0.00010008146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012907035,0.0005351413,0.0000040631858,0.00001373233,0.000048789683,0.000049382277,0.0027412893,0.0314945,0.0051749977,0.00009990827,0.0004040236,0.9593051],"study_design_scores_gemma":[0.0020552853,0.00016391506,0.00063021464,0.000052908228,0.000029087987,0.0000132590185,0.0001395153,0.96940565,0.025481924,0.000041352374,0.0015617072,0.00042515804],"about_ca_topic_score_codex":0.00007279606,"about_ca_topic_score_gemma":0.000025299722,"teacher_disagreement_score":0.95887995,"about_ca_system_score_codex":0.0001025984,"about_ca_system_score_gemma":0.000075017095,"threshold_uncertainty_score":0.978803},"labels":[],"label_agreement":null},{"id":"W2103609317","doi":"10.1109/tmm.2006.879880","title":"Communication Over an Acoustic Channel Using Data Hiding Techniques","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Computer science; Microphone; Information hiding; Transmitter; Channel (broadcasting); Word error rate; Communications system; Speech recognition; Real-time computing; Telecommunications; Artificial intelligence","score_opus":0.04696690980552407,"score_gpt":0.30863250852899593,"score_spread":0.26166559872347184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103609317","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005151714,0.000043850578,0.99293053,0.00006074977,0.00021193235,0.0002395274,0.000040228777,0.0012099817,0.00011145408],"genre_scores_gemma":[0.6241169,0.000035630048,0.37568587,0.000052443746,0.000039369752,0.000020256122,0.000020394882,0.0000145350905,0.000014607647],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870205,0.00010915342,0.00025458683,0.00044926137,0.00022118998,0.00026373033],"domain_scores_gemma":[0.99778545,0.00011444895,0.00009867798,0.0018800857,0.00005648957,0.00006485391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027620717,0.00018631887,0.00015120814,0.00029014243,0.0004023767,0.00012185213,0.0015118003,0.00011720901,0.0000054284237],"category_scores_gemma":[0.0000031077107,0.00018693165,0.000054638265,0.0003921586,0.00009113448,0.0017872,0.000017706372,0.0002848023,0.0000033693757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101165475,0.0024063005,0.00010837593,0.00008493746,0.000101850484,0.00004766346,0.001921097,0.05329877,0.38092712,0.0009166789,0.0009446861,0.5591414],"study_design_scores_gemma":[0.00016894827,0.000056784153,0.0001135394,0.00007630311,0.000021730517,0.000014727229,0.000012950604,0.83418506,0.16226846,0.0025024796,0.00030110314,0.00027791635],"about_ca_topic_score_codex":0.0002879792,"about_ca_topic_score_gemma":0.00008334587,"teacher_disagreement_score":0.7808863,"about_ca_system_score_codex":0.00005654632,"about_ca_system_score_gemma":0.00002618092,"threshold_uncertainty_score":0.7622852},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; 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","score_opus":0.005238289156049966,"score_gpt":0.205590914934917,"score_spread":0.20035262577886703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103806271","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06847168,0.00004150023,0.92874604,0.0000055473356,0.0018154393,0.00014298559,0.000022997914,0.00034733358,0.00040650155],"genre_scores_gemma":[0.9579824,0.000055066692,0.0416098,0.00001414989,0.00013933677,0.00004342424,0.000010405931,0.00007627041,0.00006911756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987978,0.00001930082,0.00035820412,0.00024602105,0.00021709157,0.00036162802],"domain_scores_gemma":[0.9991065,0.00027781952,0.00006393422,0.0003372603,0.00007531593,0.0001391586],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008333186,0.00023579563,0.00023575098,0.00015212844,0.00009658044,0.000016947626,0.00013716928,0.00019775645,0.0002153186],"category_scores_gemma":[0.000008660726,0.0002511858,0.00009715897,0.00038749605,0.00009473188,0.00012596592,0.000001347366,0.00047983177,0.00001351281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015762218,0.000094878465,0.000017504854,0.000011767513,0.000027178208,0.0000015389998,0.00007687679,0.816316,0.010687372,0.0000072225293,0.000030735457,0.17271318],"study_design_scores_gemma":[0.0005713889,0.000022497363,0.00008622259,0.000045973316,0.000029770421,0.0000034383231,0.00001603683,0.94949967,0.04940527,0.0000045644356,0.00009850061,0.00021665433],"about_ca_topic_score_codex":0.00003552527,"about_ca_topic_score_gemma":0.00010640729,"teacher_disagreement_score":0.88951075,"about_ca_system_score_codex":0.000050998904,"about_ca_system_score_gemma":0.000014073956,"threshold_uncertainty_score":0.99999404},"labels":[],"label_agreement":null},{"id":"W2105318782","doi":"10.1109/6046.865483","title":"A concealment method for shape information in MPEG-4 coded video sequences","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; McMaster University","funders":"","keywords":"Computer science; Markov random field; Decoding methods; A priori and a posteriori; Estimator; Bitstream; Artificial intelligence; Binary number; Algorithm; Computer vision; Field (mathematics); Error concealment; Pattern recognition (psychology); Image (mathematics); Image segmentation; Mathematics","score_opus":0.026183555090458905,"score_gpt":0.2959729190657412,"score_spread":0.2697893639752823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105318782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074055754,0.000026726733,0.98969203,0.0013672424,0.00035810506,0.0004065674,0.00002034394,0.00048630455,0.00023711921],"genre_scores_gemma":[0.67202145,0.00007832881,0.32682094,0.00044909233,0.000011960033,0.0003968255,0.0000025702254,0.000005757359,0.00021305314],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988494,0.000061687235,0.00033592174,0.00026043018,0.0002287799,0.00026376318],"domain_scores_gemma":[0.99909693,0.00036544437,0.00006535194,0.00035711966,0.000055080796,0.000060069073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027833035,0.00014980866,0.00018217473,0.00025735365,0.00015010462,0.00010027452,0.00058473006,0.000119896446,0.00012189656],"category_scores_gemma":[0.00001634035,0.00013277774,0.00008372284,0.0003797883,0.000047306123,0.0007903544,0.0000023349419,0.00020817573,0.00010747155],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034633707,0.0000614775,0.0000028170507,0.0000100258085,0.000008942774,0.0000012056505,0.0009989404,0.020205818,0.000840282,0.00013094285,0.00025227148,0.97745264],"study_design_scores_gemma":[0.0009208623,0.00014924744,0.000050523096,0.000056075947,0.0000057364205,0.0000052630025,0.0001148292,0.9241806,0.06847725,0.0009753567,0.004887899,0.00017635363],"about_ca_topic_score_codex":0.00010085147,"about_ca_topic_score_gemma":0.000029007608,"teacher_disagreement_score":0.97727627,"about_ca_system_score_codex":0.00007131197,"about_ca_system_score_gemma":0.00006197038,"threshold_uncertainty_score":0.54145193},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.01642296285742713,"score_gpt":0.2544252547356321,"score_spread":0.23800229187820499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106159865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018818034,0.000024985236,0.97960705,0.00027465273,0.00051775324,0.0002930339,0.00013984037,0.000210362,0.00011429794],"genre_scores_gemma":[0.63255185,0.00002507526,0.36719415,0.000047448084,0.000030878655,0.000027708144,0.000017838514,0.000013856165,0.00009120631],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985167,0.00005167955,0.00032359114,0.00039006418,0.00045951552,0.00025847394],"domain_scores_gemma":[0.99894685,0.00005995626,0.00012273836,0.0005766211,0.00016016225,0.00013367293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001442576,0.00017723492,0.00021352668,0.00013980667,0.00015732377,0.000059828744,0.0006232479,0.0001237193,0.000052804593],"category_scores_gemma":[0.000011375341,0.00016253183,0.00009430265,0.0005529066,0.00007699702,0.0005526276,0.0000075797357,0.00020030951,0.00008271399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008376309,0.00089440914,0.000019226785,0.0000319022,0.00006981103,0.000016619197,0.00087167666,0.69040954,0.022489902,0.00031804707,0.00019897881,0.28459612],"study_design_scores_gemma":[0.0021296905,0.000320901,0.0006980697,0.00016281671,0.000029247105,0.000014420412,0.00005026856,0.7999492,0.19554454,0.0004434894,0.00032866388,0.0003286367],"about_ca_topic_score_codex":0.00028007862,"about_ca_topic_score_gemma":0.000063833824,"teacher_disagreement_score":0.6137338,"about_ca_system_score_codex":0.000085658685,"about_ca_system_score_gemma":0.00013905659,"threshold_uncertainty_score":0.6627856},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Algorithm; Environmentally friendly; Computer network; Distributed computing","score_opus":0.013726833710300068,"score_gpt":0.2554394679309254,"score_spread":0.24171263422062536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108169811","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032598318,0.00006776533,0.9946141,0.0004938761,0.0003882642,0.00067766686,0.000097787946,0.00023186523,0.00016878854],"genre_scores_gemma":[0.7474286,0.00006654397,0.25106508,0.00012876085,0.00014149248,0.00068119756,0.000014028096,0.000014238454,0.00046008086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983001,0.000038334234,0.00047430847,0.0005006354,0.0003296561,0.00035700345],"domain_scores_gemma":[0.9982151,0.00052919704,0.00015962105,0.0006782446,0.00023777739,0.00018008993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002364579,0.00021971314,0.00037514052,0.00051370286,0.00022610584,0.000037417733,0.00065565936,0.00012380687,0.00007499569],"category_scores_gemma":[0.000005710591,0.00021597835,0.00034443664,0.0013702902,0.000103238955,0.00036680646,0.0000026010707,0.00019501998,0.00008715916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022168428,0.00025554156,0.00002040372,0.000007359674,0.00030915192,2.0395001e-7,0.00028562543,0.16700992,0.00022760058,0.00006695605,0.00012268001,0.8316724],"study_design_scores_gemma":[0.0009987765,0.000120580495,0.00038366445,0.000008954966,0.00042240723,0.0000013135001,0.00001915513,0.9871463,0.00385486,0.0000054405864,0.006820732,0.00021777545],"about_ca_topic_score_codex":0.000011018563,"about_ca_topic_score_gemma":0.00006385606,"teacher_disagreement_score":0.83145463,"about_ca_system_score_codex":0.000061857405,"about_ca_system_score_gemma":0.000083979896,"threshold_uncertainty_score":0.88073415},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; 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","score_opus":0.05404327102636397,"score_gpt":0.27091360558868033,"score_spread":0.21687033456231636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109729353","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03785955,0.000026917278,0.95966667,0.00019274384,0.0008556933,0.00032872366,0.000010946127,0.0009385501,0.00012017742],"genre_scores_gemma":[0.5605571,0.00004428253,0.43924233,0.00005832999,0.00003962054,0.000023040093,0.0000023544299,0.00001682972,0.00001604362],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981541,0.00007617817,0.0003637874,0.0005665096,0.00034800955,0.000491407],"domain_scores_gemma":[0.9990001,0.00006766702,0.0001195372,0.0005739887,0.00009344928,0.00014529994],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022273118,0.00029453236,0.00022208349,0.00039546617,0.00048772298,0.00013041752,0.0006143932,0.00016493865,0.000009388965],"category_scores_gemma":[0.0000067253727,0.00027194788,0.00019549647,0.00044811983,0.00013841355,0.0010302438,0.000012583201,0.0003835866,0.000027362274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013841763,0.00086033973,0.00006524137,0.000097928154,0.00008226157,0.00019911531,0.0056167953,0.4998178,0.3029479,0.0005089262,0.000015699845,0.18964958],"study_design_scores_gemma":[0.0010716557,0.00012270693,0.0002537976,0.00021359298,0.000024886926,0.0000690527,0.000034549903,0.43671218,0.5596711,0.0010595393,0.00027497232,0.00049198273],"about_ca_topic_score_codex":0.000120256605,"about_ca_topic_score_gemma":0.000022155062,"teacher_disagreement_score":0.5226976,"about_ca_system_score_codex":0.00020168576,"about_ca_system_score_gemma":0.00005813165,"threshold_uncertainty_score":0.9999733},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Scheme (mathematics); Cache; Popularity; Peer-to-peer; Position (finance); Distributed computing; Computer network","score_opus":0.02571846912565651,"score_gpt":0.2541340654331863,"score_spread":0.22841559630752978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112336602","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06427931,0.000022615455,0.932123,0.0013553273,0.00015782946,0.0006399722,0.000007979542,0.00095643685,0.00045749877],"genre_scores_gemma":[0.5275467,0.000018851904,0.47160026,0.00012601465,0.000024065705,0.00042900184,0.0000014076029,0.000016546108,0.0002371214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981665,0.000037215385,0.00031208273,0.0006245434,0.000389656,0.00047000966],"domain_scores_gemma":[0.9985654,0.0001518405,0.00006480609,0.0009788256,0.00008929808,0.00014984248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001518046,0.00024557638,0.0002331425,0.00047787747,0.00025243184,0.00005066243,0.0010926712,0.00014602019,0.000011406978],"category_scores_gemma":[0.000009848282,0.00023106813,0.0000622708,0.0013638702,0.00012534496,0.00041576344,0.000011147756,0.00053246465,0.00021940308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008564289,0.0011408998,0.0007125654,0.000025474668,0.00011022576,0.0001535514,0.00498674,0.63627124,0.00901114,0.00033200296,0.002048334,0.3451222],"study_design_scores_gemma":[0.0030509979,0.0005144013,0.005591312,0.00015240745,0.000027607088,0.0003484439,0.00016926137,0.9017306,0.07866708,0.00024299813,0.008159533,0.0013453525],"about_ca_topic_score_codex":0.00007695073,"about_ca_topic_score_gemma":0.00017562295,"teacher_disagreement_score":0.46326742,"about_ca_system_score_codex":0.00014133943,"about_ca_system_score_gemma":0.00011857075,"threshold_uncertainty_score":0.9422685},"labels":[],"label_agreement":null},{"id":"W2112755565","doi":"10.1109/tmm.2003.819745","title":"Link-Level Traffic Scheduling for Providing Predictive QoS in Wireless Multimedia Networks","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Manitoba","funders":"","keywords":"Computer science; Computer network; Time division multiple access; Quality of service; Scheduling (production processes); Wireless; Real-time computing; Telecommunications","score_opus":0.01685075312478021,"score_gpt":0.2351112498453533,"score_spread":0.2182604967205731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112755565","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04151614,0.00011968524,0.95285624,0.00005755043,0.003025214,0.0014980292,0.00008910456,0.00081375253,0.000024263472],"genre_scores_gemma":[0.8370274,0.00027290932,0.16105641,0.00002775202,0.00066412974,0.00072123745,0.000051324547,0.00015698839,0.000021840902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795705,0.000032167234,0.0005661709,0.0004968383,0.00024378388,0.0007039642],"domain_scores_gemma":[0.9989183,0.00042052416,0.000078299156,0.000289301,0.00009730562,0.00019625407],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017375496,0.00043128693,0.0004075846,0.00037149628,0.0001788545,0.000039836945,0.00021521735,0.00039128415,0.000013413384],"category_scores_gemma":[0.000019818475,0.00049759983,0.00015481892,0.00062956376,0.00008427146,0.00044191096,0.000001461459,0.00076016015,0.000018794126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011728476,0.000117823234,0.0000060312827,0.000050683386,0.000053066047,0.0000053707104,0.0008615251,0.86565506,0.0010448046,0.0000035528237,0.000009960989,0.13207482],"study_design_scores_gemma":[0.0034975833,0.00010357592,0.000066269975,0.00030353633,0.000045603378,0.0000039343167,0.00012855857,0.9815309,0.013810066,0.00001588699,0.000026037338,0.0004680727],"about_ca_topic_score_codex":0.00001105149,"about_ca_topic_score_gemma":0.00020487086,"teacher_disagreement_score":0.79551125,"about_ca_system_score_codex":0.00054476585,"about_ca_system_score_gemma":0.000069404596,"threshold_uncertainty_score":0.9997476},"labels":[],"label_agreement":null},{"id":"W2113131653","doi":"10.1109/tmm.2012.2236306","title":"VideoPuzzle: Descriptive One-Shot Video Composition","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; Ministry of Education, India; Queen's University; National Natural Science Foundation of China; Queen's University Belfast","keywords":"Shot (pellet); Computer science; Video tracking; Computer vision; Consistency (knowledge bases); Matching (statistics); Frame (networking); Video compression picture types; Artificial intelligence; Video processing; CLIPS; Task (project management); Video capture; Multimedia; Video browsing; Computer graphics (images)","score_opus":0.05150986222623572,"score_gpt":0.26949804401782856,"score_spread":0.21798818179159285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113131653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009337961,0.000061951956,0.9877637,0.0004499,0.0012381738,0.00019789279,0.000013687651,0.00024154499,0.0006951738],"genre_scores_gemma":[0.95383453,0.000037632748,0.04526934,0.0004000618,0.00013158639,0.000046979734,0.000012227999,0.00001644856,0.00025120474],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983539,0.0001490015,0.00031943957,0.0003470801,0.00042318198,0.00040742656],"domain_scores_gemma":[0.9988737,0.00015469863,0.00009498649,0.0004980892,0.00012533754,0.00025318417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030730737,0.0001993894,0.0002276726,0.00029325444,0.00030174325,0.000121504534,0.00037321742,0.000119893026,0.00014698436],"category_scores_gemma":[0.000008065436,0.00019838115,0.0001708642,0.000642001,0.000053302334,0.0014273691,0.0000031790216,0.00025209502,0.0004920833],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018760966,0.0058066105,0.0015951765,0.00006233404,0.0007883017,0.000016570943,0.019323228,0.028891591,0.21061684,0.004043526,0.0035761811,0.72509205],"study_design_scores_gemma":[0.0016774163,0.00024657155,0.007938528,0.000100765035,0.00025729,0.000022159105,0.00020336005,0.65713865,0.32886285,0.00034259597,0.0022277157,0.0009821217],"about_ca_topic_score_codex":0.00007356092,"about_ca_topic_score_gemma":0.000037358375,"teacher_disagreement_score":0.9444966,"about_ca_system_score_codex":0.00013562001,"about_ca_system_score_gemma":0.0000367146,"threshold_uncertainty_score":0.80897486},"labels":[],"label_agreement":null},{"id":"W2116525964","doi":"10.1109/tmm.2006.876227","title":"Opportunistic scheduling for streaming multimedia users in high-speed downlink packet access (HSDPA)","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Simon Fraser University; Sharif University of Technology","keywords":"Computer science; Link adaptation; Quality of service; Telecommunications link; Network packet; Robustness (evolution); Scheduling (production processes); Computer network; Real-time computing; Distributed computing; Fading; Channel (broadcasting); Mathematical optimization; Mathematics","score_opus":0.020007289529344045,"score_gpt":0.25895053686530695,"score_spread":0.2389432473359629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116525964","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08468491,0.00004399996,0.91197145,0.00005736005,0.0014679505,0.0008409548,0.00023186063,0.0006086939,0.00009281113],"genre_scores_gemma":[0.8772413,0.000122046265,0.12169181,0.00002575653,0.00024918202,0.00019125675,0.0002529413,0.0001338877,0.00009177829],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980851,0.000033670713,0.0005869366,0.00043631304,0.00024843213,0.00060956477],"domain_scores_gemma":[0.99879277,0.00054267404,0.0000899508,0.00033190544,0.00008690235,0.00015580867],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013890123,0.000393009,0.00038993356,0.00044617383,0.00014471811,0.000076423894,0.00026217353,0.00026085207,0.00007206043],"category_scores_gemma":[0.000021391634,0.00045566825,0.00012177215,0.00053664926,0.00007451483,0.00055324024,0.0000019547801,0.00043352257,0.000027756467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005423561,0.00012242667,0.00016389883,0.00006342121,0.000031359847,0.000010715697,0.00010376529,0.9522181,0.0038296075,0.00000888174,0.00007152765,0.043322045],"study_design_scores_gemma":[0.0021856516,0.00003377377,0.0007676642,0.00011221758,0.000043320557,0.0000022161769,0.000059601378,0.9828664,0.013287756,0.00009655046,0.00008003876,0.00046482414],"about_ca_topic_score_codex":0.00013054766,"about_ca_topic_score_gemma":0.0005981799,"teacher_disagreement_score":0.7925564,"about_ca_system_score_codex":0.0002927559,"about_ca_system_score_gemma":0.00004297366,"threshold_uncertainty_score":0.9997895},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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)","score_opus":0.0923963486530244,"score_gpt":0.39200836329782174,"score_spread":0.29961201464479736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123033112","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012728744,0.000045657453,0.9852079,0.00022790601,0.0003843803,0.0007864959,0.000022521994,0.00008942424,0.0005069784],"genre_scores_gemma":[0.7380856,0.0000031511793,0.26154685,0.00008152257,0.00004658214,0.00018261986,0.0000021641358,0.0000073662964,0.000044088512],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978389,0.00033224028,0.00049229176,0.0003509065,0.00075563666,0.00022998624],"domain_scores_gemma":[0.9978294,0.0009245187,0.00022002141,0.00042597388,0.00053268723,0.00006739371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027066197,0.00013971968,0.000233613,0.00029400887,0.00014786706,0.000043504147,0.00031498176,0.0000741046,0.000026119276],"category_scores_gemma":[0.00014236764,0.00013713972,0.00016126185,0.0005937444,0.000038112794,0.00057535165,0.0000021264018,0.00014382123,0.000021660644],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027655398,0.0005561373,0.000009723541,0.000047328278,0.000065512366,1.3133891e-7,0.0042035696,0.030721232,0.004996018,0.00027906388,0.00003028181,0.95906335],"study_design_scores_gemma":[0.0012880391,0.00010819389,0.0006058266,0.000016313283,0.000044918525,9.775496e-7,0.00023856488,0.75350326,0.24359347,0.00042297837,0.00003179119,0.00014567706],"about_ca_topic_score_codex":0.00007353954,"about_ca_topic_score_gemma":0.000082727194,"teacher_disagreement_score":0.9589177,"about_ca_system_score_codex":0.00020623839,"about_ca_system_score_gemma":0.0001991718,"threshold_uncertainty_score":0.55923957},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.012883741067359095,"score_gpt":0.23491841348836823,"score_spread":0.22203467242100913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124333148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1701503,0.000010171794,0.8283057,0.00063896144,0.00019714536,0.0003498788,0.0000062868335,0.000112541085,0.00022900362],"genre_scores_gemma":[0.98785657,0.000021940336,0.011348662,0.00064415147,0.000036158868,0.00004649627,0.0000104905375,0.0000066297353,0.000028916445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983867,0.00032934354,0.00040837392,0.00019212383,0.00045173135,0.00023173922],"domain_scores_gemma":[0.99911386,0.00031739936,0.0000965113,0.00037988898,0.000052212898,0.000040128205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000562759,0.00015776172,0.00015783543,0.00023419359,0.00044596026,0.0003011643,0.0005423813,0.000084781605,0.0003063846],"category_scores_gemma":[0.000008727641,0.000101412705,0.000097638615,0.0011019034,0.000044890872,0.0010876927,0.0000014815878,0.000301304,0.00012018322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029975032,0.00020662544,0.0000741931,0.000016437065,0.000054657565,0.0000030015904,0.015334153,0.50601983,0.0012642854,0.000075145625,0.00021758671,0.4767041],"study_design_scores_gemma":[0.0004575032,0.000040334235,0.0014947027,0.000023107175,0.000025838794,0.000015916581,0.00018995699,0.9959489,0.00090894377,0.00010132236,0.00066985213,0.00012363061],"about_ca_topic_score_codex":0.0003447328,"about_ca_topic_score_gemma":0.00032393166,"teacher_disagreement_score":0.8177062,"about_ca_system_score_codex":0.0000817449,"about_ca_system_score_gemma":0.00006031185,"threshold_uncertainty_score":0.413549},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.011147330945346007,"score_gpt":0.24010192086853951,"score_spread":0.2289545899231935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124636945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048863264,0.00001125941,0.9919833,0.00019453342,0.000606435,0.00017281341,0.00001706376,0.0009365619,0.0011917253],"genre_scores_gemma":[0.63034725,0.000006092504,0.36930305,0.000089519854,0.0000554578,0.000038973463,0.0000024701224,0.000013543665,0.00014364421],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986923,0.000030355863,0.00025087336,0.00040279422,0.0002572504,0.0003664668],"domain_scores_gemma":[0.9992145,0.00010646987,0.00006707257,0.0004820872,0.000054565848,0.0000753596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012103631,0.00021218417,0.0001596663,0.00027044525,0.00025837964,0.00012421412,0.0005393423,0.00010007396,0.000017112981],"category_scores_gemma":[0.0000017237755,0.00019343551,0.00016252356,0.00033982197,0.00009003878,0.00076895935,0.000003861494,0.00029929043,0.000050345694],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007145316,0.0012853165,0.00008475209,0.000033430875,0.000060579114,0.00011077358,0.0009398918,0.003007102,0.63162035,0.0008023322,0.00074097834,0.36124304],"study_design_scores_gemma":[0.0009066686,0.000111167916,0.00087854353,0.00006096888,0.000017233087,0.00007115953,0.000009592149,0.11264763,0.8790147,0.0023303349,0.003394405,0.0005576221],"about_ca_topic_score_codex":0.000101982885,"about_ca_topic_score_gemma":0.000019743942,"teacher_disagreement_score":0.6254609,"about_ca_system_score_codex":0.000038758866,"about_ca_system_score_gemma":0.000018676219,"threshold_uncertainty_score":0.78880715},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; 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","score_opus":0.020322360921013995,"score_gpt":0.2726646795590411,"score_spread":0.25234231863802714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126552487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17544046,0.000002907709,0.82271695,0.00015582268,0.00044690404,0.0007830143,0.00017124235,0.00021572153,0.00006699747],"genre_scores_gemma":[0.91829795,0.00000876492,0.080536954,0.00027330604,0.000088374436,0.00054320536,0.00018983873,0.000012530428,0.000049056787],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985423,0.00004551886,0.0003396137,0.00031224047,0.0003930056,0.00036732288],"domain_scores_gemma":[0.9987595,0.00013402967,0.00014947589,0.00036974525,0.00032405113,0.00026316647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021936806,0.00020920219,0.00019355923,0.0006989609,0.0003199819,0.00017318712,0.00022980722,0.0001576306,0.000058455254],"category_scores_gemma":[0.000013641111,0.00018242528,0.00015764286,0.0010834432,0.000028328892,0.00280999,0.0000032197381,0.00015330197,0.00060929003],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032460378,0.00049695076,0.0009453563,0.000037064743,0.00015259949,1.6011563e-7,0.0032452373,0.026090972,0.010195156,0.000018594701,0.000094879295,0.9583984],"study_design_scores_gemma":[0.0027801981,0.0006333093,0.050437953,0.000088077555,0.000319016,0.000008666739,0.00024215075,0.71516126,0.22737938,0.00013497935,0.0019064456,0.0009085355],"about_ca_topic_score_codex":0.00006673963,"about_ca_topic_score_gemma":0.000029829434,"teacher_disagreement_score":0.9574899,"about_ca_system_score_codex":0.000107152846,"about_ca_system_score_gemma":0.00003313748,"threshold_uncertainty_score":0.7831394},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; 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","score_opus":0.023193424198252974,"score_gpt":0.29175278025062074,"score_spread":0.26855935605236775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129884074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014361856,0.000013761422,0.9812048,0.0014784947,0.0016441974,0.00048379737,0.000013523484,0.00035029094,0.00044929242],"genre_scores_gemma":[0.9529734,0.0000043460855,0.04433398,0.001461672,0.00020354576,0.0004274522,0.0000026254836,0.00001873705,0.0005741986],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983504,0.00013929466,0.0002944199,0.0004088281,0.00040996645,0.0003970971],"domain_scores_gemma":[0.998946,0.00022325903,0.00007260362,0.0004833855,0.00012347588,0.00015129006],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018106408,0.00020529263,0.00020170814,0.00013600684,0.0003387839,0.0001987228,0.0005925946,0.00012187356,0.0006957088],"category_scores_gemma":[0.0000035967648,0.00019751783,0.00018700311,0.00033400417,0.000085431886,0.00051866646,0.0000029868854,0.0003218037,0.0020115965],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028222537,0.0016355086,0.000014236834,0.000047572023,0.000090466696,0.000020049678,0.0039353743,0.007266546,0.013191235,0.0003401727,0.015906159,0.9575245],"study_design_scores_gemma":[0.0030775014,0.0005481377,0.0012655879,0.000040046954,0.000050453204,0.0000075861485,0.00036854943,0.7566548,0.22468308,0.0007625063,0.01152995,0.0010117876],"about_ca_topic_score_codex":0.00030650434,"about_ca_topic_score_gemma":0.000020072725,"teacher_disagreement_score":0.9565127,"about_ca_system_score_codex":0.000093655915,"about_ca_system_score_gemma":0.00012400355,"threshold_uncertainty_score":0.99876547},"labels":[],"label_agreement":null},{"id":"W2132705791","doi":"10.1109/tmm.2008.917365","title":"Partitioning of Multiple Fine-Grained Scalable Video Sequences Concurrently Streamed to Heterogeneous Clients","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Scalability; Server; Distributed computing; Computer network; Computer architecture; Operating system","score_opus":0.03739689135751726,"score_gpt":0.26400230853123013,"score_spread":0.22660541717371288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132705791","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34517774,0.00004891659,0.65306556,0.0002457905,0.00068158715,0.00021793699,0.00003128896,0.00047890522,0.00005226691],"genre_scores_gemma":[0.96603477,0.000044994573,0.033537984,0.000080223115,0.000017681963,0.00011087977,0.0000023667126,0.000011581128,0.00015953426],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983102,0.0000672523,0.00039370253,0.00047304406,0.000398982,0.00035678828],"domain_scores_gemma":[0.99866414,0.00031594487,0.00012044402,0.0006004586,0.00013515406,0.00016385164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000105176376,0.00020691125,0.00027745488,0.0002941871,0.00032354787,0.000037612746,0.00075033173,0.000106840635,0.000037942522],"category_scores_gemma":[0.00006638172,0.00018988417,0.00013553556,0.0005845601,0.00016239185,0.00024673596,0.000011821,0.00021502524,0.00010738278],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001633136,0.0014538041,0.0032910134,0.00006438378,0.00017173837,0.00010689948,0.0035656025,0.23835252,0.06762545,0.00006874536,0.0019524309,0.6831841],"study_design_scores_gemma":[0.0009530202,0.0003691389,0.0006659324,0.00014374858,0.0000120977575,0.00003245921,0.000053089985,0.17901151,0.8180019,0.00012037032,0.00033801957,0.00029868656],"about_ca_topic_score_codex":0.00009886919,"about_ca_topic_score_gemma":0.00007556634,"teacher_disagreement_score":0.75037646,"about_ca_system_score_codex":0.000046153495,"about_ca_system_score_gemma":0.000069414724,"threshold_uncertainty_score":0.7743252},"labels":[],"label_agreement":null},{"id":"W2136662549","doi":"10.1109/tmm.2008.2004915","title":"Channel Aware Multiuser Scalable Video Streaming Over Lossy Under-Provisioned Channels: Modeling and Analysis","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Aristotle University of Thessaloniki","keywords":"Computer science; Computer network; Scalability; Packet loss; Network packet; Real-time computing; Channel (broadcasting); Forward error correction; Wireless network; Lossy compression; Latency (audio); Wireless; Decoding methods; Algorithm; Telecommunications","score_opus":0.01811287303732982,"score_gpt":0.22929125145983964,"score_spread":0.2111783784225098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136662549","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13032672,0.00016750576,0.86822885,0.000019719708,0.00045059487,0.00024200225,0.000043211727,0.0004980533,0.000023366481],"genre_scores_gemma":[0.9871864,0.0010443203,0.011309536,0.000034445246,0.00009241805,0.000068975816,0.00003901057,0.00009365964,0.00013127693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998463,0.00003200091,0.00035964116,0.00044037335,0.00027990236,0.00042510722],"domain_scores_gemma":[0.99919146,0.00014951036,0.000045540906,0.00033245562,0.00008394465,0.00019710488],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007228024,0.00033867042,0.00038267867,0.0005233958,0.00035843378,0.000034889836,0.000111915346,0.00020773581,0.00007650695],"category_scores_gemma":[0.000005283555,0.00036568634,0.0001463207,0.0009210727,0.000067068104,0.00047870248,0.000002330223,0.00035164537,0.000023928837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024346444,0.0000588002,0.000028171436,0.000021965057,0.00027048404,0.0000072352427,0.0005998351,0.9915446,0.00035360944,3.0858982e-7,0.000013346061,0.0070772804],"study_design_scores_gemma":[0.00083115575,0.000024537767,0.00014141112,0.00004843059,0.00019857068,0.0000055454207,0.00013522271,0.9952767,0.0029450185,0.000015712314,0.000006222519,0.0003714888],"about_ca_topic_score_codex":0.00008407082,"about_ca_topic_score_gemma":0.00010419098,"teacher_disagreement_score":0.8569193,"about_ca_system_score_codex":0.00013160743,"about_ca_system_score_gemma":0.000016772941,"threshold_uncertainty_score":0.99987954},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.03136254584442519,"score_gpt":0.28970169673183643,"score_spread":0.2583391508874112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137394636","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013066711,0.000004590907,0.9835987,0.00092919945,0.0004498965,0.00086317863,0.000018901781,0.00095217396,0.00011665865],"genre_scores_gemma":[0.615446,0.0000015582965,0.3834788,0.00059107196,0.000080382044,0.00032641515,0.000004834403,0.000021802092,0.00004917103],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99819195,0.00013337845,0.00031460525,0.0005675498,0.00029766824,0.00049484626],"domain_scores_gemma":[0.9986767,0.00030765592,0.00011560622,0.0006616333,0.000077686454,0.00016068004],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042890158,0.00029019272,0.00020965218,0.00035922733,0.0006808134,0.00012455681,0.0006991783,0.00014218806,0.000011703141],"category_scores_gemma":[0.000008902058,0.00024577478,0.00018937603,0.00027191584,0.0000636542,0.0006835609,0.0000028667362,0.00047726894,0.000021051304],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003542005,0.001260061,0.000042316387,0.00006439783,0.00006988737,0.000012361575,0.001999369,0.01625515,0.022949314,0.00018194689,0.00013288081,0.9566781],"study_design_scores_gemma":[0.001012321,0.0003944387,0.000025202457,0.00010482272,0.000009907114,0.0000037923098,0.000012660343,0.4060546,0.5872041,0.00033220934,0.0045200246,0.00032593054],"about_ca_topic_score_codex":0.000012366439,"about_ca_topic_score_gemma":0.000021470756,"teacher_disagreement_score":0.9563522,"about_ca_system_score_codex":0.00010316275,"about_ca_system_score_gemma":0.000029570032,"threshold_uncertainty_score":0.99999946},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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)","score_opus":0.026726019951157157,"score_gpt":0.26092621151341655,"score_spread":0.2342001915622594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137587644","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028731737,0.000078871475,0.99522936,0.00072731415,0.0004132231,0.00024243831,0.000011997599,0.0003097005,0.00011394371],"genre_scores_gemma":[0.34583881,0.00009339411,0.65335584,0.00024941098,0.00002139442,0.0000042103093,9.662501e-7,0.00002037213,0.000415587],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986675,0.000107680025,0.00021555966,0.00042919215,0.00031183424,0.00026826645],"domain_scores_gemma":[0.99895895,0.00015701457,0.000092796676,0.00049430324,0.0001567929,0.00014014193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002622013,0.00020102666,0.00019026022,0.00015952194,0.00023850381,0.0001419154,0.00022340112,0.00014382521,0.000026835862],"category_scores_gemma":[0.000054995046,0.00018875467,0.00009050887,0.00054054975,0.00013381918,0.00040623135,0.0000018581268,0.000370227,0.00003352124],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052806932,0.00033226912,0.000013357383,0.0000793081,0.000030812553,0.000029681425,0.0003468886,0.0001875504,0.97575593,0.0004636204,0.00016880085,0.022538984],"study_design_scores_gemma":[0.00041363208,0.00005901924,0.00007781735,0.000040691804,0.000016089847,0.00002412917,0.000014328315,0.16252339,0.83560807,0.00017536125,0.0008424262,0.00020506662],"about_ca_topic_score_codex":0.000004874276,"about_ca_topic_score_gemma":0.0000011681528,"teacher_disagreement_score":0.34296566,"about_ca_system_score_codex":0.0000779324,"about_ca_system_score_gemma":0.000104329156,"threshold_uncertainty_score":0.7697192},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.03457499967339993,"score_gpt":0.28112201969120565,"score_spread":0.2465470200178057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139467041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029872363,0.000033157103,0.9688623,0.00008099482,0.0006875931,0.00021966833,0.0000061465353,0.0001617863,0.000076001],"genre_scores_gemma":[0.6062096,0.000017404986,0.39362922,0.0001028989,0.000015070028,0.000007752675,0.0000027263807,0.0000051878856,0.000010147206],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899334,0.00008391892,0.00022815951,0.0003446588,0.00018747376,0.00016243571],"domain_scores_gemma":[0.99938154,0.000121916615,0.000099056015,0.00025274054,0.00005138608,0.00009335019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008325362,0.00014549846,0.00014013515,0.00009060456,0.00021277409,0.00007575539,0.00015888925,0.00007258449,0.000028772656],"category_scores_gemma":[0.000011068238,0.0001475611,0.000049813916,0.00019190424,0.00004354561,0.0010674798,0.000004034901,0.00013591799,0.000011783043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008838116,0.00008432321,0.00013415379,0.000006481405,0.00001647819,0.0000066278644,0.0013587037,0.014032834,0.0039825146,0.000013192963,0.000015223386,0.9802611],"study_design_scores_gemma":[0.0015759376,0.00003106169,0.0014465633,0.000053783948,0.000021933107,0.000010984238,0.000065060376,0.97160184,0.024415845,0.00062668807,0.000004948881,0.00014532196],"about_ca_topic_score_codex":0.0003390122,"about_ca_topic_score_gemma":0.000024837189,"teacher_disagreement_score":0.9801158,"about_ca_system_score_codex":0.00007609254,"about_ca_system_score_gemma":0.000017414019,"threshold_uncertainty_score":0.6017367},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; 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","score_opus":0.09553335985342506,"score_gpt":0.30971808113179394,"score_spread":0.21418472127836888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140334624","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010648483,0.000023384746,0.99664444,0.00010737647,0.0007916849,0.00014674306,0.00004030271,0.00015492053,0.0010263143],"genre_scores_gemma":[0.6459136,0.000027112637,0.35369253,0.00012244038,0.000016588041,0.0000045954407,0.000008236277,0.000010776599,0.00020412797],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987404,0.00006230485,0.00028878593,0.0004303113,0.0002865471,0.0001916649],"domain_scores_gemma":[0.99845123,0.000060041977,0.000100101744,0.0011740191,0.00010262878,0.00011198803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013233279,0.00014765884,0.00016590928,0.00015291698,0.00011394003,0.000019768057,0.0010864986,0.00006858652,0.00023115313],"category_scores_gemma":[0.000010712609,0.00012908694,0.000058290658,0.00023663708,0.00008495045,0.00089024066,0.00001417715,0.00022847713,0.000093887465],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025451922,0.0004420699,0.000026876174,0.00001579212,0.000019075494,0.0000069438597,0.0063001765,0.0014312367,0.029178007,0.000045628596,0.0008815565,0.9616272],"study_design_scores_gemma":[0.00077649544,0.000056231656,0.0012594762,0.000078755525,0.000016680686,0.000015160676,0.00028019026,0.93965524,0.05702945,0.00011912844,0.00048850453,0.00022469669],"about_ca_topic_score_codex":0.00008682824,"about_ca_topic_score_gemma":0.000014136099,"teacher_disagreement_score":0.9614025,"about_ca_system_score_codex":0.000020702995,"about_ca_system_score_gemma":0.00003156318,"threshold_uncertainty_score":0.5264013},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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","score_opus":0.06997677448712307,"score_gpt":0.32309600292604884,"score_spread":0.25311922843892576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140834120","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005340591,0.0000065280383,0.99667835,0.0013242593,0.0004302482,0.00050559087,0.00002931213,0.00043702865,0.00005460754],"genre_scores_gemma":[0.9248341,0.000016307107,0.07383353,0.0006253756,0.00003359431,0.00012058188,0.000010609545,0.000013755977,0.0005121911],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881774,0.000028621685,0.00032920626,0.00035232244,0.00019613179,0.00027597128],"domain_scores_gemma":[0.99902034,0.00030033075,0.00008455689,0.00022719508,0.00022855612,0.0001390448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043230224,0.00015201264,0.00015166531,0.00021684227,0.00020403923,0.000058300364,0.00027902797,0.00013182044,0.000019723238],"category_scores_gemma":[0.00001271602,0.00014120764,0.00015861135,0.00028397216,0.000064467,0.00034340366,0.00000165747,0.00020647411,0.000026188565],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001187304,0.00041591644,0.0000056607396,0.000010988462,0.000025303276,0.0000011483919,0.00041675553,0.00006262546,0.013402755,0.0008541929,0.00023023212,0.9844557],"study_design_scores_gemma":[0.0005928024,0.00014336732,0.00007052718,0.000010674927,0.00001001215,0.0000031913737,0.000051839528,0.7981567,0.199984,0.00030084586,0.00052471063,0.00015135751],"about_ca_topic_score_codex":0.000012563866,"about_ca_topic_score_gemma":0.00004202985,"teacher_disagreement_score":0.9843043,"about_ca_system_score_codex":0.00007837891,"about_ca_system_score_gemma":0.000049462426,"threshold_uncertainty_score":0.5758281},"labels":[],"label_agreement":null},{"id":"W2144708315","doi":"10.1109/tmm.2005.846778","title":"A real-time video multicast architecture for assured forwarding services","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Multicast; Computer network; Unicast; Differentiated services; Xcast; Source-specific multicast; Distributed computing; IP multicast; Quality of service; Pragmatic General Multicast; Network packet","score_opus":0.008436776112164125,"score_gpt":0.23653639051733497,"score_spread":0.22809961440517085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144708315","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035214305,0.00003152352,0.9917689,0.0023740232,0.0006572895,0.0006264491,0.00003978976,0.0006301287,0.00035046198],"genre_scores_gemma":[0.7377255,0.000023243283,0.2602731,0.00041740297,0.00036792047,0.00025013133,0.000005213428,0.000029890476,0.0009076201],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982979,0.00007056836,0.00031990782,0.00054537086,0.00029288564,0.00047332232],"domain_scores_gemma":[0.9983809,0.00067639764,0.00009091919,0.00051396847,0.000117914475,0.00021989536],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002400383,0.00026624702,0.00028577403,0.00018433001,0.00030068422,0.0001351584,0.0005997551,0.00014813083,0.00006392188],"category_scores_gemma":[0.000006152725,0.00024756225,0.00024127471,0.0002745049,0.000044483448,0.0003658306,0.0000028446143,0.00025287367,0.000297141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007685139,0.00014813498,0.0000012622295,0.000016901655,0.0000762519,0.0000025241422,0.0008605389,0.123300925,0.00519843,0.000058916823,0.00029922603,0.86996],"study_design_scores_gemma":[0.0017615882,0.0001123144,0.0000249538,0.0000454475,0.000053458636,0.000011255208,0.000020044237,0.98202443,0.0047754645,0.000051963187,0.010837975,0.00028108465],"about_ca_topic_score_codex":0.000022224069,"about_ca_topic_score_gemma":0.000099245335,"teacher_disagreement_score":0.869679,"about_ca_system_score_codex":0.00006285108,"about_ca_system_score_gemma":0.000051170082,"threshold_uncertainty_score":0.9999977},"labels":[],"label_agreement":null},{"id":"W2147267580","doi":"10.1109/tmm.2010.2089786","title":"Training Surrogate Sensors in Musical Gesture Acquisition Systems","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Music and Audio Processing","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Gesture; Computer science; Microphone; SIGNAL (programming language); Speech recognition; Gesture recognition; Data acquisition; Artificial intelligence; Musical instrument; Human–computer interaction; Signal processing; Computer vision; Computer hardware; Digital signal processing; Acoustics","score_opus":0.024755206796328224,"score_gpt":0.2567921720099744,"score_spread":0.23203696521364617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147267580","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34440425,0.0000118984535,0.6511492,0.0005537934,0.0030682618,0.00016281572,0.0000060303487,0.00021349869,0.00043023616],"genre_scores_gemma":[0.98338145,0.000004463958,0.01596899,0.00026304598,0.0001781018,0.00003232741,0.0000014930208,0.000014741286,0.00015536642],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986324,0.00006802284,0.00025830112,0.00041544682,0.00028726543,0.00033861503],"domain_scores_gemma":[0.9992111,0.00018832214,0.00006691984,0.00034177373,0.000052171927,0.0001397457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030806364,0.00017387867,0.00020082663,0.00022245751,0.00016378137,0.00014975539,0.00033046972,0.00018064278,0.000047312846],"category_scores_gemma":[0.0000095138,0.00016306581,0.0000720534,0.00041299217,0.00006886769,0.0004448559,0.0000019181782,0.0007111219,0.000097601885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004568424,0.00056118856,0.000088876644,0.00009195371,0.000041697487,0.00020431599,0.024857568,0.0551744,0.112238675,0.0005323692,0.0002951554,0.80586815],"study_design_scores_gemma":[0.0013981948,0.0000747538,0.0025356894,0.00016338992,0.000016309112,0.00011143724,0.00039108767,0.9761475,0.016813003,0.00011603447,0.0017146313,0.00051800953],"about_ca_topic_score_codex":0.000054807144,"about_ca_topic_score_gemma":0.00007996906,"teacher_disagreement_score":0.92097306,"about_ca_system_score_codex":0.000032923763,"about_ca_system_score_gemma":0.00007647744,"threshold_uncertainty_score":0.66496307},"labels":[],"label_agreement":null},{"id":"W2156364025","doi":"10.1109/tmm.2007.911243","title":"Meet In the Middle Cross-Layer Adaptation for Audiovisual Content Delivery","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Overhead (engineering); Computer network; Quality of service; Context (archaeology); Session (web analytics); Layer (electronics); Application layer; Adaptation (eye); Dynamic Adaptive Streaming over HTTP; Multimedia; Real-time computing; Quality of experience; World Wide Web; Operating system","score_opus":0.19601405534164182,"score_gpt":0.36484646825807165,"score_spread":0.16883241291642984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156364025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06835985,0.00002930637,0.92969996,0.00048218406,0.00074046315,0.00049862434,0.000014383585,0.000060655762,0.00011454918],"genre_scores_gemma":[0.950908,0.0000086730015,0.047702923,0.001009765,0.000065790824,0.00010631711,0.0000031035972,0.000008665596,0.0001867509],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986034,0.00009087371,0.00034254417,0.00029468405,0.00034304505,0.0003254725],"domain_scores_gemma":[0.99854827,0.0008567011,0.000067494475,0.00033729585,0.00012925749,0.00006099639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011416721,0.00013543667,0.00013074475,0.00015124826,0.00019770561,0.00013581879,0.00044159344,0.00007690226,0.000010659396],"category_scores_gemma":[0.000016075175,0.00010652963,0.00012635668,0.00026336225,0.00005532058,0.000503732,0.0000016968322,0.00016675521,0.000034987483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073453755,0.0034760127,0.00023341608,0.00011517647,0.00017215346,0.000094408526,0.06784083,0.03522563,0.024811324,0.003334857,0.0010658909,0.8628958],"study_design_scores_gemma":[0.005362938,0.00077560276,0.011569663,0.000082416416,0.000049460887,0.000022230244,0.0049166363,0.832456,0.1412769,0.00049303536,0.0023598175,0.00063533295],"about_ca_topic_score_codex":0.0002438522,"about_ca_topic_score_gemma":0.000910167,"teacher_disagreement_score":0.88254815,"about_ca_system_score_codex":0.00008936274,"about_ca_system_score_gemma":0.000068242036,"threshold_uncertainty_score":0.43441522},"labels":[],"label_agreement":null},{"id":"W2157007261","doi":"10.1109/tmm.2007.911224","title":"Optimal Coding of Multilayer and Multiversion Video Streams","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Scalability; Granularity; Server; Leverage (statistics); Multiple description coding; Coding (social sciences); Scalable Video Coding; Algorithm; Real-time computing; Distributed computing; Computer network; Decoding methods; Artificial intelligence; Database","score_opus":0.018741229931214805,"score_gpt":0.2590451846557991,"score_spread":0.24030395472458432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157007261","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23292552,0.000048314738,0.7661438,0.00008696141,0.00033522054,0.000101696525,0.00000519328,0.00027596613,0.0000773583],"genre_scores_gemma":[0.87581193,0.00006218692,0.12398075,0.000025565121,0.000010284291,0.000005502514,2.826908e-7,0.0000074424024,0.00009605916],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99886996,0.000026168489,0.00024878842,0.00034917393,0.00025137444,0.00025455357],"domain_scores_gemma":[0.9989489,0.0004053741,0.00009109529,0.0003824674,0.00007393743,0.00009824117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002452794,0.00015563807,0.00018144044,0.00030959424,0.00016772584,0.0000290811,0.00040685094,0.0001309442,0.000014236485],"category_scores_gemma":[0.000019453553,0.00013894001,0.00007415536,0.00028376377,0.0001268775,0.00027484706,0.000010290078,0.00025044047,0.000016501517],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006128896,0.00020057481,0.00010907112,0.000017449947,0.000026224076,0.000014033991,0.0012665231,0.003709441,0.06647601,0.00008474984,0.00007010763,0.9279645],"study_design_scores_gemma":[0.00078750856,0.00015092193,0.0007749103,0.00008412437,0.0000107774895,0.0000083355435,0.00027835666,0.25381976,0.7437371,0.00003061325,0.00014902234,0.00016856006],"about_ca_topic_score_codex":0.000048787933,"about_ca_topic_score_gemma":0.0000098818455,"teacher_disagreement_score":0.92779595,"about_ca_system_score_codex":0.000031416694,"about_ca_system_score_gemma":0.00001768215,"threshold_uncertainty_score":0.56658095},"labels":[],"label_agreement":null},{"id":"W2159329831","doi":"10.1109/tmm.2008.2004907","title":"Confidence Evolution in Multimedia Systems","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Multimedia","score_opus":0.019927659524028897,"score_gpt":0.23449570577118192,"score_spread":0.21456804624715303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159329831","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008978059,0.00010856592,0.9888575,0.00014195415,0.0012551992,0.0002557289,0.0000070497686,0.00017476441,0.00022117236],"genre_scores_gemma":[0.9858693,0.000109002525,0.013286011,0.0000612431,0.000051740553,0.000068164045,0.0000037445782,0.000011784525,0.0005389964],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817735,0.00014907947,0.00043288476,0.000469228,0.00045994768,0.00031151462],"domain_scores_gemma":[0.998903,0.00021910794,0.00009568541,0.00051967887,0.00012285447,0.00013968401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026601786,0.00018446143,0.00025837205,0.00048077392,0.0002104753,0.000058412665,0.00045412363,0.00013614002,0.00003104651],"category_scores_gemma":[0.000024166538,0.00018063918,0.000112250484,0.00092971633,0.000083322346,0.0006526729,0.0000023281896,0.00027575987,0.0003221964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000119066215,0.0023225988,0.0065724417,0.00008716092,0.00021842218,0.00041469722,0.012222239,0.8276157,0.019291416,0.0044070566,0.0011842338,0.125545],"study_design_scores_gemma":[0.00063011365,0.000048949176,0.006089246,0.000037987877,0.000010095995,0.00002423642,0.000052982883,0.99047273,0.0022556388,0.000067532324,0.00010044866,0.00021004074],"about_ca_topic_score_codex":0.0008752207,"about_ca_topic_score_gemma":0.00020835246,"teacher_disagreement_score":0.9768913,"about_ca_system_score_codex":0.00018721742,"about_ca_system_score_gemma":0.00011989336,"threshold_uncertainty_score":0.73662525},"labels":[],"label_agreement":null},{"id":"W2159414674","doi":"10.1109/tmm.2007.898937","title":"Event Dynamics Based Temporal Registration","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Temporal resolution; Event (particle physics); Artificial intelligence; Temporal database; Interpolation (computer graphics); Computer vision; Visualization; Dynamics (music); Image registration; Volume (thermodynamics); Data mining; Image (mathematics)","score_opus":0.014936738937338951,"score_gpt":0.29107506459471943,"score_spread":0.2761383256573805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159414674","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00064020883,0.0000077479,0.9966069,0.00077163096,0.0009892607,0.00014458215,0.000005496644,0.0002544843,0.00057970826],"genre_scores_gemma":[0.69648045,0.0000026085938,0.30276522,0.0003430496,0.000025441337,0.00000571924,0.0000037923603,0.0000086155405,0.00036511713],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998859,0.000026628797,0.00025832813,0.00030196348,0.00030178612,0.00025233196],"domain_scores_gemma":[0.9991662,0.0001437959,0.00007580563,0.000409473,0.00006510659,0.00013962951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030167366,0.00013132917,0.000100067766,0.00018554275,0.00016248014,0.000051937324,0.00029108246,0.00005716863,0.000033853445],"category_scores_gemma":[0.000008796353,0.00012913285,0.0000862001,0.00033333842,0.00004334234,0.00040909208,0.0000010214833,0.00022283997,0.00009471557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028384915,0.00023484528,0.000048264603,0.000006069018,0.000006653557,0.000021411755,0.00016297578,0.012322549,0.001663641,0.000218547,0.00011972991,0.9851669],"study_design_scores_gemma":[0.0005003623,0.00006592329,0.00040053073,0.000019623867,0.000003846149,0.000005557036,0.00003146104,0.97542,0.022351524,0.0001209179,0.0009268062,0.00015347078],"about_ca_topic_score_codex":0.000029511726,"about_ca_topic_score_gemma":0.00016295645,"teacher_disagreement_score":0.9850135,"about_ca_system_score_codex":0.00013789786,"about_ca_system_score_gemma":0.00006010632,"threshold_uncertainty_score":0.52658844},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.005886804516660743,"score_gpt":0.21132228290115698,"score_spread":0.20543547838449625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160624927","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49056306,0.000066096276,0.50837433,0.0000044567396,0.00050757604,0.00022890295,0.000016478776,0.00016751718,0.000071565904],"genre_scores_gemma":[0.99567163,0.00042417133,0.0036439402,0.000009758534,0.00008233889,0.000058437094,0.000004800935,0.00006198056,0.000042923784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991179,0.000046712164,0.00022568987,0.0002024589,0.00017717922,0.000230049],"domain_scores_gemma":[0.999029,0.00055872847,0.000037556216,0.00026928095,0.000019664913,0.00008580827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008466608,0.0002335745,0.0002964879,0.00017146696,0.00010134491,0.000007768319,0.00008730706,0.0001107505,0.00003389671],"category_scores_gemma":[0.0000119006545,0.0002273567,0.00007499472,0.0001707202,0.000063520296,0.0001582957,0.0000012331549,0.00024126562,0.000008177639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020862404,0.000028171105,0.00004043319,0.000056554454,0.000048657723,0.00000975768,0.00035504473,0.9536114,0.0044884835,4.345169e-7,0.00003436993,0.041118104],"study_design_scores_gemma":[0.004025266,0.00023934187,0.00023680176,0.00012717223,0.0000383456,0.0000134983375,0.0000067593214,0.8497004,0.14537446,0.0000027066749,0.000025544598,0.00020970833],"about_ca_topic_score_codex":0.000013113416,"about_ca_topic_score_gemma":0.000008968448,"teacher_disagreement_score":0.5051086,"about_ca_system_score_codex":0.000058598198,"about_ca_system_score_gemma":0.000004736975,"threshold_uncertainty_score":0.9271337},"labels":[],"label_agreement":null},{"id":"W2163362100","doi":"10.1109/tmm.2010.2099648","title":"Rate and Distortion Modeling of CGS Coded Scalable Video Content","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Residual; Encoder; Algorithm; Scalable Video Coding; Scalability; Distortion (music); Video quality; Quantization (signal processing); Motion compensation; Bandwidth (computing); Telecommunications","score_opus":0.03713383366777794,"score_gpt":0.24595360229582358,"score_spread":0.20881976862804563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163362100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3130552,0.000016461814,0.6858708,0.000170641,0.0005294261,0.00007825223,0.0000040491514,0.00024112295,0.000034048106],"genre_scores_gemma":[0.96255755,0.00003399298,0.03723806,0.000034606972,0.000011030108,0.000027923912,5.8382875e-7,0.0000070915085,0.000089158886],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914193,0.00003586681,0.00021981163,0.00029257158,0.00015417348,0.00015566451],"domain_scores_gemma":[0.99919003,0.000092899914,0.000067478104,0.00047100656,0.000095139825,0.00008347106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019672014,0.00011367304,0.00015325595,0.0001440578,0.00014445609,0.00004604518,0.00035060407,0.000107111904,0.000009460323],"category_scores_gemma":[0.000020580244,0.0000983549,0.000046533132,0.00015043875,0.00008714096,0.00037937195,0.0000046192426,0.00029979035,0.000009124389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052639152,0.0003424169,0.000043514745,0.000021582322,0.000025009998,0.0000029653015,0.0008176196,0.038351387,0.43500093,0.00039551788,0.000034294022,0.5249121],"study_design_scores_gemma":[0.00029820643,0.000067136,0.0000869896,0.000019564663,0.0000064707488,0.0000029832215,0.000040489842,0.7151108,0.28386024,0.00040383285,0.000017197048,0.00008614291],"about_ca_topic_score_codex":0.00011327443,"about_ca_topic_score_gemma":0.00006446711,"teacher_disagreement_score":0.67675936,"about_ca_system_score_codex":0.000011543459,"about_ca_system_score_gemma":0.000025432371,"threshold_uncertainty_score":0.40107965},"labels":[],"label_agreement":null},{"id":"W2163550024","doi":"10.1109/tmm.2006.876234","title":"Data transmission schemes for DVD-like interactive TV","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Multimedia Communication and Technology","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Interactive television; Multimedia; Digital television; Interactivity; Quality of service; Bandwidth (computing); The Internet; Computer network; World Wide Web; Telecommunications","score_opus":0.060943001039775076,"score_gpt":0.367640111836206,"score_spread":0.3066971107964309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163550024","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00252987,0.00013518802,0.98343325,0.00566419,0.0010545567,0.0009813235,0.00037340645,0.0005721472,0.005256081],"genre_scores_gemma":[0.9080924,0.0003019617,0.08718466,0.00021487602,0.00017768487,0.00020866196,0.00016563099,0.00003364475,0.0036205275],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99857426,0.0001391459,0.0002896741,0.00039239248,0.00026555933,0.00033897776],"domain_scores_gemma":[0.9980199,0.00086418766,0.00009271829,0.0007842685,0.00012293413,0.00011602848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038703677,0.00015925287,0.000189396,0.00020747013,0.00067719934,0.000045175268,0.0009466154,0.00024803256,0.0007340418],"category_scores_gemma":[0.00003628142,0.00015942902,0.000101673926,0.00028086014,0.00035855448,0.00035803832,0.0000039656297,0.0003508455,0.00012432344],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016375103,0.00085230934,0.000024160698,0.000014395936,0.00006166176,0.0000016408625,0.0028720398,0.000101150086,0.004101202,0.00081858557,0.009024473,0.98196465],"study_design_scores_gemma":[0.0011957213,0.000051443323,0.000053071362,0.000031107626,0.00004943776,9.5309446e-7,0.0010261876,0.031066883,0.011092673,0.0004818259,0.9547067,0.00024398965],"about_ca_topic_score_codex":0.0017428624,"about_ca_topic_score_gemma":0.0039151935,"teacher_disagreement_score":0.9817206,"about_ca_system_score_codex":0.000094835734,"about_ca_system_score_gemma":0.00014769626,"threshold_uncertainty_score":0.8037243},"labels":[],"label_agreement":null},{"id":"W2166830713","doi":"10.1109/tmm.2010.2095833","title":"Layered Multicast With Inter-Layer Network Coding for Multimedia Streaming","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Multicast; Computer science; Computer network; Source-specific multicast; Linear network coding; Pragmatic General Multicast; Xcast; Protocol Independent Multicast; Multicast address; Distributed computing; Distance Vector Multicast Routing Protocol; IP multicast; Reliable multicast","score_opus":0.03783306449584478,"score_gpt":0.28836830745662956,"score_spread":0.2505352429607848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166830713","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011473528,0.000023615132,0.984964,0.00053679995,0.0017311093,0.00067343184,0.000017489365,0.0003536184,0.00022640509],"genre_scores_gemma":[0.7177279,0.000044938115,0.28135568,0.00021157697,0.00017481013,0.00019862251,0.0000046188234,0.000030649237,0.00025119365],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820083,0.00009374587,0.00036875557,0.000555345,0.00024299874,0.00053830544],"domain_scores_gemma":[0.9972348,0.0011320503,0.00012664642,0.0009947744,0.0002626381,0.0002490889],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036816226,0.00031470577,0.00029707915,0.00016680668,0.00062660093,0.00019811041,0.0009359759,0.00014389529,0.00010617347],"category_scores_gemma":[0.000038608294,0.00027476778,0.00013425002,0.00048859575,0.00013562937,0.0004599591,0.000012815403,0.0007374169,0.0000726609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014082025,0.00037928027,0.0002515257,0.00002000185,0.00012614699,0.000008362612,0.0025559873,0.0134898275,0.030345324,0.00059324416,0.0006646599,0.95142484],"study_design_scores_gemma":[0.0018286405,0.00017541889,0.00041835004,0.0000934624,0.000026133457,0.000014520924,0.000052869917,0.97013247,0.023622192,0.000024764071,0.003204979,0.00040617154],"about_ca_topic_score_codex":0.000013395199,"about_ca_topic_score_gemma":0.0010702648,"teacher_disagreement_score":0.9566427,"about_ca_system_score_codex":0.000050215673,"about_ca_system_score_gemma":0.000085189866,"threshold_uncertainty_score":0.99997044},"labels":[],"label_agreement":null},{"id":"W2169530986","doi":"10.1109/tmm.2005.850967","title":"Progressive scalable interactive region-of-interest image coding using vector quantization","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Encoder; Vector quantization; Computational complexity theory; Scalability; Quantization (signal processing); Algorithm; Data compression; Coding (social sciences); Computer vision; Artificial intelligence; Mathematics","score_opus":0.05985769026960499,"score_gpt":0.331599419061439,"score_spread":0.27174172879183406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169530986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009117739,0.000028285545,0.98929775,0.00026881087,0.00047256096,0.00035734472,0.000023287426,0.00038615544,0.00004804644],"genre_scores_gemma":[0.5953823,0.0000148559175,0.404457,0.000030910487,0.000036939902,0.000027243466,0.0000026653172,0.000015238708,0.00003289949],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986251,0.00009603672,0.00037224666,0.00044294374,0.00020912486,0.00025454685],"domain_scores_gemma":[0.9985299,0.00026702433,0.00027165445,0.0005984921,0.00022880409,0.00010410103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009890365,0.00020233332,0.0002293993,0.00030983475,0.00019052468,0.000076171564,0.0005873672,0.00008854175,0.000049202994],"category_scores_gemma":[0.00003323202,0.00019473865,0.00009346636,0.00043186828,0.00011805647,0.0019437853,0.000012100614,0.00029866653,0.000038585767],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023050609,0.0013607363,0.000023324346,0.000082401675,0.000108809785,0.00009124823,0.00314446,0.014265274,0.43539456,0.0011039652,0.0008232823,0.54337144],"study_design_scores_gemma":[0.000253099,0.000059134745,0.0000145410795,0.00020572322,0.000009384476,0.000032999145,0.00002650583,0.44826382,0.5507742,0.00009703321,0.00012107885,0.00014243073],"about_ca_topic_score_codex":0.000021986212,"about_ca_topic_score_gemma":0.000011508264,"teacher_disagreement_score":0.5862645,"about_ca_system_score_codex":0.0001864283,"about_ca_system_score_gemma":0.000058313304,"threshold_uncertainty_score":0.79412115},"labels":[],"label_agreement":null},{"id":"W2204753431","doi":"10.1109/tmm.2015.2502067","title":"Energy-Aware and Bandwidth-Efficient Hybrid Video Streaming Over Mobile Networks","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multicast; Computer science; Unicast; Computer network; Xcast; Cellular network; Source-specific multicast; Distributed computing; Energy consumption","score_opus":0.007619853450722221,"score_gpt":0.20928318060148848,"score_spread":0.20166332715076626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2204753431","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024783213,0.00043985838,0.97255534,0.0000055300998,0.0014124668,0.00018622172,0.000024358354,0.00045558542,0.0001374102],"genre_scores_gemma":[0.9950064,0.00035435642,0.0041264053,0.000030248475,0.00014833156,0.00012219301,0.000022303017,0.00007479479,0.000114994225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989637,0.000026544729,0.00022875068,0.00026562522,0.00019709107,0.00031830155],"domain_scores_gemma":[0.99931633,0.00012846862,0.00003241942,0.00023023451,0.00005296526,0.00023956274],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006757477,0.00023920955,0.00019827491,0.00012759953,0.00009870997,0.000033492528,0.0000805681,0.00010226695,0.000047287343],"category_scores_gemma":[0.0000018384025,0.0002549655,0.000050911956,0.00021403671,0.000056532703,0.0001524227,0.0000015124406,0.0002332304,0.000011648782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018131763,0.00004250731,0.000009317729,0.0000066828657,0.000023818988,0.000005906911,0.0001295017,0.8697673,0.00007829901,0.0000010308592,0.00034828417,0.12956922],"study_design_scores_gemma":[0.00078085845,0.000048273276,0.000019405545,0.000040329687,0.000027677572,0.000008380539,0.00006821951,0.9946468,0.0033268323,0.000007026357,0.0007659118,0.00026027637],"about_ca_topic_score_codex":0.000020361666,"about_ca_topic_score_gemma":0.000028895585,"teacher_disagreement_score":0.9702232,"about_ca_system_score_codex":0.00013770182,"about_ca_system_score_gemma":0.00001537653,"threshold_uncertainty_score":0.9999903},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.031006308726461918,"score_gpt":0.2782674574003173,"score_spread":0.24726114867385535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293936600","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0477343,0.000011673327,0.95070976,0.00044643396,0.0004012298,0.00036218512,0.000040619634,0.00020805282,0.00008574827],"genre_scores_gemma":[0.8758514,0.000010154654,0.12382484,0.00015722543,0.000031276366,0.00008179814,0.000008576444,0.000010897986,0.000023812512],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985439,0.00020973789,0.00027979855,0.0003487165,0.00025322306,0.0003646284],"domain_scores_gemma":[0.9990211,0.00013806426,0.00008921664,0.00052332197,0.0000781123,0.00015018706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040973278,0.00020263226,0.00017025936,0.00014959753,0.00022191308,0.00007116734,0.0005761379,0.000112332615,0.0000038883804],"category_scores_gemma":[0.000008910857,0.00014814343,0.00009791543,0.00048073017,0.00017645062,0.000651857,0.000007950877,0.00037193613,0.000014006033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021982712,0.0046798126,0.009666594,0.00011881253,0.0005408833,0.0006159734,0.13701414,0.22565603,0.056879185,0.004110615,0.004437178,0.5540825],"study_design_scores_gemma":[0.0030160572,0.0001933085,0.0018034228,0.00011212267,0.000026689497,0.00006113066,0.0005740764,0.8470613,0.13843371,0.004481769,0.0035230883,0.0007133419],"about_ca_topic_score_codex":0.00017540573,"about_ca_topic_score_gemma":0.00022560747,"teacher_disagreement_score":0.82811713,"about_ca_system_score_codex":0.00016838063,"about_ca_system_score_gemma":0.00005747521,"threshold_uncertainty_score":0.6041114},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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)","score_opus":0.026755965340925336,"score_gpt":0.2841701884584154,"score_spread":0.25741422311749007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2301300958","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09696929,0.0000060817692,0.8983595,0.00028243774,0.0029551084,0.000666944,0.000034549714,0.000703313,0.000022778679],"genre_scores_gemma":[0.9943731,0.0000023739412,0.0043583727,0.00009676925,0.000061149294,0.0004437947,0.000002382764,0.000028342964,0.00063372974],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812406,0.00012355427,0.00042617627,0.0005814626,0.00037249675,0.00037226395],"domain_scores_gemma":[0.9989393,0.00016868635,0.00014350489,0.00039636082,0.00019261152,0.0001595849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029086656,0.00024517585,0.0002337666,0.00036884894,0.0004950242,0.000077595585,0.00033877583,0.00015285767,0.000034824643],"category_scores_gemma":[0.000011188032,0.0001862421,0.00023863984,0.00032435101,0.00007234878,0.00042409406,0.0000018253011,0.00013062186,0.00019281184],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012880472,0.00061292376,0.000015712114,0.00008458129,0.000053447653,0.00000500955,0.00013889735,0.00050840934,0.5309628,0.0003527538,0.000026837486,0.4671098],"study_design_scores_gemma":[0.0072658756,0.0020692921,0.0030619332,0.00022111506,0.00007551551,0.000018628192,0.000099533994,0.49987182,0.48635477,0.000118240765,0.00016713052,0.00067616336],"about_ca_topic_score_codex":0.00008911557,"about_ca_topic_score_gemma":0.00022047815,"teacher_disagreement_score":0.8974038,"about_ca_system_score_codex":0.0003410621,"about_ca_system_score_gemma":0.000040734398,"threshold_uncertainty_score":0.75947326},"labels":[],"label_agreement":null},{"id":"W2321027716","doi":"10.1109/tmm.2016.2538721","title":"A Geometric Approach to Server Selection for Interactive Video Streaming","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Peer-to-Peer Network Technologies","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Server; Computer science; PlanetLab; Computer network; Scalability; Relay; Backup; Operating system","score_opus":0.022012313335899803,"score_gpt":0.2598254123809206,"score_spread":0.2378130990450208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2321027716","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018443955,0.0000062690633,0.9775041,0.0013414278,0.0008634362,0.0008316881,0.000028124292,0.0008394153,0.00014161212],"genre_scores_gemma":[0.69127923,0.000003706036,0.3072677,0.0001723078,0.000055302236,0.00052628625,7.394607e-7,0.000019706196,0.0006750454],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99820405,0.00004008501,0.00024995496,0.0007085298,0.00030836428,0.0004889934],"domain_scores_gemma":[0.99833995,0.0006743185,0.00006574872,0.0005378877,0.00019867772,0.00018342826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023055624,0.00023538333,0.00022164633,0.0011624888,0.00017734997,0.000091122856,0.00080016,0.00014065902,0.0000105775425],"category_scores_gemma":[0.00010806819,0.00018535373,0.00012301414,0.0022099013,0.00003108345,0.0006083194,0.000011047639,0.00019628377,0.00021095254],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007422557,0.00029816545,0.000034147397,0.000009692118,0.00006762862,8.092486e-7,0.0005716987,0.009128523,0.008600085,0.000068329835,0.0019261534,0.97922057],"study_design_scores_gemma":[0.0032469002,0.0017780177,0.0028209435,0.00024999006,0.000080478305,0.00004662919,0.00022279761,0.3880484,0.59076446,0.0007295417,0.010559528,0.0014522629],"about_ca_topic_score_codex":0.000054652468,"about_ca_topic_score_gemma":0.000078582234,"teacher_disagreement_score":0.9777683,"about_ca_system_score_codex":0.0003370486,"about_ca_system_score_gemma":0.000049052807,"threshold_uncertainty_score":0.75585055},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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","score_opus":0.014167516585760404,"score_gpt":0.23181583405439607,"score_spread":0.21764831746863567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2328057526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011316833,0.00006948705,0.98431855,0.00069550966,0.0024034267,0.00035655402,0.0000194551,0.0007887955,0.000031370102],"genre_scores_gemma":[0.8882719,0.00014881768,0.11050208,0.000504523,0.00013215217,0.000118495416,0.0000042475517,0.000050823415,0.00026690692],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971609,0.00017575831,0.000645469,0.0008509612,0.00034811936,0.00081877096],"domain_scores_gemma":[0.9971567,0.0014901456,0.00014069022,0.000891391,0.00007513768,0.00024596017],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004291033,0.0004004288,0.00043137564,0.00036277052,0.00017750739,0.00013402461,0.00094130356,0.0002486093,0.0001540042],"category_scores_gemma":[0.000057146826,0.0003029929,0.00023936648,0.0007145375,0.00009866739,0.0007587432,0.000014460637,0.0004915548,0.00025231394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020252193,0.00036469154,0.00022230898,0.0000061642795,0.000058660604,0.0000604603,0.00052994076,0.20033537,0.000106522064,0.00003181098,0.001006918,0.7970746],"study_design_scores_gemma":[0.0056124097,0.00018748578,0.0005600847,0.00037136208,0.0000277585,0.000036840323,0.000018203626,0.99112296,0.00076727575,0.00006136339,0.00058590295,0.0006483552],"about_ca_topic_score_codex":0.000042309828,"about_ca_topic_score_gemma":0.00016166749,"teacher_disagreement_score":0.8769551,"about_ca_system_score_codex":0.00018809925,"about_ca_system_score_gemma":0.00006757035,"threshold_uncertainty_score":0.99994224},"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,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_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)","score_opus":0.08289732554385422,"score_gpt":0.30321194501167154,"score_spread":0.22031461946781733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2534681680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030284426,0.000046521385,0.99371004,0.0017875809,0.0009270839,0.00025708316,0.000010948327,0.00017182245,0.000060488037],"genre_scores_gemma":[0.80479187,0.000009439706,0.19415814,0.00027164893,0.00014613757,0.000044780856,8.825451e-7,0.000017639755,0.00055948034],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878794,0.00003276426,0.00021187443,0.00043638123,0.00022371017,0.0003073527],"domain_scores_gemma":[0.99912566,0.00020889097,0.00009908634,0.00036521035,0.000094175586,0.000106972046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016858101,0.00017109395,0.00015061279,0.0001370869,0.00038515392,0.00012605215,0.00034458068,0.00012201334,0.00002392103],"category_scores_gemma":[0.000012908144,0.00012437624,0.00012207299,0.00020926875,0.000057383146,0.0005190799,0.0000024953588,0.00015001086,0.000026367059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029683279,0.0001501634,0.000022309176,0.00003540757,0.000034479395,0.0000017849433,0.00057172385,0.033069674,0.09642805,0.00020772027,0.00050351577,0.8689455],"study_design_scores_gemma":[0.0016848636,0.00010562148,0.001487247,0.00030293642,0.000051132458,0.000036597205,0.00006810864,0.869338,0.12401602,0.0010804904,0.0012628515,0.0005661346],"about_ca_topic_score_codex":0.000013637025,"about_ca_topic_score_gemma":0.000019549216,"teacher_disagreement_score":0.86837935,"about_ca_system_score_codex":0.00012500054,"about_ca_system_score_gemma":0.000083196304,"threshold_uncertainty_score":0.50719154},"labels":[],"label_agreement":null},{"id":"W2562954379","doi":"10.1109/tmm.2016.2646182","title":"Live Broadcast With Community Interactions: Bottlenecks and Optimizations","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Multimedia Communication and Technology","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Fundamental Research Funds for the Central Universities","keywords":"Computer science; Cloud computing; Multimedia; Amateur; Latency (audio); Synchronization (alternating current); Bandwidth (computing); Context (archaeology); Live streaming; Computer network; Telecommunications; Operating system","score_opus":0.03591970123785901,"score_gpt":0.31164573865776973,"score_spread":0.27572603741991075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562954379","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08245688,0.00009773317,0.87388015,0.021981388,0.0009957995,0.0010842404,0.00016951034,0.0011926786,0.018141635],"genre_scores_gemma":[0.9777147,0.0008347744,0.017384548,0.00017095309,0.00003296131,0.00012374601,0.000004581632,0.000019575555,0.003714124],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987325,0.00042997673,0.0001856127,0.00017776695,0.00023271769,0.00024144156],"domain_scores_gemma":[0.99770975,0.001335432,0.000079076795,0.0004966487,0.00020865942,0.000170417],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00034118188,0.00013681641,0.00014642159,0.00021233466,0.001293243,0.00004170873,0.00031812114,0.00013156595,0.0009188067],"category_scores_gemma":[0.00010228942,0.00010096296,0.000041823285,0.0002609417,0.00089335034,0.00030604991,0.0000039735082,0.00046132365,0.00020351565],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014210759,0.0010031925,0.0006712746,0.000010096488,0.00014552956,0.0000034317854,0.039076373,0.00025429614,0.0015190142,0.0010021507,0.00063332764,0.9555392],"study_design_scores_gemma":[0.025420358,0.0026721072,0.012019351,0.0023229765,0.00088688015,0.00017564575,0.17914382,0.029764412,0.035192594,0.0036369008,0.70366377,0.005101188],"about_ca_topic_score_codex":0.001763897,"about_ca_topic_score_gemma":0.013025736,"teacher_disagreement_score":0.950438,"about_ca_system_score_codex":0.00010959329,"about_ca_system_score_gemma":0.000098900055,"threshold_uncertainty_score":0.9999945},"labels":[],"label_agreement":null},{"id":"W2577358469","doi":"10.1109/tmm.2017.2655423","title":"Online MoCap Data Coding With Bit Allocation, Rate Control, and Motion-Adaptive Post-Processing","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Codec; Animation; Motion capture; Artificial intelligence; Coding (social sciences); Rendering (computer graphics); Computer animation; Computer vision; Automotive industry; Motion (physics); Computer graphics (images); Computer hardware","score_opus":0.03524122005034014,"score_gpt":0.2720617423097473,"score_spread":0.23682052225940714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577358469","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064625964,0.000029996147,0.99034137,0.0026285807,0.00016569188,0.00015624485,0.00009655344,0.00008011701,0.000038873866],"genre_scores_gemma":[0.95859456,0.000055379503,0.04079217,0.00033456058,0.00004962666,0.000009661397,0.00004330101,0.000012367362,0.0001083457],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876016,0.00006774602,0.00022049919,0.0005246998,0.00022895187,0.0001979484],"domain_scores_gemma":[0.998203,0.00011088284,0.00019696911,0.0010668968,0.00030306962,0.000119206736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031755728,0.00016112499,0.0001971587,0.000115770905,0.00092535414,0.00048767775,0.00078376185,0.000062833155,0.000009118577],"category_scores_gemma":[0.000038891816,0.00013370626,0.000029963767,0.00014984814,0.00012190287,0.0018895097,0.000008524517,0.00016981065,0.000012502258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015842544,0.0006367517,0.00060953246,0.000035822435,0.00029765398,0.000021826298,0.0015374442,0.024947446,0.009099392,0.00039794826,0.00008233134,0.9621754],"study_design_scores_gemma":[0.0010184295,0.00007637827,0.008408333,0.00005020743,0.00008075857,0.000004780565,0.00007586345,0.98886114,0.0011646184,0.00004160274,0.000046739362,0.00017112317],"about_ca_topic_score_codex":0.00023505233,"about_ca_topic_score_gemma":0.00093024754,"teacher_disagreement_score":0.96391374,"about_ca_system_score_codex":0.00003139795,"about_ca_system_score_gemma":0.00009093952,"threshold_uncertainty_score":0.71171695},"labels":[],"label_agreement":null},{"id":"W2579587788","doi":"10.1109/tmm.2017.2652061","title":"CrowdTranscoding: Online Video Transcoding With Massive Viewers","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Qatar National Research Fund","keywords":"Transcoding; Computer science; Multimedia; Schedule; Quality of experience; Workload; Crowdsourcing; Phone; Quality (philosophy); Key (lock); PlanetLab; Computer network; The Internet; Quality of service; World Wide Web; Computer security; Operating system","score_opus":0.048767847598981104,"score_gpt":0.323983511580692,"score_spread":0.2752156639817109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2579587788","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007009839,0.000028605282,0.98635334,0.004420913,0.0009213474,0.00034983474,0.00004128417,0.00023168701,0.00064313173],"genre_scores_gemma":[0.8986589,0.00007904731,0.1001298,0.0005327654,0.00008309595,0.000053210188,0.0000031371962,0.000026515634,0.0004335546],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99795574,0.00008461433,0.00032930958,0.0006212113,0.00053052424,0.0004785846],"domain_scores_gemma":[0.9979094,0.00018961488,0.00017292151,0.0013669493,0.00012836023,0.00023275422],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024728058,0.00030938175,0.0003167601,0.00017532129,0.0010205632,0.0005251687,0.0013338354,0.00010882993,0.00008120116],"category_scores_gemma":[0.000011913683,0.00026353012,0.00018991211,0.00015037594,0.00021011884,0.0013994068,0.000003308617,0.00047521034,0.0000911614],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003443991,0.002333777,0.00016463245,0.00023318488,0.0005509498,0.0004490725,0.009988956,0.0130816195,0.037779726,0.0010996431,0.00051890005,0.93345517],"study_design_scores_gemma":[0.010880285,0.0016230413,0.0066334074,0.0011449031,0.00035466754,0.00009273338,0.00076848426,0.43373582,0.53493404,0.00041496588,0.0068493932,0.0025682414],"about_ca_topic_score_codex":0.00014888869,"about_ca_topic_score_gemma":0.00028586978,"teacher_disagreement_score":0.9308869,"about_ca_system_score_codex":0.00008624255,"about_ca_system_score_gemma":0.00014381044,"threshold_uncertainty_score":0.9999817},"labels":[],"label_agreement":null},{"id":"W2594405368","doi":"10.1109/tmm.2017.2678198","title":"Cross-Layer Resource Allocation for Scalable Video Over OFDMA Wireless Networks: Tradeoff Between Quality Fairness and Efficiency","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Scalability; Video quality; Mathematical optimization; Resource allocation; Wireless; Fairness measure; Quality (philosophy); Spectral efficiency; Optimization problem; Computer network; Throughput; Channel (broadcasting); Algorithm; Metric (unit); Mathematics; Telecommunications","score_opus":0.026386789740327706,"score_gpt":0.3031946810506294,"score_spread":0.2768078913103017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594405368","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21020573,0.00009660843,0.7880615,0.00007335678,0.00056072,0.00053827604,0.00008857391,0.00029098828,0.00008424785],"genre_scores_gemma":[0.9940491,0.00019995467,0.004827634,0.000022272512,0.00030229246,0.0002528185,0.000034913213,0.00009548612,0.00021557008],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838287,0.000044765875,0.0004479875,0.00044114527,0.00023330904,0.00044990293],"domain_scores_gemma":[0.9985406,0.00047149058,0.00014886244,0.00058666576,0.000084684034,0.00016774333],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031276324,0.00030421137,0.00035499767,0.00010308617,0.00076586544,0.00020201899,0.00029127553,0.00028063747,0.000031481886],"category_scores_gemma":[0.00002131908,0.00033083506,0.00010247538,0.00012927332,0.00020882396,0.00060452253,0.0000029345927,0.00031741787,0.00000608002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005383185,0.000048312002,0.0010072677,0.00006954954,0.000047058216,3.8350063e-7,0.00017451815,0.8654316,0.0007847873,0.00000859878,0.00005151093,0.13232258],"study_design_scores_gemma":[0.001500426,0.000036073165,0.021181598,0.0000881301,0.0000615502,7.1746405e-7,0.00002244463,0.9639968,0.012373643,0.000023291712,0.00033541245,0.0003798657],"about_ca_topic_score_codex":0.0000483851,"about_ca_topic_score_gemma":0.00006481946,"teacher_disagreement_score":0.78384334,"about_ca_system_score_codex":0.00010823675,"about_ca_system_score_gemma":0.000016929243,"threshold_uncertainty_score":0.99991435},"labels":[],"label_agreement":null},{"id":"W2609389548","doi":"10.1109/tmm.2017.2699082","title":"Blind Stereo Quality Assessment Based on Learned Features From Binocular Combined Images","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Artificial intelligence; Computer science; Contrast (vision); Feature (linguistics); Computer vision; Pattern recognition (psychology); Property (philosophy); Feature extraction; Stereo image; Perception; Representation (politics); Image (mathematics); Feature learning","score_opus":0.07706284941214316,"score_gpt":0.3824872957914618,"score_spread":0.30542444637931865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609389548","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014081851,0.000013951258,0.9754735,0.0065123737,0.0017339304,0.0004871085,0.0001381129,0.00026599795,0.0012931224],"genre_scores_gemma":[0.9148145,0.000015565725,0.083038144,0.001148207,0.00009076986,0.00008788894,0.000021048405,0.00002585118,0.0007580278],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968035,0.0005043843,0.00045952896,0.00086471206,0.0009204112,0.00044742186],"domain_scores_gemma":[0.9959178,0.00073871604,0.00031472,0.0026372655,0.0001479326,0.00024357202],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007279758,0.00037597716,0.00043266627,0.00020814386,0.0010206662,0.0010107721,0.0015742099,0.0001856574,0.00017189786],"category_scores_gemma":[0.000045551395,0.00034476336,0.00029455317,0.00013207365,0.00017626649,0.0008315811,0.000013418299,0.0007160692,0.00020075578],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014291136,0.008057003,0.0010929463,0.00013388084,0.0006845651,0.00028328426,0.0023492505,0.016578434,0.040998217,0.0009746792,0.0035527304,0.9238659],"study_design_scores_gemma":[0.01733882,0.0013718002,0.17825896,0.00026926535,0.0001622827,0.000002916119,0.00022284157,0.46795145,0.33004567,0.0014376446,0.0012515568,0.0016867982],"about_ca_topic_score_codex":0.0012082973,"about_ca_topic_score_gemma":0.00014828295,"teacher_disagreement_score":0.9221791,"about_ca_system_score_codex":0.00015121087,"about_ca_system_score_gemma":0.00021736156,"threshold_uncertainty_score":0.99990046},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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","score_opus":0.017050863147401944,"score_gpt":0.27612067738154655,"score_spread":0.2590698142341446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811207102","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015902658,0.0000078762005,0.9956276,0.00019023546,0.0011548907,0.00053732,0.00019553518,0.0006219396,0.00007436456],"genre_scores_gemma":[0.6948261,0.000011191316,0.30445385,0.00014420605,0.00010041071,0.00028302617,0.0000029845075,0.000016861459,0.00016135884],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985855,0.000057547502,0.00024227539,0.00046981598,0.00022123623,0.00042360747],"domain_scores_gemma":[0.99889684,0.00031972182,0.00005745244,0.0004347365,0.00014426016,0.0001469844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018609538,0.00021155777,0.00019224046,0.00020067302,0.0003519624,0.00008939091,0.00052921317,0.000106649364,0.000026792486],"category_scores_gemma":[0.00001174512,0.00018193875,0.00011246041,0.00018665497,0.00020159825,0.0003423667,0.000004565104,0.00017470741,0.000056394085],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013176489,0.0014660612,0.0000215687,0.000161523,0.0003076723,0.000090166686,0.0051214104,0.0012550141,0.10092391,0.0008004295,0.004511679,0.8840229],"study_design_scores_gemma":[0.00086608,0.0008888705,0.00009755116,0.00003234667,0.000024142875,0.000016824773,0.0000139928925,0.12473392,0.8688578,0.0027500116,0.0013724908,0.00034597013],"about_ca_topic_score_codex":0.000014190982,"about_ca_topic_score_gemma":0.000017599185,"teacher_disagreement_score":0.88367695,"about_ca_system_score_codex":0.000042665983,"about_ca_system_score_gemma":0.000029425715,"threshold_uncertainty_score":0.7419247},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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)","score_opus":0.027752562738193338,"score_gpt":0.30748212242052814,"score_spread":0.2797295596823348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884820794","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04604734,0.000009619454,0.9466936,0.00037939017,0.0036203882,0.0006780636,0.00012390118,0.0001369403,0.0023107287],"genre_scores_gemma":[0.99567634,0.00001612211,0.001682595,0.00027782636,0.0001657046,0.00025515008,0.000128293,0.000012362078,0.0017856237],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893826,0.000104732906,0.00034989568,0.00018377564,0.00018064758,0.00024270396],"domain_scores_gemma":[0.99852544,0.0007034145,0.00012722274,0.00026565403,0.00028881925,0.0000894616],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031536844,0.00012708138,0.00013557982,0.00027014333,0.00063969474,0.0000462255,0.000105134226,0.0001818627,0.0006942761],"category_scores_gemma":[0.000048721373,0.00009763687,0.00020299968,0.00051556365,0.00012265753,0.00019344233,6.734675e-7,0.00018343107,0.0011681783],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017371624,0.00058322644,0.00076386763,0.000011276267,0.0011506039,6.094949e-7,0.006718995,0.002980473,0.0013938528,0.0003089923,0.0046609053,0.97969],"study_design_scores_gemma":[0.012205456,0.001575874,0.055610888,0.000039740706,0.002074047,0.000012503781,0.0024335578,0.8058077,0.016246812,0.00019847888,0.102967836,0.00082709803],"about_ca_topic_score_codex":0.00014463927,"about_ca_topic_score_gemma":0.0017987731,"teacher_disagreement_score":0.97886294,"about_ca_system_score_codex":0.000059038164,"about_ca_system_score_gemma":0.000033347063,"threshold_uncertainty_score":0.99960953},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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","score_opus":0.03979363620046616,"score_gpt":0.2607314566692937,"score_spread":0.22093782046882754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891286892","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060343765,0.000047608726,0.9344415,0.00023135779,0.0038472246,0.00032668828,0.000046388013,0.0005165931,0.00019889239],"genre_scores_gemma":[0.99568135,0.000018809062,0.002942033,0.0002997317,0.00026387308,0.000114287715,0.000008881515,0.00001671198,0.0006543374],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983848,0.00010276605,0.0003072024,0.0005044991,0.00039248873,0.0003082873],"domain_scores_gemma":[0.9987272,0.000075403404,0.00007518091,0.00063315406,0.0003154371,0.00017364966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026823406,0.00020495719,0.00018249342,0.00019825638,0.00048982253,0.00013094749,0.0004915596,0.00012634236,0.000046499037],"category_scores_gemma":[0.000013877654,0.00019888225,0.00013531176,0.00036714957,0.000099001714,0.00057157176,0.0000053060025,0.0003729023,0.0002851179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012020075,0.0012168544,0.0005004042,0.000050813884,0.00019284153,0.000022570055,0.0049425075,0.019080361,0.015611617,0.00017896129,0.0014236271,0.95665926],"study_design_scores_gemma":[0.001081529,0.00055060437,0.0028282555,0.0000960065,0.000048099555,0.00003311349,0.00020601685,0.97028214,0.023437807,0.000053279262,0.001003991,0.00037913746],"about_ca_topic_score_codex":0.0011446757,"about_ca_topic_score_gemma":0.0002093686,"teacher_disagreement_score":0.9562801,"about_ca_system_score_codex":0.0001603243,"about_ca_system_score_gemma":0.000107810956,"threshold_uncertainty_score":0.8110183},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.03197822218404654,"score_gpt":0.2916044915662906,"score_spread":0.25962626938224403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897585580","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14567399,0.0000046185196,0.851225,0.00013565841,0.0015675577,0.0006247825,0.000057269022,0.0005443633,0.00016673037],"genre_scores_gemma":[0.96341455,0.000015146291,0.034891058,0.00014952621,0.00055125746,0.00022423605,0.00028093084,0.000042281128,0.0004310192],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99711585,0.00023895205,0.0006357386,0.00092965405,0.0004945728,0.0005852549],"domain_scores_gemma":[0.9980261,0.00038791265,0.0003181592,0.00046262977,0.0005293799,0.00027586176],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00039798033,0.00037920417,0.0004040005,0.0009940712,0.0013205375,0.00021317435,0.00035859869,0.00024091119,0.00049372157],"category_scores_gemma":[0.000035676756,0.00041181323,0.00052117923,0.00095719355,0.00015311912,0.0010919123,0.000005069062,0.00061092747,0.00064948347],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030237302,0.0009804412,0.00019884066,0.00003107332,0.0007456507,0.000007447991,0.00093098066,0.023690956,0.03570502,0.000029285113,0.00019549154,0.9371824],"study_design_scores_gemma":[0.0019509458,0.00057789,0.001716841,0.0000264603,0.0004371124,0.000014917845,0.00004747983,0.89729685,0.09649116,0.00045407948,0.0005036887,0.00048256147],"about_ca_topic_score_codex":0.00034761053,"about_ca_topic_score_gemma":0.00063259073,"teacher_disagreement_score":0.93669987,"about_ca_system_score_codex":0.0002867813,"about_ca_system_score_gemma":0.0000877313,"threshold_uncertainty_score":0.9999796},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Convolutional neural network; Benchmark (surveying); Artificial intelligence; Consistency (knowledge bases); Ground truth; Estimation; Pattern recognition (psychology); Computer vision; Speech recognition","score_opus":0.013398895468783728,"score_gpt":0.2421667185512783,"score_spread":0.22876782308249458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903559293","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48441187,0.000015948815,0.5080059,0.00006598395,0.0045056785,0.00043470133,0.00023336473,0.0010524997,0.0012740368],"genre_scores_gemma":[0.9850428,0.000028154602,0.013820834,0.00003165982,0.0007049562,0.00007373873,0.00002319002,0.00008460555,0.00019005527],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986596,0.000062379026,0.0003207687,0.0003283921,0.00025185346,0.0003769741],"domain_scores_gemma":[0.99895835,0.00037556313,0.000037644553,0.00034438004,0.0000882858,0.00019578876],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012448634,0.00029601916,0.00028602648,0.00017651115,0.00018398477,0.00006776181,0.00016096738,0.00018763449,0.00074626395],"category_scores_gemma":[0.00001801579,0.0002945505,0.00011663443,0.00023959304,0.00010054343,0.0003745411,0.0000013500376,0.0002934544,0.004171243],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006398231,0.000055018016,0.00004900152,0.000013494587,0.00008183047,0.0000071097033,0.0006550423,0.020222483,0.94523585,7.4158646e-7,0.00049657724,0.033118863],"study_design_scores_gemma":[0.000900634,0.00010968792,0.001426652,0.00008656868,0.000055442575,0.0000024831895,0.000042457024,0.12192731,0.87445325,0.000058408412,0.0005436138,0.00039350032],"about_ca_topic_score_codex":0.00041893116,"about_ca_topic_score_gemma":0.00009523004,"teacher_disagreement_score":0.500631,"about_ca_system_score_codex":0.0002020715,"about_ca_system_score_gemma":0.000031912205,"threshold_uncertainty_score":0.99995065},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.008978700472931145,"score_gpt":0.235699129488738,"score_spread":0.22672042901580686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908941882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15321481,0.000034748467,0.842289,0.00020550324,0.0025971164,0.0011190452,0.000011636015,0.00045031836,0.00007786047],"genre_scores_gemma":[0.9798762,0.000013658384,0.019120604,0.00014452108,0.0002827792,0.00037882224,0.00000574023,0.00004898816,0.00012866133],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997237,0.00024938665,0.00047460187,0.00094432186,0.00046534906,0.0006293105],"domain_scores_gemma":[0.9984491,0.0004802045,0.00019768665,0.00039623972,0.00032307827,0.0001536932],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004758566,0.00038506306,0.00039212036,0.0007428748,0.0005545814,0.00013888767,0.000414404,0.0003150715,0.00010874005],"category_scores_gemma":[0.000058176745,0.00043271607,0.00034474017,0.0008300197,0.000086805565,0.00068110385,0.0000057202924,0.000987784,0.00025339465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010258128,0.00077131024,0.000045196964,0.00006146624,0.000084478386,0.000013959479,0.0035532662,0.116087526,0.04015522,0.000023084003,0.000018437171,0.8381602],"study_design_scores_gemma":[0.0028683802,0.000805211,0.00051301584,0.00002671501,0.000020423939,0.000011791942,0.00014684231,0.8374274,0.15722096,0.00017144335,0.00039119204,0.0003966402],"about_ca_topic_score_codex":0.00031779622,"about_ca_topic_score_gemma":0.0017969576,"teacher_disagreement_score":0.8377636,"about_ca_system_score_codex":0.000679605,"about_ca_system_score_gemma":0.00009843426,"threshold_uncertainty_score":0.9998125},"labels":[],"label_agreement":null},{"id":"W2909646374","doi":"10.1109/tmm.2019.2892007","title":"Dynamic Cross-Layer Signaling Exchange for Real-Time and On-Demand Multimedia Streams","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Quality of service; Application layer; Multimedia; The Internet; Hypertext Transfer Protocol; Operating system","score_opus":0.023808484204228573,"score_gpt":0.3187754195556047,"score_spread":0.2949669353513761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909646374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2256118,0.000042127856,0.7711562,0.00035025133,0.0011020214,0.0010722239,0.00013580348,0.00026389648,0.0002656606],"genre_scores_gemma":[0.8888513,0.00010900911,0.10685144,0.0003705082,0.00007970433,0.00018460532,0.000020927499,0.000049284874,0.0034832475],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978379,0.000102604245,0.00038415208,0.0007640053,0.00040195003,0.00050941366],"domain_scores_gemma":[0.9978394,0.0010371403,0.00012034217,0.00066488987,0.000121924066,0.00021630434],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000469632,0.00032651576,0.000353697,0.0002322466,0.0002555618,0.00024733914,0.00043412548,0.00017530342,0.00019649023],"category_scores_gemma":[0.00001276236,0.00030736576,0.00016773885,0.00020929788,0.00008862212,0.000609005,0.000007037647,0.00027294908,0.0005132641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052061287,0.001802434,0.00018600271,0.00043485264,0.00038404446,0.00004092489,0.009922634,0.02538082,0.251924,0.000121667814,0.0006274604,0.7086545],"study_design_scores_gemma":[0.0027265474,0.00056936464,0.0006418999,0.00009416364,0.000035191653,0.000006489659,0.000060007464,0.93342745,0.061568305,0.000109982844,0.00030041276,0.00046019265],"about_ca_topic_score_codex":0.00009886015,"about_ca_topic_score_gemma":0.000031354823,"teacher_disagreement_score":0.9080466,"about_ca_system_score_codex":0.0001056961,"about_ca_system_score_gemma":0.00007459347,"threshold_uncertainty_score":0.99993783},"labels":[],"label_agreement":null},{"id":"W2919557871","doi":"10.1109/tmm.2019.2902099","title":"Multi-Scale Interpretation Model for Convolutional Neural Networks: Building Trust Based on Hierarchical Interpretation","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Interpretability; Computer science; Discriminative model; Artificial intelligence; Convolutional neural network; Machine learning; Interpretation (philosophy); Deep learning; Artificial neural network; Feature (linguistics); Scale (ratio)","score_opus":0.024416744323347336,"score_gpt":0.2849254407007991,"score_spread":0.2605086963774518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2919557871","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017493159,0.000008947598,0.978833,0.00047748486,0.0019017983,0.00093149993,0.000035049106,0.00026677846,0.000052266038],"genre_scores_gemma":[0.75932753,0.000001993075,0.23955083,0.00063644716,0.000049851256,0.00020529405,0.000011286707,0.000027082699,0.00018970358],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997785,0.00012713812,0.00047804453,0.0007288643,0.00039483485,0.00048612434],"domain_scores_gemma":[0.99799967,0.00096717465,0.00012833321,0.0005258612,0.0002010914,0.00017786576],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035379443,0.00028888317,0.0002597366,0.0003642843,0.00025386692,0.00015276727,0.00060786115,0.00017717933,0.000045155815],"category_scores_gemma":[0.000041170777,0.0002967906,0.0002517694,0.00036469309,0.00009980941,0.0007693412,0.0000049828095,0.00046869522,0.000110930996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036823243,0.0002780987,0.000024479754,0.00001575385,0.000015640733,0.0000010449802,0.0010360777,0.93367535,0.0038085976,0.00022798912,0.000026762486,0.060521986],"study_design_scores_gemma":[0.00077881134,0.00032733893,0.00008725024,0.00007213997,0.000017352693,0.000002164966,0.000050655875,0.9894702,0.008605626,0.00027428256,0.000013305123,0.00030091315],"about_ca_topic_score_codex":0.000023001583,"about_ca_topic_score_gemma":0.00005705921,"teacher_disagreement_score":0.74183434,"about_ca_system_score_codex":0.0002322164,"about_ca_system_score_gemma":0.00010012563,"threshold_uncertainty_score":0.99994844},"labels":[],"label_agreement":null},{"id":"W2962740177","doi":"10.1109/tmm.2019.2891418","title":"MC-SSM: Nonparametric Semantic Image Segmentation With the ICM Algorithm","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Image segmentation; Artificial intelligence; Segmentation; Scale-space segmentation; Pattern recognition (psychology); Nonparametric statistics; Algorithm; Computer vision; Mathematics","score_opus":0.008836781364611048,"score_gpt":0.2599988188402725,"score_spread":0.25116203747566146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962740177","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023235686,0.000037764432,0.9953529,0.0004430057,0.00036124172,0.00084081036,0.000010411014,0.00037526884,0.00025503253],"genre_scores_gemma":[0.31491193,0.000103692226,0.6830055,0.00053014094,0.000040985826,0.0001387925,0.0000031064938,0.000028912878,0.0012369229],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986113,0.00007114199,0.00018668434,0.00041825717,0.0004209993,0.00029165734],"domain_scores_gemma":[0.99869555,0.0003425768,0.000093602954,0.0006672022,0.00011953519,0.000081543396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018886305,0.00020540843,0.0001756403,0.00024174592,0.00018031846,0.00013970686,0.00056216156,0.00006492846,0.00006604213],"category_scores_gemma":[0.0000054875977,0.00013598787,0.000086625725,0.0011274081,0.000085831525,0.0009137096,0.0000040534496,0.00034743667,0.00053171464],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025992185,0.00016954984,0.0000127623025,0.000015468551,0.000040085833,0.00001642459,0.00062774756,0.0006928223,0.013784949,0.000019219724,0.00029524654,0.9842997],"study_design_scores_gemma":[0.0011850656,0.0006629861,0.0002772725,0.000047319303,0.000045211666,0.00004694119,0.0001465096,0.28942797,0.7062785,0.00017741449,0.0012756306,0.00042919812],"about_ca_topic_score_codex":0.000028186429,"about_ca_topic_score_gemma":0.000006682307,"teacher_disagreement_score":0.9838705,"about_ca_system_score_codex":0.000069263,"about_ca_system_score_gemma":0.000047030437,"threshold_uncertainty_score":0.6834293},"labels":[],"label_agreement":null},{"id":"W2968000510","doi":"10.1109/tmm.2019.2935683","title":"An SDN-Based Caching Decision Policy for Video Caching in Information-Centric Networking","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cache; Software-defined networking; Information-centric networking; Computer network; Leverage (statistics); Network packet; The Internet; Latency (audio); Integer programming; Distributed computing; Algorithm; Telecommunications; Artificial intelligence","score_opus":0.011053392754361456,"score_gpt":0.2563936685861719,"score_spread":0.24534027583181045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968000510","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15472014,0.000019157067,0.84278345,0.00017186282,0.0016143941,0.00044703286,0.00001315724,0.00017380154,0.000056992023],"genre_scores_gemma":[0.98278964,0.000008031685,0.01598347,0.0009895307,0.00013622873,0.000051696145,0.000008884978,0.000015321635,0.000017223823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840677,0.000088600464,0.00040974776,0.00034114913,0.00033168984,0.0004220521],"domain_scores_gemma":[0.9982963,0.0008209725,0.00011156683,0.00055205345,0.00007425602,0.0001448114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005022434,0.00020033136,0.00021328086,0.0008918141,0.00024173092,0.00024264042,0.00057958683,0.000097089585,0.000008311062],"category_scores_gemma":[0.000023370565,0.00020197596,0.0001464865,0.0006721602,0.000014589838,0.0015622146,0.0000023557084,0.00037635607,0.00009089907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064146974,0.00009867936,0.00033442158,0.000012158806,0.0000072920257,0.0000017015429,0.0006417161,0.66962665,0.0004722385,0.00003468253,0.00002154998,0.32868475],"study_design_scores_gemma":[0.0018416989,0.00011987057,0.00047981238,0.00011258039,0.00000723954,0.0000033443548,0.000043291635,0.99589604,0.0005786123,0.00006351872,0.0006206971,0.00023329479],"about_ca_topic_score_codex":0.0010164853,"about_ca_topic_score_gemma":0.00023747499,"teacher_disagreement_score":0.82806945,"about_ca_system_score_codex":0.00023386149,"about_ca_system_score_gemma":0.00017673952,"threshold_uncertainty_score":0.8236341},"labels":[],"label_agreement":null},{"id":"W2979618460","doi":"10.1109/tmm.2019.2946094","title":"Light Field Super-Resolution Using Edge-Preserved Graph-Based Regularization","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Light field; Computer science; Computer vision; Artificial intelligence; Iterative reconstruction; Graph; Image resolution; Regularization (linguistics); Field (mathematics); Algorithm; Mathematics; Theoretical computer science","score_opus":0.019042495396324948,"score_gpt":0.2674966561232707,"score_spread":0.2484541607269458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979618460","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004406594,0.000039717826,0.99263114,0.0007553407,0.00083836773,0.00036787547,0.0000035830997,0.000787969,0.00016939323],"genre_scores_gemma":[0.43678337,0.0000051064494,0.56267977,0.00026334185,0.000026997543,0.000028088694,0.000002470249,0.000020021955,0.0001908215],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985396,0.00008404453,0.00026688748,0.00050696504,0.00029395966,0.00030854574],"domain_scores_gemma":[0.9986872,0.0001593173,0.00009506172,0.0007928385,0.00017064226,0.00009495327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016069735,0.00020649,0.00017991695,0.0003626491,0.00020837106,0.00011613033,0.00061482843,0.0001655129,0.000056995912],"category_scores_gemma":[0.000024411136,0.00021226161,0.00011416663,0.00070377917,0.000032490556,0.0010904608,0.0000051574107,0.0003001451,0.000065406006],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010754551,0.0005234373,0.00015225567,0.00010375247,0.000037745307,0.000011044216,0.00050808757,0.05955519,0.8472916,0.00024440818,0.00037125198,0.091093674],"study_design_scores_gemma":[0.00032003282,0.00007980805,0.000021631198,0.00005911102,0.000008352578,0.000002368202,0.0000039903007,0.59508234,0.40324163,0.0008731969,0.00014652133,0.00016100223],"about_ca_topic_score_codex":0.000036067704,"about_ca_topic_score_gemma":0.0000115310095,"teacher_disagreement_score":0.53552717,"about_ca_system_score_codex":0.000098281496,"about_ca_system_score_gemma":0.00010092616,"threshold_uncertainty_score":0.8655777},"labels":[],"label_agreement":null},{"id":"W2995274323","doi":"10.1109/tmm.2019.2957991","title":"Intra Coding Strategy for Video Error Resiliency: Behavioral Analysis","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Retransmission; Coding tree unit; Context-adaptive binary arithmetic coding; Coding (social sciences); Lossy compression; Packet loss; Algorithm; Data compression; Intra-frame; Network packet; Real-time computing; Decoding methods; Artificial intelligence; Mathematics; Computer network; Statistics","score_opus":0.046172945870424545,"score_gpt":0.31659480145492336,"score_spread":0.2704218555844988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995274323","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09109799,0.000026852074,0.9066078,0.00027677658,0.00079103344,0.00035340106,0.000026422147,0.00069933303,0.00012039778],"genre_scores_gemma":[0.95992583,0.000011064747,0.039162282,0.000074800126,0.00001705068,0.00012744676,0.0000026360678,0.000013130421,0.0006657732],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983747,0.000044442873,0.00030800243,0.0006006439,0.0002931788,0.00037901266],"domain_scores_gemma":[0.9985806,0.00026454063,0.00010252862,0.0008555823,0.00009771333,0.00009906495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018454473,0.0002098756,0.00031195514,0.000582888,0.00021945761,0.00013761007,0.00092050154,0.00014108936,0.000099716046],"category_scores_gemma":[0.000008883088,0.00019050996,0.0002964989,0.0010081937,0.00005907758,0.00038613452,0.000006161089,0.00030008482,0.00012945967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001359245,0.0009503408,0.00059279334,0.000054926833,0.000475645,0.000016680497,0.0013259284,0.14797767,0.03168428,0.0014250966,0.0010972695,0.81426346],"study_design_scores_gemma":[0.0009845368,0.00054567953,0.00066311663,0.000042140047,0.00022787096,0.000004249361,0.00027386768,0.8575427,0.1381584,0.0006326753,0.00047635756,0.0004484362],"about_ca_topic_score_codex":0.000059938593,"about_ca_topic_score_gemma":0.000055626475,"teacher_disagreement_score":0.8688278,"about_ca_system_score_codex":0.00006045206,"about_ca_system_score_gemma":0.00006580823,"threshold_uncertainty_score":0.77687705},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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)","score_opus":0.02954615709882722,"score_gpt":0.2871377927065734,"score_spread":0.25759163560774617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003423830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056992564,0.000013523807,0.98794794,0.00459194,0.00047779805,0.00060073583,0.00006163355,0.00047357817,0.00013359761],"genre_scores_gemma":[0.5473573,0.000003945435,0.45133245,0.00070482877,0.00023208659,0.0002782416,0.000020889278,0.000017893051,0.000052340838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985922,0.00006432397,0.00027722216,0.0004915754,0.00026291728,0.00031178686],"domain_scores_gemma":[0.99889946,0.00040046938,0.00009474799,0.0002922171,0.00010648238,0.0002066453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015727317,0.0001765225,0.00015718529,0.00006546501,0.00037494896,0.000087827444,0.00035407,0.00007751896,0.00006951528],"category_scores_gemma":[0.000025380383,0.00019055011,0.0001197095,0.00036585567,0.00005279952,0.0003484878,0.0000040713426,0.00030539322,0.0003039661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001748564,0.00039525735,0.00023666481,0.00007565265,0.00016285149,0.000006661555,0.0032513451,0.68982285,0.06766746,0.0030980108,0.0054058935,0.22970249],"study_design_scores_gemma":[0.0009602084,0.00011839403,0.0017605877,0.000010536581,0.000020908421,0.0000040376203,0.000021375621,0.9848876,0.010516799,0.00021213364,0.0012732965,0.00021411608],"about_ca_topic_score_codex":0.00006236133,"about_ca_topic_score_gemma":0.000011588933,"teacher_disagreement_score":0.54165804,"about_ca_system_score_codex":0.00007207753,"about_ca_system_score_gemma":0.0000672161,"threshold_uncertainty_score":0.7770408},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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","score_opus":0.053731053981725715,"score_gpt":0.30024683099678834,"score_spread":0.2465157770150626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005647066","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006195379,0.00005193142,0.9921075,0.00046643242,0.00035940757,0.00027040113,0.00002765696,0.00016229785,0.000358971],"genre_scores_gemma":[0.9266984,0.00003347577,0.07248442,0.0005380512,0.000054642358,0.00005187552,0.0000014574869,0.000011865639,0.00012582321],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986779,0.00008559841,0.00031811057,0.000354404,0.0003407399,0.00022325029],"domain_scores_gemma":[0.99901897,0.00022842,0.00009538621,0.0004003083,0.00008881164,0.00016809333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011531718,0.00015533736,0.00022112878,0.00008401218,0.00009189397,0.000044080534,0.0005139525,0.00006801576,0.00013221525],"category_scores_gemma":[0.000011274359,0.00015159717,0.00014021037,0.00031391505,0.000058700352,0.00041148442,0.000004942355,0.00024028988,0.00026147155],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000463084,0.0006336987,0.000027499884,0.00009875575,0.00011445128,0.000035509278,0.022480953,0.0081878,0.03036868,0.000099820136,0.0006052233,0.9373013],"study_design_scores_gemma":[0.0013987089,0.0011946879,0.00030831087,0.00008010287,0.00005352574,0.0000062086506,0.001147941,0.5220977,0.47155008,0.000085386026,0.0016299342,0.00044739075],"about_ca_topic_score_codex":0.000102840975,"about_ca_topic_score_gemma":0.000016650369,"teacher_disagreement_score":0.9368539,"about_ca_system_score_codex":0.000034165372,"about_ca_system_score_gemma":0.000085539905,"threshold_uncertainty_score":0.61819535},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; 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","score_opus":0.07359573018393849,"score_gpt":0.2848202832929515,"score_spread":0.211224553109013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008273026","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024419061,0.000014844618,0.99310035,0.0019719931,0.0008095219,0.00081064476,0.00036550342,0.00044927752,0.00003594489],"genre_scores_gemma":[0.8682713,0.000031542146,0.12628503,0.0039847745,0.0003147536,0.00058920664,0.0003776315,0.00002970706,0.00011609107],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998134,0.00009427879,0.00042731786,0.0006251404,0.00035540492,0.00036383639],"domain_scores_gemma":[0.9983015,0.00073735503,0.0001661418,0.00022229094,0.0002866234,0.0002861036],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016043206,0.00028929856,0.0002684642,0.0002382039,0.0004685034,0.000116041934,0.00028717206,0.00023551493,0.00025016002],"category_scores_gemma":[0.000038828766,0.00031173148,0.00037257333,0.00034510478,0.00006968458,0.0006637464,0.0000021538099,0.00040859514,0.00017667993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016005136,0.0013864917,0.000013251334,0.00010842417,0.000262699,0.000010124547,0.00073548144,0.6286922,0.007305226,0.000093626244,0.0077194883,0.35207245],"study_design_scores_gemma":[0.0060421745,0.00026993582,0.000038572696,0.000034886656,0.00006359039,0.000005121469,0.000023758008,0.97292393,0.019301312,0.00075777917,0.00019173513,0.00034717418],"about_ca_topic_score_codex":0.000016197899,"about_ca_topic_score_gemma":0.000045636578,"teacher_disagreement_score":0.8668153,"about_ca_system_score_codex":0.00007731941,"about_ca_system_score_gemma":0.00016198399,"threshold_uncertainty_score":0.9999335},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.011251000751002212,"score_gpt":0.2371668884619035,"score_spread":0.2259158877109013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008774220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009246869,0.0000019412264,0.9874277,0.00095399487,0.00011377746,0.00052308297,0.000010908154,0.0015811994,0.00014053882],"genre_scores_gemma":[0.6944738,0.000004084322,0.3052718,0.00014185323,0.000011092936,0.000057050158,0.000004464948,0.00001166042,0.000024212097],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989486,0.00005970596,0.0002316293,0.00029325593,0.0003113135,0.00015554736],"domain_scores_gemma":[0.9993186,0.0000624708,0.00011774733,0.00024524712,0.00017423482,0.000081702885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007812086,0.00013458512,0.00015145092,0.000113548944,0.0000698786,0.000039820236,0.00029050757,0.000054205557,0.000024411516],"category_scores_gemma":[0.000013980463,0.00012002643,0.00003982086,0.0006618829,0.0000727899,0.00061567425,0.0000022011775,0.00012642976,0.000051624127],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021400234,0.000584104,0.000057639376,0.00013725285,0.000070878115,0.000019494077,0.005204307,0.002621651,0.96177304,0.00010093886,0.0006659944,0.028550692],"study_design_scores_gemma":[0.00039131468,0.00028751127,0.00021988913,0.00002482616,0.00000937162,0.0000015662168,0.000012169725,0.44613054,0.55281335,0.000009630244,0.000015495929,0.00008435514],"about_ca_topic_score_codex":0.000013943789,"about_ca_topic_score_gemma":0.000008410485,"teacher_disagreement_score":0.6852269,"about_ca_system_score_codex":0.00004610193,"about_ca_system_score_gemma":0.000082272956,"threshold_uncertainty_score":0.48945358},"labels":[],"label_agreement":null},{"id":"W3010142250","doi":"10.1109/tmm.2020.2976985","title":"Feature-Flow Interpretation of Deep Convolutional Neural Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Interpretability; Computer science; Feature (linguistics); Convolutional neural network; Artificial intelligence; Interpretation (philosophy); Visualization; Representation (politics); Pattern recognition (psychology); Feature extraction; Deep learning; Machine learning; Data mining","score_opus":0.020010300986707825,"score_gpt":0.25059093384670444,"score_spread":0.2305806328599966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010142250","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015918128,0.00006697641,0.99454814,0.0022785233,0.0010215605,0.00020405234,0.0000097291295,0.00016656758,0.00011265613],"genre_scores_gemma":[0.9492998,0.0000113857295,0.049795188,0.0007268764,0.00008627854,0.000024728604,0.000004060059,0.000012322736,0.000039334518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987278,0.000089219335,0.00025776983,0.00036240206,0.00030077237,0.00026203017],"domain_scores_gemma":[0.9990062,0.0002850021,0.000098223856,0.0002753869,0.00015755082,0.00017764325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010748557,0.00016114955,0.00018840551,0.00010168214,0.000116889205,0.000049175203,0.00052072975,0.00010695349,0.00007904692],"category_scores_gemma":[0.000028819046,0.0001645196,0.00013886915,0.0005352691,0.0001079442,0.0005089553,0.000004180806,0.00034698672,0.00009850819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005342352,0.000074883625,0.000013716548,0.000008920719,0.000024110017,0.000006433646,0.0019970885,0.7858123,0.0012977152,0.00017004897,0.000184294,0.21035706],"study_design_scores_gemma":[0.00017015042,0.00016900485,0.00007328562,0.0000109001685,0.00001235161,0.0000042817105,0.000082770006,0.97687715,0.022316573,0.000071623654,0.00008902389,0.00012286617],"about_ca_topic_score_codex":0.000020120802,"about_ca_topic_score_gemma":0.00003874593,"teacher_disagreement_score":0.947708,"about_ca_system_score_codex":0.000046584846,"about_ca_system_score_gemma":0.00003657976,"threshold_uncertainty_score":0.67089146},"labels":[],"label_agreement":null},{"id":"W3016418421","doi":"10.1109/tmm.2020.2987710","title":"A Hierarchical Visual Feature-Based Approach For Image Sonification","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Sonification; Timbre; Pixel; Feature (linguistics); Pattern recognition (psychology)","score_opus":0.05264982290819236,"score_gpt":0.3096435393697308,"score_spread":0.25699371646153846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016418421","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008794312,0.0000011682617,0.98513335,0.0038176272,0.0004221363,0.0007151754,0.00022569357,0.00029334927,0.00059720635],"genre_scores_gemma":[0.9660685,0.000004335216,0.030543087,0.0024726398,0.00018447397,0.0003038814,0.000020607551,0.000040158113,0.00036231408],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985818,0.0001012478,0.00020572089,0.00056539354,0.00025523268,0.00029057259],"domain_scores_gemma":[0.9987306,0.00070395914,0.00006723084,0.00019980747,0.00005365553,0.0002447481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003253001,0.00019704275,0.00017913617,0.00012593757,0.0003238602,0.00007673193,0.00018357909,0.00012484471,0.00012916877],"category_scores_gemma":[0.00013142511,0.00018962774,0.00022327411,0.00030501463,0.000119573924,0.00022195066,6.0848055e-7,0.00051845587,0.00021080604],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006193573,0.00071969075,0.0000011343208,0.000033438893,0.0000114688455,0.0000056640397,0.0005699713,0.0074202097,0.9723059,0.000026404874,0.0011890737,0.017097674],"study_design_scores_gemma":[0.0006827307,0.00017327958,0.000007678583,0.0000035033297,0.000022826875,0.0000050175136,0.00005034299,0.4365641,0.5599757,0.000006025535,0.0023795513,0.00012929075],"about_ca_topic_score_codex":0.0000054714405,"about_ca_topic_score_gemma":0.0000023002467,"teacher_disagreement_score":0.9572742,"about_ca_system_score_codex":0.000047196485,"about_ca_system_score_gemma":0.00006896414,"threshold_uncertainty_score":0.7732794},"labels":[],"label_agreement":null},{"id":"W3085394716","doi":"10.1109/tmm.2020.3023282","title":"<i>NDN-MMRA</i>: Multi-Stage Multicast Rate Adaptation in Named Data Networking WLAN","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China Stem Cell and Translational Research; Higher Education Discipline Innovation Project; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Multicast; Computer science; Source-specific multicast; Xcast; Computer network; Pragmatic General Multicast; IP multicast; Protocol Independent Multicast; Distance Vector Multicast Routing Protocol; Reliable multicast; Reliability (semiconductor)","score_opus":0.184290102837671,"score_gpt":0.30088695674190535,"score_spread":0.11659685390423435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3085394716","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009362269,0.00007921351,0.9871761,0.0012694685,0.0013316429,0.00032502727,0.00008064417,0.00033928256,0.00003631248],"genre_scores_gemma":[0.96691006,0.000079993886,0.0314546,0.001194882,0.00014132603,0.000029101924,0.00003181221,0.000025625704,0.00013258596],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786454,0.00022245475,0.00040355205,0.00081118086,0.00029553092,0.00040275886],"domain_scores_gemma":[0.9983722,0.00037162594,0.000100121455,0.00086492073,0.000052045103,0.00023910972],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036750385,0.00024797386,0.0002582895,0.0001624817,0.00017005007,0.00015369584,0.001172658,0.00010615687,0.000020533833],"category_scores_gemma":[0.000036170397,0.00025778922,0.00008576918,0.000580118,0.000054941378,0.0007823855,0.000018415156,0.00057641836,0.00019191869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002998868,0.0009018529,0.00027097508,0.00006595926,0.00012750053,0.00031129323,0.013617561,0.3390976,0.034675714,0.000031397984,0.0003771576,0.6102231],"study_design_scores_gemma":[0.0016788536,0.000063234686,0.00030868585,0.00005169702,0.000015869718,0.000002980315,0.00022467725,0.99583066,0.000729868,0.0000024288822,0.00081442157,0.00027665755],"about_ca_topic_score_codex":0.00043952785,"about_ca_topic_score_gemma":0.00072428334,"teacher_disagreement_score":0.9575478,"about_ca_system_score_codex":0.00005525913,"about_ca_system_score_gemma":0.000076826014,"threshold_uncertainty_score":0.9999874},"labels":[],"label_agreement":null},{"id":"W3092407891","doi":"10.1109/tmm.2020.3028479","title":"A New Multihypothesis-Based Compressed Video Sensing Reconstruction System","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Higher Education Discipline Innovation Project; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Residual; Pattern recognition (psychology); Preprocessor; Set (abstract data type); Decoding methods; Block (permutation group theory); Computer vision; Algorithm; Mathematics","score_opus":0.024228699760776157,"score_gpt":0.21012078754607738,"score_spread":0.18589208778530122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092407891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011721574,0.00004249413,0.9827468,0.00015542257,0.0009924846,0.00029362267,0.000019830459,0.0034990197,0.0005287519],"genre_scores_gemma":[0.87254393,0.000009692422,0.12703983,0.00014476587,0.00017163232,0.0000073652022,0.0000022775232,0.000065628956,0.000014890921],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893,0.000051689738,0.00028785024,0.00028774166,0.00019160406,0.00025111402],"domain_scores_gemma":[0.99922335,0.00017027456,0.000045052588,0.00026498784,0.00005210876,0.00024423818],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000040280105,0.00026092705,0.00028449993,0.00014080812,0.00010766554,0.00004876183,0.00012100728,0.00015216526,0.00006031892],"category_scores_gemma":[0.0000066695006,0.00027997931,0.00014360584,0.00024605356,0.00003607113,0.00011248351,6.209583e-7,0.0003339244,0.00013988283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008735813,0.000018248202,0.000003460424,0.00006238143,0.000083086976,0.00002327508,0.0003164067,0.40901965,0.15338655,0.0000015431066,0.0013580167,0.43564],"study_design_scores_gemma":[0.0004428237,0.000029274843,0.000010963063,0.00013563655,0.000035864494,0.000012196814,0.000053051604,0.58724195,0.4114945,0.0000025966253,0.00036642278,0.00017473986],"about_ca_topic_score_codex":0.00008654783,"about_ca_topic_score_gemma":0.00001778694,"teacher_disagreement_score":0.8608223,"about_ca_system_score_codex":0.00010143774,"about_ca_system_score_gemma":0.0000356733,"threshold_uncertainty_score":0.99996525},"labels":[],"label_agreement":null},{"id":"W3093959361","doi":"10.1109/tmm.2020.3034530","title":"Anisotropic Graph Convolutional Network for Semi-Supervised Learning","year":2020,"lang":"en","type":"preprint","venue":"IEEE Transactions on Multimedia","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Graph; Smoothing; Node (physics); Artificial intelligence; Theoretical computer science; Pattern recognition (psychology); Machine learning; Computer vision","score_opus":0.031109731398920697,"score_gpt":0.26490500993720517,"score_spread":0.23379527853828447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093959361","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00064750784,0.00027945,0.98988,0.0012132657,0.0056738956,0.0011644093,0.00011463224,0.000984582,0.000042244672],"genre_scores_gemma":[0.4491241,0.00036779937,0.54726654,0.00089873985,0.0010242691,0.0008022287,0.00011769222,0.00010501102,0.0002936421],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99664193,0.00018407225,0.0005618859,0.0013593805,0.0004988708,0.0007538543],"domain_scores_gemma":[0.99757755,0.0008359312,0.00025960756,0.00077170227,0.00019947386,0.00035573446],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015579387,0.0005950445,0.0006014436,0.00020908218,0.00054886437,0.0001570944,0.0013072139,0.0005040176,0.000036864323],"category_scores_gemma":[0.000022703196,0.0006470763,0.0006479785,0.0006203852,0.00014554564,0.00027921612,0.000031497846,0.0021954891,0.00006082164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007638789,0.000085182226,0.000032234588,0.00007121787,0.00014573443,0.000012489725,0.00026295544,0.9598563,0.0003686751,0.00040711308,0.0017378202,0.036943946],"study_design_scores_gemma":[0.0011236405,0.00023072393,0.00013158398,0.000142133,0.00007311762,0.000009070878,0.00001178032,0.9838064,0.00073410233,0.010860729,0.0022222733,0.0006544685],"about_ca_topic_score_codex":0.000017270282,"about_ca_topic_score_gemma":0.000023612496,"teacher_disagreement_score":0.44847658,"about_ca_system_score_codex":0.000121509416,"about_ca_system_score_gemma":0.00018932913,"threshold_uncertainty_score":0.999598},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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)","score_opus":0.02995517247449536,"score_gpt":0.2519905603140803,"score_spread":0.22203538783958493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095715663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08559571,0.00007780875,0.91191006,0.0019043104,0.0002618643,0.00012440041,0.000014757744,0.000089284666,0.000021826807],"genre_scores_gemma":[0.98119676,0.000024713201,0.01749473,0.0011654842,0.0000744027,0.000009389269,0.0000036011422,0.000010985069,0.000019937965],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990895,0.00004537276,0.0001517664,0.0003557668,0.00021006042,0.00014754469],"domain_scores_gemma":[0.9995909,0.000047526155,0.00006772053,0.00014926362,0.000015243925,0.00012938156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000052418305,0.00013217988,0.0001121519,0.000059878745,0.00025681648,0.00013551749,0.00015829832,0.000079703605,0.000026488266],"category_scores_gemma":[0.0000022562901,0.00013147296,0.000030811116,0.00020908866,0.000100335754,0.0005184944,0.0000033339893,0.00017869801,0.0000320368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026797778,0.00012797999,0.0001366915,0.00003209471,0.000026312067,0.000015087223,0.005666955,0.009946051,0.09061014,0.000012084797,0.00029891508,0.8931009],"study_design_scores_gemma":[0.0005001194,0.00009236451,0.0026097335,0.000019216228,0.000021666154,0.000015143004,0.00038558987,0.966739,0.029168442,0.000038179103,0.00020262868,0.00020792574],"about_ca_topic_score_codex":0.000009099162,"about_ca_topic_score_gemma":0.000007232558,"teacher_disagreement_score":0.95679295,"about_ca_system_score_codex":0.00006425793,"about_ca_system_score_gemma":0.000044268512,"threshold_uncertainty_score":0.5361312},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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)","score_opus":0.05101680530524946,"score_gpt":0.2914439014463287,"score_spread":0.24042709614107924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117941406","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006666917,0.00005264966,0.9900967,0.00030213862,0.0018514708,0.00053009996,0.00010719727,0.00031605433,0.00007676689],"genre_scores_gemma":[0.41941556,0.000005440141,0.5790783,0.00028970384,0.0001228105,0.00042956532,0.000005470996,0.000035427056,0.0006176976],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792135,0.00018814275,0.0002952476,0.00059762184,0.00044359142,0.00055405917],"domain_scores_gemma":[0.99838084,0.00066902215,0.00012673449,0.0005812077,0.000110344816,0.00013187001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009082154,0.00024071147,0.00028290894,0.00013778398,0.0009915063,0.00010480562,0.00067773473,0.00006296783,0.000057228197],"category_scores_gemma":[0.000010788755,0.00022848298,0.00015536587,0.00063786336,0.0000657561,0.00029731763,0.0000073883216,0.0004117082,0.000021066364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000254664,0.00069823134,0.0010434019,0.0000385627,0.00015304633,0.000023218146,0.0017900617,0.33878496,0.001358475,0.000056088422,0.0010947217,0.6547046],"study_design_scores_gemma":[0.004504392,0.0006859181,0.008332728,0.00004132533,0.000055490964,0.000045415953,0.00015371112,0.9674406,0.008378312,0.00012894942,0.009482069,0.000751107],"about_ca_topic_score_codex":0.000057425656,"about_ca_topic_score_gemma":0.00044338487,"teacher_disagreement_score":0.65395343,"about_ca_system_score_codex":0.00010815626,"about_ca_system_score_gemma":0.00014978209,"threshold_uncertainty_score":0.9317266},"labels":[],"label_agreement":null},{"id":"W3134101093","doi":"10.1109/tmm.2021.3062481","title":"AdaCrowd: Unlabeled Scene Adaptation for Crowd Counting","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Manitoba; Huawei Technologies (Canada); Simon Fraser University","funders":"University of Manitoba","keywords":"Computer science; Artificial intelligence; Benchmark (surveying); Adaptation (eye); Computer vision; Code (set theory); Counting problem; Image (mathematics); Pattern recognition (psychology); Algorithm","score_opus":0.04566899353662336,"score_gpt":0.3040500184347916,"score_spread":0.2583810248981683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134101093","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047966274,0.00008864462,0.99200803,0.00058850937,0.0018913944,0.00021011288,0.000023211602,0.00024853877,0.00014492008],"genre_scores_gemma":[0.49360904,0.000030281635,0.5055782,0.00027335968,0.00008213482,0.00006504386,0.000006196335,0.0000170551,0.00033867918],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852854,0.00012579859,0.00027786358,0.00046734768,0.0002688854,0.00033158125],"domain_scores_gemma":[0.99825907,0.0007983777,0.000081226375,0.00045329184,0.00031339022,0.00009462578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005016342,0.00016576635,0.00020773122,0.00011166027,0.00029001042,0.00014508062,0.00028537074,0.00010143519,0.000034188088],"category_scores_gemma":[0.000069024194,0.00017593856,0.0001486081,0.0004932529,0.00003236818,0.00039706807,0.0000019769507,0.00019631394,0.00005550571],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035654575,0.00025645783,0.000045083052,0.00004219027,0.00007561299,0.000022035285,0.00111003,0.022686372,0.020200735,0.000201411,0.0001932128,0.95513123],"study_design_scores_gemma":[0.0014224317,0.00008217379,0.00055789575,0.00004891163,0.000024696144,0.000017962098,0.00008428946,0.75272566,0.24174581,0.0004511044,0.0025406526,0.00029839907],"about_ca_topic_score_codex":0.000028437009,"about_ca_topic_score_gemma":0.00015453453,"teacher_disagreement_score":0.9548328,"about_ca_system_score_codex":0.000054047523,"about_ca_system_score_gemma":0.00018263615,"threshold_uncertainty_score":0.71745664},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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","score_opus":0.015151951062559208,"score_gpt":0.2686879798880343,"score_spread":0.25353602882547505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134680050","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001944322,0.00007963932,0.9949894,0.00056142494,0.0016956837,0.000111573274,0.000005319379,0.000268221,0.0003444401],"genre_scores_gemma":[0.5952859,0.000056134268,0.4029947,0.0008219446,0.00004534056,0.000022693906,0.000006495379,0.000013239237,0.00075355056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987929,0.00007500332,0.00025860913,0.0003657273,0.00024521947,0.00026254248],"domain_scores_gemma":[0.99912876,0.00009402259,0.00006374016,0.00043851917,0.00014878609,0.00012617497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001036588,0.00014854061,0.0001521267,0.00014073204,0.00021454312,0.000093630246,0.00023369717,0.00007447826,0.0002276735],"category_scores_gemma":[0.000013206967,0.0001489074,0.00011035866,0.0004187562,0.00003877385,0.00097370794,0.0000042378197,0.00026196663,0.00035578263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039643528,0.00029717988,0.00003489601,0.000015378237,0.000045125555,0.000025288697,0.0013079459,0.020178845,0.17017199,0.00032358736,0.00045856813,0.80710155],"study_design_scores_gemma":[0.0006923688,0.000029591314,0.00009760388,0.00002621143,0.000007981657,0.000027921187,0.000028562707,0.89253855,0.10355275,0.00053334056,0.002304286,0.00016081713],"about_ca_topic_score_codex":0.000029930323,"about_ca_topic_score_gemma":0.000020911672,"teacher_disagreement_score":0.8723597,"about_ca_system_score_codex":0.000086493696,"about_ca_system_score_gemma":0.00007026373,"threshold_uncertainty_score":0.6072268},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Video streaming; Multimedia; Mobile computing; Server; Computer network","score_opus":0.018291561687785625,"score_gpt":0.2392927899908476,"score_spread":0.221001228303062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138954746","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13739054,0.00016917552,0.8619817,0.000053061467,0.00013313713,0.00016634479,0.000023560371,0.000054388864,0.000028092429],"genre_scores_gemma":[0.9724418,0.000042043477,0.027303848,0.000069694404,0.0000073674046,0.00004555297,0.000004021974,0.000008914579,0.0000767855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873567,0.00018455971,0.0002166808,0.00045752552,0.00022635548,0.0001792408],"domain_scores_gemma":[0.9986818,0.0006548292,0.00007569989,0.00036094282,0.00011312391,0.00011361418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021783818,0.00015576174,0.00029546674,0.00035547308,0.00016218919,0.00007545299,0.0001481467,0.00007129634,0.000018126624],"category_scores_gemma":[0.0000120233635,0.00015469588,0.00012280476,0.00068603794,0.00006935609,0.00024099198,0.0000037696682,0.0002024054,0.0000022769418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008300107,0.00058249437,0.00027306916,0.000037216956,0.0014399642,0.00004226461,0.0037089589,0.49691296,0.065653875,0.000018432633,0.000009065475,0.4312387],"study_design_scores_gemma":[0.0005018434,0.000109672765,0.00080710964,0.00002593647,0.00044771735,0.0000053964695,0.00014299728,0.92639947,0.07138515,0.000010256449,0.000008973225,0.00015548721],"about_ca_topic_score_codex":0.0002176858,"about_ca_topic_score_gemma":0.000086197695,"teacher_disagreement_score":0.83505124,"about_ca_system_score_codex":0.000038259284,"about_ca_system_score_gemma":0.0000949866,"threshold_uncertainty_score":0.6308315},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.004761814742510791,"score_gpt":0.24114036950292658,"score_spread":0.2363785547604158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3182688976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018166987,0.00015101192,0.9800181,0.000042241158,0.00018138206,0.00015508241,0.000029851768,0.000478026,0.0007773196],"genre_scores_gemma":[0.74801123,0.00053400605,0.25105283,0.00013836705,0.000038788665,0.000052652795,0.00003801822,0.000040205054,0.00009390518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990892,0.000046368412,0.00020889581,0.00028619287,0.00015884402,0.00021049645],"domain_scores_gemma":[0.99932855,0.00019604834,0.000027140206,0.00026848793,0.00005929542,0.000120503835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007401083,0.00018830429,0.00022319361,0.0002737955,0.00015655086,0.00004906857,0.000050088554,0.00010221967,0.00029138546],"category_scores_gemma":[0.000008185818,0.00020397434,0.000082349776,0.0006882051,0.000048184887,0.00018703884,0.0000017131978,0.0002339505,0.00001188249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072453855,0.00017427145,0.00010675917,0.000062032144,0.00014937174,0.000048171707,0.00023444707,0.6761241,0.12205669,0.000003441065,0.00028958605,0.20067865],"study_design_scores_gemma":[0.0004315873,0.000060370934,0.0012088877,0.000043434706,0.00015921795,0.0000039851966,0.00002272211,0.8305997,0.16701953,0.00012709953,0.00011826637,0.00020514357],"about_ca_topic_score_codex":0.000005688373,"about_ca_topic_score_gemma":0.00004268046,"teacher_disagreement_score":0.7298443,"about_ca_system_score_codex":0.000049942977,"about_ca_system_score_gemma":0.000010613033,"threshold_uncertainty_score":0.8317832},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","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","score_opus":0.03562366763511123,"score_gpt":0.3555343833712776,"score_spread":0.31991071573616636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194484130","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003245039,0.000025985251,0.99656874,0.0013263217,0.0004321971,0.00026474035,0.000022102617,0.00091423385,0.0001211892],"genre_scores_gemma":[0.21060371,0.000008242398,0.7885842,0.0002280436,0.000026910859,0.00020359093,0.00001105255,0.000018767005,0.00031552505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834985,0.00017263574,0.00029910548,0.0005447978,0.00031709994,0.000316506],"domain_scores_gemma":[0.9983804,0.00049879093,0.00009319509,0.0005230356,0.0004002457,0.00010431148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038543425,0.0001832319,0.00020215141,0.000118820026,0.00035417642,0.00015080275,0.00036191833,0.00009465655,0.000035524245],"category_scores_gemma":[0.00008352407,0.00019674798,0.00015789107,0.00035460322,0.00008336848,0.00061075785,0.000003646719,0.00037553706,0.000021665799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007104942,0.0014135777,0.000031578114,0.00018422393,0.000052878837,0.000035428344,0.0006267314,0.03067356,0.42173657,0.00030963615,0.0005506982,0.5443141],"study_design_scores_gemma":[0.00061160896,0.000120031276,0.00010603443,0.000028423636,0.000010742627,0.0000040489676,0.000023242508,0.6949009,0.30230036,0.0006705475,0.0010245377,0.00019958471],"about_ca_topic_score_codex":0.000014543669,"about_ca_topic_score_gemma":0.000019045987,"teacher_disagreement_score":0.6642273,"about_ca_system_score_codex":0.00017355321,"about_ca_system_score_gemma":0.00032003262,"threshold_uncertainty_score":0.80231494},"labels":[],"label_agreement":null},{"id":"W3197746894","doi":"10.1109/tmm.2021.3066118","title":"A Discriminative Vectorial Framework for Multi-Modal Feature Representation","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Discriminative model; Computer science; Pattern recognition (psychology); Artificial intelligence; Feature (linguistics); Modal; Feature learning; Representation (politics); Feature extraction; Machine learning","score_opus":0.06014157342650117,"score_gpt":0.37407016042469166,"score_spread":0.3139285869981905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197746894","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001948341,0.00006558278,0.9958653,0.00073607394,0.0022711023,0.00043009658,0.000054757682,0.0003458203,0.00003644269],"genre_scores_gemma":[0.09307202,0.00007716423,0.90557605,0.00019247313,0.00023046319,0.00019078882,0.0000107202195,0.000020492409,0.00062982726],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99869174,0.00007598099,0.00017984759,0.0005382988,0.00025273857,0.00026138694],"domain_scores_gemma":[0.9984511,0.0005945999,0.00007446393,0.0004976203,0.00027901333,0.00010318752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007137961,0.00018072965,0.0001979748,0.00010056484,0.00021747797,0.00010438965,0.00030550212,0.00017262709,0.000019075853],"category_scores_gemma":[0.0001819198,0.00017464816,0.00018732506,0.00049735303,0.000053337968,0.00060515944,0.000004279763,0.00038421198,0.000018045712],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023785792,0.0009260616,0.000010039478,0.000046336805,0.00011525142,0.00006803168,0.0046569947,0.00063981954,0.033071138,0.0043389504,0.0011356217,0.9547539],"study_design_scores_gemma":[0.0010226744,0.00018061388,0.00017640133,0.000062470695,0.00003150771,0.000011232651,0.00013226677,0.072583646,0.91692066,0.0072073643,0.0013919633,0.00027916717],"about_ca_topic_score_codex":0.000008480394,"about_ca_topic_score_gemma":0.000011789864,"teacher_disagreement_score":0.95447475,"about_ca_system_score_codex":0.00007952858,"about_ca_system_score_gemma":0.00008679595,"threshold_uncertainty_score":0.7121945},"labels":[],"label_agreement":null},{"id":"W3211570189","doi":"10.1109/tmm.2021.3125803","title":"Towards Real-Time Video Caching at Edge Servers: A Cost-Aware Deep Q-Learning Solution","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Australian Research Council; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Server; Cache; Popularity; Enhanced Data Rates for GSM Evolution; Context (archaeology); Computer network; Video quality; Quality of service; Artificial intelligence","score_opus":0.019707513507182903,"score_gpt":0.2438425595940053,"score_spread":0.2241350460868224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211570189","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05458421,0.000090898626,0.94166714,0.00069543574,0.0014202221,0.00020069022,0.000017387421,0.0006801565,0.00064387283],"genre_scores_gemma":[0.9892856,0.00018136976,0.0067100436,0.0002669909,0.00011076432,0.000058560305,0.000020702648,0.000033341395,0.0033326652],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978004,0.000295065,0.00030981397,0.0006696992,0.00045217003,0.00047281827],"domain_scores_gemma":[0.9986306,0.00026849366,0.00009582843,0.000577802,0.00018800181,0.00023928263],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002998223,0.00026425248,0.00027162704,0.00018554927,0.0006980615,0.0001767171,0.00042456156,0.00016408387,0.00018149943],"category_scores_gemma":[0.00003086904,0.0002873898,0.00027221112,0.00045233234,0.000046883815,0.00059925305,0.000017802355,0.00058908964,0.0006982964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016562936,0.00060493755,0.00010924601,0.00006696993,0.00026553322,0.00051862723,0.0049414597,0.1434148,0.1382253,0.00004389166,0.00055947714,0.7110841],"study_design_scores_gemma":[0.0009854133,0.00011231394,0.00028886338,0.00009696768,0.000053575324,0.00011238152,0.000121701225,0.96231943,0.0344233,0.000019428337,0.0010659143,0.00040073032],"about_ca_topic_score_codex":0.0005863786,"about_ca_topic_score_gemma":0.0004913458,"teacher_disagreement_score":0.9349571,"about_ca_system_score_codex":0.00043758634,"about_ca_system_score_gemma":0.00014326953,"threshold_uncertainty_score":0.9999578},"labels":[],"label_agreement":null},{"id":"W4205823760","doi":"10.1109/tmm.2021.3132156","title":"Caching in Dynamic Environments: A Near-Optimal Online Learning Approach","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Computer science; Regret; Dynamic web page; Sublinear function; Popularity; The Internet; Artificial intelligence; World Wide Web; Machine learning; Web page","score_opus":0.013097787125850736,"score_gpt":0.23073989068507492,"score_spread":0.21764210355922417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205823760","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26663545,0.000067397246,0.7325766,0.00016766056,0.0003046106,0.00007124146,0.000006997556,0.00009764761,0.000072386225],"genre_scores_gemma":[0.9459183,0.00007755164,0.052406475,0.00014244708,0.000015083464,0.000020005897,0.00001563389,0.000016948185,0.0013875418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849313,0.0001707274,0.00024026944,0.0005077774,0.00027826722,0.000309813],"domain_scores_gemma":[0.9993381,0.00012796636,0.00004594163,0.00037331015,0.000015013958,0.000099701065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001612774,0.00017420787,0.00018741586,0.00013394053,0.00020960324,0.000111542824,0.0003136569,0.00009004923,0.000023010676],"category_scores_gemma":[0.000009802026,0.00018867211,0.00012162077,0.00031905397,0.000044308406,0.00032225752,0.000005121002,0.0008029143,0.00006839708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013442841,0.00067742524,0.00006960488,0.0000070196897,0.00002649596,0.00007879737,0.0017030475,0.87401545,0.009970343,0.0000038652515,0.0000045727684,0.11342993],"study_design_scores_gemma":[0.00073459494,0.000045591176,0.0008038689,0.000029777306,0.00001065927,0.000037259626,0.00020816909,0.99675125,0.0008877645,0.0000038617677,0.00028996952,0.00019724837],"about_ca_topic_score_codex":0.00011300053,"about_ca_topic_score_gemma":0.00009473888,"teacher_disagreement_score":0.6801701,"about_ca_system_score_codex":0.00013948894,"about_ca_system_score_gemma":0.000056455283,"threshold_uncertainty_score":0.76938254},"labels":[],"label_agreement":null},{"id":"W4206004830","doi":"10.1109/tmm.2022.3141888","title":"Fast Human Pose Estimation in Compressed Videos","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; Huawei Technologies (Canada); McMaster University","funders":"","keywords":"Computer science; Image warping; Artificial intelligence; Computer vision; Pose; Dynamic time warping; Frame (networking); Motion estimation; Inter frame; Discrete cosine transform; Motion (physics); Pattern recognition (psychology); Reference frame; Image (mathematics)","score_opus":0.020368275768489933,"score_gpt":0.2654056436825718,"score_spread":0.24503736791408187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206004830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05532707,0.000006456302,0.9420974,0.00030687728,0.001005319,0.00030259756,0.000028972509,0.0002582862,0.00066699355],"genre_scores_gemma":[0.9903917,0.0000027055476,0.008683182,0.00029880667,0.000026653683,0.00020761203,0.000017665103,0.000012634441,0.000359091],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987023,0.00014432317,0.00026798208,0.00033244296,0.00033964438,0.0002133166],"domain_scores_gemma":[0.9994098,0.00010346605,0.00007263542,0.00030619794,0.0000322652,0.00007561157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017063592,0.0001326105,0.0001340737,0.00042950455,0.0005067369,0.000071302915,0.00036316508,0.000040684336,0.00075055286],"category_scores_gemma":[0.0000026169644,0.00015422862,0.00007577339,0.00045016728,0.000028474844,0.0004527613,0.000004830396,0.0004175525,0.00019074423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004747725,0.0017354055,0.000030806787,0.000020737894,0.000037668175,0.00006217167,0.004020078,0.46001735,0.026513504,0.00028246868,0.0010100703,0.50622225],"study_design_scores_gemma":[0.0014199009,0.00019801079,0.0010375618,0.000018257093,0.000010120155,0.000019700077,0.00012504592,0.9683235,0.027267847,0.0006516356,0.0006587717,0.0002696288],"about_ca_topic_score_codex":0.000101128455,"about_ca_topic_score_gemma":0.00007789298,"teacher_disagreement_score":0.93506455,"about_ca_system_score_codex":0.00013675184,"about_ca_system_score_gemma":0.000035631143,"threshold_uncertainty_score":0.82180274},"labels":[],"label_agreement":null},{"id":"W4210706233","doi":"10.1109/tmm.2013.2294427","title":"IEEE Transactions on Multimedia publication information","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Multimedia Communication and Technology","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Standards Association; Toronto Metropolitan University; McMaster University","funders":"","keywords":"Computer science; Multimedia; World Wide Web; Information retrieval","score_opus":0.02528765922651188,"score_gpt":0.29539117555783234,"score_spread":0.27010351633132046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210706233","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031865737,0.000010337757,0.9642953,0.0084602125,0.0028672528,0.0010570331,0.000095660194,0.00130002,0.01872759],"genre_scores_gemma":[0.9832814,0.00026585962,0.0123147,0.0014530425,0.00014582953,0.00047020338,0.000045518333,0.00003807976,0.0019854112],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99703074,0.00050037313,0.0006696563,0.0003989941,0.0007877604,0.000612461],"domain_scores_gemma":[0.9967585,0.0011263925,0.00027264855,0.0009978131,0.0004397874,0.00040483277],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00096780184,0.00032973333,0.00032319772,0.00087274594,0.0012889164,0.00016589415,0.00081585627,0.00059200655,0.0020412356],"category_scores_gemma":[0.00020483883,0.00034935412,0.00022762366,0.0009289474,0.0005901029,0.0011302967,9.489917e-7,0.0009636478,0.0041710963],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007732967,0.0005910824,0.000010524739,0.000012475668,0.000056790283,3.6146102e-7,0.009213409,0.0016073628,0.0005425945,0.0006532104,0.001702185,0.9855327],"study_design_scores_gemma":[0.005662715,0.00056457263,0.0005704234,0.0001203205,0.0002001781,0.0000072903667,0.0039551533,0.35860023,0.054741003,0.0005732115,0.5734115,0.0015933771],"about_ca_topic_score_codex":0.0010550604,"about_ca_topic_score_gemma":0.00188561,"teacher_disagreement_score":0.9839393,"about_ca_system_score_codex":0.00031172333,"about_ca_system_score_gemma":0.00020935397,"threshold_uncertainty_score":0.9998959},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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)","score_opus":0.01730875617166179,"score_gpt":0.26466604474233724,"score_spread":0.24735728857067546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210825863","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027241383,0.00004216564,0.97061694,0.00025564927,0.0010460683,0.0005473839,0.000014825644,0.00017911039,0.000056502926],"genre_scores_gemma":[0.86979556,0.0000801924,0.12895037,0.00009174766,0.000038107628,0.00093425455,0.000011436836,0.000011943007,0.000086374166],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986589,0.00011647837,0.00025233984,0.00038884196,0.0002155663,0.00036789593],"domain_scores_gemma":[0.99945235,0.000119120756,0.00007045804,0.00029545501,0.000019323357,0.000043261734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036561006,0.00015337278,0.00017245093,0.00029900315,0.00028820636,0.000028952303,0.000403066,0.00005979042,0.000010080741],"category_scores_gemma":[0.0000017305906,0.00015797743,0.00009476225,0.00056031626,0.000026937603,0.0002675435,0.0000071628883,0.00034581457,8.886109e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069668156,0.0011718535,0.0026517843,0.00007217542,0.00003961581,0.00006745669,0.0077207726,0.2509626,0.0143215805,0.00037831406,0.00061006006,0.7213071],"study_design_scores_gemma":[0.004968394,0.0012959915,0.0073398594,0.0003167963,0.000029506717,0.00002243995,0.0002031695,0.71718603,0.24646045,0.01048534,0.01025028,0.0014417409],"about_ca_topic_score_codex":0.000068867084,"about_ca_topic_score_gemma":0.00026380672,"teacher_disagreement_score":0.8425542,"about_ca_system_score_codex":0.00022557547,"about_ca_system_score_gemma":0.000027888995,"threshold_uncertainty_score":0.64421326},"labels":[],"label_agreement":null},{"id":"W4225936863","doi":"10.1109/tmm.2022.3199102","title":"Auto-Weighted Layer Representation Based View Synthesis Distortion Estimation for 3-D Video Coding","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Science Foundation of Shandong Province; Nanyang Technological University","keywords":"Distortion (music); Computer science; Representation (politics); Coding (social sciences); Nonlinear distortion; Pixel; Nonlinear system; Algorithm; Function (biology); Layer (electronics); Artificial intelligence; Pattern recognition (psychology); Mathematics; Telecommunications; Statistics; Bandwidth (computing)","score_opus":0.043864244168868624,"score_gpt":0.2913793639308624,"score_spread":0.24751511976199375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225936863","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034331235,0.000033666995,0.99249685,0.0012798421,0.0011236704,0.00047611754,0.000041028732,0.001071552,0.00004416516],"genre_scores_gemma":[0.88186884,0.000006795261,0.11614998,0.00017999591,0.000015755317,0.0016416557,0.000008645557,0.00001661017,0.00011173233],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998383,0.00015175725,0.0003214364,0.0004913847,0.00041527027,0.00023716672],"domain_scores_gemma":[0.9982613,0.0008547761,0.00016458874,0.0005834689,0.00007517268,0.00006072386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031311647,0.00016765509,0.00019499878,0.0003578896,0.0008713616,0.00008855786,0.000558046,0.00005974705,0.0001048159],"category_scores_gemma":[0.00007123008,0.0001705193,0.0001577982,0.0005894723,0.00003750109,0.00035665568,0.00000814683,0.00024469988,0.000023423107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007039086,0.00029007928,0.000009453527,0.00003488736,0.000029026902,0.000004227123,0.00038248562,0.11359775,0.008902171,0.00024500946,0.001300164,0.87513435],"study_design_scores_gemma":[0.00038367955,0.00009090596,0.000051889638,0.000034721183,0.000026111185,0.000003686727,0.000058905236,0.81058747,0.18696311,0.0004927763,0.0011409956,0.0001657737],"about_ca_topic_score_codex":0.000036240326,"about_ca_topic_score_gemma":0.0000043227374,"teacher_disagreement_score":0.87843573,"about_ca_system_score_codex":0.00020427241,"about_ca_system_score_gemma":0.000068119545,"threshold_uncertainty_score":0.6953575},"labels":[],"label_agreement":null},{"id":"W4226035400","doi":"10.1109/tmm.2022.3169055","title":"Focal Stack Image Compression Based on Basis-Quadtree Representation","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Quadtree; Computer science; Encoder; Basis (linear algebra); Algorithm; Data compression; Basis function; Coding (social sciences); Group of pictures; Artificial intelligence; Computer vision; Decoding methods; Mathematics","score_opus":0.024338337571105362,"score_gpt":0.3051349810832933,"score_spread":0.2807966435121879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226035400","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009251541,0.000007890837,0.99475616,0.0013646779,0.0013242301,0.0002761426,0.00003736225,0.0003256059,0.0009827927],"genre_scores_gemma":[0.80792964,0.0000059294507,0.18999189,0.0013998172,0.000037957278,0.00012844014,0.0000122954525,0.000025773868,0.00046824434],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793506,0.0002393928,0.00026384293,0.00057123555,0.0007031303,0.0002873419],"domain_scores_gemma":[0.9985685,0.00039965773,0.00009169105,0.0007358519,0.000055306384,0.00014902724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018455597,0.00018551665,0.00016637128,0.00031159923,0.0006415756,0.00008177193,0.0005543388,0.00003356872,0.00058131805],"category_scores_gemma":[0.000013527613,0.00018575217,0.00013650407,0.00051289884,0.000054382544,0.00050131057,0.000009852633,0.0005148454,0.00015828587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016683862,0.00079894305,0.000022130256,0.000007871658,0.000011295767,0.000043883665,0.00055157515,0.27938733,0.01915166,0.000038361122,0.002385143,0.69743496],"study_design_scores_gemma":[0.0012287012,0.00021638429,0.00031273408,0.000018405606,0.000006647427,0.0000048775064,0.000102276215,0.94454235,0.051101975,0.000083881874,0.0021830571,0.00019871416],"about_ca_topic_score_codex":0.000026267433,"about_ca_topic_score_gemma":0.000004193185,"teacher_disagreement_score":0.8070045,"about_ca_system_score_codex":0.0001411223,"about_ca_system_score_gemma":0.000058156205,"threshold_uncertainty_score":0.7574753},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.03848727033689459,"score_gpt":0.2986878252806538,"score_spread":0.2602005549437592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226294111","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019794872,0.000038267248,0.99264646,0.0007852361,0.0030407887,0.00073283206,0.00011854326,0.00031732675,0.00034105583],"genre_scores_gemma":[0.95294654,0.000022869956,0.044232517,0.00025249433,0.00007671475,0.0009854155,0.000020722358,0.000032264954,0.0014304401],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99797356,0.00020809444,0.00034771563,0.00062472455,0.00045751382,0.00038841603],"domain_scores_gemma":[0.9986126,0.00043877607,0.00012284674,0.000600703,0.000113313334,0.00011178243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080140296,0.00021452042,0.00024053961,0.00030236435,0.0007816197,0.00009917049,0.0005956286,0.000076060314,0.00015233616],"category_scores_gemma":[0.000018000097,0.00023563147,0.00025380295,0.00053677225,0.000039049726,0.00039434456,0.0000017089841,0.00032846737,0.000041653373],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023408717,0.0005593326,0.0000041217963,0.00005482556,0.00010920935,0.000005745349,0.010402048,0.05121644,0.068582326,0.00005414427,0.00065754703,0.8681202],"study_design_scores_gemma":[0.002286863,0.0005224825,0.00048424923,0.00001936871,0.000044557422,0.00000999422,0.0005779073,0.47628066,0.5157511,0.000858834,0.0026052129,0.0005587337],"about_ca_topic_score_codex":0.00008594681,"about_ca_topic_score_gemma":0.00008278113,"teacher_disagreement_score":0.9509671,"about_ca_system_score_codex":0.00013055006,"about_ca_system_score_gemma":0.000096558164,"threshold_uncertainty_score":0.9608772},"labels":[],"label_agreement":null},{"id":"W4234514911","doi":"10.1109/tmm.2018.2831999","title":"IEEE Transactions on Multimedia","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Multimedia Communication and Technology","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Università degli Studi di Perugia; University of Science and Technology of China; Simon Fraser University; Tongji University; University of Hong Kong; Institute for Infocomm Research; Academia Sinica; Beijing University of Posts and Telecommunications; Samsung; Korea University; Nanyang Technological University; Microsoft Research Asia; Indiana University Bloomington; Dartmouth College; De Montfort University; Microsoft Research; Università degli Studi di Torino; LOEWE Zentrum AdRIA; Università Degli Studi di Modena e Reggio Emila; Universitat Politècnica de Catalunya; University of Technology Sydney; Cardiff University; Cisco Systems","keywords":"Computer science; Multimedia; Computer network; World Wide Web","score_opus":0.04197486961567568,"score_gpt":0.3322802217173517,"score_spread":0.290305352101676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234514911","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020752858,0.000057482357,0.9297821,0.0073180795,0.009792168,0.0016872352,0.00020983834,0.002325092,0.028075159],"genre_scores_gemma":[0.9769498,0.00043919485,0.014730965,0.0008694642,0.00039419797,0.00027789894,0.0000063479083,0.00006860269,0.006263519],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99675524,0.00045172122,0.00053991785,0.00065605243,0.0007648635,0.0008322228],"domain_scores_gemma":[0.99678254,0.0011141844,0.00016319858,0.0011300603,0.00031126034,0.00049877824],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006126995,0.00039502763,0.00039345966,0.0006424846,0.0018389105,0.00008062042,0.0009918388,0.0006511506,0.006256395],"category_scores_gemma":[0.000069076195,0.00041091425,0.00029671908,0.0009498708,0.0019281076,0.00030243146,0.0000010543461,0.0011007196,0.005754092],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002482245,0.0018073377,0.000019179746,0.000011420632,0.0001823389,0.000010394313,0.022457825,0.00043430616,0.0048233056,0.00023371211,0.0025710391,0.96720093],"study_design_scores_gemma":[0.00987922,0.0020406877,0.000561063,0.0003102663,0.00048705415,0.00002433565,0.010922344,0.13599387,0.44427225,0.0009005069,0.39135334,0.003255051],"about_ca_topic_score_codex":0.0015890728,"about_ca_topic_score_gemma":0.008944672,"teacher_disagreement_score":0.96394587,"about_ca_system_score_codex":0.00030204945,"about_ca_system_score_gemma":0.0002939085,"threshold_uncertainty_score":0.9998343},"labels":[],"label_agreement":null},{"id":"W4235839885","doi":"10.1109/tmm.2020.2973164","title":"IEEE Transactions on Multimedia","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Multimedia Communication and Technology","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Standards Association","funders":"Institute for Infocomm Research; Hefei University of Technology; University of Miami; Northwestern University; Hefei University; University of Science and Technology of China; Hong Kong University of Science and Technology; Università di Catania; Ball State University; Nanjing University of Science and Technology; Tongji University; Università degli Studi di Perugia; Tsinghua University; Federation University Australia; University of Hong Kong; Northwestern Polytechnical University; Murdoch University; National University of Singapore; Microsoft Research; Nanjing University; Charles Sturt University; Microsoft Research Asia; University of Technology Sydney; Cisco Systems","keywords":"Computer science; Multimedia; Computer network","score_opus":0.05512351067365925,"score_gpt":0.3193603287911619,"score_spread":0.26423681811750266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235839885","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009002941,0.00008212985,0.93973374,0.031470552,0.004012798,0.0016467426,0.00025607622,0.0024531211,0.011341916],"genre_scores_gemma":[0.98242927,0.00064602925,0.012009082,0.0025896507,0.00024139219,0.0002637917,0.000008694429,0.00007011173,0.0017420106],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99686366,0.00043948795,0.000545251,0.0006702775,0.0007638137,0.00071750913],"domain_scores_gemma":[0.99706477,0.0011032851,0.00015529363,0.0007575889,0.00016733566,0.00075174595],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003670308,0.00039279368,0.00043474085,0.00035624224,0.0011714223,0.00007617682,0.0009832807,0.00057397544,0.0043179933],"category_scores_gemma":[0.00009040493,0.00041780644,0.0003396809,0.0009776711,0.0008235359,0.00028572054,0.0000010819274,0.0013911544,0.0037094282],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034435693,0.0015980409,0.000021056576,0.000028017197,0.00023177273,0.000025734142,0.041092824,0.004888005,0.006803783,0.00020626285,0.0031750663,0.94158506],"study_design_scores_gemma":[0.011393598,0.0015940735,0.00028776532,0.00021785534,0.00054456084,0.000013331648,0.016010428,0.3463203,0.25995418,0.00035176336,0.3599633,0.0033488316],"about_ca_topic_score_codex":0.00081694254,"about_ca_topic_score_gemma":0.0019207959,"teacher_disagreement_score":0.9734263,"about_ca_system_score_codex":0.00021032224,"about_ca_system_score_gemma":0.00027527602,"threshold_uncertainty_score":0.9998274},"labels":[],"label_agreement":null},{"id":"W4248438633","doi":"10.1109/tmm.2020.2980724","title":"IEEE Transactions on Multimedia","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Multimedia Communication and Technology","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Standards Association","funders":"Institute for Infocomm Research; Hefei University of Technology; University of Miami; Northwestern University; Hefei University; University of Science and Technology of China; Hong Kong University of Science and Technology; Università di Catania; Ball State University; Nanjing University of Science and Technology; Tongji University; Università degli Studi di Perugia; Tsinghua University; Federation University Australia; University of Hong Kong; Northwestern Polytechnical University; Murdoch University; National University of Singapore; Microsoft Research; Nanjing University; Charles Sturt University; Microsoft Research Asia; University of Technology Sydney; Cisco Systems","keywords":"Computer science; Multimedia; Computer network","score_opus":0.05512351067365925,"score_gpt":0.3193603287911619,"score_spread":0.26423681811750266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248438633","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009002941,0.00008212985,0.93973374,0.031470552,0.004012798,0.0016467426,0.00025607622,0.0024531211,0.011341916],"genre_scores_gemma":[0.98242927,0.00064602925,0.012009082,0.0025896507,0.00024139219,0.0002637917,0.000008694429,0.00007011173,0.0017420106],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99686366,0.00043948795,0.000545251,0.0006702775,0.0007638137,0.00071750913],"domain_scores_gemma":[0.99706477,0.0011032851,0.00015529363,0.0007575889,0.00016733566,0.00075174595],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003670308,0.00039279368,0.00043474085,0.00035624224,0.0011714223,0.00007617682,0.0009832807,0.00057397544,0.0043179933],"category_scores_gemma":[0.00009040493,0.00041780644,0.0003396809,0.0009776711,0.0008235359,0.00028572054,0.0000010819274,0.0013911544,0.0037094282],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034435693,0.0015980409,0.000021056576,0.000028017197,0.00023177273,0.000025734142,0.041092824,0.004888005,0.006803783,0.00020626285,0.0031750663,0.94158506],"study_design_scores_gemma":[0.011393598,0.0015940735,0.00028776532,0.00021785534,0.00054456084,0.000013331648,0.016010428,0.3463203,0.25995418,0.00035176336,0.3599633,0.0033488316],"about_ca_topic_score_codex":0.00081694254,"about_ca_topic_score_gemma":0.0019207959,"teacher_disagreement_score":0.9734263,"about_ca_system_score_codex":0.00021032224,"about_ca_system_score_gemma":0.00027527602,"threshold_uncertainty_score":0.9998274},"labels":[],"label_agreement":null},{"id":"W4250597793","doi":"10.1109/tmm.2020.2966202","title":"IEEE Transactions on Multimedia","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Multimedia Communication and Technology","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Standards Association","funders":"Institute for Infocomm Research; Hefei University of Technology; University of Miami; Northwestern University; Hefei University; University of Science and Technology of China; Hong Kong University of Science and Technology; Università di Catania; Ball State University; Nanjing University of Science and Technology; Tongji University; Università degli Studi di Perugia; Tsinghua University; Federation University Australia; University of Hong Kong; Northwestern Polytechnical University; Beijing University of Posts and Telecommunications; Murdoch University; University of Technology Sydney; National University of Singapore; Microsoft Research; Nanjing University; Charles Sturt University; Microsoft Research Asia","keywords":"Computer science; Multimedia; Computer network","score_opus":0.05512351067365925,"score_gpt":0.3193603287911619,"score_spread":0.26423681811750266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250597793","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009002941,0.00008212985,0.93973374,0.031470552,0.004012798,0.0016467426,0.00025607622,0.0024531211,0.011341916],"genre_scores_gemma":[0.98242927,0.00064602925,0.012009082,0.0025896507,0.00024139219,0.0002637917,0.000008694429,0.00007011173,0.0017420106],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99686366,0.00043948795,0.000545251,0.0006702775,0.0007638137,0.00071750913],"domain_scores_gemma":[0.99706477,0.0011032851,0.00015529363,0.0007575889,0.00016733566,0.00075174595],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003670308,0.00039279368,0.00043474085,0.00035624224,0.0011714223,0.00007617682,0.0009832807,0.00057397544,0.0043179933],"category_scores_gemma":[0.00009040493,0.00041780644,0.0003396809,0.0009776711,0.0008235359,0.00028572054,0.0000010819274,0.0013911544,0.0037094282],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034435693,0.0015980409,0.000021056576,0.000028017197,0.00023177273,0.000025734142,0.041092824,0.004888005,0.006803783,0.00020626285,0.0031750663,0.94158506],"study_design_scores_gemma":[0.011393598,0.0015940735,0.00028776532,0.00021785534,0.00054456084,0.000013331648,0.016010428,0.3463203,0.25995418,0.00035176336,0.3599633,0.0033488316],"about_ca_topic_score_codex":0.00081694254,"about_ca_topic_score_gemma":0.0019207959,"teacher_disagreement_score":0.9734263,"about_ca_system_score_codex":0.00021032224,"about_ca_system_score_gemma":0.00027527602,"threshold_uncertainty_score":0.9998274},"labels":[],"label_agreement":null},{"id":"W4254988289","doi":"10.1109/tmm.2018.2824220","title":"IEEE Transactions on Multimedia","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Multimedia Communication and Technology","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Università degli Studi di Perugia; University of Science and Technology of China; Simon Fraser University; Tongji University; University of Hong Kong; Institute for Infocomm Research; Academia Sinica; Beijing University of Posts and Telecommunications; Samsung; Korea University; Nanyang Technological University; Microsoft Research Asia; Indiana University Bloomington; Dartmouth College; De Montfort University; Microsoft Research; Università degli Studi di Torino; LOEWE Zentrum AdRIA; Università Degli Studi di Modena e Reggio Emila; Universitat Politècnica de Catalunya; University of Technology Sydney; Cardiff University; Cisco Systems","keywords":"Computer science; Multimedia; Computer network","score_opus":0.04197486961567568,"score_gpt":0.3322802217173517,"score_spread":0.290305352101676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254988289","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020752858,0.000057482357,0.9297821,0.0073180795,0.009792168,0.0016872352,0.00020983834,0.002325092,0.028075159],"genre_scores_gemma":[0.9769498,0.00043919485,0.014730965,0.0008694642,0.00039419797,0.00027789894,0.0000063479083,0.00006860269,0.006263519],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99675524,0.00045172122,0.00053991785,0.00065605243,0.0007648635,0.0008322228],"domain_scores_gemma":[0.99678254,0.0011141844,0.00016319858,0.0011300603,0.00031126034,0.00049877824],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006126995,0.00039502763,0.00039345966,0.0006424846,0.0018389105,0.00008062042,0.0009918388,0.0006511506,0.006256395],"category_scores_gemma":[0.000069076195,0.00041091425,0.00029671908,0.0009498708,0.0019281076,0.00030243146,0.0000010543461,0.0011007196,0.005754092],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002482245,0.0018073377,0.000019179746,0.000011420632,0.0001823389,0.000010394313,0.022457825,0.00043430616,0.0048233056,0.00023371211,0.0025710391,0.96720093],"study_design_scores_gemma":[0.00987922,0.0020406877,0.000561063,0.0003102663,0.00048705415,0.00002433565,0.010922344,0.13599387,0.44427225,0.0009005069,0.39135334,0.003255051],"about_ca_topic_score_codex":0.0015890728,"about_ca_topic_score_gemma":0.008944672,"teacher_disagreement_score":0.96394587,"about_ca_system_score_codex":0.00030204945,"about_ca_system_score_gemma":0.0002939085,"threshold_uncertainty_score":0.9998343},"labels":[],"label_agreement":null},{"id":"W4255254988","doi":"10.1109/tmm.2014.2298932","title":"IEEE Transactions on Multimedia publication information","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Multimedia Communication and Technology","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Standards Association; Toronto Metropolitan University; McMaster University","funders":"","keywords":"Computer science; Multimedia; Computer network; World Wide Web","score_opus":0.02528765922651188,"score_gpt":0.29539117555783234,"score_spread":0.27010351633132046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255254988","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031865737,0.000010337757,0.9642953,0.0084602125,0.0028672528,0.0010570331,0.000095660194,0.00130002,0.01872759],"genre_scores_gemma":[0.9832814,0.00026585962,0.0123147,0.0014530425,0.00014582953,0.00047020338,0.000045518333,0.00003807976,0.0019854112],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99703074,0.00050037313,0.0006696563,0.0003989941,0.0007877604,0.000612461],"domain_scores_gemma":[0.9967585,0.0011263925,0.00027264855,0.0009978131,0.0004397874,0.00040483277],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00096780184,0.00032973333,0.00032319772,0.00087274594,0.0012889164,0.00016589415,0.00081585627,0.00059200655,0.0020412356],"category_scores_gemma":[0.00020483883,0.00034935412,0.00022762366,0.0009289474,0.0005901029,0.0011302967,9.489917e-7,0.0009636478,0.0041710963],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007732967,0.0005910824,0.000010524739,0.000012475668,0.000056790283,3.6146102e-7,0.009213409,0.0016073628,0.0005425945,0.0006532104,0.001702185,0.9855327],"study_design_scores_gemma":[0.005662715,0.00056457263,0.0005704234,0.0001203205,0.0002001781,0.0000072903667,0.0039551533,0.35860023,0.054741003,0.0005732115,0.5734115,0.0015933771],"about_ca_topic_score_codex":0.0010550604,"about_ca_topic_score_gemma":0.00188561,"teacher_disagreement_score":0.9839393,"about_ca_system_score_codex":0.00031172333,"about_ca_system_score_gemma":0.00020935397,"threshold_uncertainty_score":0.9998959},"labels":[],"label_agreement":null},{"id":"W4255279474","doi":"10.1109/tmm.2020.2993156","title":"IEEE Transactions on Multimedia","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Multimedia Communication and Technology","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Standards Association","funders":"Institute for Infocomm Research; Hefei University of Technology; University of Miami; Northwestern University; Hefei University; University of Science and Technology of China; Hong Kong University of Science and Technology; Università di Catania; Ball State University; Nanjing University of Science and Technology; Tongji University; Università degli Studi di Perugia; Tsinghua University; Federation University Australia; University of Hong Kong; Northwestern Polytechnical University; Murdoch University; National University of Singapore; Microsoft Research; Nanjing University; Charles Sturt University; Microsoft Research Asia; University of Technology Sydney; Cisco Systems","keywords":"Computer science; Multimedia; Computer network","score_opus":0.05512351067365925,"score_gpt":0.3193603287911619,"score_spread":0.26423681811750266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255279474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009002941,0.00008212985,0.93973374,0.031470552,0.004012798,0.0016467426,0.00025607622,0.0024531211,0.011341916],"genre_scores_gemma":[0.98242927,0.00064602925,0.012009082,0.0025896507,0.00024139219,0.0002637917,0.000008694429,0.00007011173,0.0017420106],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99686366,0.00043948795,0.000545251,0.0006702775,0.0007638137,0.00071750913],"domain_scores_gemma":[0.99706477,0.0011032851,0.00015529363,0.0007575889,0.00016733566,0.00075174595],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003670308,0.00039279368,0.00043474085,0.00035624224,0.0011714223,0.00007617682,0.0009832807,0.00057397544,0.0043179933],"category_scores_gemma":[0.00009040493,0.00041780644,0.0003396809,0.0009776711,0.0008235359,0.00028572054,0.0000010819274,0.0013911544,0.0037094282],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034435693,0.0015980409,0.000021056576,0.000028017197,0.00023177273,0.000025734142,0.041092824,0.004888005,0.006803783,0.00020626285,0.0031750663,0.94158506],"study_design_scores_gemma":[0.011393598,0.0015940735,0.00028776532,0.00021785534,0.00054456084,0.000013331648,0.016010428,0.3463203,0.25995418,0.00035176336,0.3599633,0.0033488316],"about_ca_topic_score_codex":0.00081694254,"about_ca_topic_score_gemma":0.0019207959,"teacher_disagreement_score":0.9734263,"about_ca_system_score_codex":0.00021032224,"about_ca_system_score_gemma":0.00027527602,"threshold_uncertainty_score":0.9998274},"labels":[],"label_agreement":null},{"id":"W4285129685","doi":"10.1109/tmm.2022.3185929","title":"Unsupervised Single-Image Reflection Removal","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Benchmark (surveying); Convolutional neural network; Reflection (computer programming); Image (mathematics); Process (computing); Deep learning; Feature (linguistics); Pattern recognition (psychology); Artificial neural network; Feature extraction; Supervised learning; Image quality; Computer vision","score_opus":0.027653636013142982,"score_gpt":0.2694604788557707,"score_spread":0.2418068428426277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285129685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035656388,0.000016612634,0.98973656,0.0006191776,0.0016673825,0.00033108375,0.000012889184,0.0012716849,0.0027789935],"genre_scores_gemma":[0.6302487,0.00001131153,0.3669298,0.0005329364,0.00005760399,0.000275493,0.0000047509043,0.000029942576,0.0019094094],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982166,0.00017703282,0.00025281232,0.00048775823,0.0005437467,0.00032200766],"domain_scores_gemma":[0.99903524,0.00009670912,0.000067936584,0.0006485746,0.00006721885,0.00008433164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028918337,0.00018115346,0.00014685755,0.00033470997,0.0005608729,0.00009635805,0.00069513643,0.000047455553,0.0004870207],"category_scores_gemma":[0.0000071182917,0.00020628353,0.00011682138,0.0007577768,0.0000521707,0.00058942434,0.000009988971,0.0004674888,0.00013938815],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005361232,0.0008324397,0.0000010336055,0.00000925377,0.000029379575,0.000092563874,0.0013415964,0.0014169953,0.59805167,0.00006980881,0.0019752663,0.3961264],"study_design_scores_gemma":[0.00074764626,0.00059490243,0.000017492412,0.000010720931,0.00001602415,0.00011018033,0.00007493235,0.14867693,0.8371685,0.0002501087,0.011969136,0.00036339252],"about_ca_topic_score_codex":0.000039354294,"about_ca_topic_score_gemma":0.000010966796,"teacher_disagreement_score":0.6266831,"about_ca_system_score_codex":0.00033141082,"about_ca_system_score_gemma":0.000057726527,"threshold_uncertainty_score":0.8411998},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.018101136092844125,"score_gpt":0.2926798188489161,"score_spread":0.27457868275607195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285163934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03441493,0.000029706964,0.9563072,0.00023602456,0.0076974514,0.0006918993,0.00005176738,0.0005000873,0.00007095024],"genre_scores_gemma":[0.98594934,0.000010663976,0.011064574,0.0005241624,0.000117413096,0.00089778675,0.000013072547,0.00003594525,0.0013870165],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771416,0.00018181452,0.00040634323,0.0006778214,0.0005018029,0.0005180828],"domain_scores_gemma":[0.9989162,0.00018373072,0.00013308454,0.0005195857,0.00010385636,0.00014352481],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00047525016,0.00024838198,0.00020981282,0.0002600065,0.0014494945,0.00016113599,0.00052829663,0.00009112141,0.00020537773],"category_scores_gemma":[0.000009074944,0.000266864,0.00031617726,0.0007558559,0.000054891585,0.00037720133,0.000009978127,0.0004443435,0.00010652463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025759757,0.00079460046,0.00005056196,0.00003208118,0.0001087394,0.000009952877,0.0015911272,0.49558714,0.017285336,0.00007587591,0.0010521751,0.4831548],"study_design_scores_gemma":[0.0014336591,0.0009361678,0.0011560884,0.000009920763,0.000029951118,0.000033767585,0.000109592795,0.9710336,0.01827154,0.00051316706,0.0060537085,0.00041884565],"about_ca_topic_score_codex":0.000042712898,"about_ca_topic_score_gemma":0.00014374807,"teacher_disagreement_score":0.95153445,"about_ca_system_score_codex":0.00030288292,"about_ca_system_score_gemma":0.00005317909,"threshold_uncertainty_score":0.99997836},"labels":[],"label_agreement":null},{"id":"W4285222939","doi":"10.1109/tmm.2022.3177942","title":"VWP:An Efficient DRL-Based Autonomous Driving Model","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Convergence (economics); Collision; Task (project management); Mode (computer interface); Feature (linguistics); Function (biology); Artificial intelligence; Encoder; Adversarial system; Real-time computing; Algorithm; Simulation; Engineering","score_opus":0.010230341570072467,"score_gpt":0.21343052868102938,"score_spread":0.20320018711095691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285222939","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2660878,0.00002135763,0.7305219,0.0001056575,0.00064818905,0.00021693567,0.000071410184,0.0018992269,0.00042748728],"genre_scores_gemma":[0.99072516,0.0000036224242,0.008635805,0.00010252023,0.000019117773,0.00025530765,0.000011360544,0.00006639941,0.00018072505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878716,0.000043974735,0.00026089614,0.00030667562,0.00021881833,0.0003824891],"domain_scores_gemma":[0.999296,0.00008325662,0.000029594292,0.00045011067,0.00001797633,0.0001230501],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017465412,0.0002243149,0.00019752773,0.00027478472,0.00052200945,0.00001310697,0.00030540506,0.00013499348,0.00045931304],"category_scores_gemma":[0.0000020803284,0.0002654701,0.00011782691,0.0002534706,0.00007623752,0.000065197186,0.0000023364375,0.00083602377,0.00009223224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016804563,0.00023271194,0.000007728802,0.000006717543,0.00002586738,0.000009077387,0.00043565652,0.9638694,0.0034570773,0.000021392256,0.000038308077,0.03187924],"study_design_scores_gemma":[0.0005828788,0.000089083354,0.000074680975,0.000004493755,0.000027963883,0.00000700127,0.000095369185,0.97952265,0.018970542,0.000028001348,0.0003178606,0.00027947713],"about_ca_topic_score_codex":0.000012173119,"about_ca_topic_score_gemma":0.000031277483,"teacher_disagreement_score":0.7246373,"about_ca_system_score_codex":0.00034402034,"about_ca_system_score_gemma":0.00008022136,"threshold_uncertainty_score":0.99997973},"labels":[],"label_agreement":null},{"id":"W4313316212","doi":"10.1109/tmm.2022.3233306","title":"Cycle Consistency Based Pseudo Label and Fine Alignment for Unsupervised Domain Adaptation","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Consistency (knowledge bases); Data mining; Machine learning","score_opus":0.029257884249003458,"score_gpt":0.2524013322969264,"score_spread":0.22314344804792294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313316212","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009482267,0.000043587068,0.98703796,0.0019064205,0.00061582203,0.0005211213,0.00006369936,0.0001742217,0.00015492633],"genre_scores_gemma":[0.6927133,0.0000055805995,0.30558926,0.00093068054,0.000017852812,0.00042256832,0.000014793482,0.000017826475,0.0002881103],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985623,0.00015716215,0.0002613508,0.00040884383,0.00035981162,0.00025054096],"domain_scores_gemma":[0.99898094,0.0004602821,0.000080273436,0.00028662378,0.00005805322,0.00013383296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003783286,0.00015787492,0.00015818732,0.00019301787,0.00070739567,0.00007214876,0.00024962582,0.000040848056,0.00015717786],"category_scores_gemma":[0.000011958936,0.00017352951,0.00007785407,0.00030103925,0.00005439192,0.00019343205,0.000004319451,0.00019666563,0.000016894051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034939044,0.0013063895,0.000033478264,0.000062399224,0.00010923337,0.000022746653,0.010840036,0.43181556,0.017691929,0.0030693882,0.0005864216,0.53411305],"study_design_scores_gemma":[0.0034455825,0.00033734893,0.00014789963,0.0000075401254,0.000018107457,0.000007336911,0.0005525117,0.98912513,0.0015310772,0.0004685152,0.0041432925,0.00021567062],"about_ca_topic_score_codex":0.00003017805,"about_ca_topic_score_gemma":0.000029781057,"teacher_disagreement_score":0.68323106,"about_ca_system_score_codex":0.00009186615,"about_ca_system_score_gemma":0.00010547014,"threshold_uncertainty_score":0.7076328},"labels":[],"label_agreement":null},{"id":"W4327664521","doi":"10.1109/tmm.2023.3257566","title":"Federated Adversarial Domain Hallucination for Privacy-Preserving Domain Generalization","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"Science and Technology Commission of Shanghai Municipality","keywords":"Computer science; Domain (mathematical analysis); Artificial intelligence; Entropy (arrow of time); Generalization; Segmentation; Machine learning; Deep learning; Pattern recognition (psychology); Mathematics","score_opus":0.030672194975276946,"score_gpt":0.2805723767357126,"score_spread":0.24990018176043566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327664521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063345754,0.000007174259,0.987945,0.0020354926,0.0018406279,0.0006314488,0.000018034863,0.0008514916,0.00033612605],"genre_scores_gemma":[0.53460073,0.00003353383,0.46214977,0.00047778344,0.00024249898,0.0003167043,0.00010387137,0.00005516184,0.0020199441],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813825,0.00017649287,0.0003515662,0.00051299576,0.00041952255,0.00040116798],"domain_scores_gemma":[0.99886733,0.00035814504,0.000121950354,0.00033892653,0.00017261435,0.00014105297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057561655,0.00020783716,0.00018217483,0.00044068633,0.00066880824,0.0002566642,0.0004621463,0.00014656101,0.00008372946],"category_scores_gemma":[0.00005046171,0.00022212173,0.00012303771,0.0010325554,0.000035535406,0.0006790434,0.000007999064,0.00019564493,0.00024378317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030598757,0.00041381837,0.000031966338,0.00010277111,0.00018434144,0.000023155482,0.0142568415,0.46327898,0.08399051,0.002678169,0.009190443,0.425543],"study_design_scores_gemma":[0.0021702445,0.00010697774,0.00023129216,0.000025244333,0.000009720908,0.0000029617077,0.0001899223,0.980353,0.005802117,0.0014828581,0.00936132,0.0002643259],"about_ca_topic_score_codex":0.000032784847,"about_ca_topic_score_gemma":0.00004110875,"teacher_disagreement_score":0.5282662,"about_ca_system_score_codex":0.000119966935,"about_ca_system_score_gemma":0.000092295755,"threshold_uncertainty_score":0.9057861},"labels":[],"label_agreement":null},{"id":"W4366378394","doi":"10.1109/tmm.2023.3268369","title":"Discriminative Identity-Feature Exploring and Differential Aware Learning for Unsupervised Person Re-Identification","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Dalian Science and Technology Innovation Fund; Fundamental Research Funds for the Central Universities; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Discriminative model; Computer science; Artificial intelligence; Salient; Pattern recognition (psychology); Redundancy (engineering); Machine learning; Feature learning; Robustness (evolution); Identification (biology)","score_opus":0.11069083483817996,"score_gpt":0.3299612520130516,"score_spread":0.21927041717487164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366378394","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10101592,0.000018146533,0.89599466,0.0006849068,0.0015377312,0.0002944044,0.000017260958,0.00042425358,0.000012708142],"genre_scores_gemma":[0.9859859,0.0001274603,0.012834381,0.000020675174,0.00009628783,0.0002791364,0.000019752943,0.000024756815,0.0006116792],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985724,0.00016862979,0.00017021626,0.000512601,0.0002813797,0.0002947636],"domain_scores_gemma":[0.9988654,0.0005858688,0.00006944222,0.00028233542,0.00010229075,0.00009465282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043155448,0.00017859579,0.00019293898,0.000309688,0.0005240099,0.00024208773,0.00029825905,0.00008858995,0.0000100002735],"category_scores_gemma":[0.000054085063,0.00017563901,0.000120051765,0.0005426174,0.0000499662,0.0010944665,0.0000044341605,0.0003185486,0.000034438744],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008346349,0.00016243287,0.0004276042,0.00020532223,0.00014203478,0.000015028817,0.027368626,0.008337482,0.029739585,0.00021464737,0.00022535451,0.9330784],"study_design_scores_gemma":[0.0015936239,0.00019031296,0.04586183,0.00011876996,0.00006848869,0.000004986496,0.0029462664,0.89062905,0.057382923,0.0004169572,0.00028301805,0.00050376053],"about_ca_topic_score_codex":0.00002654577,"about_ca_topic_score_gemma":0.0000626578,"teacher_disagreement_score":0.9325746,"about_ca_system_score_codex":0.0000411879,"about_ca_system_score_gemma":0.000021514908,"threshold_uncertainty_score":0.7162351},"labels":[],"label_agreement":null},{"id":"W4385945197","doi":"10.1109/tmm.2023.3306072","title":"Hierarchical Independent Coding Scheme for Varifocal Multiview Images Based on Angular-Focal Joint Prediction","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image and Video Stabilization","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Coding (social sciences); Joint (building); Computer vision; Artificial intelligence; Scheme (mathematics); Mathematics","score_opus":0.03556230861831092,"score_gpt":0.27820497460947025,"score_spread":0.24264266599115933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385945197","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019305925,0.0000080755335,0.9938763,0.0010596542,0.001376662,0.0007813996,0.0001326521,0.0007539063,0.00008078588],"genre_scores_gemma":[0.9161658,0.000026006248,0.08246339,0.0004386546,0.00016523259,0.00042045678,0.00005555832,0.000042091466,0.00022279732],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775815,0.00014527622,0.00039523616,0.0006679128,0.00057064096,0.0004627985],"domain_scores_gemma":[0.9984858,0.00058835384,0.00007645627,0.0005186144,0.00014500513,0.00018574284],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00060052855,0.00025655743,0.0002501788,0.0004885534,0.00038643743,0.00013753767,0.00033503067,0.0001806449,0.000042672924],"category_scores_gemma":[0.00008659049,0.0002459332,0.00022915652,0.00072988606,0.00008141343,0.0004446089,0.0000054509956,0.00042540635,0.00017779676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048538513,0.0024106884,0.00017487568,0.0003629114,0.00015166201,0.000088005574,0.0021675508,0.15242787,0.09065739,0.0007123269,0.0038213301,0.74654],"study_design_scores_gemma":[0.0015348837,0.00035431952,0.0018071122,0.00008814887,0.000021322001,0.0000040015584,0.000021560007,0.92065835,0.07483335,0.00011844884,0.0003381972,0.00022031293],"about_ca_topic_score_codex":0.000017204706,"about_ca_topic_score_gemma":0.000011852291,"teacher_disagreement_score":0.91423523,"about_ca_system_score_codex":0.00013728775,"about_ca_system_score_gemma":0.00012370352,"threshold_uncertainty_score":0.9999993},"labels":[],"label_agreement":null},{"id":"W4386108371","doi":"10.1109/tmm.2023.3307933","title":"Learning Representations by Contrastive Spatio-Temporal Clustering for Skeleton-Based Action Recognition","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Cluster analysis; Artificial intelligence; Discriminative model; Feature learning; Pattern recognition (psychology); Regularization (linguistics); Machine learning","score_opus":0.04889612834000338,"score_gpt":0.30536831814105614,"score_spread":0.25647218980105274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386108371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028142879,0.0000029399769,0.96815497,0.0005947481,0.0012562299,0.0006351012,0.00013371349,0.00095273246,0.00012671492],"genre_scores_gemma":[0.9814731,0.000026664515,0.015832348,0.00020491148,0.000135099,0.0006604529,0.00061509386,0.000035060708,0.0010172602],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843776,0.00012516616,0.00032665394,0.0004985408,0.00027842418,0.00033347463],"domain_scores_gemma":[0.99860644,0.00070239796,0.00015267046,0.00020941645,0.00020110203,0.00012799188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025950474,0.0001956253,0.00017288471,0.00043053177,0.000618465,0.00015106014,0.00017390269,0.00012753565,0.0001222371],"category_scores_gemma":[0.00004214686,0.00022221929,0.00016573166,0.0006052199,0.000044055843,0.0006786138,0.0000018067993,0.0003095105,0.000507722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018340368,0.0003009857,0.000040989402,0.00006060754,0.00008461936,0.0000062306085,0.0010589046,0.054936476,0.0120360255,0.0000052902487,0.004807136,0.92647934],"study_design_scores_gemma":[0.0016149092,0.00027321413,0.00017621256,0.000051621304,0.000031795138,0.000002984299,0.00023463627,0.90743554,0.08757323,0.00020571215,0.0021205049,0.00027962044],"about_ca_topic_score_codex":0.00008533985,"about_ca_topic_score_gemma":0.00015946,"teacher_disagreement_score":0.9533302,"about_ca_system_score_codex":0.000104650244,"about_ca_system_score_gemma":0.00006756557,"threshold_uncertainty_score":0.90618396},"labels":[],"label_agreement":null},{"id":"W4388430467","doi":"10.1109/tmm.2023.3330522","title":"Realistic Depth Image Synthesis for 3D Hand Pose Estimation","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Pose; Computer vision; Artificial intelligence; Image (mathematics); 3D pose estimation","score_opus":0.03247850330242019,"score_gpt":0.2885760705584957,"score_spread":0.2560975672560755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388430467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030119226,0.0000030235958,0.9940374,0.00044209464,0.0010955678,0.00036791092,0.00008768869,0.000562516,0.00039189035],"genre_scores_gemma":[0.7711336,0.00003415399,0.22690728,0.00016952048,0.00013561921,0.00056038337,0.000040519863,0.000032216813,0.0009866892],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888337,0.000049762726,0.00022955058,0.00035090058,0.00021680903,0.000269612],"domain_scores_gemma":[0.99866515,0.0007417963,0.000067272485,0.0003097612,0.00010629702,0.00010972294],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020702035,0.00015179852,0.0001477472,0.0003547484,0.00042098842,0.00018748404,0.00022755904,0.00008543349,0.00009623812],"category_scores_gemma":[0.000059673686,0.0001527397,0.00011625828,0.0004348739,0.000057075886,0.0005227677,0.0000015308475,0.0001238265,0.0012489422],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028932092,0.00013962424,7.8944095e-7,0.000042046173,0.00003554291,0.000009918606,0.00054072007,0.005182222,0.0056168935,0.00004979073,0.0024655443,0.985888],"study_design_scores_gemma":[0.0004451986,0.00007419643,0.00020501512,0.000043954988,0.00004214322,0.0000065198083,0.00002429315,0.90592927,0.091838405,0.0006512468,0.00054921146,0.00019055398],"about_ca_topic_score_codex":0.000032251806,"about_ca_topic_score_gemma":0.000052072144,"teacher_disagreement_score":0.98569745,"about_ca_system_score_codex":0.000051702533,"about_ca_system_score_gemma":0.000045619814,"threshold_uncertainty_score":0.9995287},"labels":[],"label_agreement":null},{"id":"W4390577888","doi":"10.1109/tmm.2023.3345180","title":"Disentangled Representation Learning for Controllable Person Image Generation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Feature learning; Encoder; Artificial intelligence; Transformer; Component (thermodynamics); Segmentation; Pattern recognition (psychology); Representation (politics); Computer vision; Machine learning","score_opus":0.053821500640456724,"score_gpt":0.3332741873070718,"score_spread":0.2794526866666151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390577888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004351746,0.00013048951,0.9910552,0.00087039726,0.0024869477,0.00037326716,0.000010570731,0.00048755264,0.00023384763],"genre_scores_gemma":[0.83647764,0.00003706807,0.16171797,0.000070392496,0.0002585241,0.00017508441,0.000014026166,0.0000223905,0.0012269242],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878824,0.0001528342,0.00017852693,0.00045083126,0.0002050038,0.0002245893],"domain_scores_gemma":[0.9989551,0.0006004136,0.000037025166,0.00024177268,0.00009684811,0.00006886673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041611135,0.00013640952,0.00015478762,0.00017626086,0.00024938368,0.00038281782,0.0001681399,0.00006766493,0.000033407105],"category_scores_gemma":[0.000042816126,0.00012920948,0.00016050175,0.00035645772,0.000026923057,0.00068968744,7.946359e-7,0.00019976641,0.00009525662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049615082,0.00009822098,0.000033324082,0.0000493193,0.00010551298,0.000019362782,0.0023313852,0.035007775,0.18112569,0.00018484832,0.0010637315,0.77993125],"study_design_scores_gemma":[0.00059092714,0.0000922542,0.0000812001,0.000021478048,0.000022916587,0.0000054277907,0.00005011666,0.90590477,0.0918995,0.000087085886,0.0011052755,0.00013905983],"about_ca_topic_score_codex":0.000031336884,"about_ca_topic_score_gemma":0.000027955683,"teacher_disagreement_score":0.870897,"about_ca_system_score_codex":0.00006507267,"about_ca_system_score_gemma":0.000050955317,"threshold_uncertainty_score":0.526901},"labels":[],"label_agreement":null},{"id":"W4393285750","doi":"10.1109/tmm.2024.3382889","title":"SPACE: Self-Supervised Dual Preference Enhancing Network for Multimodal Recommendation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Key Research and Development Projects of Shaanxi Province; Fundamental Research Funds for the Central Universities; State Key Laboratory of Integrated Services Networks; National Natural Science Foundation of China","keywords":"Computer science; Preference; Modality (human–computer interaction); Recommender system; Artificial intelligence; Dual (grammatical number); Space (punctuation); Machine learning; Representation (politics); Task (project management); Information retrieval; Human–computer interaction; Natural language processing","score_opus":0.03291627563722623,"score_gpt":0.2738679133519815,"score_spread":0.24095163771475528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393285750","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007499744,0.000065314176,0.9905427,0.0012515772,0.004327135,0.0008662753,0.000041030446,0.001807944,0.00034802998],"genre_scores_gemma":[0.53764987,0.000059679496,0.46111056,0.000111707945,0.00029426915,0.00046757978,0.000011980068,0.000030205083,0.00026415076],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982522,0.00010442579,0.00039147548,0.0006244481,0.00020214463,0.00042531925],"domain_scores_gemma":[0.99870247,0.0006077261,0.000057389334,0.0004094742,0.00008757449,0.00013535394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005142485,0.00025555215,0.00023733954,0.00019773627,0.0002721859,0.0003151352,0.00033532703,0.00016610292,0.000067058696],"category_scores_gemma":[0.000006618144,0.00023778566,0.00017889262,0.00042510888,0.00001810338,0.00069436996,0.0000046609407,0.00032450608,0.00009436168],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030060457,0.0002657015,0.000008335913,0.00021230892,0.00018299774,0.000008428345,0.0032943536,0.0017877836,0.0041092485,0.0009653917,0.009105701,0.9800297],"study_design_scores_gemma":[0.00042930664,0.00021017485,0.0000248778,0.00016241163,0.00002869459,0.000014939163,0.00003181453,0.9422507,0.039495833,0.00047505746,0.016561652,0.0003144843],"about_ca_topic_score_codex":0.00008368608,"about_ca_topic_score_gemma":0.00011894005,"teacher_disagreement_score":0.9797152,"about_ca_system_score_codex":0.00013699172,"about_ca_system_score_gemma":0.00010739683,"threshold_uncertainty_score":0.9696618},"labels":[],"label_agreement":null},{"id":"W4394586022","doi":"10.1109/tmm.2024.3375774","title":"Progressive Learning Model for Big Data Analysis Using Subnetwork and Moore-Penrose Inverse","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; Vector Institute; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Subnetwork; Computer science; Moore–Penrose pseudoinverse; Big data; Theoretical computer science; Artificial intelligence; Inverse; Algorithm; Data mining; Computer network; Mathematics","score_opus":0.07840007998234619,"score_gpt":0.323889116765693,"score_spread":0.2454890367833468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394586022","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01394667,0.00029177684,0.9841853,0.00018949552,0.0007941893,0.00020190752,0.000043281343,0.00032968086,0.000017735063],"genre_scores_gemma":[0.8098154,0.000029810572,0.18932536,0.000047618472,0.00016051307,0.000029148921,0.000026164367,0.000021879383,0.0005441023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852115,0.000076468845,0.00019917809,0.0007042382,0.00020400813,0.00029493304],"domain_scores_gemma":[0.9988545,0.00031302078,0.0000512787,0.00059831055,0.00004531962,0.00013754603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038664474,0.00018069301,0.0002134829,0.0003970527,0.00034705573,0.00030077793,0.00047147926,0.000087569606,0.0000075727608],"category_scores_gemma":[0.000021147966,0.00016585003,0.000113216935,0.0009294144,0.00006459496,0.00042545065,0.000013192078,0.00034460583,0.000012856201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000111924455,0.000039806342,0.000048448794,0.000029374984,0.00023311027,0.000011274001,0.0015097709,0.7361066,0.00011180561,0.000012469386,0.00009379764,0.26179236],"study_design_scores_gemma":[0.0002624169,0.000043317617,0.000030133142,0.000043434262,0.0003512427,0.000008332384,0.00003121179,0.99847275,0.00012576696,0.00008040331,0.0003609172,0.00019008484],"about_ca_topic_score_codex":0.00008041508,"about_ca_topic_score_gemma":0.00008705005,"teacher_disagreement_score":0.79586875,"about_ca_system_score_codex":0.00003410569,"about_ca_system_score_gemma":0.00008868185,"threshold_uncertainty_score":0.6763168},"labels":[],"label_agreement":null},{"id":"W4401634887","doi":"10.1109/tmm.2024.3443613","title":"CarveNet: Carving Point-Block for Complex 3D Shape Completion","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"JST-Mirai Program; Natural Sciences and Engineering Research Council of Canada; Info-communications Media Development Authority; National Research Foundation Singapore","keywords":"Computer science; Carving; Block (permutation group theory); Point (geometry); Completion (oil and gas wells); Artificial intelligence; Computer graphics (images); Geometry; Mathematics; Geology","score_opus":0.03044060495744065,"score_gpt":0.25823436409810724,"score_spread":0.22779375914066657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401634887","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008718365,0.0002781381,0.9879978,0.00013186462,0.0010835751,0.0002062624,0.00024802506,0.00095604017,0.00037997038],"genre_scores_gemma":[0.9855201,0.00006582608,0.0136076845,0.00004559512,0.00019212248,0.000090860725,0.000047955175,0.000074947995,0.0003548863],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990086,0.000017681103,0.0002596332,0.00026870353,0.00016928428,0.0002760765],"domain_scores_gemma":[0.99945706,0.00019084835,0.000011859758,0.0001936859,0.00004902032,0.00009752078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011673088,0.00020561279,0.00022870305,0.00025697262,0.0001394827,0.000078591176,0.00010900507,0.00009922733,0.0002177687],"category_scores_gemma":[0.0000034464117,0.00021274915,0.0002502098,0.0002445143,0.000027380518,0.00010467935,5.401553e-7,0.00023735293,0.00019138935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007800703,0.000028328774,0.0000018854062,0.00014653015,0.0001792229,0.0000036866375,0.00033870363,0.8595577,0.014442391,0.0000027124966,0.0008643052,0.124426775],"study_design_scores_gemma":[0.00025532895,0.000030791456,0.000024878314,0.00008779496,0.00014769708,0.0000054624243,0.000049002698,0.9925321,0.0032695306,0.000018783241,0.0033501135,0.00022856591],"about_ca_topic_score_codex":0.000043332373,"about_ca_topic_score_gemma":0.00009451447,"teacher_disagreement_score":0.97680175,"about_ca_system_score_codex":0.00010899477,"about_ca_system_score_gemma":0.000016315062,"threshold_uncertainty_score":0.8675659},"labels":[],"label_agreement":null},{"id":"W4402125019","doi":"10.1109/tmm.2024.3453044","title":"Coarse-to-Fine Target Detection for HFSWR With Spatial-Frequency Analysis and Subnet Structure","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Nuclear Physics and Applications","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Subnet; Computer science; Telecommunications; Computer network","score_opus":0.005298795199876363,"score_gpt":0.23133639660283284,"score_spread":0.22603760140295648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402125019","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1199026,0.000008144985,0.8782009,0.00017023495,0.00014837987,0.00039508447,0.0010032939,0.0000943543,0.00007700122],"genre_scores_gemma":[0.9848749,0.0000011310984,0.014498381,0.000022772452,0.00018574849,0.0001729722,0.000060551454,0.00004323165,0.00014030037],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922967,0.000012087419,0.00013805124,0.00033946137,0.00010630139,0.0001744448],"domain_scores_gemma":[0.99952334,0.00008035823,0.000028491117,0.00019989298,0.00005414031,0.00011379995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000035345056,0.00016370506,0.00017316182,0.00019156594,0.00017594926,0.00008367219,0.000066418266,0.000038402006,0.0002354736],"category_scores_gemma":[4.1759708e-7,0.00013981348,0.00011521204,0.0005765151,0.0000378853,0.00008842651,7.8425415e-7,0.00016441013,0.000028780853],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000116900315,0.00030730903,0.00061040145,0.00006049461,0.0022281872,0.0000015762688,0.0010441198,0.024613937,0.12373056,0.001304803,0.00016181728,0.8458199],"study_design_scores_gemma":[0.0014853107,0.00056812697,0.0035652812,0.00006418591,0.002575102,0.0000023814018,0.00021822944,0.560994,0.4145525,0.0092973085,0.0057208235,0.0009567398],"about_ca_topic_score_codex":0.00050437485,"about_ca_topic_score_gemma":0.0005337648,"teacher_disagreement_score":0.8649723,"about_ca_system_score_codex":0.00002081183,"about_ca_system_score_gemma":0.00002729552,"threshold_uncertainty_score":0.5701428},"labels":[],"label_agreement":null},{"id":"W4402125480","doi":"10.1109/tmm.2024.3453059","title":"DBSR: Quadratic Conditional Diffusion Model for Blind Cardiac MRI Super-Resolution","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Computer science; Quadratic equation; Diffusion; Diffusion MRI; Artificial intelligence; Pattern recognition (psychology); Algorithm; Magnetic resonance imaging; Mathematics; Radiology; Medicine","score_opus":0.03570155834236143,"score_gpt":0.3406171578198018,"score_spread":0.30491559947744035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402125480","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034982725,0.00015922678,0.9911816,0.0018675401,0.0003672885,0.0014324349,0.0008630777,0.00046285638,0.00016770542],"genre_scores_gemma":[0.8233213,0.00030560824,0.16968523,0.00021628398,0.00023694723,0.0016871291,0.00038622224,0.000062127474,0.0040991916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988526,0.0000138172845,0.00027005863,0.0003829152,0.00023996619,0.00024063335],"domain_scores_gemma":[0.999228,0.00021375336,0.00002574159,0.00027148286,0.00010361319,0.00015740615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009908712,0.0001886885,0.00023088075,0.00020138166,0.00021767731,0.000025630346,0.000060642567,0.00016114023,0.000118475094],"category_scores_gemma":[0.0000071548234,0.000171421,0.0002793532,0.00021157725,0.0000921753,0.00013662699,9.2234563e-7,0.00029987475,0.00009889705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015936458,0.0030796658,0.000019340274,0.0007929664,0.00039424436,0.000025597474,0.0025655737,0.43874767,0.29066005,0.0067780353,0.036237624,0.21910556],"study_design_scores_gemma":[0.000886023,0.00015708953,0.000025931828,0.00011453659,0.0002238773,0.0000110523615,0.000047799876,0.9703406,0.018830914,0.0016090256,0.007578388,0.00017475785],"about_ca_topic_score_codex":0.000011644422,"about_ca_topic_score_gemma":0.000010956418,"teacher_disagreement_score":0.82149637,"about_ca_system_score_codex":0.00015198746,"about_ca_system_score_gemma":0.00012854916,"threshold_uncertainty_score":0.6990345},"labels":[],"label_agreement":null},{"id":"W4405755248","doi":"10.1109/tmm.2024.3521796","title":"Context-Enriched Contrastive Loss: Enhancing Presentation of Inherent Sample Connections in Contrastive Learning Framework","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Innovative Teaching and Learning Methods","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Context (archaeology); Presentation (obstetrics); Sample (material); Natural language processing; Artificial intelligence","score_opus":0.0375956953616343,"score_gpt":0.37898254576200135,"score_spread":0.34138685040036704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405755248","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17921205,0.00023628396,0.81650025,0.0001672857,0.0025886206,0.0004636991,0.00006225697,0.00022695429,0.00054262293],"genre_scores_gemma":[0.99014336,0.00001647048,0.008694054,0.0000672999,0.00014202125,0.00023735334,0.000018264625,0.00005445403,0.000626724],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9966627,0.001508164,0.00064305123,0.00053216366,0.00024977172,0.00040416457],"domain_scores_gemma":[0.9894403,0.0099489605,0.00016159951,0.00019192771,0.00017463209,0.000082569495],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012293296,0.00027760107,0.00044263605,0.0006419215,0.00018878163,0.00004209055,0.00012057417,0.000288336,0.0015042817],"category_scores_gemma":[0.00070916716,0.00027879234,0.00015742109,0.000919158,0.00023019337,0.00014793267,0.0000015649839,0.0021954388,0.00013482211],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001690355,0.0017901995,0.005642125,0.00020852103,0.0015261574,0.00010315733,0.20830272,0.037348025,0.046849024,0.009732257,0.00020357997,0.6866039],"study_design_scores_gemma":[0.023945814,0.0074841357,0.2774397,0.008241894,0.0016236111,0.00017746708,0.19615011,0.26391813,0.19077583,0.012133772,0.012991351,0.005118197],"about_ca_topic_score_codex":0.0017430185,"about_ca_topic_score_gemma":0.0003382929,"teacher_disagreement_score":0.8109313,"about_ca_system_score_codex":0.0001884531,"about_ca_system_score_gemma":0.00009409049,"threshold_uncertainty_score":0.99996644},"labels":[],"label_agreement":null},{"id":"W4405778930","doi":"10.1109/tmm.2024.3521788","title":"FER-Former: Multimodal Transformer for Facial Expression Recognition","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"China Scholarship Council","keywords":"Computer science; Facial expression recognition; Facial expression; Transformer; Facial recognition system; Speech recognition; Artificial intelligence; Feature extraction; Pattern recognition (psychology); Electrical engineering; Voltage; Engineering","score_opus":0.048615069730560095,"score_gpt":0.33256329620026037,"score_spread":0.2839482264697003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405778930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0432652,0.00011237865,0.933796,0.00045667606,0.011561483,0.001434153,0.0012622054,0.00073629693,0.007375622],"genre_scores_gemma":[0.9867583,0.00007860799,0.0060653077,0.000257059,0.0005190572,0.0011114115,0.0002689169,0.0000883458,0.0048530316],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982988,0.000082298924,0.00039674807,0.00056642067,0.00022785607,0.00042786397],"domain_scores_gemma":[0.9991846,0.00031130173,0.000041332285,0.00019103548,0.00009339449,0.00017830639],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00020990957,0.0002830347,0.00022075968,0.00040229177,0.0002566073,0.0000639394,0.00010155503,0.0003642399,0.006269921],"category_scores_gemma":[0.000007726889,0.0002603522,0.0003814885,0.00022998714,0.00007918381,0.0002967329,2.947073e-7,0.00043739536,0.003236461],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050269434,0.0004796988,0.0000013672934,0.00007782052,0.0001105341,0.000008129786,0.0032158191,0.000051756644,0.03059911,0.000011626916,0.0031495823,0.9617919],"study_design_scores_gemma":[0.013387961,0.0021243705,0.000568158,0.0010325032,0.000800736,0.00014834647,0.0028998798,0.03461527,0.8261353,0.0017467655,0.11443628,0.0021044775],"about_ca_topic_score_codex":0.000038540256,"about_ca_topic_score_gemma":0.000058745267,"teacher_disagreement_score":0.9596874,"about_ca_system_score_codex":0.000074886375,"about_ca_system_score_gemma":0.000045632907,"threshold_uncertainty_score":0.99998486},"labels":[],"label_agreement":null},{"id":"W4405779020","doi":"10.1109/tmm.2024.3521780","title":"A Twist Representation and Shape Refinement Method for Human Mesh Recovery","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Natural Resources","keywords":"Computer science; Twist; Representation (politics); Theoretical computer science; Algorithm; Mathematics","score_opus":0.029722834564563563,"score_gpt":0.31474052916319634,"score_spread":0.2850176945986328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405779020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010654173,0.00026147044,0.98759234,0.0001410945,0.00054808997,0.00015636842,0.00007173482,0.00038871274,0.00018603787],"genre_scores_gemma":[0.941584,0.00023310097,0.056772668,0.000035744644,0.00010718851,0.00016906949,0.000026748348,0.000045702956,0.0010257895],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929285,0.000018615547,0.00019585034,0.00024576604,0.00010561503,0.00014128318],"domain_scores_gemma":[0.99958664,0.00017893093,0.000009979564,0.00014096322,0.000023827115,0.000059654598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015280087,0.0001259986,0.00014415942,0.00020399614,0.00009533016,0.000067640394,0.000045156685,0.00006557716,0.00012431829],"category_scores_gemma":[0.0000042061138,0.00012508448,0.00013113009,0.00016845165,0.000013493118,0.00008949924,3.9487983e-7,0.0001401057,0.000024165787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000149151,0.000034874753,0.0000011742661,0.00015119629,0.00029950743,0.0000044679587,0.0006409812,0.31724846,0.025621729,0.0000121930825,0.00097244885,0.65499806],"study_design_scores_gemma":[0.00023039358,0.000041153253,0.000008253775,0.00006595157,0.00016726274,0.000002800093,0.00006015431,0.9838281,0.014714515,0.00018598902,0.0005600594,0.00013534626],"about_ca_topic_score_codex":0.000057674006,"about_ca_topic_score_gemma":0.00006946217,"teacher_disagreement_score":0.93092984,"about_ca_system_score_codex":0.00004847472,"about_ca_system_score_gemma":0.000007866226,"threshold_uncertainty_score":0.5100797},"labels":[],"label_agreement":null},{"id":"W4405844857","doi":"10.1109/tmm.2024.3521781","title":"MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-Resolution","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Science Foundation of Beijing Municipality","keywords":"Computer science; Image resolution; Computer vision; Field (mathematics); Artificial intelligence; Resolution (logic); Light field; Optics; Computer graphics (images); Physics; Mathematics","score_opus":0.025156000052270384,"score_gpt":0.3172584365749431,"score_spread":0.29210243652267276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405844857","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000254914,0.00018701194,0.9940879,0.003357002,0.0014907785,0.00027170026,0.000068769186,0.0002259323,0.000056006815],"genre_scores_gemma":[0.40064386,0.0001339194,0.59796846,0.0004952452,0.0001751956,0.000061350795,0.000042513802,0.000024088873,0.0004553439],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990184,0.000024740884,0.00016040861,0.00046080272,0.00015726575,0.00017839398],"domain_scores_gemma":[0.9992026,0.00022860893,0.000018342804,0.00043784425,0.000034667228,0.000077948585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013438598,0.000117389674,0.0000921786,0.0001445805,0.00016039285,0.00019469614,0.0002992157,0.000047976093,0.000042242846],"category_scores_gemma":[0.000015696765,0.00011042654,0.00004216078,0.000157174,0.000026827496,0.0012309925,0.000007290935,0.00014277546,0.00004484595],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021116894,0.0000475528,0.0000012745757,0.000034639037,0.000016927836,0.000007441263,0.00058485416,0.00013241667,0.029465739,0.00005756304,0.0020804072,0.96755004],"study_design_scores_gemma":[0.00040219497,0.00008774093,0.000021935879,0.00004656284,0.000019084211,0.000009318164,0.00003459174,0.92810106,0.06060409,0.00018444001,0.01036218,0.00012682412],"about_ca_topic_score_codex":0.000044054897,"about_ca_topic_score_gemma":0.00003475072,"teacher_disagreement_score":0.96742326,"about_ca_system_score_codex":0.000029311543,"about_ca_system_score_gemma":0.000032912325,"threshold_uncertainty_score":0.45030636},"labels":[],"label_agreement":null},{"id":"W4405907397","doi":"10.1109/tmm.2024.3521770","title":"Towards Neural Codec-Empowered 360$^\\circ$ Video Streaming: A Saliency-Aided Synergistic Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Codec; Encoding (memory); Visualization; Artificial intelligence; Multimedia; Computer vision; Computer hardware","score_opus":0.025621884409199228,"score_gpt":0.2840532735675514,"score_spread":0.2584313891583522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405907397","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012995904,0.00012113426,0.9773389,0.0003993024,0.0051393085,0.00036990442,0.000037276714,0.0011511361,0.0024471143],"genre_scores_gemma":[0.98449755,0.00002990495,0.013414552,0.00015147355,0.0001245925,0.00014040375,0.000010946927,0.00004089881,0.0015896654],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972126,0.00016439073,0.0004979067,0.0009580485,0.0006553396,0.00051172526],"domain_scores_gemma":[0.99871314,0.00018483592,0.00006675673,0.00065534853,0.00009813526,0.00028176873],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002908026,0.00036693728,0.00029942085,0.0005347204,0.0003658761,0.0004419276,0.0006696963,0.0001866812,0.00016990514],"category_scores_gemma":[0.000021610373,0.00033724826,0.0003230446,0.0012074074,0.00012366902,0.00075312395,0.0000072016446,0.0005622861,0.0005380979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009254037,0.0015555777,0.000009919808,0.000269369,0.0002583501,0.0001466696,0.0051250393,0.025594346,0.036906928,0.0023124642,0.0011859504,0.9265428],"study_design_scores_gemma":[0.00054133026,0.00025977695,0.00017147034,0.000064757514,0.000053601365,0.00006349059,0.00012439316,0.9810032,0.016073013,0.00018108622,0.0010474358,0.000416441],"about_ca_topic_score_codex":0.00013479101,"about_ca_topic_score_gemma":0.000040205432,"teacher_disagreement_score":0.97150165,"about_ca_system_score_codex":0.000164261,"about_ca_system_score_gemma":0.00012208027,"threshold_uncertainty_score":0.999908},"labels":[],"label_agreement":null},{"id":"W4406857318","doi":"10.1109/tmm.2025.3535333","title":"CLIP-AE: A Multi-Modal Unsupervised Images Enhancement Method Based on High-Order Adaptive Curve for Visual Disbalance Defects","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Modal; Artificial intelligence; Computer vision; Pattern recognition (psychology); Materials science","score_opus":0.01867822461644182,"score_gpt":0.3499249939261524,"score_spread":0.3312467693097106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406857318","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018386802,0.000059370792,0.9781387,0.0014244583,0.00053785555,0.000981643,0.00013534329,0.00014534121,0.00019050186],"genre_scores_gemma":[0.7865228,0.000040352246,0.20890248,0.0010464923,0.0000700442,0.0004791469,0.000054916436,0.000038879632,0.0028449045],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979358,0.0001815999,0.0004033886,0.0006787919,0.00036355574,0.00043689748],"domain_scores_gemma":[0.9980191,0.0009813684,0.00009147859,0.00039857245,0.00032546232,0.00018404039],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033719293,0.00037144753,0.0005975285,0.00044619798,0.00023917702,0.000036199395,0.00012585644,0.00013879917,0.00015224761],"category_scores_gemma":[0.000093502014,0.00031856954,0.00041979543,0.00063843775,0.00011221612,0.00008548901,0.0000015886453,0.0004297341,0.00006397916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011458129,0.015089106,0.0017198898,0.0010004956,0.002638182,0.0000977126,0.0011421884,0.07319373,0.35891506,0.00003913201,0.0022317078,0.5324747],"study_design_scores_gemma":[0.006549776,0.0007654612,0.0020214866,0.00037343148,0.0008011812,0.0000012462675,0.00014438039,0.7198853,0.2689412,0.00001048335,0.00023939085,0.0002666459],"about_ca_topic_score_codex":0.00021485322,"about_ca_topic_score_gemma":0.000033865745,"teacher_disagreement_score":0.7692362,"about_ca_system_score_codex":0.00017343958,"about_ca_system_score_gemma":0.00018916269,"threshold_uncertainty_score":0.9999266},"labels":[],"label_agreement":null},{"id":"W4406857611","doi":"10.1109/tmm.2025.3535365","title":"Cross-Modal Progressive Perspective Matching Network for Remote Sensing Image-Text Retrieval","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus","funders":"National Natural Science Foundation of China","keywords":"Computer science; Perspective (graphical); Image retrieval; Matching (statistics); Information retrieval; Artificial intelligence; Modal; Image matching; Image (mathematics); Computer vision","score_opus":0.014731840927615985,"score_gpt":0.3401474703342695,"score_spread":0.32541562940665353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406857611","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008548308,0.00021861256,0.9947273,0.00068735797,0.0012549843,0.00095082144,0.000025054498,0.0007655202,0.0005155148],"genre_scores_gemma":[0.11347359,0.000046357083,0.88470316,0.0004212751,0.00019286048,0.000009761105,0.0000025313216,0.000031538773,0.0011189227],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979747,0.00007881613,0.00036112295,0.00074255804,0.00028184272,0.0005609894],"domain_scores_gemma":[0.99789757,0.0006656718,0.0001367323,0.0006104497,0.00057562836,0.000113961585],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033901908,0.00029889902,0.00031529932,0.00022536301,0.0006487479,0.0003187503,0.0005159596,0.00017069058,0.000013422466],"category_scores_gemma":[0.000091894435,0.00029659085,0.00025237407,0.0008835723,0.00019252466,0.0008675505,0.000010671239,0.0005157532,0.000022084381],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005970057,0.00012574827,0.0000022845559,0.000058914535,0.00014477476,0.00005752312,0.0015308178,0.0041025025,0.012473938,0.00086326443,0.0005329007,0.9795103],"study_design_scores_gemma":[0.0015910652,0.0003158618,0.00009129778,0.0003522207,0.00006482635,0.00003292445,0.0001832315,0.4440601,0.50043976,0.05110072,0.0011850226,0.00058299117],"about_ca_topic_score_codex":0.00005627655,"about_ca_topic_score_gemma":0.000008776376,"teacher_disagreement_score":0.9789273,"about_ca_system_score_codex":0.0002837847,"about_ca_system_score_gemma":0.0001654497,"threshold_uncertainty_score":0.9999486},"labels":[],"label_agreement":null},{"id":"W4406857612","doi":"10.1109/tmm.2025.3535363","title":"Hand Gesture Recognition From an Open-Set Perspective","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Perspective (graphical); Gesture; Gesture recognition; Set (abstract data type); Artificial intelligence; Human–computer interaction; Speech recognition; Computer vision; Programming language","score_opus":0.03744854021147527,"score_gpt":0.31677089472382225,"score_spread":0.279322354512347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406857612","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008204585,0.00007547361,0.9824819,0.0018118105,0.0026170136,0.0006780383,0.00026899297,0.0002710108,0.0035911894],"genre_scores_gemma":[0.96949124,0.000032021617,0.027996242,0.0011015411,0.0001488907,0.00024161773,0.000042858792,0.000019740357,0.00092585094],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981696,0.00025924892,0.00028889687,0.0007504984,0.00026986646,0.0002619166],"domain_scores_gemma":[0.99841815,0.00033764463,0.000081709084,0.0006634215,0.00032720077,0.000171876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023321976,0.00023785616,0.0002779721,0.00030031323,0.00039612898,0.0006044083,0.0009598605,0.00021064121,0.00016246384],"category_scores_gemma":[0.000020369418,0.00022560863,0.000084797204,0.00064199505,0.00007869647,0.0010611492,0.000007850772,0.0004094612,0.0005613449],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001820325,0.0012895992,0.000036358913,0.000020323936,0.0003404529,0.00004307077,0.014227827,0.00047343984,0.011739867,0.00030112136,0.0025734634,0.9687725],"study_design_scores_gemma":[0.020515958,0.002079168,0.009633732,0.001592048,0.000589016,0.00009727064,0.010035029,0.21162038,0.66029143,0.04773079,0.031929158,0.003886017],"about_ca_topic_score_codex":0.0011218014,"about_ca_topic_score_gemma":0.0010776527,"teacher_disagreement_score":0.9648864,"about_ca_system_score_codex":0.00015436245,"about_ca_system_score_gemma":0.00014654327,"threshold_uncertainty_score":0.9200053},"labels":[],"label_agreement":null},{"id":"W4409129305","doi":"10.1109/tmm.2025.3557613","title":"Adversarial Geometric Attacks for 3D Point Cloud Object Tracking","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Computer science; Point cloud; Adversarial system; Object (grammar); Computer vision; Artificial intelligence; Point (geometry); Cloud computing; Video tracking; Tracking (education); Computer security; Geometry; Mathematics","score_opus":0.017827179790808584,"score_gpt":0.2931662891273019,"score_spread":0.2753391093364933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409129305","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00082229904,0.00004340193,0.9865263,0.0007033291,0.009900656,0.0006660304,0.0000200313,0.0004861658,0.0008317487],"genre_scores_gemma":[0.6709618,0.00001436559,0.32726884,0.0003553566,0.00032889043,0.00012033235,0.000004489474,0.000029415742,0.0009165081],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977638,0.0001362262,0.00044784768,0.0007289733,0.00037690502,0.000546254],"domain_scores_gemma":[0.99724096,0.0016404468,0.00013264183,0.0006909334,0.0001680489,0.00012697221],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005953411,0.00031082184,0.0003554826,0.00097623124,0.00054630026,0.00016299803,0.0009362731,0.00022096817,0.00006958441],"category_scores_gemma":[0.00022053834,0.0003220435,0.0002973903,0.0017243363,0.0000815075,0.0005912502,0.000010408124,0.0006629739,0.00007538654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017122479,0.0002744237,0.000041341227,0.000053902622,0.00015917746,0.000011199125,0.0009190801,0.30263758,0.0005403762,0.0004966464,0.0008893902,0.6938057],"study_design_scores_gemma":[0.0033120962,0.00018816127,0.0002910295,0.00008054246,0.00010072389,0.0000065677586,0.00007916796,0.97952193,0.0103393905,0.0003909583,0.0052432297,0.000446213],"about_ca_topic_score_codex":0.00007794976,"about_ca_topic_score_gemma":0.000030104029,"teacher_disagreement_score":0.69335943,"about_ca_system_score_codex":0.00023997836,"about_ca_system_score_gemma":0.00020684932,"threshold_uncertainty_score":0.99992317},"labels":[],"label_agreement":null},{"id":"W4412030561","doi":"10.1109/tmm.2025.3586133","title":"Pathology-Preserving Transformer Based on Multicolor Space for Low-Quality Medical Image Enhancement","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Computing and Algorithms","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Computer vision; Color space; Image enhancement; Image quality; Transformer; Pattern recognition (psychology); Image (mathematics)","score_opus":0.02166934755931343,"score_gpt":0.37545870745807697,"score_spread":0.35378935989876353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412030561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012917545,0.000018166997,0.97457665,0.0069044647,0.0017874993,0.00079852145,0.00004793708,0.00019599801,0.0027531944],"genre_scores_gemma":[0.914409,0.000074429736,0.08009372,0.0017252978,0.00021669918,0.00042993855,0.000009041359,0.000026748849,0.003015143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978221,0.00027321716,0.00035675743,0.00045797322,0.0005685741,0.00052139314],"domain_scores_gemma":[0.99733114,0.001968814,0.00006762138,0.0002836478,0.0001361025,0.00021267676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011027216,0.00019900982,0.00027511205,0.00016195404,0.00083178363,0.000038613765,0.0003549698,0.00023523733,0.0004114017],"category_scores_gemma":[0.0003228336,0.00019237751,0.00021013658,0.00031480446,0.0003310361,0.00010547584,0.0000012137582,0.0003962067,0.00004643216],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007916019,0.002706033,0.000019725985,0.00020343528,0.00010928271,0.000025394907,0.008715638,0.01630073,0.014985197,0.00040959037,0.0010205677,0.9547128],"study_design_scores_gemma":[0.008426407,0.0005334095,0.00029505242,0.00068902876,0.00012817106,6.29438e-7,0.003527216,0.7793956,0.18323582,0.00095106725,0.021924917,0.0008927132],"about_ca_topic_score_codex":0.00018974922,"about_ca_topic_score_gemma":0.0005029836,"teacher_disagreement_score":0.9538201,"about_ca_system_score_codex":0.00018319326,"about_ca_system_score_gemma":0.00034410466,"threshold_uncertainty_score":0.78449273},"labels":[],"label_agreement":null},{"id":"W4414166064","doi":"10.1109/tmm.2025.3607712","title":"Oversampling With GAN via Meta-Learning for Imbalanced Data","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Oversampling; Discriminator; Generator (circuit theory); Consistency (knowledge bases); Mode (computer interface); Pattern recognition (psychology); Contrast (vision); Bayesian probability","score_opus":0.061232010547466464,"score_gpt":0.31698107717266627,"score_spread":0.2557490666251998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414166064","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000044863275,0.00003085523,0.9971856,0.0007742013,0.0003503749,0.00053206226,0.00010021652,0.00075534434,0.00022650842],"genre_scores_gemma":[0.41811526,0.000028965076,0.58052945,0.00030742135,0.000021362139,0.00031540045,0.0000711605,0.00001798389,0.0005930087],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843746,0.000057826514,0.00026427108,0.0007200157,0.00022776783,0.00029263424],"domain_scores_gemma":[0.9975522,0.00054270326,0.000109903405,0.0015957331,0.00012854996,0.0000709153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000335144,0.00020792203,0.00028347707,0.0002472357,0.00029249754,0.00012520091,0.0014078972,0.00008891647,0.000021226402],"category_scores_gemma":[0.000033978624,0.00017625789,0.000086360145,0.0005478192,0.00006681449,0.00075029855,0.00000866016,0.00031171978,0.000020195179],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003410921,0.00080714765,0.000111687055,0.00018305342,0.0025631147,0.000009079457,0.0008360415,0.038622938,0.05240256,0.00221508,0.0047523035,0.8971559],"study_design_scores_gemma":[0.0008509209,0.000118871685,0.00014368835,0.0000377451,0.0003021145,0.000003788718,0.00002626964,0.8511928,0.13310342,0.0003003078,0.013641411,0.00027866536],"about_ca_topic_score_codex":0.00003003101,"about_ca_topic_score_gemma":0.00003136908,"teacher_disagreement_score":0.8968772,"about_ca_system_score_codex":0.00006662571,"about_ca_system_score_gemma":0.00010721474,"threshold_uncertainty_score":0.7187588},"labels":[],"label_agreement":null},{"id":"W4416214872","doi":"10.1109/tmm.2025.3632665","title":"Rethinking the Influence of Distribution Adjustment in Incremental Semantic Segmentation","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Segmentation; Feature (linguistics); Subspace topology; Feature vector; Stability (learning theory); Incremental learning; Regularization (linguistics); Domain knowledge; Feature learning","score_opus":0.0182876295252716,"score_gpt":0.272980366762932,"score_spread":0.25469273723766045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416214872","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28745776,0.00014138939,0.7094353,0.0007850835,0.0012917257,0.0007190865,0.000030078585,0.000044511038,0.00009508057],"genre_scores_gemma":[0.99474967,0.0003009495,0.00416476,0.0003982689,0.00002232845,0.00008724682,0.000015127625,0.000010956318,0.00025067318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99698925,0.00051884505,0.00090158154,0.0005365505,0.0006583897,0.00039537923],"domain_scores_gemma":[0.9982677,0.0006434686,0.00031479564,0.00052924664,0.0001664002,0.00007835286],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009749389,0.00028926026,0.0002984434,0.00035354827,0.00040863847,0.00011283372,0.00060870644,0.0001642147,0.0000683591],"category_scores_gemma":[0.000052388,0.00026409395,0.00015240633,0.0016039726,0.00027314128,0.0005763724,0.000010484286,0.00075311924,0.000047297566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016072688,0.0007250361,0.00045300435,0.00013778947,0.00011941269,0.0000066894754,0.018092168,0.56722575,0.019863965,0.00058853463,0.000045328226,0.39258158],"study_design_scores_gemma":[0.002494776,0.00020980717,0.060044788,0.0009363877,0.000116314186,0.000004242789,0.0014475195,0.8872381,0.046646226,0.00043401157,0.00011165127,0.00031619082],"about_ca_topic_score_codex":0.00048924657,"about_ca_topic_score_gemma":0.00028474888,"teacher_disagreement_score":0.7072919,"about_ca_system_score_codex":0.0004708807,"about_ca_system_score_gemma":0.0002686453,"threshold_uncertainty_score":0.9999811},"labels":[],"label_agreement":null},{"id":"W4416214887","doi":"10.1109/tmm.2025.3632696","title":"Fast and Effective Overwrite Attack Against DNN-Based Image Watermarking Models","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Chongqing; National Natural Science Foundation of China","keywords":"Digital watermarking; Robustness (evolution); Watermark; Noise (video); Image (mathematics); Watermarking attack; Vulnerability (computing); Artificial neural network","score_opus":0.012829775666558433,"score_gpt":0.2746773812058975,"score_spread":0.2618476055393391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416214887","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020574559,0.00014870451,0.9704215,0.0009156966,0.0044538984,0.0013928502,0.00007251321,0.00026803554,0.0017522256],"genre_scores_gemma":[0.9150996,0.000111766385,0.08290602,0.0006939687,0.00013690203,0.00015472055,0.0000059886893,0.000066466084,0.0008245249],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994996,0.00080954493,0.0007973647,0.0016806886,0.00069130305,0.0010251033],"domain_scores_gemma":[0.9958572,0.0021050042,0.00026643559,0.0011344425,0.00028066363,0.00035628828],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007749634,0.00087489164,0.0007950323,0.0009208973,0.0012649064,0.0006359278,0.0009700651,0.00046706246,0.00006046923],"category_scores_gemma":[0.000067610126,0.0009402554,0.00038025476,0.0011529417,0.00062736013,0.0012014363,0.00003115123,0.0019792528,0.000100342455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017514867,0.0002440907,0.000016962662,0.00015434784,0.00014526169,0.000047807793,0.0015499042,0.63519496,0.0032366307,0.000019868854,0.000030616793,0.35918438],"study_design_scores_gemma":[0.003758341,0.0001711234,0.00026505865,0.00085032644,0.00019976863,0.000004931036,0.00009815883,0.97225285,0.021383893,0.00008625471,0.00016314191,0.0007661493],"about_ca_topic_score_codex":0.00009770968,"about_ca_topic_score_gemma":0.000034935318,"teacher_disagreement_score":0.89452505,"about_ca_system_score_codex":0.0004917536,"about_ca_system_score_gemma":0.00036029273,"threshold_uncertainty_score":0.9993048},"labels":[],"label_agreement":null},{"id":"W4417470361","doi":"10.1109/tmm.2025.3645632","title":"StereoMamba+: A Novel Stereo Image Super-Resolution Framework With Adaptive Dependency Capture and Enhanced Feature Fusion","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Telus","keywords":"Stereo image; Epipolar geometry; Block (permutation group theory); Convolutional neural network; Feature extraction; Computational complexity theory; Stereo cameras; Computer stereo vision; Stereopsis; Pattern recognition (psychology)","score_opus":0.011490956799198978,"score_gpt":0.2671166456396222,"score_spread":0.2556256888404232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417470361","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019004733,0.001132398,0.9910396,0.0019044013,0.0013015365,0.0015023905,0.00015886898,0.00072904606,0.00033128483],"genre_scores_gemma":[0.42813808,0.0002759599,0.56969744,0.00037913673,0.000050374896,0.00015886805,0.0000035658811,0.000049041508,0.0012475607],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995415,0.00020886851,0.00062763196,0.0019529619,0.00082379585,0.00097179733],"domain_scores_gemma":[0.9966981,0.000582158,0.00032439575,0.0013331803,0.00071662996,0.0003455349],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0003007897,0.0009955422,0.00075128564,0.00074929255,0.0010313089,0.00058509863,0.0010341045,0.00090471696,0.000050347186],"category_scores_gemma":[0.000063241285,0.0009227488,0.00018830046,0.0017975653,0.0007470797,0.002498194,0.000038259233,0.0026519324,0.000025206999],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023715496,0.0020353699,0.00001577868,0.0005202792,0.00037858353,0.00010166579,0.0121725,0.0011686368,0.24823502,0.00029790122,0.00030576135,0.73239696],"study_design_scores_gemma":[0.00440409,0.0014824381,0.0004304191,0.0059440024,0.00046835266,0.00012852582,0.0009789874,0.694675,0.2869564,0.002525645,0.00020595251,0.0018001698],"about_ca_topic_score_codex":0.00011284962,"about_ca_topic_score_gemma":0.00021517595,"teacher_disagreement_score":0.7305968,"about_ca_system_score_codex":0.00037921587,"about_ca_system_score_gemma":0.00053949334,"threshold_uncertainty_score":0.999649},"labels":[],"label_agreement":null},{"id":"W7085164132","doi":"10.1109/tmm.2025.3618564","title":"Learning Efficient and Adaptive Cross-Channel Dependencies for Weakly-Supervised Object Detection","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Infections and bacterial resistance","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Taishan Scholar Project of Shandong Province; Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Object detection; Convolution (computer science); Feature (linguistics); Focus (optics); Object (grammar); Pattern recognition (psychology); Code (set theory); Feature extraction","score_opus":0.008559026826373104,"score_gpt":0.25747324855363685,"score_spread":0.24891422172726374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7085164132","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51720774,0.000055848548,0.48172185,0.000013351692,0.000560692,0.00024176565,0.00002769355,0.000023319499,0.0001477382],"genre_scores_gemma":[0.9971603,0.00008248221,0.00097336946,0.00003780872,0.00007495362,0.00016715837,0.00000890655,0.000014976224,0.0014800157],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923944,0.00003715946,0.00014855839,0.00031369523,0.0000690164,0.00019212348],"domain_scores_gemma":[0.9995932,0.000064305044,0.000036267833,0.00012919224,0.00013189264,0.00004516595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011033463,0.00014231206,0.000114601615,0.00009545414,0.00041994682,0.00004619751,0.00005393066,0.00015628754,0.000009147628],"category_scores_gemma":[0.00003974384,0.00014056054,0.00010037872,0.00010787155,0.000088856636,0.000005597882,0.0000020772297,0.00013068883,0.0000040331975],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077324087,0.000103605526,0.000023942595,0.000029785508,0.00007904746,4.597342e-7,0.00010965788,0.023639902,0.93526584,0.00000524691,0.000013340159,0.03995592],"study_design_scores_gemma":[0.0010596458,0.0005130957,0.0009129599,0.000027944072,0.000042817053,0.000003493144,0.00017823688,0.039517846,0.9563516,0.000015876141,0.0012024274,0.00017407988],"about_ca_topic_score_codex":0.000037330126,"about_ca_topic_score_gemma":0.00022179344,"teacher_disagreement_score":0.48074847,"about_ca_system_score_codex":0.00003244592,"about_ca_system_score_gemma":0.000052186166,"threshold_uncertainty_score":0.57318926},"labels":[],"label_agreement":null}]}