{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":3,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":3,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"7ca4f43b15d9","filters":{"venue":"2021 International Conference on Visual Communications and Image Processing (VCIP)"}},"results":[{"id":"W4205259689","doi":"10.1109/vcip53242.2021.9675357","title":"Scalable Privacy in Multi-Task Image Compression","year":2021,"lang":"en","type":"article","venue":"2021 International Conference on Visual Communications and Image Processing (VCIP)","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Inference; Representation (politics); Task (project management); Scalability; Decoding methods; Image compression; Artificial intelligence; Mutual information; Image (mathematics); Encoding (memory); Image segmentation; Segmentation; Object (grammar); Computer vision; Data mining; Machine learning; Image processing; Algorithm; Database","authors":[{"name":"Saeed Ranjbar Alvar","is_ca":true},{"name":"Ivan V. Bajić","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06381441385878117,"gpt":0.381663597190849,"spread":0.3178491833320678,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005919974,0.0007800882,0.001545134,0.0007808253,0.001018331,0.002299284,0.001949874,0.001774445,0.001381323],"category_scores_gemma":[0.02253883,0.0006235009,0.0008211812,0.001417867,0.002917751,0.004544612,0.004513585,0.002681632,0.0003685573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001830278,"about_ca_system_score_gemma":0.001989481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001959695,"about_ca_topic_score_gemma":0.001366936,"domain_scores_codex":[0.9956679,0.001729546,0.000228745,0.0007407127,0.001171371,0.0004616709],"domain_scores_gemma":[0.9834902,0.01055764,0.001053259,0.003717983,0.0008803626,0.0003005054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009230141,0.0001483382,0.001743226,0.0002001711,0.0001191696,0.0005198185,0.0003576019,0.740887,0.01025938,0.1362382,0.002858251,0.1057458],"study_design_scores_gemma":[0.00001794881,0.00003406757,0.0001785808,0.000007826938,0.000007629246,0.00007066233,0.00002477594,0.93976,0.002538888,0.05697057,0.0003786185,0.00001038356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03077013,0.0005655344,0.9650218,0.001188034,0.00004337845,0.00005094801,0.0001772727,0.0004724392,0.001710431],"genre_scores_gemma":[0.8974181,0.0005420638,0.09834991,0.0003400488,0.0002213579,0.000141786,0.0002961853,0.0001125703,0.002578017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005919974,"threshold_uncertainty_score":0.03130823,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4205657202","doi":"10.1109/vcip53242.2021.9675428","title":"Inter-Observer Visual Congruency in Video-Viewing","year":2021,"lang":"en","type":"article","venue":"2021 International Conference on Visual Communications and Image Processing (VCIP)","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Computer vision; Artificial intelligence; Observer (physics); Video tracking; Eye tracking; Optical flow; Channel (broadcasting); Video processing; Image (mathematics)","authors":[{"name":"Jiaomin Yue","is_ca":false},{"name":"Qiang Lu","is_ca":false},{"name":"Dandan Zhu","is_ca":false},{"name":"Xiongkuo Min","is_ca":false},{"name":"Xiao–Ping Zhang","is_ca":true},{"name":"Guangtao Zhai","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06627026081564653,"gpt":0.3795205785594798,"spread":0.3132503177438333,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001043893,0.0002574724,0.0003911115,0.0009944831,0.0001776342,0.0006213445,0.0002572087,0.0002804694,0.000979569],"category_scores_gemma":[0.01051271,0.0001366749,0.0002556871,0.0008201251,0.0002508934,0.0006596737,0.0006177038,0.0002859606,0.0001958967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003950905,"about_ca_system_score_gemma":0.0001981648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004213551,"about_ca_topic_score_gemma":0.004789088,"domain_scores_codex":[0.999241,0.0001547669,0.00004943818,0.0002965994,0.000186318,0.0000719652],"domain_scores_gemma":[0.9964719,0.001831391,0.0006001721,0.0003284719,0.0006393846,0.0001287761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.006465626,0.0003809343,0.2931991,0.001485098,0.000790131,0.0007333949,0.00337338,0.01362532,0.371223,0.002815502,0.005429577,0.3004789],"study_design_scores_gemma":[0.0000574955,0.0004452312,0.9088576,0.00004606673,0.0002305805,0.0005737525,0.0005279233,0.05251237,0.03183324,0.002221598,0.002630392,0.00006374396],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9622746,0.001274516,0.03082234,0.00006077061,0.00008655775,0.00009746113,0.0009828954,0.0002792922,0.004121596],"genre_scores_gemma":[0.9959793,0.0001425374,0.002820488,0.00002054208,0.00002334688,0.00002174799,0.0006564992,0.00002867369,0.0003068364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004213551,"threshold_uncertainty_score":0.008378029,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4205894626","doi":"10.1109/vcip53242.2021.9675344","title":"DFTS2: Deep Feature Transmission Simulation for Collaborative Intelligence","year":2021,"lang":"en","type":"article","venue":"2021 International Conference on Visual Communications and Image Processing (VCIP)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Channel (broadcasting); Packet loss; Enhanced Data Rates for GSM Evolution; Feature (linguistics); Inference; Network packet; Transmission (telecommunications); Artificial intelligence; Imperfect; Path (computing); Cloud computing; Real-time computing; Machine learning; Data mining; Distributed computing; Computer network; Telecommunications","authors":[{"name":"Ashiv Dhondea","is_ca":true},{"name":"Robert Cohen","is_ca":true},{"name":"Ivan V. Bajić","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03738514842136979,"gpt":0.3969215276612447,"spread":0.3595363792398749,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008960053,0.0005866052,0.000557453,0.0003933152,0.0005659782,0.0007106575,0.001434515,0.00108493,0.003695461],"category_scores_gemma":[0.003355647,0.0003146071,0.0007325389,0.0003447692,0.0006394665,0.0008948637,0.0008249741,0.00120096,0.0002689589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001055199,"about_ca_system_score_gemma":0.001330333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01348884,"about_ca_topic_score_gemma":0.007459019,"domain_scores_codex":[0.9997411,0.0001009474,0.00001422892,0.00003136316,0.00007474828,0.00003767012],"domain_scores_gemma":[0.9984291,0.001104231,0.0000870988,0.0001263156,0.0001708817,0.00008226758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005145085,0.00003246029,0.0007598042,0.00001701932,0.00001258939,0.00002624175,0.00003625156,0.9875545,0.0007097918,0.006479922,0.0005953332,0.00372468],"study_design_scores_gemma":[0.000003165847,0.000003683275,0.00001658233,6.731632e-7,6.309319e-7,0.000001740905,0.000001890787,0.9991583,0.0001600042,0.0005411374,0.0001110347,0.000001101445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1431009,0.0001339203,0.8439982,0.0004574068,0.0001169056,0.000136667,0.0005971774,0.003100837,0.008358068],"genre_scores_gemma":[0.8468791,0.0001010042,0.1496527,0.0001157534,0.00002384437,0.0002203964,0.0005214696,0.000303281,0.002182523],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01348884,"threshold_uncertainty_score":0.02682066,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}