{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":2,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":2,"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":"55947200fcc1","filters":{"venue":"WSEAS Transactions on Signal Processing archive"}},"results":[{"id":"W2485089284","doi":"10.5555/2037123.2037126","title":"A scalable architecture for H.264/AVC variable block size motion estimation on FPGAs","year":2011,"lang":"en","type":"article","venue":"WSEAS Transactions on Signal Processing archive","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Video Graphics Array; Field-programmable gate array; Computer science; Scalability; Lookup table; Computer hardware; Motion estimation; Frame rate; Block (permutation group theory); Encoding (memory); Block size; Computer architecture; Embedded system; Key (lock); Algorithm; Artificial intelligence","authors":[{"name":"Theepan Moorthy","is_ca":true},{"name":"Phoebe Ping Chen","is_ca":true},{"name":"Andy Ye","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02570247222306756,"gpt":0.2401655372336952,"spread":0.2144630650106276,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001416591,0.0002598634,0.0001562058,0.000296969,0.0001556436,0.0002177522,0.0004282901,0.0001750784,0.002104977],"category_scores_gemma":[0.0002406497,0.000107036,0.0001451536,0.000215395,0.0000972546,0.0003742003,0.0001246739,0.0002293505,0.0003880306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003172613,"about_ca_system_score_gemma":0.0003904212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00264872,"about_ca_topic_score_gemma":0.004561411,"domain_scores_codex":[0.9998982,0.00001428492,0.000005427563,0.00001906529,0.00004531987,0.00001768006],"domain_scores_gemma":[0.9999284,0.00001637992,0.000009089445,0.00001063176,0.00002939674,0.00000614669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000390071,0.000114803,0.001657189,0.000312592,0.00006464003,0.0005838131,0.00008639618,0.08688735,0.4134289,0.01381537,0.008628658,0.4740301],"study_design_scores_gemma":[0.0001681585,0.00138371,0.006592354,0.00009628814,0.00007957013,0.0009161432,0.00008583324,0.794505,0.1594987,0.003077011,0.03353378,0.00006352481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1981143,0.001984899,0.7763952,0.0003325487,0.0001811434,0.0003517526,0.0002412986,0.004265198,0.01813362],"genre_scores_gemma":[0.6516862,0.0005521489,0.341736,0.0001035222,0.00004353628,0.0001110504,0.0003244752,0.00004932365,0.005393797],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00264872,"threshold_uncertainty_score":0.007041812,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W146176842","doi":"","title":"An adaptive stochastic-resonance-based detector and its application in watermark extraction","year":2011,"lang":"en","type":"article","venue":"WSEAS Transactions on Signal Processing archive","topic":"stochastic dynamics and bifurcation","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Stochastic resonance; Watermark; Detector; Gaussian noise; Noise (video); Computer science; Amplitude; Digital watermarking; SIGNAL (programming language); Gaussian; Additive white Gaussian noise; Algorithm; Acoustics; Control theory (sociology); Physics; White noise; Telecommunications; Optics; Artificial intelligence; Image (mathematics)","authors":[{"name":"Gencheng Guo","is_ca":true},{"name":"Mrinal Mandal","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01676301046604524,"gpt":0.2464122296835917,"spread":0.2296492192175464,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005711776,0.0002970188,0.0003893206,0.0003576853,0.0001422348,0.000271964,0.0004789942,0.000732509,0.0005256575],"category_scores_gemma":[0.001588675,0.0001792613,0.0002790712,0.0002905498,0.0004503626,0.0006486842,0.0003709465,0.0003197256,0.0002532771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000192167,"about_ca_system_score_gemma":0.0001617446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001717658,"about_ca_topic_score_gemma":0.0001998954,"domain_scores_codex":[0.9996043,0.0001010471,0.00001947451,0.00009668429,0.0001590463,0.0000194951],"domain_scores_gemma":[0.9994761,0.0002859586,0.00006642052,0.00005240973,0.0001025587,0.00001656633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003290879,0.00008878346,0.001198803,0.0002404657,0.00007546324,0.0003432729,0.0001395986,0.05640397,0.698451,0.0233943,0.0005018259,0.2188334],"study_design_scores_gemma":[0.00002537479,0.000270312,0.000704944,0.00001258638,0.00003183088,0.000562356,0.00001618301,0.8475237,0.1461515,0.002393057,0.002269125,0.00003906869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0387409,0.0004985419,0.9590566,0.000133447,0.00003841279,0.00002772295,0.00001405955,0.0002654826,0.001224878],"genre_scores_gemma":[0.6058751,0.000521144,0.391597,0.0001304247,0.00005530514,0.00004268016,0.00003225372,0.00003172268,0.001714459],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.000732509,"threshold_uncertainty_score":0.003020704,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}