{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":4,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":4,"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":"bdde3283bac9","filters":{"venue":"2022 30th European Signal Processing Conference (EUSIPCO)"}},"results":[{"id":"W4312569779","doi":"10.23919/eusipco55093.2022.9909584","title":"A Minimum Variance Distortionless Response Spectral Estimator with Kronecker Product Filters","year":2022,"lang":"en","type":"article","venue":"2022 30th European Signal Processing Conference (EUSIPCO)","topic":"Speech and Audio Processing","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":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Kronecker product; Estimator; Kronecker delta; Minimum-variance unbiased estimator; Mathematics; Generalization; Algorithm; Spectral density estimation; Product (mathematics); Filter (signal processing); Estimation theory; Fourier transform; Mathematical optimization; Computer science; Statistics; Mathematical analysis","authors":[{"name":"Xianrui Wang","is_ca":false},{"name":"Jacob Benesty","is_ca":true},{"name":"Gongping Huang","is_ca":false},{"name":"Jingdong Chen","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01902118202833152,"gpt":0.230387675097611,"spread":0.2113664930692795,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001799391,0.0005189444,0.0004440515,0.0002866731,0.001777154,0.001095317,0.002297221,0.00003072506,0.0005725962],"category_scores_gemma":[0.0001141326,0.000483171,0.00009771024,0.001484312,0.000279472,0.001378102,0.0009162858,0.0008271696,0.0001072463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002596558,"about_ca_system_score_gemma":0.001587916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008824652,"about_ca_topic_score_gemma":0.000003524769,"domain_scores_codex":[0.9947864,0.000923449,0.0005918203,0.001547753,0.001224974,0.0009256214],"domain_scores_gemma":[0.9978752,0.0001048547,0.000560588,0.0008496795,0.0003020525,0.0003076713],"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":[0.003961659,0.001386687,0.002961122,0.0005085078,0.0001822202,0.00477199,0.01466013,0.005718851,0.262526,0.001606193,0.007563668,0.694153],"study_design_scores_gemma":[0.01793233,0.01097346,0.08443812,0.00363611,0.0006622447,0.009416736,0.00780061,0.4112031,0.1939314,0.006090612,0.2371418,0.01677337],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2484339,0.001198373,0.7339765,0.004299915,0.0005546988,0.0006949274,0.00004224911,0.0009279242,0.009871531],"genre_scores_gemma":[0.9541403,0.000004915582,0.04217395,0.0006502451,0.0002020369,0.00006813018,0.00002133792,0.00008277144,0.00265628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7057065,"threshold_uncertainty_score":0.9999416,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4312979902","doi":"10.23919/eusipco55093.2022.9909619","title":"Non-Intrusive Signal Analysis for Room Adaptation of ASR Models","year":2022,"lang":"en","type":"article","venue":"2022 30th European Signal Processing Conference (EUSIPCO)","topic":"Speech and Audio Processing","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":"Nuance Communications (Canada)","funders":"","keywords":"PESQ; Computer science; Speech recognition; Intelligibility (philosophy); Codec; Speech coding; Pattern recognition (psychology); Artificial intelligence; Noise reduction; Speech enhancement","authors":[{"name":"Ge Li","is_ca":true},{"name":"Dushyant Sharma","is_ca":false},{"name":"Patrick A. Naylor","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04283224857805892,"gpt":0.2539396269958251,"spread":0.2111073784177661,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001362634,0.0003759993,0.0005845536,0.0006412275,0.001046781,0.0005008992,0.001968231,0.00004637134,0.0003329871],"category_scores_gemma":[0.00003718983,0.0003931714,0.0003059795,0.002478978,0.0001022502,0.001274846,0.0008079365,0.0004246239,0.00001147229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000113294,"about_ca_system_score_gemma":0.0008589081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003641239,"about_ca_topic_score_gemma":0.0000111166,"domain_scores_codex":[0.9962568,0.000305862,0.0007879852,0.001023857,0.001008863,0.0006165974],"domain_scores_gemma":[0.9975184,0.0001367173,0.0008832713,0.0004933819,0.0007829868,0.0001852627],"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":[0.000178417,0.0003494252,0.0006315035,0.0002615857,0.000383594,0.00007698146,0.009003579,0.3677342,0.03248697,0.002118699,0.0003995344,0.5863755],"study_design_scores_gemma":[0.0007431288,0.0004032758,0.0004266699,0.00006598902,0.0002337492,0.00001485375,0.001214662,0.9800343,0.01160315,0.004044577,0.000686256,0.0005293654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02193214,0.0004841982,0.9711043,0.0002551653,0.0001144692,0.0003697463,0.0000504903,0.0001853949,0.005504054],"genre_scores_gemma":[0.9583141,0.000009112201,0.04040296,0.0003522111,0.0001203078,0.00007255422,0.00007481085,0.00004718527,0.0006067803],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9363819,"threshold_uncertainty_score":0.999852,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4312300771","doi":"10.23919/eusipco55093.2022.9909919","title":"Boundary Enhanced Semantic Segmentation for High Resolution Electron Microscope Images","year":2022,"lang":"en","type":"article","venue":"2022 30th European Signal Processing Conference (EUSIPCO)","topic":"Integrated Circuits and Semiconductor Failure Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Infineon Technologies (Canada)","funders":"","keywords":"Computer science; Segmentation; Leverage (statistics); Artificial intelligence; Encoder; Image segmentation; Computer vision; Artificial neural network; Boundary (topology); Domain (mathematical analysis); Pattern recognition (psychology)","authors":[{"name":"Matthias Pollach","is_ca":false},{"name":"Felix Schiegg","is_ca":false},{"name":"Matthias Ludwig","is_ca":true},{"name":"Ann-Christin Bette","is_ca":true},{"name":"Alois Knoll","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01209613445298991,"gpt":0.2290229453689072,"spread":0.2169268109159173,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005397986,0.00032611,0.0003163114,0.0002411762,0.000885239,0.0003825846,0.0004147638,0.00004339661,0.0009069728],"category_scores_gemma":[0.00001894994,0.0003479463,0.000111111,0.0005457727,0.00007412225,0.0003965632,0.00007546411,0.0005215513,0.00003906395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002919999,"about_ca_system_score_gemma":0.0002033713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003754559,"about_ca_topic_score_gemma":0.00001346121,"domain_scores_codex":[0.9978932,0.0002137476,0.0004738617,0.0005023505,0.0003703155,0.000546517],"domain_scores_gemma":[0.9992477,0.00003882556,0.0001676955,0.0002197312,0.0002452342,0.00008085345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003932993,0.00003847631,0.000009279921,0.0001335552,0.00007570079,0.000009783727,0.0008522513,0.005738474,0.9617216,0.0001529808,0.003435568,0.02779298],"study_design_scores_gemma":[0.001676822,0.0007229911,0.0003251232,0.00019584,0.000472657,0.00004064161,0.002898769,0.08670275,0.8873476,0.001239857,0.01684647,0.001530439],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6228241,0.003452023,0.3585174,0.0002423855,0.0006700644,0.0009365325,0.000246757,0.001022147,0.01208857],"genre_scores_gemma":[0.996366,0.00006189432,0.0007203662,0.0001728863,0.000186485,0.0001027565,0.0005594611,0.0001096283,0.001720582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3735418,"threshold_uncertainty_score":0.9998972,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313037550","doi":"10.23919/eusipco55093.2022.9909661","title":"Contrastive Learning for Time Series on Dynamic Graphs","year":2022,"lang":"en","type":"article","venue":"2022 30th European Signal Processing Conference (EUSIPCO)","topic":"Time Series Analysis and Forecasting","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":"McGill University","funders":"","keywords":"Computer science; Series (stratigraphy); Graph; Artificial intelligence; Time series; Piecewise; Multivariate statistics; Anomaly detection; Machine learning; Pattern recognition (psychology); Theoretical computer science; Mathematics","authors":[{"name":"Yitian Zhang","is_ca":true},{"name":"Florence Regol","is_ca":true},{"name":"Antonios Valkanas","is_ca":true},{"name":"Mark Coates","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01455236412292159,"gpt":0.2247749059684716,"spread":0.2102225418455499,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001157815,0.0003612083,0.0004143369,0.0002586675,0.002142365,0.000716433,0.001342201,0.00003091525,0.0007093715],"category_scores_gemma":[0.00009147331,0.000358706,0.0002043165,0.0008641648,0.0001403882,0.0007049176,0.0007617659,0.0006414087,0.00008783703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009877564,"about_ca_system_score_gemma":0.0002368438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006639207,"about_ca_topic_score_gemma":0.000002753897,"domain_scores_codex":[0.9968531,0.0004988955,0.0004881424,0.0009209815,0.0006062183,0.0006327008],"domain_scores_gemma":[0.998587,0.0001629284,0.0004699144,0.0003348673,0.0002983512,0.0001469905],"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":[0.0004366596,0.0003135632,0.0002329436,0.0001439523,0.0002069821,0.0002143579,0.006063363,0.02875427,0.02178242,0.0302758,0.001569557,0.9100062],"study_design_scores_gemma":[0.0007998691,0.001794585,0.0006430844,0.0001114905,0.00006660083,0.00006646752,0.001183522,0.9618042,0.0002860656,0.002920411,0.02948036,0.0008433448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02710268,0.0007685657,0.9176411,0.001491102,0.0004109401,0.0009039657,0.00007497795,0.001223373,0.0503833],"genre_scores_gemma":[0.9878439,0.000009184961,0.00373181,0.0003448106,0.00006972881,0.00005818814,0.00006730756,0.00006058401,0.007814428],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9607413,"threshold_uncertainty_score":0.9998865,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}