{"meta":{"query_hash":"bdde3283bac9","filters":{"venue":"2022 30th European Signal Processing Conference (EUSIPCO)"},"cohort_total":4,"direct_labels_cover":0,"predictions_cover":4,"exported":4,"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/bdde3283bac9","api":"https://metacan.xera.ac/api/v1/cohort?venue=2022+30th+European+Signal+Processing+Conference+%28EUSIPCO%29"},"results":[{"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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)","score_opus":0.012096134452989913,"score_gpt":0.22902294536890716,"score_spread":0.21692681091591726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312300771","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":[0.62282413,0.0034520226,0.35851738,0.00024238547,0.0006700644,0.0009365325,0.000246757,0.0010221472,0.012088573],"genre_scores_gemma":[0.99636596,0.00006189432,0.0007203662,0.00017288628,0.00018648505,0.00010275651,0.00055946107,0.00010962833,0.0017205821],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978932,0.00021374763,0.00047386173,0.0005023505,0.00037031554,0.000546517],"domain_scores_gemma":[0.99924767,0.000038825565,0.0001676955,0.00021973117,0.00024523423,0.00008085345],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005397986,0.00032610996,0.00031631143,0.00024117624,0.00088523905,0.00038258464,0.00041476378,0.000043396612,0.0009069728],"category_scores_gemma":[0.000018949935,0.00034794633,0.00011111097,0.0005457727,0.000074122254,0.00039656318,0.00007546411,0.0005215513,0.000039063947],"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":[0.000039329927,0.000038476308,0.000009279921,0.00013355516,0.00007570079,0.000009783727,0.0008522513,0.0057384744,0.9617216,0.0001529808,0.0034355677,0.027792979],"study_design_scores_gemma":[0.0016768217,0.00072299107,0.0003251232,0.00019584,0.00047265698,0.000040641615,0.002898769,0.08670275,0.88734764,0.001239857,0.016846469,0.0015304394],"about_ca_topic_score_codex":0.000037545586,"about_ca_topic_score_gemma":0.000013461215,"teacher_disagreement_score":0.3735418,"about_ca_system_score_codex":0.00029199992,"about_ca_system_score_gemma":0.0002033713,"threshold_uncertainty_score":0.99989724},"labels":[],"label_agreement":null},{"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.019021182028331522,"score_gpt":0.23038767509761102,"score_spread":0.2113664930692795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312569779","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":[0.24843386,0.0011983732,0.73397654,0.0042999154,0.0005546988,0.0006949274,0.000042249107,0.0009279242,0.009871531],"genre_scores_gemma":[0.9541403,0.000004915582,0.04217395,0.00065024506,0.00020203694,0.00006813018,0.000021337924,0.00008277144,0.0026562803],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947864,0.000923449,0.0005918203,0.0015477528,0.0012249738,0.00092562137],"domain_scores_gemma":[0.99787515,0.000104854655,0.00056058797,0.0008496795,0.00030205247,0.0003076713],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0017993908,0.0005189444,0.00044405152,0.00028667305,0.001777154,0.0010953173,0.0022972212,0.000030725063,0.0005725962],"category_scores_gemma":[0.00011413264,0.00048317097,0.00009771024,0.0014843115,0.00027947203,0.0013781021,0.0009162858,0.00082716957,0.00010724631],"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":[0.0039616586,0.0013866869,0.0029611217,0.0005085078,0.00018222025,0.0047719902,0.014660128,0.005718851,0.26252598,0.0016061931,0.0075636683,0.694153],"study_design_scores_gemma":[0.017932326,0.010973456,0.08443812,0.0036361096,0.00066224474,0.009416736,0.0078006103,0.41120312,0.19393145,0.0060906117,0.23714185,0.016773371],"about_ca_topic_score_codex":0.000008824652,"about_ca_topic_score_gemma":0.0000035247688,"teacher_disagreement_score":0.7057065,"about_ca_system_score_codex":0.00025965585,"about_ca_system_score_gemma":0.001587916,"threshold_uncertainty_score":0.99994165},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.042832248578058923,"score_gpt":0.25393962699582506,"score_spread":0.21110737841776614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312979902","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":[0.021932136,0.0004841982,0.9711043,0.00025516533,0.00011446916,0.00036974633,0.000050490304,0.0001853949,0.005504054],"genre_scores_gemma":[0.95831406,0.000009112201,0.040402956,0.00035221112,0.00012030779,0.00007255422,0.00007481085,0.000047185265,0.0006067803],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962568,0.00030586196,0.00078798516,0.0010238569,0.0010088629,0.00061659736],"domain_scores_gemma":[0.99751836,0.00013671725,0.0008832713,0.00049338193,0.00078298675,0.0001852627],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013626341,0.0003759993,0.0005845536,0.0006412275,0.0010467807,0.0005008992,0.0019682313,0.000046371337,0.00033298714],"category_scores_gemma":[0.000037189828,0.00039317145,0.00030597954,0.0024789777,0.000102250204,0.0012748464,0.0008079365,0.0004246239,0.00001147229],"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":[0.00017841703,0.0003494252,0.00063150347,0.00026158572,0.00038359396,0.00007698146,0.009003579,0.36773422,0.03248697,0.0021186988,0.0003995344,0.5863755],"study_design_scores_gemma":[0.0007431288,0.00040327577,0.00042666987,0.00006598902,0.00023374919,0.0000148537465,0.001214662,0.9800343,0.011603152,0.0040445765,0.000686256,0.00052936544],"about_ca_topic_score_codex":0.00003641239,"about_ca_topic_score_gemma":0.0000111166,"teacher_disagreement_score":0.93638194,"about_ca_system_score_codex":0.000113293994,"about_ca_system_score_gemma":0.0008589081,"threshold_uncertainty_score":0.999852},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.014552364122921595,"score_gpt":0.22477490596847155,"score_spread":0.21022254184554995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313037550","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":[0.027102679,0.00076856575,0.9176411,0.0014911017,0.00041094012,0.0009039657,0.00007497795,0.0012233728,0.0503833],"genre_scores_gemma":[0.98784393,0.000009184961,0.00373181,0.00034481063,0.00006972881,0.00005818814,0.00006730756,0.000060584014,0.007814428],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99685305,0.0004988955,0.0004881424,0.0009209815,0.0006062183,0.0006327008],"domain_scores_gemma":[0.99858695,0.00016292837,0.00046991443,0.0003348673,0.00029835122,0.00014699045],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0011578151,0.00036120825,0.00041433694,0.0002586675,0.0021423653,0.000716433,0.0013422014,0.000030915253,0.0007093715],"category_scores_gemma":[0.00009147331,0.00035870596,0.0002043165,0.00086416485,0.00014038819,0.0007049176,0.00076176587,0.00064140867,0.000087837034],"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":[0.00043665955,0.0003135632,0.00023294362,0.00014395226,0.00020698206,0.00021435795,0.006063363,0.02875427,0.021782419,0.030275797,0.0015695572,0.91000617],"study_design_scores_gemma":[0.00079986907,0.0017945848,0.0006430844,0.00011149045,0.000066600835,0.000066467524,0.0011835216,0.9618042,0.00028606557,0.0029204108,0.029480357,0.0008433448],"about_ca_topic_score_codex":0.000006639207,"about_ca_topic_score_gemma":0.0000027538968,"teacher_disagreement_score":0.9607413,"about_ca_system_score_codex":0.000098775636,"about_ca_system_score_gemma":0.0002368438,"threshold_uncertainty_score":0.9998865},"labels":[],"label_agreement":null}]}