{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":157,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":157,"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"},"query_hash":"65fb48c233b2","filters":{"venue":"IEEE Signal Processing Letters"}},"results":[{"id":"W1644173899","doi":"10.1109/lsp.2015.2487369","title":"A Patch-Structure Representation Method for Quality Assessment of Contrast Changed Images","year":2015,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":426,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Contrast (vision); Artificial intelligence; Computer science; Image quality; Representation (politics); Quality (philosophy); Pattern recognition (psychology); Computer vision; Perception; Feature (linguistics); Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.1106064119134101,"gpt":0.4258384142188925,"spread":0.3152320023054824,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001541964,0.0001900452,0.0003684811,0.0001115113,0.0001221538,0.000247862,0.0005291987,0.00006422951,0.000003408335],"category_scores_gemma":[0.00005042274,0.0001727878,0.000106273,0.0003112759,0.00007138118,0.0008899,0.00006252936,0.0001589195,6.852094e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001011209,"about_ca_system_score_gemma":0.0002703302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001998647,"about_ca_topic_score_gemma":0.000008400884,"domain_scores_codex":[0.9976287,0.0004263397,0.000517926,0.0004875486,0.0006193868,0.0003200507],"domain_scores_gemma":[0.9983722,0.0002720889,0.000491075,0.0003427266,0.0004073355,0.0001145591],"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":[0.00008237656,0.0001601314,0.001044369,0.0005247266,0.00008373125,0.00002071292,0.004370675,0.001826448,0.7915007,0.0009927776,0.006398945,0.1929944],"study_design_scores_gemma":[0.004639355,0.0004144072,0.004094578,0.0002649766,0.00009744119,0.00004083391,0.001004468,0.3010452,0.6767412,0.00995408,0.00082,0.0008833967],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01403969,0.00007834989,0.9779931,0.007058817,0.0002389736,0.0003855752,0.00002810913,0.00008785777,0.00008948924],"genre_scores_gemma":[0.6421089,6.325378e-7,0.3560694,0.001564764,0.0001770091,0.0000402641,0.00001142296,0.00001077053,0.00001687742],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6280692,"threshold_uncertainty_score":0.7046082,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2407561938","doi":"10.1109/lsp.2015.2438008","title":"Median Filtering Forensics Based on Convolutional Neural Networks","year":2015,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":418,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; National Science Foundation","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Pooling; Image (mathematics); Pattern recognition (psychology); Filter (signal processing); Median filter; Residual; Computer vision; Image processing; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.02658748460942762,"gpt":0.2257852914226731,"spread":0.1991978068132455,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002834266,0.0002164049,0.0001717878,0.0001607201,0.0001212804,0.0003942377,0.0004980103,0.0000698335,0.000002434219],"category_scores_gemma":[0.00004628791,0.0002077303,0.00006734312,0.00042193,0.000163224,0.0008974693,0.00005210405,0.0002718543,0.00002278698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001509649,"about_ca_system_score_gemma":0.0001212077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007152374,"about_ca_topic_score_gemma":0.000003009493,"domain_scores_codex":[0.9981369,0.00004813803,0.0002414537,0.0004335734,0.0006871921,0.0004527598],"domain_scores_gemma":[0.9991267,0.0001062456,0.0001293879,0.0002565988,0.0001083302,0.0002727446],"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":[0.00005304758,0.00004557649,0.0001161037,0.00002553958,0.000009576118,0.0001138468,0.0002603242,0.6151883,0.00189633,0.0001364069,0.00961211,0.3725428],"study_design_scores_gemma":[0.0004537779,0.0001439909,0.00009758949,0.00006853505,0.000004818299,0.00002957104,0.000007933323,0.9958655,0.002108494,0.0006269502,0.0003485985,0.0002442499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01870868,0.00002045818,0.9741956,0.003921425,0.002147004,0.0001142091,0.000001666406,0.0003332217,0.0005577479],"genre_scores_gemma":[0.9772369,8.350712e-8,0.01389269,0.008185061,0.0006271563,0.0000167621,0.000007199541,0.00002214122,0.00001205827],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9603029,"threshold_uncertainty_score":0.8470995,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2068403421","doi":"10.1109/lsp.2014.2372333","title":"No-Reference Quality Assessment of Contrast-Distorted Images Based on Natural Scene Statistics","year":2014,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":340,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Scene statistics; Contrast (vision); Artificial intelligence; Computer science; Image quality; Support vector machine; Distortion (music); Mean opinion score; Entropy (arrow of time); Pattern recognition (psychology); Quality Score; Quality (philosophy); Regression; Computer vision; Statistics; Image (mathematics); Mathematics; Perception; Metric (unit)","retraction":null,"screen_n_in":null,"score":{"opus":0.03557197115822961,"gpt":0.3340860052783217,"spread":0.2985140341200921,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001126695,0.0002730828,0.0004058776,0.0001323205,0.000195682,0.0002727624,0.0007759972,0.0000583457,0.00001817425],"category_scores_gemma":[0.00008462527,0.0002462158,0.00007335198,0.0002467804,0.000187848,0.0004768429,0.00005125002,0.0003796812,0.00001446241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001316309,"about_ca_system_score_gemma":0.0002881725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001005199,"about_ca_topic_score_gemma":0.000004677087,"domain_scores_codex":[0.9970114,0.0004758248,0.0006060645,0.0005483615,0.0009400809,0.000418262],"domain_scores_gemma":[0.9979907,0.0005294601,0.0004844343,0.0004830876,0.0004022515,0.0001101205],"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":[0.0001181174,0.0008076947,0.002032692,0.001162474,0.00006858481,0.00003660181,0.0002714519,0.006198265,0.7618989,0.005360265,0.009819296,0.2122256],"study_design_scores_gemma":[0.00132626,0.0003383854,0.02517164,0.0003084005,0.00002816509,0.00000204371,0.00001225128,0.9357303,0.03555056,0.0003964455,0.0005487192,0.0005868428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007599418,0.00001790124,0.9894521,0.00139345,0.0003313618,0.0001564127,0.00003124297,0.0001191097,0.0008990355],"genre_scores_gemma":[0.8499739,5.432926e-7,0.1436113,0.006233582,0.00009282956,0.00001222309,0.00002010845,0.00001366467,0.00004182443],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.929532,"threshold_uncertainty_score":0.999999,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2912581987","doi":"10.1109/lsp.2019.2895749","title":"Medical Image Fusion via Convolutional Sparsity Based Morphological Component Analysis","year":2019,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":305,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Component (thermodynamics); Sparse approximation; Image fusion; Convolutional neural network; Image (mathematics); Computer vision; Component analysis; Representation (politics); Pixel","retraction":null,"screen_n_in":null,"score":{"opus":0.007747057653277735,"gpt":0.2258603778043704,"spread":0.2181133201510927,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002984824,0.000251999,0.0003677096,0.0002750025,0.0001119046,0.00004944601,0.0003222722,0.0001502324,0.001954356],"category_scores_gemma":[0.00001486923,0.0002361172,0.0001732382,0.0005564045,0.0001788794,0.0002453991,0.00004775684,0.0004512199,0.0001541373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000147053,"about_ca_system_score_gemma":0.00003518786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001065724,"about_ca_topic_score_gemma":0.000001367665,"domain_scores_codex":[0.9980779,0.00005882781,0.0003419922,0.0003800154,0.0007590505,0.0003822415],"domain_scores_gemma":[0.9993683,0.00009327746,0.00007889219,0.0002117256,0.00007418834,0.0001736514],"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":[0.00002888657,0.00005412268,0.00172637,0.00008204889,0.00005605241,0.0001288056,0.00001832482,0.04157112,0.9486531,0.000001420951,0.002363474,0.005316276],"study_design_scores_gemma":[0.0004368173,0.00003149147,0.004393434,0.00007037057,0.00009217957,0.00002457414,0.000005390677,0.9151044,0.07900741,0.00005907879,0.0003837399,0.0003911098],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3636671,0.00004910345,0.6348695,0.0004566057,0.0000927065,0.0001150321,0.000007026945,0.0005850602,0.0001578692],"genre_scores_gemma":[0.971437,0.000003629052,0.02628906,0.002062762,0.00009556103,0.00001432082,0.00005297567,0.00003101113,0.00001362047],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8735333,"threshold_uncertainty_score":0.998958,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2103636088","doi":"10.1109/lsp.2003.813679","title":"Speech probability distribution","year":2003,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":298,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Decorrelation; Generalized gamma distribution; Distribution (mathematics); Marginal distribution; Mathematics; Generalized integer gamma distribution; Speech processing; Speech recognition; Probability distribution; Multivariate statistics; Gaussian; Gamma distribution; Multivariate normal distribution; Joint probability distribution; Discrete cosine transform; Computer science; Random variable; Statistics; Artificial intelligence; Mathematical analysis; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.01804183266396142,"gpt":0.2324819737331489,"spread":0.2144401410691875,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005768498,0.0002164852,0.0001833769,0.00005823473,0.0003671476,0.0005276593,0.0005813432,0.0000702182,0.00001711584],"category_scores_gemma":[0.00006231927,0.0002005367,0.00007622961,0.0006520365,0.0001152195,0.001075912,0.00003430084,0.0002472724,0.00005568169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001272374,"about_ca_system_score_gemma":0.000185574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003947673,"about_ca_topic_score_gemma":9.25834e-7,"domain_scores_codex":[0.9980541,0.00009933966,0.0002900884,0.0005912965,0.0004378665,0.0005273426],"domain_scores_gemma":[0.9992315,0.00004418849,0.0001536482,0.0003182048,0.000116873,0.0001355525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001344843,0.0001937537,0.001860918,0.0002396395,0.00001845279,0.0001003937,0.0004105406,0.0007185754,0.4808607,0.0008887757,0.005330619,0.5093642],"study_design_scores_gemma":[0.0004396213,0.00004855947,0.0005865929,0.0001232565,0.00001223343,0.00014161,0.00001391932,0.003454244,0.9770061,0.01131987,0.006302535,0.000551411],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.167688,0.00014093,0.8288153,0.001867329,0.0002162329,0.0001262275,0.000001733291,0.0003115462,0.0008327315],"genre_scores_gemma":[0.9194753,0.000001028907,0.07819356,0.0021524,0.0001057702,0.00001378759,0.000003409973,0.00001276179,0.00004196817],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7517873,"threshold_uncertainty_score":0.8177651,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2145296449","doi":"10.1109/lsp.2009.2016449","title":"Proportional Fair Multiuser Scheduling in LTE","year":2009,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":249,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Telecommunications link; Computer science; Scheduling (production processes); Maximum throughput scheduling; Proportionally fair; Fairness measure; Throughput; Computer network; Mathematical optimization; Round-robin scheduling; Real-time computing; Fair-share scheduling; Distributed computing; Mathematics; Telecommunications; Quality of service; Wireless","retraction":null,"screen_n_in":null,"score":{"opus":0.008065413057944063,"gpt":0.2187135508866586,"spread":0.2106481378287146,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009458774,0.0001762604,0.0001535256,0.0001457866,0.00006692105,0.0000528313,0.000112308,0.0000684232,0.00001204247],"category_scores_gemma":[0.000005760054,0.0001915331,0.00003189396,0.0003578633,0.00003197604,0.0004999571,0.000004456505,0.0002675095,0.00001265383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001177692,"about_ca_system_score_gemma":0.00002054503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000102739,"about_ca_topic_score_gemma":0.000001679341,"domain_scores_codex":[0.9989467,0.00001313201,0.0002833298,0.0002176133,0.0002068256,0.0003323518],"domain_scores_gemma":[0.9997628,0.00001621458,0.00005329732,0.00008235012,0.00003256944,0.00005275047],"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":[0.000008944564,0.00001579244,0.0002880378,0.00002991391,0.000003208343,0.00001717032,0.0001165371,0.8801755,0.08472091,0.000007389348,0.0001226533,0.03449394],"study_design_scores_gemma":[0.0003429987,0.00001148237,0.001256262,0.0001733298,0.000004489006,0.000005855243,0.00001347779,0.9894379,0.00824967,0.0001512983,0.00008858913,0.0002646233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3494838,0.0001723034,0.6491827,0.0004273336,0.0001039446,0.0001204555,7.099401e-7,0.0003506367,0.0001581246],"genre_scores_gemma":[0.9602463,0.000007747381,0.03863633,0.0007979979,0.000243607,0.00001274052,0.000009917054,0.00003537875,0.000009985755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6107625,"threshold_uncertainty_score":0.7810494,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2153435651","doi":"10.1109/lsp.2005.847886","title":"A simplified clipping and filtering technique for PAR reduction in OFDM systems","year":2005,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":245,"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":"Clipping (morphology); Fast Fourier transform; Algorithm; Computer science; Iterative method; Reduction (mathematics); Noise (video); Orthogonal frequency-division multiplexing; Mathematics; Telecommunications; Artificial intelligence; Channel (broadcasting)","retraction":null,"screen_n_in":null,"score":{"opus":0.01683630079010304,"gpt":0.2405425269981602,"spread":0.2237062262080571,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002479665,0.0001725037,0.0001887913,0.0002219994,0.00009865387,0.0001064435,0.00008849564,0.00008994861,0.000002272483],"category_scores_gemma":[0.000008050251,0.0001972356,0.00003106904,0.0001812701,0.000050242,0.0003843373,0.000008555477,0.0001951272,0.000002114615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001631838,"about_ca_system_score_gemma":0.00001371599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000781898,"about_ca_topic_score_gemma":9.569126e-7,"domain_scores_codex":[0.9989918,0.00001891266,0.0003279035,0.000243189,0.0001101605,0.0003080451],"domain_scores_gemma":[0.9997381,0.00002812257,0.00006051074,0.00009495949,0.00002476484,0.00005355872],"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":[0.00001073785,0.000005801457,0.00002558221,0.0003978061,0.000007992482,0.000001439232,0.000273523,0.1647626,0.8243494,0.000003035496,0.0007104112,0.009451727],"study_design_scores_gemma":[0.0008937746,0.00004249525,0.000198507,0.001220586,0.00002948037,0.0003120882,0.0003251344,0.3630214,0.6270214,0.00008911247,0.006066903,0.0007791552],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.549377,0.0004571181,0.448016,0.0005089788,0.0004355091,0.000626152,0.000004111472,0.0004119163,0.0001632854],"genre_scores_gemma":[0.9921014,0.000008791776,0.006705137,0.00008586382,0.0006765843,0.0003519215,0.000002710045,0.00005096718,0.0000166578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4427244,"threshold_uncertainty_score":0.8043035,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2054040518","doi":"10.1109/97.889636","title":"Wavelet speech enhancement based on the Teager energy operator","year":2001,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":194,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Energy operator; Speech enhancement; Wavelet; Speech recognition; Energy (signal processing); Computer science; Noise (video); A priori and a posteriori; Artificial intelligence; Pattern recognition (psychology); Noise measurement; Wavelet transform; Noise reduction; Mathematics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.01549480607661621,"gpt":0.2246815998051548,"spread":0.2091867937285386,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004590565,0.0002953041,0.000197022,0.0001395751,0.0005820969,0.0008272921,0.001163499,0.00006259501,0.0001269715],"category_scores_gemma":[0.0000199997,0.0002028164,0.00008676652,0.0006859229,0.0001084642,0.0005299971,0.0000606541,0.0002688213,0.00008011402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009442612,"about_ca_system_score_gemma":0.0001826879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000110228,"about_ca_topic_score_gemma":0.000001726728,"domain_scores_codex":[0.9976701,0.00009851775,0.0003004929,0.0006134377,0.0007145907,0.0006028363],"domain_scores_gemma":[0.9990395,0.0001243,0.0001546583,0.0004624606,0.00009533469,0.0001237376],"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":[0.00002656322,0.0001157563,0.00005232571,0.00002435913,0.00001334103,0.0001953788,0.0001856795,0.001092809,0.5174167,0.0000782535,0.01175327,0.4690455],"study_design_scores_gemma":[0.0003613874,0.00009100889,0.00003831231,0.0001979728,0.000007988145,0.00003033539,0.0000159403,0.09952317,0.8876404,0.0001973874,0.01150087,0.0003951978],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06252097,0.00009960493,0.9049305,0.02903893,0.0002777685,0.0001122222,8.543742e-7,0.0002053336,0.002813823],"genre_scores_gemma":[0.8881803,0.000003972725,0.01811329,0.09296055,0.0003783847,0.00003424762,0.000001360301,0.000025054,0.0003028294],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8868172,"threshold_uncertainty_score":0.8270614,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2588196171","doi":"10.1109/lsp.2017.2669333","title":"Light-Field Image Super-Resolution Using Convolutional Neural Network","year":2017,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":193,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Research Foundation of Korea; Ministère de l'Éducation, du Loisir et du Sport Québec","keywords":"Convolutional neural network; Image resolution; Computer science; Light field; Artificial intelligence; Angular resolution (graph drawing); Computer vision; Field (mathematics); Image quality; Sample (material); Artificial neural network; Resolution (logic); Image (mathematics); Mathematics; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.02746942230664988,"gpt":0.2939887329418525,"spread":0.2665193106352026,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002430782,0.0002046404,0.0001807365,0.00007409784,0.001756586,0.001096085,0.001086911,0.00005416672,0.00001420912],"category_scores_gemma":[0.00003889353,0.0001981501,0.00008712722,0.0001305415,0.0001146933,0.002982649,0.0002048151,0.0002979882,0.00002496744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000698709,"about_ca_system_score_gemma":0.00008125628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002652675,"about_ca_topic_score_gemma":0.000001384235,"domain_scores_codex":[0.9982702,0.00004996137,0.0002573047,0.0004849405,0.0003822966,0.0005553185],"domain_scores_gemma":[0.9989942,0.00005011485,0.0002472392,0.0004770282,0.0001096815,0.0001217113],"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":[0.00003160247,0.00003846651,0.001000795,0.00004298252,0.00001411668,0.0001067604,0.0002507149,0.02043454,0.8489284,0.0003828605,0.01141068,0.1173581],"study_design_scores_gemma":[0.000327725,0.00002637168,0.0008068117,0.0001425002,0.000008154493,0.00005663802,0.000007187873,0.9855566,0.01068307,0.0007707335,0.001312524,0.0003016852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03285884,0.0001716043,0.9526138,0.01293758,0.0007982442,0.00008617634,5.705239e-7,0.0001617484,0.0003714628],"genre_scores_gemma":[0.85779,0.000001477069,0.1348669,0.006547161,0.0007442163,0.000003687088,7.744562e-7,0.00001637896,0.00002938229],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.965122,"threshold_uncertainty_score":0.9999409,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2033400894","doi":"10.1109/lsp.2003.811586","title":"Multiwavelets denoising using neighboring coefficients","year":2003,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":171,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Noise reduction; Thresholding; Wavelet; Pattern recognition (psychology); Artificial intelligence; Video denoising; Computer science; Extension (predicate logic); Term (time); Image denoising; Wavelet transform; Mathematics; Step detection; Algorithm; Computer vision; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.03591050733372826,"gpt":0.288264375172295,"spread":0.2523538678385668,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001018191,0.0003478412,0.0003158426,0.0002999435,0.0006956639,0.0008720154,0.0008030165,0.00009651328,0.00001140859],"category_scores_gemma":[0.00007348706,0.0003483652,0.0001232972,0.000918551,0.0001175222,0.001177152,0.00007152504,0.0003592398,0.00003127207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001630963,"about_ca_system_score_gemma":0.0001800912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002060273,"about_ca_topic_score_gemma":3.344492e-7,"domain_scores_codex":[0.9969929,0.0003361384,0.0004458809,0.0007487777,0.0006524161,0.000823844],"domain_scores_gemma":[0.9988774,0.0001477149,0.0002249182,0.0004142549,0.0001505522,0.0001851728],"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":[0.00001059435,0.00006371288,0.0001979806,0.00006004458,0.00001642378,0.0001915167,0.0007602592,0.01643611,0.8947228,0.000233135,0.0001253839,0.08718207],"study_design_scores_gemma":[0.001712328,0.00005529331,0.0002102668,0.0004340333,0.00004548089,0.0002921378,0.00004651798,0.4617149,0.5321056,0.0007337902,0.001539027,0.00111058],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1704774,0.0002533908,0.8277752,0.0001529507,0.0005654409,0.00012219,4.863278e-7,0.000223111,0.0004297738],"genre_scores_gemma":[0.7466384,9.655784e-7,0.25093,0.002218114,0.0001317976,0.000003469842,4.583007e-7,0.00003525372,0.00004161993],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5768453,"threshold_uncertainty_score":0.9998968,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4213363428","doi":"10.1109/lsp.2022.3152108","title":"Joint Parameter and Time-Delay Estimation for a Class of Nonlinear Time-Series Models","year":2022,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":142,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Nonlinear system; Autoregressive model; Computer science; Series (stratigraphy); Estimation theory; Time series; Nonlinear autoregressive exogenous model; Algorithm; Identification (biology); Mathematical optimization; Control theory (sociology); Mathematics; Artificial intelligence; Machine learning; Statistics; Control (management)","retraction":null,"screen_n_in":null,"score":{"opus":0.01215193104042406,"gpt":0.2083848560866763,"spread":0.1962329250462522,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001717386,0.0001173807,0.0001963031,0.00009085933,0.0001281726,0.00004600887,0.00006177889,0.00003164459,0.00001928027],"category_scores_gemma":[0.000005693484,0.0001216389,0.00005323782,0.0000990949,0.00003503883,0.0002216426,0.00001054678,0.0001097149,0.000004212879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004569941,"about_ca_system_score_gemma":0.00001436442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004353554,"about_ca_topic_score_gemma":2.534732e-7,"domain_scores_codex":[0.999266,0.00002684727,0.0002486227,0.0001419032,0.0001637763,0.0001528586],"domain_scores_gemma":[0.9997579,0.00004057353,0.00007017596,0.00007114349,0.00002641784,0.00003376912],"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":[0.0000436999,0.000007746909,9.887876e-7,0.000157904,0.00002348744,0.000001326765,0.0002522751,0.6790302,0.3096494,0.000002554614,0.001012159,0.009818239],"study_design_scores_gemma":[0.0003663544,0.00007877631,0.000001462429,0.00003140306,0.00001815836,0.00002402279,0.00003248669,0.9876485,0.01081071,0.0001587935,0.000693242,0.0001360528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3850483,0.0002836447,0.6125359,0.0006711454,0.0002337632,0.0005480947,0.000059108,0.0003501828,0.0002699191],"genre_scores_gemma":[0.9939305,5.969264e-7,0.005436684,0.0002714769,0.00006455204,0.0001356373,0.000009076668,0.00003508045,0.0001164427],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6088822,"threshold_uncertainty_score":0.4960292,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1981093646","doi":"10.1109/lsp.2010.2096466","title":"Joint Relay Selection and Power Allocation for Two-Way Relay Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":140,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta; Ontario Tech University","funders":"","keywords":"Relay; Maximization; Transceiver; Selection (genetic algorithm); Computer science; Transmitter power output; Relay channel; Signal-to-noise ratio (imaging); Power (physics); Channel (broadcasting); Mathematical optimization; Joint (building); Control theory (sociology); Mathematics; Topology (electrical circuits); Telecommunications; Wireless; Transmitter; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02201918150435418,"gpt":0.2610998891457004,"spread":0.2390807076413463,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005945592,0.000151606,0.000132916,0.00009753829,0.0005564495,0.0003796534,0.0003536939,0.00007138895,0.000008139936],"category_scores_gemma":[0.00002684452,0.0001454597,0.00004250337,0.0003366364,0.00006705883,0.0006106315,0.00006746111,0.0004153785,0.000003919212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003468819,"about_ca_system_score_gemma":0.00004656256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003236401,"about_ca_topic_score_gemma":0.00002788181,"domain_scores_codex":[0.9989062,0.00007378739,0.0002435092,0.0003711946,0.0001455985,0.0002597096],"domain_scores_gemma":[0.9992406,0.0001001119,0.0001465035,0.0002494043,0.0001818326,0.00008154132],"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":[0.00001815256,0.00004065171,0.0001496983,0.00002033593,0.0000170631,0.000001174371,0.0009906687,0.03060587,0.748997,0.002713031,0.004018484,0.2124279],"study_design_scores_gemma":[0.0004128639,0.00005377464,0.0005647971,0.00005705141,0.000008562259,0.00002387864,0.000006320152,0.9853513,0.009957409,0.00008719548,0.003234955,0.0002419453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1102817,0.0001754693,0.8829154,0.005809604,0.0003539736,0.0002038246,2.106861e-7,0.0001544835,0.0001053998],"genre_scores_gemma":[0.9603418,0.00001577294,0.03621256,0.003086487,0.0002196654,0.00004778605,0.000002885895,0.0000166563,0.00005642441],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9547454,"threshold_uncertainty_score":0.5931674,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2941547246","doi":"10.1109/lsp.2019.2910403","title":"Single Image Dehazing with a Generic Model-Agnostic Convolutional Neural Network","year":2019,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":131,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Convolutional neural network; Image (mathematics); Artificial intelligence; Atmosphere (unit); Simple (philosophy); Artificial neural network; Plug and play; Computer vision; Pattern recognition (psychology)","retraction":null,"screen_n_in":null,"score":{"opus":0.01494816148333204,"gpt":0.2192740983239218,"spread":0.2043259368405898,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002097052,0.0002660092,0.0002180684,0.0001038217,0.0002122862,0.0004229896,0.0007430056,0.00004845171,0.00001334734],"category_scores_gemma":[0.000005514084,0.0002408346,0.00005469093,0.0005299818,0.0001288822,0.001531238,0.0001092373,0.0002466574,0.00004637603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001281309,"about_ca_system_score_gemma":0.0001231544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005465887,"about_ca_topic_score_gemma":0.000001024254,"domain_scores_codex":[0.9979436,0.00005342564,0.0002576281,0.0005919554,0.0005214277,0.0006319716],"domain_scores_gemma":[0.9992176,0.00006727316,0.0001871196,0.0003239579,0.0001211302,0.00008288575],"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":[0.00002061727,0.00004652871,0.000294253,0.00005811283,0.00001291347,0.00004539678,0.0001571329,0.2688176,0.7168955,0.00007502639,0.003746512,0.009830464],"study_design_scores_gemma":[0.0002987276,0.0001333361,0.00009390974,0.0001749585,0.00001293465,0.00006114664,0.000002641691,0.964392,0.03405637,0.0003054278,0.00007984754,0.0003886822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1661949,0.000119054,0.831643,0.000834332,0.0001418298,0.0002247472,7.616142e-7,0.0004928593,0.0003484932],"genre_scores_gemma":[0.7469522,6.474576e-7,0.248501,0.004270042,0.0001615363,0.00002712035,0.000002747429,0.00002643268,0.00005836158],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6955745,"threshold_uncertainty_score":0.9820949,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2789151399","doi":"10.1109/lsp.2018.2799699","title":"AUV-Aided Joint Localization and Time Synchronization for Underwater Acoustic Sensor Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":126,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Research and Development Corporation of Newfoundland and Labrador","keywords":"Synchronization (alternating current); Computer science; Underwater; Scalability; Wireless sensor network; Real-time computing; Underwater acoustic communication; Nonlinear system; Network packet; Measure (data warehouse); Range (aeronautics); Underwater acoustics; Control theory (sociology); Telecommunications; Artificial intelligence; Engineering; Computer network; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.01031725898693007,"gpt":0.2051855735355893,"spread":0.1948683145486592,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001067765,0.0001769319,0.0001584587,0.0001221502,0.0002266772,0.0001341262,0.00007500916,0.000136814,0.00002196947],"category_scores_gemma":[0.00001713763,0.0001701344,0.00002726717,0.0002124567,0.0001691062,0.0002074391,0.00001238515,0.00008910493,0.00001971467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000798795,"about_ca_system_score_gemma":0.00001292864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001837732,"about_ca_topic_score_gemma":0.000001613778,"domain_scores_codex":[0.9991369,0.00001446527,0.0002323081,0.0002127908,0.0001110602,0.0002924397],"domain_scores_gemma":[0.9996786,0.00002951518,0.00005133578,0.00009153505,0.0001098584,0.00003912684],"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":[0.00001508012,0.000006745851,0.00005393626,0.0002433918,0.00002329148,0.000002117769,0.0002353593,0.8652595,0.1124075,0.000009937683,0.006167535,0.01557554],"study_design_scores_gemma":[0.0002794789,0.00004328162,0.00001720707,0.00009764669,0.00003364123,0.000007578139,0.00003187727,0.9491439,0.04967545,0.000138293,0.000311855,0.0002198088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01346582,0.00009459158,0.9846646,0.0002260488,0.0001831113,0.0002347846,0.000002564634,0.001070071,0.00005846524],"genre_scores_gemma":[0.9960852,0.000008169408,0.00252375,0.0008590529,0.0003661564,0.00002226545,0.00001864764,0.00007436155,0.0000424584],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9826193,"threshold_uncertainty_score":0.6937881,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2159693415","doi":"10.1109/lsp.2003.817852","title":"Adaptive beamforming with sidelobe control: a second-order cone programming approach","year":2003,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Adaptive beamformer; Beamforming; Second-order cone programming; Minimum-variance unbiased estimator; Control theory (sociology); Cone (formal languages); Computer science; Mathematics; Convex optimization; Mathematical optimization; Regular polygon; Algorithm; Control (management); Telecommunications; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.01425674183187548,"gpt":0.227591839497446,"spread":0.2133350976655706,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004866942,0.0002524753,0.0003156263,0.0002104072,0.0002087067,0.0002339888,0.000435411,0.0000688912,0.000007795089],"category_scores_gemma":[0.00003138515,0.0002203152,0.00005784158,0.0008255095,0.0001959726,0.0012265,0.00002187326,0.0002358957,0.000003067533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007175087,"about_ca_system_score_gemma":0.0002191399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001870445,"about_ca_topic_score_gemma":0.000002306834,"domain_scores_codex":[0.998166,0.00009726685,0.0003847991,0.0004863548,0.00046741,0.0003981717],"domain_scores_gemma":[0.998877,0.00009041034,0.0003824121,0.0002528269,0.0003021451,0.00009522027],"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":[0.0002608948,0.0008994343,0.001147351,0.001135666,0.0004265787,0.00009305041,0.007686752,0.04695871,0.2065954,0.01609608,0.001466317,0.7172338],"study_design_scores_gemma":[0.003544143,0.0009860588,0.0001664918,0.0009102346,0.000123532,0.0004719215,0.0004994688,0.5991449,0.3861281,0.002493815,0.003624103,0.001907244],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007615666,0.00006321951,0.9889854,0.0002086943,0.00005930773,0.0004207179,0.000001267204,0.0004873292,0.002158454],"genre_scores_gemma":[0.5639712,1.81955e-7,0.4352356,0.0006705657,0.00001827233,0.0000664907,7.232377e-7,0.00001691514,0.00002005407],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7153265,"threshold_uncertainty_score":0.8984193,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4213366994","doi":"10.1109/lsp.2022.3150258","title":"CNN-RNN and Data Augmentation Using Deep Convolutional Generative Adversarial Network for Environmental Sound Classification","year":2022,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Music and Audio Processing","field":"Computer Science","cited_by":118,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Kids Brain Health Network","keywords":"Computer science; Artificial intelligence; Deep learning; Convolutional neural network; Recurrent neural network; Normalization (sociology); Transfer of learning; Feature extraction; Pattern recognition (psychology); Generative adversarial network; Feature (linguistics); Feature learning; Machine learning; Spectrogram; Generative grammar; Artificial neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.07475645323796545,"gpt":0.2863002993979042,"spread":0.2115438461599388,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.000492412,0.0001627205,0.0001398406,0.00006240523,0.00162839,0.0003180867,0.0005701435,0.00003353046,0.00002333344],"category_scores_gemma":[0.00000567551,0.0001820519,0.00002987746,0.0001797277,0.0001403621,0.001200603,0.0002717354,0.0001318653,0.000001296712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002133641,"about_ca_system_score_gemma":0.0001290599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005295794,"about_ca_topic_score_gemma":0.000001232818,"domain_scores_codex":[0.9982762,0.0001038821,0.0002654631,0.0006699229,0.0003784431,0.0003060705],"domain_scores_gemma":[0.9993305,0.00007865141,0.0002807637,0.0002286026,0.00001934093,0.00006219777],"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":[0.0001791481,0.0001423604,0.001343477,0.0001128913,0.00009693112,0.00001231265,0.002930125,0.3739235,0.4460857,0.000813145,0.009769082,0.1645914],"study_design_scores_gemma":[0.0006585901,0.00003820235,0.000322695,0.00001423605,0.00003354566,0.00002541715,0.0001703009,0.9958134,0.0004506659,0.001237974,0.0009981397,0.0002367942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06995725,0.0003278333,0.9275495,0.001378899,0.0004416506,0.0002419965,0.00003754456,0.00004877119,0.00001649804],"genre_scores_gemma":[0.8919743,0.000001698028,0.1025597,0.004400355,0.0007718157,0.00004536475,0.0002123086,0.00001638541,0.00001804458],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8249898,"threshold_uncertainty_score":0.9996713,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2092305139","doi":"10.1109/lsp.2014.2381458","title":"FSITM: A Feature Similarity Index For Tone-Mapped Images","year":2014,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Pattern recognition (psychology); Feature (linguistics); Similarity (geometry); Feature extraction; Range (aeronautics); Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01226481585074939,"gpt":0.2747091463863644,"spread":0.262444330535615,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005276925,0.0002584962,0.0002392145,0.0001495659,0.0003018212,0.0005277337,0.001039145,0.0001055316,0.000004008181],"category_scores_gemma":[0.00002875393,0.0002460482,0.00009813347,0.0003168275,0.0001074575,0.001074278,0.0001034288,0.0002875772,0.000008957196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006592385,"about_ca_system_score_gemma":0.0000601888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006921854,"about_ca_topic_score_gemma":0.000001146726,"domain_scores_codex":[0.9982268,0.00006912941,0.0002161941,0.000595389,0.000381368,0.0005111034],"domain_scores_gemma":[0.9991006,0.0001010675,0.0001749737,0.000394702,0.0001439547,0.00008471265],"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":[0.00004564148,0.00008972939,0.0002276836,0.0003135406,0.00002420977,0.00002797266,0.0005059373,0.0001592905,0.6389408,0.0002253574,0.1180053,0.2414345],"study_design_scores_gemma":[0.001174537,0.0002084522,0.0003951185,0.000265795,0.0000260211,0.00003644967,0.00001134057,0.1353961,0.8291466,0.003563673,0.02889188,0.0008840605],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00534479,0.00006565,0.9807022,0.01210464,0.0001897066,0.0003499344,0.000002842931,0.000747052,0.0004931805],"genre_scores_gemma":[0.7962143,0.000001127888,0.1951944,0.007850838,0.0003884966,0.00009498298,0.000003873082,0.00002638038,0.000225646],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7908695,"threshold_uncertainty_score":0.9999992,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2101773947","doi":"10.1109/lsp.2005.849497","title":"A novel chaos-based high-resolution imaging technique and its application to through-the-wall imaging","year":2005,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Radar imaging; Computer science; Image resolution; Radar; Modulation (music); Noise (video); Signal-to-noise ratio (imaging); Resolution (logic); Doppler effect; Medical imaging; Artificial intelligence; Physics; Image (mathematics); Telecommunications; Acoustics","retraction":null,"screen_n_in":null,"score":{"opus":0.007447983199257703,"gpt":0.2197049830795154,"spread":0.2122569998802576,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002745586,0.0002613878,0.0001902675,0.0001840564,0.0002730175,0.0001944116,0.0002287691,0.00003409214,0.000005781365],"category_scores_gemma":[0.000006367893,0.0002465507,0.00005778564,0.0003786546,0.00006205264,0.0003312602,0.00002198343,0.0002576596,0.00002772189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001548996,"about_ca_system_score_gemma":0.00001824943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001218065,"about_ca_topic_score_gemma":0.000008913085,"domain_scores_codex":[0.9986786,0.00002312995,0.000288594,0.0003825914,0.0002171427,0.0004099844],"domain_scores_gemma":[0.9995533,0.00003363096,0.00006988038,0.0002072153,0.00005562744,0.00008033364],"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":[0.000004466668,0.00001062052,0.00005111588,0.00007789599,0.0000102774,0.00000162535,0.0002644193,0.1917217,0.7811341,0.000003602809,0.0008586241,0.02586147],"study_design_scores_gemma":[0.0002045939,0.000003388036,0.0001084431,0.0001513681,0.00004593269,0.00003109486,0.00001782663,0.7978987,0.1978989,0.00001793253,0.003303529,0.0003182605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04279621,0.0005957464,0.9382592,0.01746579,0.00004375687,0.0002613218,0.000005305946,0.0005096536,0.00006303535],"genre_scores_gemma":[0.9761838,0.000003655125,0.01662663,0.006715156,0.0002397004,0.0001480695,0.000008659161,0.00006332184,0.00001098022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9333876,"threshold_uncertainty_score":0.9999987,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2155213659","doi":"10.1109/lsp.2006.884038","title":"Time Delay Estimation via Minimum Entropy","year":2007,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":88,"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","funders":"","keywords":"Multilateration; Computer science; Algorithm; Reverberation; Entropy (arrow of time); Additive white Gaussian noise; Gaussian; Speech recognition; White noise; Mathematics; Telecommunications; Acoustics","retraction":null,"screen_n_in":null,"score":{"opus":0.008847824756960781,"gpt":0.2346610019047672,"spread":0.2258131771478064,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005988018,0.0002309829,0.0001895787,0.0002132101,0.0003311218,0.0004237995,0.0006923579,0.00008018998,0.00002245976],"category_scores_gemma":[0.00001671772,0.0002210462,0.00007077041,0.0005793686,0.0001150861,0.00129053,0.00005308701,0.0002172087,0.0002822924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000937101,"about_ca_system_score_gemma":0.00008737024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003462431,"about_ca_topic_score_gemma":5.442751e-7,"domain_scores_codex":[0.9979805,0.00003094591,0.0003669406,0.0004903639,0.0005121585,0.0006191318],"domain_scores_gemma":[0.9991957,0.00008580268,0.0002245432,0.0002367848,0.00009308472,0.0001640706],"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":[0.00001253427,0.00002904365,0.00003547233,0.00003297691,0.000007490949,0.00008493343,0.0004051148,0.001371033,0.6389084,0.000005948435,0.001414711,0.3576924],"study_design_scores_gemma":[0.0004223866,0.00005696635,0.0001535102,0.0001323757,0.00001498747,0.0001437605,0.00001109405,0.3181016,0.6791296,0.0008401362,0.0005458945,0.0004477453],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1431946,0.0001211145,0.8538158,0.001933726,0.000191951,0.00008725709,3.519976e-7,0.0003395428,0.0003156047],"genre_scores_gemma":[0.8223228,4.744019e-7,0.1723057,0.004981521,0.0002777075,0.000003905926,0.000002933011,0.00002042849,0.00008447359],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6815101,"threshold_uncertainty_score":0.9014004,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2096084339","doi":"10.1109/lsp.2010.2051574","title":"A Template Matching Procedure for Automatic Target Recognition in Synthetic Aperture Sonar Imagery","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":83,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Sonar; Synthetic aperture sonar; Artificial intelligence; Computer science; Computer vision; Pattern recognition (psychology); Template matching; Automatic target recognition; Matching (statistics); Shadow (psychology); Synthetic aperture radar; Receiver operating characteristic; Image (mathematics); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02077882598449221,"gpt":0.2480856596607653,"spread":0.2273068336762731,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006307217,0.0002139783,0.0002171185,0.0002907375,0.0002736414,0.0003232192,0.0002315889,0.0001285639,0.0004458276],"category_scores_gemma":[0.00006473692,0.0001834037,0.00006034637,0.0002936767,0.0001270218,0.0005277121,0.000007621966,0.0006129857,0.0001199437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001288695,"about_ca_system_score_gemma":0.0001550414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001776183,"about_ca_topic_score_gemma":0.0003383528,"domain_scores_codex":[0.9981949,0.00005859557,0.0003108575,0.0004258037,0.0003910331,0.0006187502],"domain_scores_gemma":[0.9991791,0.0004176571,0.0001027622,0.0001091226,0.00006357821,0.0001277496],"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":[0.0001761688,0.00009788774,0.00883341,0.00233165,0.00003354175,0.0002288544,0.002249021,0.03288074,0.6798168,2.766764e-7,0.002001214,0.2713504],"study_design_scores_gemma":[0.0005365955,0.00007358438,0.003175774,0.0003923735,0.00002056122,0.0001687687,0.0001297794,0.9857835,0.00571518,0.003154719,0.000369078,0.0004800792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8978611,0.00004954327,0.09802515,0.002802353,0.0001861091,0.0006153299,0.00009487634,0.0001383966,0.0002271338],"genre_scores_gemma":[0.9533753,0.000001797648,0.04457748,0.001669305,0.0002051859,0.00002371244,0.0001012453,0.00001927726,0.00002666906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9529028,"threshold_uncertainty_score":0.7478988,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2024165348","doi":"10.1109/lsp.2010.2102017","title":"Relay Selection in Dual-Hop Vehicular Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":76,"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":"Relay; Selection (genetic algorithm); Computer science; Rayleigh fading; Channel (broadcasting); Algorithm; Artificial intelligence; Fading; Computer network; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.01665802098274453,"gpt":0.25113018062649,"spread":0.2344721596437455,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005261423,0.0001430851,0.0001317819,0.0001452577,0.0002819129,0.0002946304,0.0004473081,0.00008337385,0.00001626161],"category_scores_gemma":[0.00001548345,0.000141924,0.00004038657,0.0008531556,0.00006256175,0.0005923013,0.00008926687,0.0007779399,0.00001612805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004457994,"about_ca_system_score_gemma":0.00005917557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005960424,"about_ca_topic_score_gemma":0.00006561045,"domain_scores_codex":[0.9988075,0.0001238083,0.0002405318,0.0003343159,0.0001912677,0.0003025407],"domain_scores_gemma":[0.9994508,0.00007069487,0.00009674484,0.0002338124,0.00008060753,0.00006736057],"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":[0.000009631962,0.00006982034,0.001473193,0.00001419675,0.000009298922,0.00003607247,0.0009469327,0.1323804,0.664246,0.0008603765,0.002803372,0.1971507],"study_design_scores_gemma":[0.0002491735,0.00001752685,0.002006069,0.0000619125,0.000002802394,0.00003460573,0.000004723177,0.9904583,0.004099755,0.00004628953,0.002793743,0.0002251434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3211078,0.0001733669,0.6740083,0.003954589,0.0002754857,0.0001002202,6.538381e-8,0.0001691077,0.000211069],"genre_scores_gemma":[0.9872453,0.00002142229,0.009029054,0.003400159,0.0002320253,0.00001932098,0.000001124515,0.0000128559,0.00003868862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8580778,"threshold_uncertainty_score":0.5787492,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2109018632","doi":"10.1109/lsp.2009.2030856","title":"Adaptive Noise Variance Estimation in BayesShrink","year":2009,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":73,"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":"Variance (accounting); Noise (video); Computer science; Autocorrelation; Noise measurement; Residual; Statistics; Noise reduction; Image denoising; Pattern recognition (psychology); Estimation; Algorithm; Gradient noise; Artificial intelligence; Mathematics; Image (mathematics); Noise floor","retraction":null,"screen_n_in":null,"score":{"opus":0.01947403965786431,"gpt":0.2724771068508148,"spread":0.2530030671929505,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000621535,0.0001986714,0.0002136164,0.0002249609,0.0001498455,0.0003495919,0.0006397526,0.00006644202,0.000004381129],"category_scores_gemma":[0.00002533848,0.0001973448,0.00005198907,0.0007364111,0.00005100266,0.001470413,0.00002680037,0.0002872837,0.00002985655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008777908,"about_ca_system_score_gemma":0.00009353139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001520195,"about_ca_topic_score_gemma":7.329509e-7,"domain_scores_codex":[0.9982919,0.0001631814,0.0003052323,0.0004801011,0.0003552662,0.0004043465],"domain_scores_gemma":[0.9993834,0.00009019382,0.0001360908,0.0002486059,0.00006565566,0.00007605286],"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":[0.00004972063,0.00007576509,0.00002894252,0.00002009844,0.000004423826,0.000194398,0.001066001,0.0385707,0.1960139,0.0005967219,0.0005836107,0.7627957],"study_design_scores_gemma":[0.0007989904,0.0001147315,0.003065518,0.0002431191,0.000007340273,0.00003665283,0.000008152991,0.9570131,0.02699916,0.01124667,0.00007013024,0.0003964864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01023089,0.0001355422,0.9846853,0.003804723,0.0001537959,0.000119385,3.615157e-7,0.0001678227,0.0007021595],"genre_scores_gemma":[0.7074929,9.612055e-7,0.2865313,0.00585593,0.00007307781,0.000005517708,6.076635e-7,0.000007669216,0.00003204024],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9184424,"threshold_uncertainty_score":0.8047487,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2143309866","doi":"10.1109/97.988716","title":"Robust array interpolation using second-order cone programming","year":2002,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Interpolation (computer graphics); Robustness (evolution); Mathematical optimization; Second-order cone programming; Computer science; Multivariate interpolation; Convex optimization; Algorithm; Stairstep interpolation; Mathematics; Bilinear interpolation; Nearest-neighbor interpolation; Regular polygon; Artificial intelligence; Computer vision; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.04750596592998896,"gpt":0.2604011387072404,"spread":0.2128951727772515,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002533501,0.0001732033,0.0001908257,0.0002546449,0.0001722326,0.0002892149,0.0004266318,0.00006459194,0.0000772042],"category_scores_gemma":[0.00002487817,0.0001807676,0.00005716094,0.0007191909,0.0001103231,0.001420102,0.00003327908,0.0001773599,0.00001090707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000719989,"about_ca_system_score_gemma":0.00003470649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001766728,"about_ca_topic_score_gemma":0.000001667354,"domain_scores_codex":[0.9985992,0.00005854783,0.0003896875,0.0003587617,0.0003277825,0.0002660681],"domain_scores_gemma":[0.9991421,0.00005020915,0.0003355937,0.0002174169,0.0001926483,0.00006202635],"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":[0.000004891041,0.00009062317,0.0002193594,0.0001749583,0.00002134613,0.000006546265,0.001676373,0.005736986,0.727703,0.00009751074,0.001122546,0.2631459],"study_design_scores_gemma":[0.0001728359,0.00004609965,0.00004239621,0.0002608829,0.00001136319,0.00003324202,0.0000189099,0.8358731,0.1625147,0.0001897469,0.0005630379,0.0002736387],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02803893,0.00006679893,0.9699423,0.0005459571,0.000200735,0.000176597,6.993097e-7,0.0004743578,0.0005535998],"genre_scores_gemma":[0.6119179,3.549559e-7,0.3874908,0.0004779648,0.00005838954,0.000009970759,7.214812e-7,0.00001383716,0.00003011269],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8301361,"threshold_uncertainty_score":0.7371489,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2153200902","doi":"10.1109/lsp.2011.2106119","title":"Robust Recursive Least-Squares Adaptive-Filtering Algorithm for Impulsive-Noise Environments","year":2011,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Recursive least squares filter; Algorithm; Adaptive filter; A priori and a posteriori; Computer science; Noise (video); Signal processing; Mathematics; Artificial intelligence; Digital signal processing","retraction":null,"screen_n_in":null,"score":{"opus":0.04455036622239422,"gpt":0.2249580311770806,"spread":0.1804076649546864,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001267709,0.0004577153,0.0003370542,0.0001842423,0.0001911748,0.00005480251,0.0003946534,0.0001194286,0.00003267848],"category_scores_gemma":[0.000007676364,0.0005024601,0.0001302477,0.0001438624,0.0001703776,0.0006648261,0.00005060964,0.0003083032,0.0000204751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002395123,"about_ca_system_score_gemma":0.00001518965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009180845,"about_ca_topic_score_gemma":7.947709e-7,"domain_scores_codex":[0.9981734,0.00002763331,0.0003669857,0.0005089146,0.0002470048,0.000676028],"domain_scores_gemma":[0.9993916,0.00004740616,0.0001329489,0.0002439877,0.00004066727,0.0001433924],"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":[0.00007905218,0.00005513947,0.00002386556,0.0001548761,0.00009336533,0.00005321612,0.001088435,0.03352164,0.6517876,0.00001997791,0.001005733,0.3121171],"study_design_scores_gemma":[0.0008814722,0.0003773191,0.0003614691,0.0006367554,0.00008724085,0.00004265739,0.0002261875,0.228659,0.7636011,0.00142029,0.002264339,0.001442246],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01163351,0.0002209204,0.9862752,0.0000296344,0.0002562827,0.0004994442,0.00005816996,0.0007697772,0.0002570372],"genre_scores_gemma":[0.5040956,0.00001555191,0.4948529,0.0002453357,0.0002935969,0.000263273,0.00001273667,0.0001736262,0.00004731604],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4924621,"threshold_uncertainty_score":0.9997427,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2735133106","doi":"10.1109/lsp.2017.2724848","title":"Adaptive Kalman Filtering by Covariance Sampling","year":2017,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kalman filter; Covariance; Covariance intersection; Noise (video); Computer science; White noise; Noise measurement; Gaussian noise; Covariance matrix; Algorithm; A priori and a posteriori; Estimation of covariance matrices; Artificial intelligence; Extended Kalman filter; Mathematics; Control theory (sociology); Pattern recognition (psychology); Statistics; Noise reduction","retraction":null,"screen_n_in":null,"score":{"opus":0.04286700277901982,"gpt":0.2741864044847018,"spread":0.231319401705682,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003003453,0.0002280613,0.0002083544,0.00005675569,0.001557809,0.001582425,0.001965077,0.00007736225,0.00001313358],"category_scores_gemma":[0.00002375647,0.0002254547,0.00006205517,0.000100729,0.0001709052,0.001494827,0.0002215103,0.0003408908,0.0000421157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003942564,"about_ca_system_score_gemma":0.00003797312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004878564,"about_ca_topic_score_gemma":0.000001661038,"domain_scores_codex":[0.9982023,0.00004146967,0.0002613673,0.0006355588,0.0003609265,0.0004983827],"domain_scores_gemma":[0.9986085,0.00008386739,0.0003290713,0.0007882759,0.00006421161,0.0001261294],"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":[0.00005301442,0.00008322638,0.0004437845,0.00008797861,0.00004717652,0.0001552064,0.001053912,0.02071978,0.4003268,0.0006774238,0.07037418,0.5059775],"study_design_scores_gemma":[0.001617284,0.000165593,0.002214355,0.00141208,0.00004041692,0.0001419308,0.00006141815,0.894275,0.05302186,0.002106866,0.04275554,0.002187649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01108515,0.0001729513,0.9848781,0.002322577,0.000707992,0.00007963522,0.00001090494,0.000274063,0.0004686384],"genre_scores_gemma":[0.8829359,0.000006032095,0.1140895,0.002467442,0.0003931632,0.000009384587,0.000004835874,0.00002241045,0.00007131347],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8735552,"threshold_uncertainty_score":0.999742,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2058687912","doi":"10.1109/lsp.2012.2194142","title":"Time-Frequency Analysis via Ramanujan Sums","year":2012,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Fractal and DNA sequence analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":56,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Time–frequency analysis; Kernel (algebra); Signal processing; Algorithm; Ramanujan's sum; Mathematics; Computer science; Discrete mathematics; Digital signal processing; Pure mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.007960043356324654,"gpt":0.2325196263715792,"spread":0.2245595830152546,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002499818,0.0002204295,0.0002431556,0.0001661422,0.0001629288,0.00006079591,0.000243567,0.0001211486,0.0001023745],"category_scores_gemma":[0.00001145819,0.0001979994,0.000285741,0.0005856528,0.0001688796,0.00003437656,0.00003192023,0.0001217054,0.0001319775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002545095,"about_ca_system_score_gemma":0.00003219847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005950761,"about_ca_topic_score_gemma":0.000007068784,"domain_scores_codex":[0.998593,0.00006046447,0.0002522152,0.0003559024,0.0002404892,0.0004979341],"domain_scores_gemma":[0.9993498,0.000009180031,0.0001399905,0.0002634814,0.00006492813,0.0001726256],"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":[0.00001778241,0.0000572638,0.008830362,0.00001617786,0.0003636861,0.000004917694,0.00007290709,0.0007841756,0.9819217,8.653391e-7,0.00131094,0.006619269],"study_design_scores_gemma":[0.0006279883,0.0001941077,0.007293984,0.00004063533,0.002492496,0.00004589103,0.0001020573,0.007414099,0.9744382,0.0001117735,0.0057055,0.001533297],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9493166,0.0009757327,0.048482,0.0005458054,0.00006450556,0.00006454491,0.000005848242,0.0000517664,0.0004932491],"genre_scores_gemma":[0.9951692,0.000008974882,0.0007880324,0.002933757,0.0006321009,0.00001254605,0.0001175208,0.00003187282,0.0003059792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04769397,"threshold_uncertainty_score":0.807418,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2002330609","doi":"10.1109/lsp.2005.843775","title":"Ramanujan sums and discrete Fourier transforms","year":2005,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Fractal and DNA sequence analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":54,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Arithmetic function; Mathematics; Ramanujan's sum; Discrete Fourier transform (general); Computation; Fourier transform; Discrete mathematics; Integer (computer science); Fourier analysis; Algebra over a field; Algorithm; Fractional Fourier transform; Pure mathematics; Mathematical analysis; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.006671642077613602,"gpt":0.2301548136345087,"spread":0.2234831715568951,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009957275,0.0001463942,0.0001190179,0.0000401512,0.0001275123,0.00007521032,0.0001077634,0.00007460007,0.00001799359],"category_scores_gemma":[0.000004050804,0.0001180162,0.00007486311,0.00007717648,0.0001460979,0.00002273041,0.00001343741,0.000091824,0.000007631575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009200216,"about_ca_system_score_gemma":0.00002379156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000160851,"about_ca_topic_score_gemma":0.00001882023,"domain_scores_codex":[0.9991604,0.00001566111,0.0001515103,0.0002951019,0.0001351595,0.000242147],"domain_scores_gemma":[0.9997275,0.00000405549,0.00004868894,0.0001086465,0.00002413811,0.00008700038],"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":[0.0000381204,0.00001303806,0.0004314688,0.00002234943,0.0000291444,0.000004998669,0.00009511351,0.0003902971,0.9036694,0.000001997239,0.0008385356,0.0944656],"study_design_scores_gemma":[0.0008469115,0.0001884339,0.0007340661,0.00006472019,0.0001187426,0.00005461751,0.0001171186,0.005963645,0.9448163,0.000122523,0.04627368,0.0006992262],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9695475,0.0006898172,0.02507386,0.004193739,0.00002328017,0.00006015124,0.000004227082,0.00001711808,0.0003903053],"genre_scores_gemma":[0.9938393,0.00004046133,0.0009552264,0.004399481,0.0004252687,0.000008157126,0.00002525554,0.00001734378,0.0002894885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09376638,"threshold_uncertainty_score":0.4812562,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3193636205","doi":"10.1109/lsp.2021.3104503","title":"Sparse Bayesian Learning Using Generalized Double Pareto Prior for DOA Estimation","year":2021,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Guangzhou Municipal Science and Technology Bureau; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Hyperparameter; Prior probability; Convergence (economics); Computer science; Bayesian probability; Algorithm; Pareto principle; Bayesian inference; Direction of arrival; Hyperparameter optimization; Mathematical optimization; Artificial intelligence; Pattern recognition (psychology); Mathematics; Support vector machine","retraction":null,"screen_n_in":null,"score":{"opus":0.03780766673129853,"gpt":0.3025401547987712,"spread":0.2647324880674726,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003745075,0.0001768823,0.0002460346,0.0001702545,0.0003191117,0.0003556538,0.0003313504,0.00007078788,0.00000890419],"category_scores_gemma":[0.00005447631,0.0001934328,0.00009851062,0.000559871,0.0000584669,0.001024553,0.00005050945,0.0001440261,0.000002343906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001013918,"about_ca_system_score_gemma":0.0002383218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000276347,"about_ca_topic_score_gemma":0.000001961903,"domain_scores_codex":[0.9984592,0.00007794186,0.0004108738,0.0004324069,0.0003389086,0.0002807103],"domain_scores_gemma":[0.9989779,0.00008260968,0.000348254,0.0002209473,0.0002995429,0.00007072627],"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":[0.0000392501,0.00005580202,0.0001485516,0.0003145147,0.0000252923,0.00001385051,0.000715371,0.2228307,0.6229005,0.0009068272,0.0004112511,0.151638],"study_design_scores_gemma":[0.0003566789,0.00002078301,0.00002194702,0.0001498414,0.00001562188,0.00001842379,0.000009833517,0.6342287,0.3640106,0.0008702479,0.0001399024,0.0001573674],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05396175,0.00005384902,0.9441888,0.0008759697,0.0001984924,0.0002289123,0.000001017456,0.0003985252,0.00009262774],"genre_scores_gemma":[0.5119039,8.097771e-7,0.4876703,0.0002991414,0.00005416669,0.0000272743,0.000005004874,0.00001635173,0.00002301739],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4579422,"threshold_uncertainty_score":0.7887961,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2980241036","doi":"10.1109/lsp.2019.2945683","title":"Joint Design of Measurement Matrix and Sparse Support Recovery Method via Deep Auto-Encoder","year":2019,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Exploit; Encoder; Thresholding; Joint (building); Artificial intelligence; Sparse matrix; Computational complexity theory; Matrix (chemical analysis); Compressed sensing; Algorithm; Computation; Signal processing; Pattern recognition (psychology); Computer engineering; Computer hardware; Digital signal processing; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.03331377140293759,"gpt":0.2434455680669855,"spread":0.2101317966640479,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006090328,0.0002450641,0.0003562817,0.0001709542,0.00004720288,0.00006212696,0.0001349161,0.00009472737,0.00004226814],"category_scores_gemma":[0.000007868156,0.0002401836,0.00006903003,0.0001410223,0.00004628802,0.0002354082,0.00002360324,0.000207557,0.00001576842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008386043,"about_ca_system_score_gemma":0.00003910912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001481916,"about_ca_topic_score_gemma":6.681515e-7,"domain_scores_codex":[0.9985618,0.0000794567,0.0003635419,0.0002855733,0.0004019957,0.0003076115],"domain_scores_gemma":[0.9994519,0.00004674408,0.0001191877,0.0002073083,0.0001065211,0.00006833916],"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":[0.00002490876,0.00001300263,0.00003134424,0.0001582758,0.00004967572,0.00001247552,0.0001697654,0.131892,0.8375432,0.000001825204,0.001634618,0.02846885],"study_design_scores_gemma":[0.0002858815,0.0001097051,0.0001427062,0.0003433607,0.00006653488,0.00004982537,0.00002434708,0.4867198,0.5112321,0.0004458801,0.0002113399,0.0003684845],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03534437,0.0004701585,0.9630665,0.0001114544,0.0001871853,0.0002694941,0.000001251291,0.0003654562,0.0001841597],"genre_scores_gemma":[0.882069,0.00001473568,0.1174555,0.0003131985,0.00006377749,0.00001037234,0.000001196616,0.00005440415,0.00001788368],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8467246,"threshold_uncertainty_score":0.9794403,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2168375507","doi":"10.1109/lsp.2011.2152393","title":"Joint DOD and DOA Estimation for MIMO Array With Velocity Receive Sensors","year":2011,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"MIMO; Direction of arrival; Joint (building); Computer science; Sensor array; Constraint (computer-aided design); Algorithm; Pairing; Key (lock); Mathematics; Telecommunications; Engineering; Channel (broadcasting); Antenna (radio); Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.03297963045655949,"gpt":0.2480927040236721,"spread":0.2151130735671126,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002910314,0.0001458438,0.0001757635,0.0001473418,0.0001516164,0.0001010415,0.0002065504,0.00004754988,0.00000284378],"category_scores_gemma":[0.00002951012,0.0001263801,0.00003261254,0.0002508507,0.00014638,0.0007991704,0.00001616843,0.00009244785,0.000001705503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003815758,"about_ca_system_score_gemma":0.00005954439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003494156,"about_ca_topic_score_gemma":0.000001516851,"domain_scores_codex":[0.9989996,0.00003591002,0.0002454954,0.0003243887,0.0002153103,0.0001793167],"domain_scores_gemma":[0.9992782,0.00005757267,0.0002683841,0.0001612263,0.0001758937,0.00005868701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000181289,0.0001758312,0.0003232348,0.0008885263,0.00007737833,0.00001160901,0.01345713,0.003711218,0.4313105,0.001210666,0.002071467,0.5465811],"study_design_scores_gemma":[0.0003122992,0.0002100354,0.0009326924,0.0002827918,0.00002185728,0.00002971711,0.00002793597,0.1815931,0.8118108,0.004463932,0.00004212637,0.0002727026],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07704622,0.00001086143,0.9214452,0.000635482,0.0000644375,0.0002533664,0.000001711521,0.0002626751,0.0002800327],"genre_scores_gemma":[0.5336316,5.612135e-7,0.4660488,0.0002596845,0.0000157423,0.00002802526,7.857558e-7,0.000008699409,0.000006148251],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5463085,"threshold_uncertainty_score":0.5153631,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2167012018","doi":"10.1109/lsp.2005.861598","title":"Nonintrusive speech quality estimation using Gaussian mixture models","year":2006,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Mixture model; Computer science; Consistency (knowledge bases); Speech recognition; Pattern recognition (psychology); Gaussian; Quality (philosophy); Estimation theory; Artificial intelligence; Expectation–maximization algorithm; Speech processing; Algorithm; Mathematics; Statistics; Maximum likelihood","retraction":null,"screen_n_in":null,"score":{"opus":0.02569519222903556,"gpt":0.2754719879494821,"spread":0.2497767957204465,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004482956,0.0003347207,0.0003107645,0.0002133759,0.0005591863,0.0009545854,0.0007608483,0.0001299663,0.000008028459],"category_scores_gemma":[0.00001213759,0.0003206929,0.0001068618,0.0007351314,0.0001284765,0.002511668,0.00007585093,0.0003294064,0.00001988179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001739173,"about_ca_system_score_gemma":0.0002224562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001821594,"about_ca_topic_score_gemma":0.000009573195,"domain_scores_codex":[0.9973429,0.00009864553,0.0005214634,0.0007132255,0.0006919135,0.0006318139],"domain_scores_gemma":[0.9989295,0.00005551082,0.0004000054,0.000342234,0.000156919,0.0001158576],"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":[0.00001727043,0.00006626706,0.0002189732,0.0001821522,0.00001420279,0.0001102447,0.0005069888,0.2055923,0.6349539,0.0003715109,0.00106624,0.1568999],"study_design_scores_gemma":[0.000367406,0.00001720482,0.0002527045,0.0002216554,0.00001803525,0.0001059121,0.00001839534,0.6851981,0.2987978,0.01443642,0.00004187706,0.000524538],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2138413,0.0001819984,0.7825838,0.002033456,0.0001801347,0.0001209498,0.000002083701,0.0002961683,0.000760055],"genre_scores_gemma":[0.687393,4.693544e-7,0.3102115,0.001998133,0.0003274207,0.000005112108,0.000004207409,0.00002261769,0.00003751958],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4796058,"threshold_uncertainty_score":0.9999245,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2124541692","doi":"10.1109/lsp.2010.2047958","title":"OFDM Transmission for Time-Based Range Estimation","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Cramér–Rao bound; Orthogonal frequency-division multiplexing; Upper and lower bounds; Estimator; Maximum likelihood; Transmission (telecommunications); Channel (broadcasting); Algorithm; Estimation theory; Statistics; Range (aeronautics); Signal-to-noise ratio (imaging); Mathematics; Computer science; Telecommunications; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.006878368925534709,"gpt":0.2130724378088517,"spread":0.206194068883317,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001090947,0.0001372109,0.0001174326,0.0001188262,0.0001239215,0.00006550663,0.0001354953,0.0001223993,0.000044125],"category_scores_gemma":[0.00001112168,0.000129794,0.00005398947,0.0001476185,0.00005750197,0.0001644357,0.000002015502,0.0001769911,0.00002087548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002151406,"about_ca_system_score_gemma":0.00002005106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001096406,"about_ca_topic_score_gemma":4.615381e-7,"domain_scores_codex":[0.9993534,0.000005571536,0.0001650938,0.0001397334,0.0001272455,0.0002089247],"domain_scores_gemma":[0.9997592,0.00004227611,0.00003103703,0.00009177,0.00003963073,0.00003609246],"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":[0.0000152294,0.000008630772,0.00001824536,0.000212304,0.000004493392,0.000001208129,0.00007361937,0.1266336,0.712385,0.000008055817,0.002957997,0.1576817],"study_design_scores_gemma":[0.0003267991,0.00001192505,0.00002061759,0.00003877194,0.00001132117,0.000001177675,0.000002726238,0.6662185,0.3313606,0.0001119652,0.001762156,0.0001333719],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07726176,0.00003595625,0.9205263,0.0007230581,0.0001578239,0.0001905864,0.00000493521,0.000979773,0.0001198402],"genre_scores_gemma":[0.9697221,5.731583e-7,0.02955725,0.0005031935,0.0000915647,0.00004579646,0.00001914785,0.00004060534,0.0000197972],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8924603,"threshold_uncertainty_score":0.5292847,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1972011722","doi":"10.1109/lsp.2010.2092427","title":"Performance Analysis of Relay Selection With Feedback Delay and Channel Estimation Errors","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":47,"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":"Relay; Channel state information; Computer science; Relay channel; Selection (genetic algorithm); Channel (broadcasting); Bit error rate; Cooperative diversity; Control theory (sociology); Telecommunications; Fading; Wireless; Artificial intelligence; Power (physics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01605796510181597,"gpt":0.2433800180086934,"spread":0.2273220529068774,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002560119,0.0001032075,0.0001465511,0.0002551787,0.000247856,0.0001007569,0.0002712105,0.000035529,0.000004900237],"category_scores_gemma":[0.000006093446,0.00008730172,0.00002557449,0.001342145,0.00009449319,0.0006161488,0.00003534124,0.0002136753,0.000001230389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001540474,"about_ca_system_score_gemma":0.00003736317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000533412,"about_ca_topic_score_gemma":0.0000433715,"domain_scores_codex":[0.9992619,0.00003893203,0.0001714671,0.0002199713,0.0001728621,0.0001348739],"domain_scores_gemma":[0.9994421,0.00004034988,0.0001604286,0.0001781924,0.0001353456,0.00004361213],"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":[0.0000386018,0.00005851359,0.01038782,0.0000705486,0.0001948695,0.000001670408,0.003565324,0.4550372,0.2708701,0.0001449398,0.0001488205,0.2594816],"study_design_scores_gemma":[0.0001298804,0.00004256391,0.01363007,0.00004043547,0.00006500636,0.000009103604,0.000006884637,0.9791645,0.006767645,0.000003608836,0.00002469755,0.0001156254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.570935,0.00002105889,0.4283428,0.0005604248,0.00002464449,0.00003932397,1.563059e-7,0.00003656125,0.00004006793],"genre_scores_gemma":[0.9730106,0.00001321733,0.0265597,0.0003660418,0.00002110146,0.000007583955,0.000002236996,0.000006025673,0.00001347664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5241272,"threshold_uncertainty_score":0.3560061,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2025445694","doi":"10.1109/lsp.2010.2091405","title":"Wideband Spectrum Sensing for Cognitive Radios With Correlated Subband Occupancy","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":47,"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":"Wideband; Cognitive radio; Estimator; Detector; Statistic; Algorithm; Energy (signal processing); Maximum a posteriori estimation; Mathematics; Binary number; Statistics; Computer science; Telecommunications; Wireless; Electronic engineering; Engineering; Maximum likelihood","retraction":null,"screen_n_in":null,"score":{"opus":0.009559461532204896,"gpt":0.2246553620021963,"spread":0.2150959004699914,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003234466,0.000380533,0.000373327,0.0001979902,0.0006353841,0.0006771619,0.0003210394,0.0001196203,0.000007020997],"category_scores_gemma":[0.00003137089,0.0003304298,0.0001082265,0.0006081703,0.0002582269,0.0007313524,0.00003218399,0.0006452602,0.000008068872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004725003,"about_ca_system_score_gemma":0.0001758991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002297389,"about_ca_topic_score_gemma":0.00007143965,"domain_scores_codex":[0.9976683,0.00005490662,0.0003220557,0.0008047202,0.0003653953,0.000784685],"domain_scores_gemma":[0.9986987,0.0004220275,0.0002478992,0.0002373982,0.0002055364,0.0001884328],"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":[0.0008116379,0.0001488639,0.002180991,0.0001673341,0.0002565815,0.0007956399,0.002819325,0.0009855047,0.4580619,0.0003793399,0.00223898,0.5311539],"study_design_scores_gemma":[0.005877448,0.0006141291,0.002142983,0.001296483,0.0002497452,0.001875493,0.000114312,0.8288537,0.1542381,0.001808864,0.0008918509,0.00203688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3317091,0.00007229359,0.665317,0.001703063,0.0004272053,0.0003355005,0.000003544313,0.0002065387,0.0002257037],"genre_scores_gemma":[0.9784725,0.000002190103,0.01821855,0.002534639,0.0006675634,0.000006633919,0.000007958703,0.00005185094,0.00003806439],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8278682,"threshold_uncertainty_score":0.9999148,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2087364536","doi":"10.1109/lsp.2014.2362932","title":"Constant Modulus Blind Adaptive Beamforming Based on Unscented Kalman Filtering","year":2014,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":46,"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":"Kalman filter; Constant (computer programming); Algorithm; Beamforming; Control theory (sociology); Noise (video); Extended Kalman filter; Adaptive filter; Covariance matrix; Covariance; Mathematics; Computer science; Artificial intelligence; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.02240396972066053,"gpt":0.249937865640121,"spread":0.2275338959194605,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005953068,0.0002525371,0.0002186159,0.000297923,0.0002833178,0.0003689314,0.0006570705,0.00007705409,0.000006304204],"category_scores_gemma":[0.00002163575,0.0002432068,0.00007674408,0.0004128205,0.0001088502,0.0006671151,0.00005313879,0.000325126,0.0000197932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009283016,"about_ca_system_score_gemma":0.00009550001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001284281,"about_ca_topic_score_gemma":0.00000236461,"domain_scores_codex":[0.9981039,0.0001443571,0.0003209061,0.0005345598,0.000497863,0.0003983992],"domain_scores_gemma":[0.9990919,0.000114593,0.0002208563,0.0003603527,0.00009714245,0.0001152051],"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":[0.0003195196,0.0003347787,0.0001439643,0.0001664172,0.00004453797,0.00006879195,0.003444772,0.1657649,0.5385188,0.007154695,0.003866998,0.2801718],"study_design_scores_gemma":[0.0005464898,0.0001797425,0.0000307101,0.0002540233,0.000005816317,0.000006720419,0.00001267882,0.8996539,0.09777486,0.0004398962,0.0007842994,0.0003109168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01882348,0.000008638276,0.9758937,0.002791276,0.0001428248,0.0002010443,0.000001548327,0.0006251645,0.001512346],"genre_scores_gemma":[0.8945866,2.862896e-7,0.09134289,0.01390731,0.00009449536,0.00002324369,0.000002827467,0.00002345438,0.0000188544],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8845508,"threshold_uncertainty_score":0.9917687,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3114119488","doi":"10.1109/lsp.2020.3044775","title":"Time Difference of Arrival Estimation Based on a Kronecker Product Decomposition","year":2020,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":45,"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 Stem Cell and Translational Research","keywords":"Multilateration; Impulse response; Reverberation; Kronecker delta; Computer science; Impulse (physics); Kronecker product; Algorithm; Beamforming; Finite impulse response; Mathematics; Acoustics; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.01576175296495566,"gpt":0.2425808381854463,"spread":0.2268190852204907,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001485923,0.0001896082,0.0002122018,0.0001049485,0.0001427367,0.0001843788,0.0004984802,0.00003571632,0.00001354203],"category_scores_gemma":[0.00003462316,0.0001750467,0.0000592828,0.0004758729,0.00009165781,0.0004944847,0.00002973046,0.0001734651,0.00005249813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003681673,"about_ca_system_score_gemma":0.0001247052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001229329,"about_ca_topic_score_gemma":5.24658e-8,"domain_scores_codex":[0.9984252,0.00005784283,0.0002832374,0.0004976884,0.0004771589,0.0002588892],"domain_scores_gemma":[0.9993223,0.00005472847,0.000240834,0.0001886763,0.00008164007,0.0001117981],"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":[0.00003996615,0.00005065616,0.00005590127,0.0001283014,0.000004641362,0.00001013476,0.0003883514,0.05113801,0.7814484,0.000002146865,0.0003027732,0.1664307],"study_design_scores_gemma":[0.0002064265,0.00008975081,0.0002297826,0.0001719172,0.000006795693,0.000002909796,0.00000179294,0.5716787,0.4274101,0.00005452546,0.0000104798,0.0001368506],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1952925,0.00002991274,0.7939579,0.01029468,0.00005218668,0.0001166734,0.000001541333,0.0001358683,0.0001187462],"genre_scores_gemma":[0.8981404,1.929292e-7,0.09585451,0.00585463,0.0001205761,0.000006925402,0.000004849605,0.00001311673,0.000004781941],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.702848,"threshold_uncertainty_score":0.7138196,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3028989134","doi":"10.1109/lsp.2020.3043990","title":"Multi-Volumetric Refocusing of Light Fields","year":2020,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary; University of Victoria","funders":"","keywords":"Finite impulse response; Filter (signal processing); Computer science; Reduction (mathematics); Filter design; Algorithm; Planar; Light field; Computer vision; Artificial intelligence; Mathematics; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.03168523418857924,"gpt":0.2721402795135794,"spread":0.2404550453250002,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001047361,0.0001285965,0.0001792765,0.0001346202,0.0001163874,0.000116672,0.0005955765,0.00003578883,0.000007754154],"category_scores_gemma":[0.00003822628,0.0001182666,0.00006293474,0.0009544555,0.00004294325,0.0007502819,0.00009128588,0.0002033736,0.00001683072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002005923,"about_ca_system_score_gemma":0.00004262635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004190572,"about_ca_topic_score_gemma":1.573584e-7,"domain_scores_codex":[0.9988306,0.00003274083,0.0002654021,0.0003505036,0.000281454,0.0002393615],"domain_scores_gemma":[0.9994529,0.00003377114,0.0001639176,0.0001671918,0.00007164043,0.0001106074],"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":[0.000006725108,0.00002549636,0.0001379575,0.00008399571,0.000005871703,0.0000252817,0.00122983,0.001304325,0.6671295,0.00001101101,0.001378573,0.3286614],"study_design_scores_gemma":[0.0004842885,0.00006020008,0.0001750312,0.0001379329,0.000007314281,0.00000881153,0.00003600724,0.7896266,0.2067457,0.00005171143,0.002389496,0.0002768937],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007578756,0.0002813314,0.9783386,0.01334275,0.0001186939,0.00005588053,3.418194e-7,0.0001413615,0.0001423078],"genre_scores_gemma":[0.7581096,0.000002097872,0.2317977,0.009979642,0.00008634844,0.000001327805,2.077328e-7,0.000009635047,0.00001343173],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7883223,"threshold_uncertainty_score":0.4822772,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2102842504","doi":"10.1109/lsp.2004.827917","title":"Exact Fractional-Order Differentiators for Polynomial Signals","year":2004,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Control Systems Design","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Differentiator; Mathematics; Fractional calculus; Polynomial; Integer (computer science); Applied mathematics; Impulse response; Impulse (physics); Mathematical analysis; Computer science; Bandwidth (computing)","retraction":null,"screen_n_in":null,"score":{"opus":0.01104053293707896,"gpt":0.2291901033403009,"spread":0.2181495704032219,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001217091,0.0003147483,0.0003159602,0.0001513282,0.0001875052,0.0001245444,0.0001955493,0.0001142707,0.00002784385],"category_scores_gemma":[0.00001511388,0.0003208785,0.0001265248,0.0002026434,0.00005241224,0.0004623422,0.000006708219,0.0002307653,0.00003897114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002692865,"about_ca_system_score_gemma":0.0000616437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009875868,"about_ca_topic_score_gemma":0.000002311687,"domain_scores_codex":[0.9984834,0.00002032961,0.0003761328,0.0003218353,0.0002849648,0.0005133873],"domain_scores_gemma":[0.9994474,0.0001105493,0.0001031297,0.0001443103,0.00007531893,0.0001192732],"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":[0.00004013736,0.00001871185,0.00002181284,0.0001326897,0.00006200255,0.000006406556,0.0001018487,0.4620812,0.5298922,0.000008580704,0.001789107,0.005845262],"study_design_scores_gemma":[0.01920004,0.0004520006,0.001189839,0.00157309,0.0005215183,0.0001726682,0.0002769388,0.3205456,0.6182477,0.004531921,0.02801575,0.005272881],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08776668,0.0003500209,0.9097133,0.0004819736,0.0006354304,0.000403477,0.0000136015,0.0005062138,0.000129261],"genre_scores_gemma":[0.9932449,0.000002109723,0.004546234,0.0008221149,0.001050673,0.0001699489,0.000009871427,0.00011369,0.00004040946],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9054782,"threshold_uncertainty_score":0.9999243,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2152487627","doi":"10.1109/lsp.2005.847859","title":"Analytically derived TOA-DOA distributions of uplink/downlink wireless-cellular multipaths arisen from scatterers with an inverted-parabolic spatial distribution around the mobile","year":2005,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Telecommunications link; Omnidirectional antenna; Azimuth; Base station; Computer science; Physics; Telecommunications; Antenna (radio); Optics","retraction":null,"screen_n_in":null,"score":{"opus":0.01316436813286531,"gpt":0.22122602214297,"spread":0.2080616540101047,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001846094,0.0003234425,0.0003235091,0.00007422466,0.0002672236,0.0001541141,0.0003058296,0.0001238611,0.00003288721],"category_scores_gemma":[0.000007865883,0.0002552299,0.00009744867,0.0002793913,0.0002482502,0.0003983096,0.00002200124,0.0004037145,0.00001558247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001596212,"about_ca_system_score_gemma":0.00006883391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001628166,"about_ca_topic_score_gemma":0.00006970826,"domain_scores_codex":[0.998257,0.00006615781,0.0005258434,0.0003819751,0.0003542398,0.0004147436],"domain_scores_gemma":[0.9991497,0.0000653083,0.0001598345,0.0003170724,0.0001484046,0.0001596197],"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":[0.00005140368,0.00007503742,0.0002299212,0.00004538609,0.00007716894,0.00000416387,0.0004685274,0.2406856,0.7235231,0.000002013139,0.0001058031,0.03473187],"study_design_scores_gemma":[0.0004932184,0.00004781697,0.0002642164,0.00009515644,0.0001099968,0.000003363667,0.00009648917,0.7432666,0.2552378,0.00002495712,0.0000833752,0.0002769446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4957886,0.00007972055,0.503424,0.0002941436,0.00004191476,0.0001754444,0.00008318009,0.0001065744,0.000006413859],"genre_scores_gemma":[0.9952624,0.00001112964,0.003108619,0.00042752,0.0003920079,0.00005812976,0.0006875332,0.00004982397,0.000002816128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.502581,"threshold_uncertainty_score":0.99999,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2158386631","doi":"10.1109/lsp.2008.2008482","title":"A Robust Adaptive Dimension Reduction Technique With Application to Array Processing","year":2008,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":42,"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":"Robustness (evolution); Orthogonality; Algorithm; Computer science; Preprocessor; Adaptive filter; Dimension (graph theory); Matrix (chemical analysis); Reduction (mathematics); Signal processing; Noise reduction; Dimensionality reduction; Mathematics; Digital signal processing; Artificial intelligence; Computer hardware","retraction":null,"screen_n_in":null,"score":{"opus":0.02274200159910228,"gpt":0.2385570505773691,"spread":0.2158150489782668,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002710734,0.0002183069,0.0002188433,0.0003552247,0.0003436169,0.00008382694,0.0004327714,0.00007448327,0.00000128361],"category_scores_gemma":[0.00001011204,0.0001991543,0.000039345,0.001272054,0.0001535422,0.001214453,0.00003315853,0.0001933686,0.000007028837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001435245,"about_ca_system_score_gemma":0.00017578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003695132,"about_ca_topic_score_gemma":0.000001046987,"domain_scores_codex":[0.9982678,0.00005497219,0.0003405163,0.000573605,0.0005081502,0.0002550055],"domain_scores_gemma":[0.9988775,0.00002201614,0.0003504592,0.0002942442,0.0003587574,0.0000970571],"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":[0.0000446105,0.00006262967,0.00003181799,0.00006194392,0.000005670444,0.000004748191,0.001139428,0.01354625,0.9023055,0.00005704265,0.0004113936,0.08232893],"study_design_scores_gemma":[0.0001332467,0.0002150671,0.0001577025,0.0004352903,0.00001051528,0.0002451987,0.00002837862,0.04261817,0.9554592,0.0002837715,0.00009257383,0.0003209314],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01947244,0.00002187319,0.9777732,0.0009981372,0.00004514188,0.0006596532,6.171828e-7,0.0007751046,0.0002537882],"genre_scores_gemma":[0.5986897,6.973918e-7,0.4007717,0.0002562435,0.00004673245,0.0002059833,0.000001211982,0.00001742043,0.00001032269],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5792172,"threshold_uncertainty_score":0.8121278,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2107363208","doi":"10.1109/lsp.2004.842286","title":"Optimal step size of the adaptive multichannel LMS algorithm for blind SIMO identification","year":2005,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":42,"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","funders":"","keywords":"Algorithm; Computer science; Adaptive filter; System identification; Adaptive system; Norm (philosophy); Least mean squares filter; Computational complexity theory; Adaptive algorithm; Control theory (sociology); Mathematics; Artificial intelligence; Data modeling","retraction":null,"screen_n_in":null,"score":{"opus":0.02324323567497061,"gpt":0.2721660751831672,"spread":0.2489228395081966,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005287341,0.0001502904,0.0001516107,0.0000813412,0.0002057373,0.0001951739,0.000833324,0.00007144012,0.000002294776],"category_scores_gemma":[0.00003029614,0.0001222414,0.0001038598,0.0003335477,0.0001212584,0.0007803983,0.00006090892,0.000158956,0.000004511942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005414398,"about_ca_system_score_gemma":0.00009342777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000685717,"about_ca_topic_score_gemma":0.00000210361,"domain_scores_codex":[0.998598,0.00008330198,0.0003731066,0.0003537799,0.0003663999,0.0002254532],"domain_scores_gemma":[0.9988703,0.0001849478,0.000367243,0.0002965622,0.0002383366,0.00004262336],"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":[0.00004497175,0.0001854876,0.000007901472,0.00004977329,0.00003814109,9.729209e-7,0.005310819,0.04200837,0.2754182,0.0003629294,0.003514346,0.6730581],"study_design_scores_gemma":[0.0003644673,0.00003860294,0.00006472701,0.00004147034,0.00001109316,0.000002593687,0.00004679745,0.7444052,0.254428,0.0001326964,0.0003333685,0.0001309431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02025714,0.00004099024,0.9745269,0.004411886,0.0001139963,0.0004691711,0.000006937612,0.0001454729,0.0000274477],"genre_scores_gemma":[0.6184543,4.996989e-7,0.3796164,0.001675992,0.0001197769,0.00005147838,8.985422e-7,0.00001151366,0.00006912674],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7023969,"threshold_uncertainty_score":0.4984862,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2014316832","doi":"10.1109/lsp.2007.913136","title":"New Design of Robust $H_{\\infty}$ Filters for 2-D Systems","year":2008,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Stability and Control of Uncertain Systems","field":"Engineering","cited_by":41,"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":"Filtering theory; Mathematics; Applied mathematics; Matrix (chemical analysis); Matrix algebra; Linear matrix inequality; Computer science; Algorithm; Control theory (sociology); Mathematical optimization; Artificial intelligence; Eigenvalues and eigenvectors; Control (management)","retraction":null,"screen_n_in":null,"score":{"opus":0.04310504232231796,"gpt":0.2107964113959364,"spread":0.1676913690736185,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000261927,0.0002312748,0.0004002184,0.0001106122,0.0001201718,0.00004899483,0.000248393,0.00009594152,0.000008856388],"category_scores_gemma":[0.00001618109,0.0002277629,0.0001170035,0.0001834096,0.00007581307,0.0002428158,0.000005354452,0.0001236303,0.000006507315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009022715,"about_ca_system_score_gemma":0.00009806221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000626607,"about_ca_topic_score_gemma":9.275072e-7,"domain_scores_codex":[0.9985947,0.00004298789,0.0004738096,0.0002367843,0.0002768018,0.00037491],"domain_scores_gemma":[0.9992903,0.0002252742,0.0001131268,0.0001798656,0.00008088002,0.0001105317],"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":[0.0000648957,0.000009485449,0.00007275533,0.0005658139,0.00005387186,0.000006479834,0.0006332681,0.8472734,0.131593,0.000005676334,0.01653372,0.003187683],"study_design_scores_gemma":[0.001967415,0.0001614049,0.00009725506,0.0005662285,0.00007909458,0.00006678021,0.0002552214,0.9762788,0.01741862,0.00004097,0.002404978,0.0006632619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02221498,0.001156154,0.974991,0.0001664922,0.000555916,0.0005529057,0.000009115827,0.0002298043,0.0001236322],"genre_scores_gemma":[0.9908794,0.000005613729,0.008218978,0.0001801617,0.0004932915,0.00007998063,0.00000324655,0.0000531377,0.00008616043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9686645,"threshold_uncertainty_score":0.9287903,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2129688096","doi":"10.1109/lsp.2011.2140396","title":"Enhanced Seam Carving via Integration of Energy Gradient Functionals","year":2011,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Michigan State University","keywords":"Seam carving; Retargeting; Maxima and minima; Computer science; Energy (signal processing); Carving; Artificial intelligence; Computer vision; Process (computing); Image (mathematics); Pattern recognition (psychology); Mathematics; Engineering; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.03306421402048659,"gpt":0.2460750728747452,"spread":0.2130108588542586,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001886206,0.0001261683,0.0001300722,0.0001799538,0.0001383978,0.00005460975,0.0002842911,0.00004485237,0.00002760732],"category_scores_gemma":[0.000005656599,0.0001153242,0.00007846978,0.000403579,0.00005533947,0.0006378631,0.00002705363,0.00009119032,0.000009510678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004663496,"about_ca_system_score_gemma":0.00003228857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007682011,"about_ca_topic_score_gemma":0.000009590154,"domain_scores_codex":[0.9988456,0.00006222039,0.000290354,0.0003031754,0.0003089033,0.0001897544],"domain_scores_gemma":[0.9994338,0.00001596806,0.0002018933,0.0001485823,0.0001430282,0.0000567709],"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":[0.00001242273,0.00005594234,0.00003105589,0.00001927433,0.000009000325,0.000001790141,0.0009445404,0.0001787454,0.8524025,0.000671398,0.0001039984,0.1455693],"study_design_scores_gemma":[0.0001909495,0.0001285992,0.001434979,0.00008801663,0.00001065462,0.00001286251,0.00005041731,0.06080058,0.9356111,0.001401451,0.00006892607,0.0002014431],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1430483,0.00002554298,0.8556161,0.0001358973,0.00042769,0.0000405523,3.343442e-7,0.0001045478,0.0006010277],"genre_scores_gemma":[0.9935668,0.000001536212,0.005332035,0.0009479196,0.0000766993,0.00001517976,0.000001788827,0.00000855808,0.0000495143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8505185,"threshold_uncertainty_score":0.4702785,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2145473440","doi":"10.1109/lsp.2009.2036879","title":"Motion Vector Outlier Rejection Cascade for Global Motion Estimation","year":2009,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":40,"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":"Outlier; Cascade; Computer science; Artificial intelligence; Motion vector; Anomaly detection; Motion estimation; Noise (video); Pattern recognition (psychology); Computer vision; Image (mathematics); Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01720391461006942,"gpt":0.2899850123693512,"spread":0.2727810977592818,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001955021,0.0001548879,0.000123374,0.00008636272,0.000300681,0.0002922033,0.0002388749,0.00005209963,0.000001668012],"category_scores_gemma":[0.00002976933,0.0001540021,0.00006572182,0.0003669554,0.00002614414,0.001717841,0.00001209388,0.0001073015,0.00001162397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002028422,"about_ca_system_score_gemma":0.00002898534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003270137,"about_ca_topic_score_gemma":3.988463e-7,"domain_scores_codex":[0.9987611,0.00003366028,0.0002349476,0.0004138161,0.0002717602,0.0002847832],"domain_scores_gemma":[0.9994953,0.00002057968,0.0001607805,0.0001560357,0.0000989439,0.00006835103],"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":[0.00001305583,0.00003174038,0.00002075501,0.00001973041,0.000002256167,0.00000207983,0.0001447676,0.01609536,0.06186247,0.0002597073,0.0006523738,0.9208957],"study_design_scores_gemma":[0.0004032237,0.0000759695,0.002159347,0.00007212273,0.000008832676,0.00002567515,0.000008490118,0.9759868,0.01677523,0.003999071,0.0002813408,0.0002038757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007463215,0.00004113058,0.9848535,0.006640092,0.0003899354,0.0002149085,0.000001381551,0.0003197571,0.00007607407],"genre_scores_gemma":[0.8359646,6.656829e-7,0.1601933,0.003604935,0.0001889673,0.00001023659,0.000005950201,0.00000732217,0.0000240839],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9598914,"threshold_uncertainty_score":0.6280022,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2116394319","doi":"10.1109/lsp.2004.842263","title":"Approximate ML detection for MIMO systems using multistage sphere decoding","year":2005,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":39,"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":"Quadrature amplitude modulation; MIMO; Decoding methods; Constellation diagram; Algorithm; Signal-to-noise ratio (imaging); Computer science; Constellation; Generalization; QAM; SIGNAL (programming language); Mathematics; Telecommunications; Bit error rate; Physics; Channel (broadcasting)","retraction":null,"screen_n_in":null,"score":{"opus":0.02536225704379829,"gpt":0.26590775703556,"spread":0.2405454999917617,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000172685,0.000219397,0.0002077273,0.0001115632,0.0002579428,0.0001462979,0.0002412655,0.00009333572,0.000003361599],"category_scores_gemma":[0.000008325372,0.0002488465,0.00006060861,0.0001712354,0.00004766307,0.0005769915,0.00001642428,0.0002126686,0.000003839058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003091017,"about_ca_system_score_gemma":0.00001267957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001079761,"about_ca_topic_score_gemma":0.000007428936,"domain_scores_codex":[0.9989154,0.00002572304,0.000349777,0.000223198,0.0001453408,0.0003405458],"domain_scores_gemma":[0.9994652,0.0000629329,0.0001225124,0.0002300905,0.00006204989,0.00005721532],"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":[0.000006120779,0.000006025742,0.000006611651,0.0002732057,0.000008619629,6.711875e-7,0.00009399738,0.3621804,0.5477515,0.000004512754,0.00006592155,0.0896024],"study_design_scores_gemma":[0.0001728937,0.000006779414,0.000002242282,0.0001826363,0.00001196972,0.0000100308,0.00004878473,0.7169792,0.2809604,0.00001539051,0.001386523,0.0002231487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1146101,0.0006770094,0.8829609,0.00004772347,0.0001292801,0.0003689141,0.000006173063,0.001130757,0.00006910467],"genre_scores_gemma":[0.8630276,0.00001193083,0.1363767,0.0001119527,0.0002470895,0.0001183724,0.000004367509,0.00008980044,0.00001210793],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7484176,"threshold_uncertainty_score":0.9999964,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2165644958","doi":"10.1109/lsp.2009.2017477","title":"Snake Validation: A PCA-Based Outlier Detection Method","year":2009,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":39,"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":"Initialization; Principal component analysis; Artificial intelligence; Outlier; Pattern recognition (psychology); Anomaly detection; Computer science; Object detection; Computer vision; Object (grammar); Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01507572033221373,"gpt":0.2664874165739154,"spread":0.2514116962417017,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002100962,0.0002018027,0.0001526652,0.0001435731,0.0002146892,0.0002012271,0.0001812683,0.00008562581,0.00002046486],"category_scores_gemma":[0.000005252049,0.000209046,0.00006617769,0.0004013306,0.00003293276,0.0002778443,0.000004018572,0.0002455387,0.00002136178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008322009,"about_ca_system_score_gemma":0.00002624721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002471684,"about_ca_topic_score_gemma":4.097024e-7,"domain_scores_codex":[0.9989894,0.00002399716,0.0002466002,0.0002532044,0.0002055266,0.0002812397],"domain_scores_gemma":[0.9996105,0.00002514434,0.00006443403,0.0001689007,0.00006585533,0.00006518697],"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":[0.00000560885,0.00001993544,0.000003895438,0.00005695012,0.000004588413,0.00000284669,0.00006426488,0.03220024,0.6108118,0.000005461525,0.001240283,0.3555841],"study_design_scores_gemma":[0.0001880233,0.00002683395,0.00005861923,0.0000688837,0.00002784239,0.00001069412,0.000006165239,0.3064854,0.6888447,0.0006461416,0.003351038,0.0002856427],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009723892,0.00008898663,0.9860981,0.001684121,0.00006783671,0.000155947,0.000002266211,0.0014574,0.0007214423],"genre_scores_gemma":[0.8840686,0.000001216985,0.1133813,0.002225575,0.0002013796,0.00005409289,0.000007483606,0.00003638629,0.00002391686],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8743447,"threshold_uncertainty_score":0.8524649,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2420110223","doi":"10.1109/lsp.2016.2572666","title":"Fold-based Kolmogorov–Smirnov Modulation Classifier","year":2016,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Rail Traffic Control and Safety; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Classifier (UML); Computer science; Pattern recognition (psychology); Robustness (evolution); Artificial intelligence; Speech recognition; Phase-shift keying; Machine learning; Algorithm; Bit error rate; Decoding methods","retraction":null,"screen_n_in":null,"score":{"opus":0.02862253564090129,"gpt":0.2433321534024782,"spread":0.2147096177615769,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003695723,0.0002740521,0.0002088889,0.0002903141,0.0002759222,0.0003187201,0.0007763911,0.0001210371,0.00003557097],"category_scores_gemma":[0.00002908612,0.0002109493,0.0001027157,0.0005946872,0.0001381665,0.001687726,0.00004036479,0.0001406972,0.0001677717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002462823,"about_ca_system_score_gemma":0.0001774027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006707519,"about_ca_topic_score_gemma":0.000001670927,"domain_scores_codex":[0.9974819,0.0001385937,0.0004538194,0.0007576562,0.0007105141,0.0004574974],"domain_scores_gemma":[0.9985762,0.0002024672,0.0003436466,0.0005150574,0.0002172175,0.0001453884],"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":[0.00001468787,0.00004428017,0.0009266633,0.00002463522,0.000007959101,0.000006453535,0.00009165952,0.004580793,0.7337713,0.0006071875,0.001279883,0.2586445],"study_design_scores_gemma":[0.001196751,0.00006770626,0.02054593,0.0002964482,0.00001480801,0.000009463861,0.000005954939,0.8341608,0.1393932,0.002124389,0.001488491,0.0006960665],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07228924,0.00001982138,0.9068348,0.01966308,0.0002998307,0.0001933224,0.000002478948,0.0004640838,0.0002333654],"genre_scores_gemma":[0.9778569,5.451974e-7,0.01868862,0.002933423,0.0002755161,0.00005158715,0.000003778794,0.00003441732,0.0001552294],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9055676,"threshold_uncertainty_score":0.8602262,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3127483027","doi":"10.1109/lsp.2021.3055748","title":"Robust Minimum Error Entropy Based Cubature Information Filter With Non-Gaussian Measurement Noise","year":2021,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Outlier; Entropy (arrow of time); Gaussian; Linearization; Entropy estimation; Algorithm; Mathematics; Gaussian noise; Computer science; Filter (signal processing); Noise measurement; Mathematical optimization; Statistics; Artificial intelligence; Noise reduction; Nonlinear system; Estimator","retraction":null,"screen_n_in":null,"score":{"opus":0.02402433614496674,"gpt":0.2108463214194965,"spread":0.1868219852745298,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003078985,0.0002798994,0.0002209008,0.000137021,0.0003340487,0.0008774252,0.0005935431,0.0001057456,0.00004305336],"category_scores_gemma":[0.00001664423,0.0002282553,0.00007440845,0.0005874023,0.00006950489,0.001582335,0.00005942009,0.0003955273,0.00004654955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009541414,"about_ca_system_score_gemma":0.0003046112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007799221,"about_ca_topic_score_gemma":0.00000491636,"domain_scores_codex":[0.997582,0.00008150764,0.0003596882,0.0004634909,0.001030842,0.000482451],"domain_scores_gemma":[0.9987097,0.00003919127,0.0002273476,0.0004916093,0.0003694154,0.0001627646],"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":[0.0002835665,0.0003619445,0.0009191644,0.0007056682,0.0001124191,0.000625405,0.003244562,0.514899,0.1742675,0.0001256441,0.2024946,0.1019605],"study_design_scores_gemma":[0.001984783,0.000123287,0.001280019,0.001002301,0.00005548471,0.00009079199,0.0001043581,0.9308003,0.04507311,0.0000318377,0.01856406,0.000889648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009575807,0.00007246373,0.9801257,0.009064525,0.0005585946,0.000139813,0.00000727819,0.0002093411,0.0002464409],"genre_scores_gemma":[0.8843392,0.000001361509,0.09960933,0.01566512,0.0002672165,0.0000209241,0.00005210932,0.00002003879,0.00002474886],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8805164,"threshold_uncertainty_score":0.9307981,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2145313093","doi":"10.1109/lsp.2009.2026119","title":"Robust Affine Invariant Region-Based Shape Descriptors: The ICA Zernike Moment Shape Descriptor and the Whitening Zernike Moment Shape Descriptor","year":2009,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":34,"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":"Zernike polynomials; Affine transformation; Artificial intelligence; Invariant (physics); Pattern recognition (psychology); Moment (physics); Mathematics; Velocity Moments; Feature extraction; Shape analysis (program analysis); Computer vision; Image retrieval; Computer science; Image (mathematics); Geometry; Wavefront; Optics; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.03754018332489837,"gpt":0.2249990465451662,"spread":0.1874588632202679,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001427742,0.0006441472,0.0005346302,0.0002565735,0.001292479,0.002083767,0.002111166,0.0001650632,0.00004869639],"category_scores_gemma":[0.00006395969,0.0004001178,0.0002259104,0.001029729,0.0006750763,0.001175976,0.0001807753,0.000712392,0.00001870983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003135584,"about_ca_system_score_gemma":0.0002617518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003804514,"about_ca_topic_score_gemma":0.000002259239,"domain_scores_codex":[0.9955661,0.0003993872,0.0008577395,0.001121572,0.001139941,0.0009152946],"domain_scores_gemma":[0.9977802,0.0002331581,0.0005996483,0.0008148192,0.0003130383,0.0002591031],"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":[0.000603704,0.0005061885,0.0003376943,0.0002204424,0.0001324255,0.0001569643,0.004905586,0.0008702301,0.4246081,0.002958397,0.02733899,0.5373613],"study_design_scores_gemma":[0.002259294,0.0003620382,0.001262613,0.0006291249,0.0001239877,0.0001184181,0.0002423013,0.9442904,0.04006516,0.0007372738,0.008856772,0.001052624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03051371,0.001149906,0.8725867,0.09356646,0.0003434099,0.0009925374,0.000004016806,0.0006648791,0.0001784311],"genre_scores_gemma":[0.953704,0.00004370215,0.01638744,0.02898896,0.0004341837,0.0001654812,0.000007020465,0.00004972439,0.00021946],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9434202,"threshold_uncertainty_score":0.9998451,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2888468122","doi":"10.1109/lsp.2018.2865829","title":"Wasserstein-Distance-Based Gaussian Mixture Reduction","year":2018,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaussian; Reduction (mathematics); Divergence (linguistics); Mixture model; Computer science; Algorithm; Kullback–Leibler divergence; Exponential function; Moment (physics); Matching (statistics); Artificial intelligence; Mathematics; Pattern recognition (psychology); Statistics; Physics; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.01843390335571768,"gpt":0.2669182976932998,"spread":0.2484843943375821,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005879035,0.0002607744,0.0002210858,0.0002077718,0.0005328035,0.000620862,0.0008235229,0.0001021936,0.00002290326],"category_scores_gemma":[0.00001613808,0.0002377666,0.0001033458,0.0007975702,0.0002834881,0.0009411975,0.00003487948,0.0002864338,0.00007546745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001017192,"about_ca_system_score_gemma":0.0001706004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001257725,"about_ca_topic_score_gemma":0.000001752531,"domain_scores_codex":[0.9978368,0.0001988745,0.0002997607,0.0006481328,0.0004990295,0.0005174198],"domain_scores_gemma":[0.9990075,0.00004701256,0.0001877598,0.0004360897,0.000186559,0.0001351197],"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":[0.00007426494,0.00004714513,0.00002342738,0.00007139628,0.00001357576,0.0000664114,0.0007099571,0.0003766688,0.8708862,0.0001727264,0.01097465,0.1165836],"study_design_scores_gemma":[0.001359738,0.0002813707,0.0001685964,0.0004058395,0.00003907018,0.000115967,0.00004290505,0.1211671,0.8630024,0.002107671,0.01042497,0.0008843945],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01465878,0.0001350392,0.9768966,0.005909026,0.0008767357,0.0001149713,9.392357e-7,0.0003437866,0.001064113],"genre_scores_gemma":[0.8407284,5.434696e-7,0.1527779,0.005222065,0.00103681,0.00000942784,0.000002110035,0.00002515608,0.0001976139],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8260696,"threshold_uncertainty_score":0.9695839,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}