{"meta":{"query_hash":"69cdc10ee4ab","filters":{"venue":"IET Signal Processing"},"cohort_total":49,"direct_labels_cover":0,"predictions_cover":49,"exported":49,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/69cdc10ee4ab","api":"https://metacan.xera.ac/api/v1/cohort?venue=IET+Signal+Processing"},"results":[{"id":"W1491512592","doi":"10.1049/iet-spr.2010.0367","title":"Robust equalisation for inter symbol interference communication channels","year":2012,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; University of Victoria","funders":"","keywords":"Equaliser; Channel (broadcasting); Computer science; Constraint (computer-aided design); Intersymbol interference; Interference (communication); Communications system; Symbol (formal); Transmission (telecommunications); Bit error rate; Algorithm; Nyquist ISI criterion; Stability (learning theory); Control theory (sociology); Mathematics; Telecommunications; Artificial intelligence","score_opus":0.07592863168739125,"score_gpt":0.2993637370544824,"score_spread":0.22343510536709113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1491512592","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005885304,0.0017072953,0.990199,0.000088136956,0.00007011223,0.00026339796,0.000004024627,0.0007247991,0.0010578847],"genre_scores_gemma":[0.94962573,0.00006355978,0.049773145,0.0000620699,0.00009670763,0.00024758783,0.000048007325,0.00004552377,0.000037647416],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992608,0.00003911345,0.00027193894,0.00009818965,0.000082388826,0.0002475669],"domain_scores_gemma":[0.99931693,0.0001200459,0.000085978085,0.00030402243,0.000117111675,0.000055917346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031450248,0.00013994455,0.00013817483,0.00008726947,0.00013803014,0.00007017495,0.00035070188,0.00008261566,0.000025222731],"category_scores_gemma":[0.00002837722,0.00014814328,0.000036620488,0.00013219794,0.00004754371,0.0010142094,0.00006965481,0.0001875372,0.000010159567],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005303028,0.00015736834,0.00053285033,0.00095712085,0.000051023628,1.4077504e-7,0.013363098,0.043546654,0.063146174,0.004517858,0.0024241942,0.8712505],"study_design_scores_gemma":[0.00040269783,0.000059760274,0.00009588533,0.0009170847,0.000030950996,0.0000075396565,0.0012452091,0.74829304,0.2350918,0.008094445,0.0050863153,0.0006752853],"about_ca_topic_score_codex":0.0000022309587,"about_ca_topic_score_gemma":0.0000013938355,"teacher_disagreement_score":0.9437404,"about_ca_system_score_codex":0.000101220605,"about_ca_system_score_gemma":0.0000101126525,"threshold_uncertainty_score":0.6041108},"labels":[],"label_agreement":null},{"id":"W1946870626","doi":"10.1049/iet-spr.2014.0347","title":"Incremental algorithm for finding principal curves","year":2015,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Toronto Rehabilitation Institute","funders":"","keywords":"Algorithm; Dimensionality reduction; Principal component analysis; Data set; Set (abstract data type); Computer science; Representation (politics); Subspace topology; Principal (computer security); Curse of dimensionality; Sequence (biology); Mathematics; Pattern recognition (psychology); Artificial intelligence","score_opus":0.060690993363435954,"score_gpt":0.29223754994973955,"score_spread":0.2315465565863036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1946870626","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002248245,0.0012097806,0.9946332,0.00026240028,0.00008384799,0.00013155647,0.0000036231945,0.000120278666,0.0013070478],"genre_scores_gemma":[0.6470637,0.000004783067,0.35198158,0.0003664515,0.0002657426,0.000025053005,0.000009860795,0.000015533857,0.00026735335],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874365,0.000022705346,0.0002549887,0.00032268924,0.00032991156,0.00032605897],"domain_scores_gemma":[0.9993627,0.00003563746,0.00015654703,0.00013405942,0.0001729688,0.00013806422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006556765,0.00013238857,0.00018009804,0.00007007913,0.0002530302,0.00029772602,0.0004582823,0.00003786907,0.000011867212],"category_scores_gemma":[0.00003285205,0.00011829933,0.00007797147,0.0003379253,0.000033016935,0.0009068246,0.00019458939,0.00008898246,0.000009667093],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004809341,0.000034446126,0.00017239674,0.000085776606,0.000019244892,0.0000061806345,0.0006116594,0.00019829803,0.00048419027,0.0003897295,0.0010791941,0.9969141],"study_design_scores_gemma":[0.00035886292,0.00012260019,0.000044760014,0.00022095324,0.000020047113,0.000021067797,0.00018712704,0.99094397,0.0026170285,0.001143946,0.0040932107,0.00022642316],"about_ca_topic_score_codex":0.000014828591,"about_ca_topic_score_gemma":0.000002485444,"teacher_disagreement_score":0.99668765,"about_ca_system_score_codex":0.00005755943,"about_ca_system_score_gemma":0.00014518645,"threshold_uncertainty_score":0.48241067},"labels":[],"label_agreement":null},{"id":"W1994048562","doi":"10.1049/iet-spr.2011.0234","title":"Electroencephalogram signals classification for sleep-state decision – a Riemannian geometry approach","year":2012,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Metric (unit); Weighting; Pattern recognition (psychology); Artificial intelligence; Electroencephalography; Spectral density; Mathematics; Computer science; Feature (linguistics); SIGNAL (programming language); Statistics; Psychology","score_opus":0.0543687326056482,"score_gpt":0.31593944389252443,"score_spread":0.26157071128687626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994048562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34097517,0.0004270084,0.65702724,0.000120217665,0.00014875375,0.00040534916,0.000009473373,0.00017129628,0.0007155025],"genre_scores_gemma":[0.9770504,0.000006945086,0.021546766,0.0007068123,0.00036655422,0.000108652246,0.000009492158,0.000041226005,0.00016312745],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99759835,0.00010223631,0.00041904944,0.0006016474,0.0004453191,0.0008333842],"domain_scores_gemma":[0.99874437,0.00047408912,0.0002642437,0.00019955618,0.00011405965,0.00020369576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006814949,0.00026325454,0.00026325372,0.00023073656,0.00044700105,0.00035410633,0.00046521047,0.00010562732,0.000020807327],"category_scores_gemma":[0.00019324766,0.00022221598,0.00011609643,0.00067266496,0.00013136867,0.0010556706,0.000065792,0.0002467831,0.000023248956],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014176405,0.00035435642,0.0006553274,0.00014240453,0.000007760239,0.0000014658906,0.0010264051,0.00078318757,0.6289065,0.0002138351,0.0007552304,0.36701176],"study_design_scores_gemma":[0.0008410836,0.00044089628,0.0016108972,0.00017108051,0.000042231386,0.00011289043,0.00025188673,0.39481422,0.5898819,0.004645432,0.0064949975,0.00069247704],"about_ca_topic_score_codex":0.0000023275882,"about_ca_topic_score_gemma":3.3006057e-7,"teacher_disagreement_score":0.63607526,"about_ca_system_score_codex":0.00006547933,"about_ca_system_score_gemma":0.000052602296,"threshold_uncertainty_score":0.9061705},"labels":[],"label_agreement":null},{"id":"W1998992100","doi":"10.1049/iet-spr.2013.0019","title":"Application of the Mittag–Leffler expansion to sampling discontinuous signals","year":2013,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Control Systems and Identification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sampling (signal processing); Computer science; Applied mathematics; Mathematics; Algorithm; Filter (signal processing); Computer vision","score_opus":0.010766564008475449,"score_gpt":0.22598645808189108,"score_spread":0.21521989407341563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998992100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42519566,0.00046318313,0.57261026,0.00023062885,0.0001188358,0.0007006851,0.0000030332806,0.000113611975,0.0005640785],"genre_scores_gemma":[0.9990385,0.000001483684,0.00046770994,0.000036088903,0.00013428496,0.00014901126,0.0000028769612,0.000024518527,0.00014553427],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913776,0.000018318946,0.00032823783,0.00015770974,0.00019344373,0.0001645537],"domain_scores_gemma":[0.99951744,0.000028105229,0.00008937418,0.00019184046,0.0001243275,0.00004893584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015096775,0.00010962221,0.0001508427,0.00005390943,0.00009611892,0.000101156525,0.00017762999,0.000051547526,0.000028282646],"category_scores_gemma":[0.0000139753365,0.00008151711,0.000054583765,0.00024294856,0.000016182814,0.00029841036,0.000026183423,0.000080421894,0.000058187932],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019615218,0.000009477732,0.00032256707,0.0001028619,0.0000061906353,2.7873185e-8,0.0003635667,0.020978391,0.7738784,0.000014913921,0.00031502574,0.20400664],"study_design_scores_gemma":[0.0005970196,0.000045336077,0.05198164,0.0009232713,0.00006725579,0.000006178637,0.00087277376,0.73282135,0.20228265,0.002618165,0.0071072066,0.0006771782],"about_ca_topic_score_codex":0.00013717468,"about_ca_topic_score_gemma":0.000011092443,"teacher_disagreement_score":0.71184295,"about_ca_system_score_codex":0.00003245966,"about_ca_system_score_gemma":0.000014051987,"threshold_uncertainty_score":0.33241713},"labels":[],"label_agreement":null},{"id":"W2027471805","doi":"10.1049/iet-spr.2012.0315","title":"Modified student's <i>t</i> ‐hidden Markov model for pattern recognition and classification","year":2013,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Hidden Markov model; Computer science; Pattern recognition (psychology); Artificial intelligence; Speech recognition; Markov model; Markov chain; Machine learning","score_opus":0.04534824788077283,"score_gpt":0.2844448863382583,"score_spread":0.2390966384574855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027471805","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058548864,0.00008252242,0.93961966,0.0008386461,0.00003933757,0.00026194064,0.0000029545251,0.00013763858,0.00046843055],"genre_scores_gemma":[0.924599,0.00000439708,0.07451831,0.0004015389,0.00009817907,0.000114697556,0.00001429142,0.000012321366,0.00023729447],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902165,0.00003575572,0.00018594343,0.00035718558,0.00019019945,0.00020924014],"domain_scores_gemma":[0.9994789,0.00005410509,0.00012077953,0.00012447771,0.00014804622,0.00007368953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002397246,0.00012005848,0.00011252138,0.000059718266,0.00022219501,0.00055215356,0.00026152804,0.000055663993,0.0000063021703],"category_scores_gemma":[0.000011659039,0.000108833854,0.000029169094,0.000105435836,0.000024387908,0.0007813555,0.00006964413,0.00012703172,0.000021714137],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023836305,0.00003076427,0.00018747272,0.000056008717,0.0000040018567,3.1983075e-7,0.00077335624,0.00028181856,0.0015608035,0.000016540826,0.00024037542,0.99684614],"study_design_scores_gemma":[0.00031627476,0.000039612933,0.0014141613,0.00005049819,0.0000072083994,0.0000044580906,0.000047734193,0.99007267,0.00011251723,0.007760601,0.000018436971,0.00015584285],"about_ca_topic_score_codex":0.000019854291,"about_ca_topic_score_gemma":0.0000012147532,"teacher_disagreement_score":0.99669033,"about_ca_system_score_codex":0.000016296273,"about_ca_system_score_gemma":0.000035382713,"threshold_uncertainty_score":0.5324429},"labels":[],"label_agreement":null},{"id":"W2029176866","doi":"10.1049/iet-spr.2012.0192","title":"Compressive sensing‐based speech enhancement in non‐sparse noisy environments","year":2013,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Noise (video); Compressed sensing; Algorithm; Sparse approximation; Constraint (computer-aided design); Gaussian noise; Upper and lower bounds; Noise measurement; Additive white Gaussian noise; Speech recognition; White noise; Artificial intelligence; Mathematics; Noise reduction; Image (mathematics); Telecommunications","score_opus":0.013199191067251398,"score_gpt":0.23335122364706098,"score_spread":0.2201520325798096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029176866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16931961,0.0002697237,0.8270984,0.0006743911,0.000110852845,0.00037936438,6.818155e-7,0.000099586265,0.0020473828],"genre_scores_gemma":[0.8603352,0.0000038039138,0.13810895,0.0012000347,0.00008174333,0.000023331138,0.0000047428334,0.000021929061,0.0002202637],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99746597,0.00006383297,0.0004656598,0.00071896234,0.0005788735,0.0007067107],"domain_scores_gemma":[0.9990434,0.00006315119,0.00027713133,0.00036071197,0.00007328345,0.00018230469],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002666856,0.00032174122,0.0003180743,0.00021009835,0.00025108777,0.0006088433,0.00070909574,0.00010877097,0.00013782902],"category_scores_gemma":[0.00001635287,0.0003061021,0.00006487734,0.00052121235,0.00010069118,0.0014226186,0.00021262275,0.0003166681,0.00034521718],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011039986,0.0002067921,0.00074857264,0.0000958157,0.000008989712,0.00010672739,0.0006358342,0.0011994772,0.3162705,0.00000387991,0.0004442444,0.6802681],"study_design_scores_gemma":[0.00085517444,0.00007598152,0.0014788083,0.00045037683,0.000006569463,0.000017531109,0.000068833586,0.28202242,0.7129318,0.0011745264,0.00047387395,0.0004441094],"about_ca_topic_score_codex":0.0000768942,"about_ca_topic_score_gemma":0.000008238443,"teacher_disagreement_score":0.6910156,"about_ca_system_score_codex":0.00014455586,"about_ca_system_score_gemma":0.00016178587,"threshold_uncertainty_score":0.9999391},"labels":[],"label_agreement":null},{"id":"W2037094860","doi":"10.1049/iet-spr.2014.0120","title":"Instantaneous fundamental frequency estimation of non‐stationary periodic signals using non‐linear recursive filters","year":2015,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Harmonics; Fundamental frequency; SIGNAL (programming language); Kalman filter; Control theory (sociology); Extended Kalman filter; Computer science; Harmonic; Algorithm; Instantaneous phase; Mathematics; Filter (signal processing); Acoustics; Artificial intelligence; Engineering; Computer vision; Physics","score_opus":0.035272134597393826,"score_gpt":0.2965130527574068,"score_spread":0.261240918160013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037094860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35457575,0.00042170126,0.64393765,0.00011984892,0.000112615206,0.00015365568,0.000007463524,0.00006349603,0.0006078147],"genre_scores_gemma":[0.69064283,0.0000023126602,0.30905998,0.00015028595,0.000089849185,0.000005735645,0.000012533102,0.000017680939,0.000018775845],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997775,0.000059347494,0.00056391105,0.0004934941,0.0006920713,0.00041614176],"domain_scores_gemma":[0.99853486,0.00006599662,0.0005287208,0.0002203432,0.0004349678,0.00021510912],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004341411,0.00026553296,0.0003224856,0.0002451283,0.00032145332,0.00027513492,0.0005840918,0.000102260456,0.000022501237],"category_scores_gemma":[0.00006903364,0.00026467635,0.000076795244,0.0007783111,0.00016914254,0.0020233488,0.0001282388,0.00020798875,0.000018720168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013679353,0.000243361,0.0019064721,0.00058836164,0.000057775993,0.00035412182,0.01621666,0.11149933,0.515721,0.00006036898,0.00017680503,0.35303894],"study_design_scores_gemma":[0.0007769369,0.0003096802,0.0001338634,0.00070584135,0.000028922108,0.00028693525,0.0010768942,0.7694975,0.22111405,0.005614433,0.000018158189,0.0004367519],"about_ca_topic_score_codex":0.00003970086,"about_ca_topic_score_gemma":0.0000014337255,"teacher_disagreement_score":0.6579982,"about_ca_system_score_codex":0.00022068883,"about_ca_system_score_gemma":0.0011139354,"threshold_uncertainty_score":0.99998057},"labels":[],"label_agreement":null},{"id":"W2041785516","doi":"10.1049/iet-spr.2009.0222","title":"Optimal look-up table-based data hiding","year":2011,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Lookup table; Computer science; Robustness (evolution); Information hiding; Embedding; Algorithm; Distortion (music); Artificial intelligence; Telecommunications","score_opus":0.07718592920681396,"score_gpt":0.28451623171260065,"score_spread":0.2073303025057867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041785516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002306773,0.00031061677,0.9941354,0.000059531878,0.00010409207,0.000100553436,0.000005573243,0.0007891248,0.00218838],"genre_scores_gemma":[0.6113431,0.0000024706233,0.38839456,0.00016300006,0.000042900872,0.000007442593,0.00000829564,0.000011687754,0.000026582124],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984357,0.000044510103,0.00024424188,0.0006058228,0.00026297595,0.00040677493],"domain_scores_gemma":[0.998859,0.000036631915,0.00015107032,0.00077065243,0.000089371,0.00009327684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004277099,0.00019154305,0.00016867541,0.00014932739,0.0003369826,0.0002436961,0.0021788029,0.00007677195,0.00001772869],"category_scores_gemma":[0.0000124814,0.00017383364,0.000040251103,0.0005128785,0.000093823706,0.0022484318,0.00052619807,0.00020914826,0.000006747689],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021101494,0.00038842048,0.004077988,0.0004548778,0.00005655909,0.0002873874,0.0041097146,0.00088477397,0.019649567,0.008096494,0.002581911,0.9592013],"study_design_scores_gemma":[0.0006666469,0.00022226835,0.00020761247,0.00058033655,0.000032223525,0.00005269003,0.00007824943,0.70358264,0.27013078,0.017781667,0.0057219886,0.0009429003],"about_ca_topic_score_codex":0.000013965541,"about_ca_topic_score_gemma":7.728251e-7,"teacher_disagreement_score":0.9582584,"about_ca_system_score_codex":0.000017915081,"about_ca_system_score_gemma":0.0001328428,"threshold_uncertainty_score":0.70887303},"labels":[],"label_agreement":null},{"id":"W2048464858","doi":"10.1049/iet-spr.2011.0260","title":"Source enumeration in large arrays using moments of eigenvalues and relatively few samples","year":2012,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Eigenvalues and eigenvectors; Estimator; Mathematics; Linear subspace; Probability density function; Subspace topology; Gaussian; Noise (video); Applied mathematics; Algorithm; Covariance matrix; Gaussian noise; Enumeration; Statistics; Computer science; Mathematical analysis; Combinatorics; Artificial intelligence; Pure mathematics","score_opus":0.04890445795822267,"score_gpt":0.30865551413293024,"score_spread":0.25975105617470756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048464858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33814785,0.00043538498,0.66109973,0.00004843761,0.000012818948,0.00007746494,7.931949e-7,0.00005218122,0.00012530746],"genre_scores_gemma":[0.8934603,0.000005835869,0.10638578,0.00009212431,0.00002738739,0.000004401045,0.0000016915596,0.000007926172,0.000014602029],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901104,0.00011491904,0.00025918617,0.0001793953,0.00021424613,0.00022123248],"domain_scores_gemma":[0.9995567,0.000047882055,0.00018754722,0.00009521871,0.000062774954,0.000049843835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000754021,0.000101578524,0.00014451062,0.00015549177,0.00013425142,0.00010079239,0.00014779867,0.000063494736,0.000004328603],"category_scores_gemma":[0.000026419179,0.00010018335,0.000019337276,0.00030688205,0.000045452678,0.0016427039,0.00008706462,0.00011762281,0.000001040846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056240693,0.00085199316,0.35489687,0.0004994659,0.000048539638,0.0000027239926,0.16491508,0.0032387332,0.25076616,0.045702487,0.000085838714,0.17893586],"study_design_scores_gemma":[0.0009555632,0.00014882053,0.045640495,0.00064781826,0.00002295737,0.00002102348,0.0011564828,0.63225085,0.29255623,0.025213828,0.0006993868,0.0006865398],"about_ca_topic_score_codex":0.000028641018,"about_ca_topic_score_gemma":0.0000033521067,"teacher_disagreement_score":0.6290121,"about_ca_system_score_codex":0.000034130215,"about_ca_system_score_gemma":0.000057063604,"threshold_uncertainty_score":0.40853584},"labels":[],"label_agreement":null},{"id":"W2058994495","doi":"10.1049/iet-spr.2010.0196","title":"Joint complex diversity coding and channel coding over space, time and frequency","year":2011,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Decoding methods; Computer science; Algorithm; Coding (social sciences); Space–time code; Coding gain; Joint (building); Variable-length code; Diversity scheme; Theoretical computer science; Mathematics; Block code; Fading; Statistics; Engineering","score_opus":0.05386931931262866,"score_gpt":0.23819316153535922,"score_spread":0.18432384222273057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058994495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46393713,0.0033292868,0.5062959,0.000108602144,0.00005598851,0.0004916306,0.000028729777,0.0025476052,0.023205122],"genre_scores_gemma":[0.9798184,0.00016916957,0.019894613,0.00003752755,0.000020977419,0.0000051463408,0.000004378305,0.0000273533,0.000022427497],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993518,0.000018373263,0.00015800196,0.00016823666,0.00010593039,0.00019763135],"domain_scores_gemma":[0.9996589,0.000027819295,0.000062198626,0.00013061865,0.00004230055,0.00007820371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012947952,0.00015376134,0.00018381202,0.000093982155,0.00035390173,0.000045360168,0.00012953627,0.00006636869,0.00006122009],"category_scores_gemma":[0.0000088437855,0.0001680931,0.000018583101,0.00010965183,0.00011097881,0.00047939707,0.0002714778,0.00018554585,0.000003909278],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004931351,0.000109167675,0.011541513,0.0015171982,0.00012739227,0.00004564133,0.019832598,0.00071801816,0.8650049,0.0039030786,0.0011813552,0.09596984],"study_design_scores_gemma":[0.0012024128,0.00014829134,0.04805587,0.0013987009,0.00009299561,0.000084549145,0.000696625,0.7664318,0.14542276,0.034434363,0.00030753305,0.0017240852],"about_ca_topic_score_codex":0.000039192037,"about_ca_topic_score_gemma":0.000004241528,"teacher_disagreement_score":0.7657138,"about_ca_system_score_codex":0.000044645752,"about_ca_system_score_gemma":0.0000068265917,"threshold_uncertainty_score":0.6854638},"labels":[],"label_agreement":null},{"id":"W2081344000","doi":"10.1049/iet-spr.2011.0328","title":"Gaussian mixture model approximation of total spatial power spectral density for multiple incoherently distributed sources","year":2013,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Gaussian; Spectral density; Covariance; Mixture model; Algorithm; Gaussian process; Mixture distribution; Probability density function; Mathematics; Computer science; Pattern recognition (psychology); Statistics; Artificial intelligence; Physics","score_opus":0.011848888785694432,"score_gpt":0.23961381886696337,"score_spread":0.22776493008126894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081344000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1750408,0.000017015394,0.82407045,0.00011950441,0.0000387445,0.0004040836,0.00001555728,0.00017452704,0.00011931379],"genre_scores_gemma":[0.78018975,1.7419629e-7,0.21968782,0.000015903603,0.000023367134,0.000042750606,0.000018564444,0.000009273907,0.000012404389],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988104,0.00002980865,0.00038540576,0.00027417982,0.00029888735,0.00020129624],"domain_scores_gemma":[0.9989178,0.00006559003,0.00038396125,0.0001755937,0.00039375224,0.000063305684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017363417,0.00014974311,0.00022713852,0.00010141548,0.000121967765,0.00013306663,0.00029379138,0.00012607768,0.000012284068],"category_scores_gemma":[0.0000752443,0.00013741475,0.00008493573,0.00024978188,0.000073594776,0.0010116219,0.00007931042,0.00012889464,0.0000017896618],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021853957,0.0011711244,0.0063459435,0.0016641726,0.00010844598,0.0000021091857,0.0075326283,0.06665672,0.5630472,0.008021654,0.0016747103,0.34355676],"study_design_scores_gemma":[0.00018550942,0.00007112045,0.0012759794,0.00006861357,0.000006389117,0.0000031421619,0.000020161817,0.7137063,0.27000767,0.014541625,0.0000021470967,0.00011133725],"about_ca_topic_score_codex":0.00013815874,"about_ca_topic_score_gemma":0.000007810336,"teacher_disagreement_score":0.64704955,"about_ca_system_score_codex":0.000043856086,"about_ca_system_score_gemma":0.000107941174,"threshold_uncertainty_score":0.56036115},"labels":[],"label_agreement":null},{"id":"W2088652862","doi":"10.1049/iet-spr.2008.0203","title":"Least square identification of alias components of linear periodically time-varying systems and optimal training signal design","year":2010,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Alias; Control theory (sociology); Finite impulse response; Oversampling; Infinite impulse response; Mathematics; Algorithm; Computer science; Digital filter; Filter (signal processing); Telecommunications","score_opus":0.03735958056389142,"score_gpt":0.26243967094003434,"score_spread":0.2250800903761429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088652862","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32716987,0.00022071869,0.67205757,0.000004411907,0.000042748015,0.00019039183,0.000016689957,0.00023015689,0.00006746444],"genre_scores_gemma":[0.91736823,0.000004241282,0.08245502,0.0000021922026,0.00007169802,0.000019089686,0.000013427936,0.00005059077,0.00001550673],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864656,0.00003853777,0.00057273207,0.00023242447,0.0002779911,0.0002317489],"domain_scores_gemma":[0.9993084,0.00007914726,0.00023323286,0.0001334985,0.00016750007,0.00007820326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003991257,0.00020349548,0.0003405451,0.00013699433,0.00011473929,0.000059606926,0.0001886297,0.00012509599,0.000018930266],"category_scores_gemma":[0.00003050269,0.00021689206,0.0000431842,0.00015538589,0.00017811214,0.00037884637,0.000042207103,0.00029749764,0.0000024948565],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003492778,0.000019692972,0.000055953646,0.00054010487,0.000018085722,0.000003199389,0.0009291993,0.09973826,0.88903546,0.000019387524,0.0000056642575,0.0096000675],"study_design_scores_gemma":[0.00020649306,0.00006734795,0.0002558865,0.00051890104,0.000022187498,0.000028189288,0.00013204478,0.76946324,0.22900715,0.00006282814,0.000031906242,0.0002038091],"about_ca_topic_score_codex":0.000006109143,"about_ca_topic_score_gemma":1.7402381e-7,"teacher_disagreement_score":0.669725,"about_ca_system_score_codex":0.000022992419,"about_ca_system_score_gemma":0.000038100672,"threshold_uncertainty_score":0.88446015},"labels":[],"label_agreement":null},{"id":"W2090348733","doi":"10.1049/iet-spr.2009.0050","title":"Signal detection performance in Rayleigh fading environments with a moving antenna","year":2010,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Multipath propagation; Antenna (radio); Computer science; Rayleigh fading; Decorrelation; Antenna diversity; Fading; Narrowband; Diversity gain; Omnidirectional antenna; Electronic engineering; Acoustics; Telecommunications; Physics; Algorithm; Engineering; Decoding methods","score_opus":0.01484430547172742,"score_gpt":0.24286693810126092,"score_spread":0.2280226326295335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090348733","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52137977,0.00007280014,0.47789323,0.0000727175,0.000025766016,0.00011612033,1.2414804e-7,0.00006444197,0.00037502617],"genre_scores_gemma":[0.9850584,0.000016243239,0.014695827,0.000049377984,0.00006672441,0.000041612566,9.609608e-7,0.000019568011,0.00005123296],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982921,0.00008511626,0.00025428497,0.00040352944,0.00051049824,0.00045447296],"domain_scores_gemma":[0.99921954,0.0000950143,0.00012492486,0.00041298033,0.00004796792,0.000099574754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006364354,0.0001623946,0.0001500441,0.00022536259,0.00037267004,0.0003325758,0.0010505958,0.00008911421,0.000018901846],"category_scores_gemma":[0.000010544118,0.00014510714,0.000023545364,0.00073595863,0.00011463823,0.0015748157,0.00030270882,0.0008715617,0.00001962599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044613804,0.00009221066,0.017955199,0.000051652245,0.0000067428837,0.000017003627,0.0010654775,0.0072565703,0.34305584,0.00005140807,0.0000015774889,0.6304017],"study_design_scores_gemma":[0.00037189963,0.00008100916,0.015480896,0.00016161517,0.0000018319765,0.00003980792,0.000040507966,0.9552446,0.028083859,0.00011798591,0.00016480994,0.00021116647],"about_ca_topic_score_codex":0.000015678488,"about_ca_topic_score_gemma":0.000052510517,"teacher_disagreement_score":0.94798803,"about_ca_system_score_codex":0.00009043996,"about_ca_system_score_gemma":0.000105038285,"threshold_uncertainty_score":0.59172976},"labels":[],"label_agreement":null},{"id":"W2107676201","doi":"10.1049/iet-spr.2010.0262","title":"Signal denoising using neighbouring dual-tree complex wavelet coefficients","year":2012,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Noise reduction; Wavelet; Complex wavelet transform; Preprocessor; Pattern recognition (psychology); Artificial intelligence; Video denoising; Computer science; Non-local means; SIGNAL (programming language); Wavelet transform; Invariant (physics); Image denoising; Step detection; Signal processing; Noise (video); Algorithm; Mathematics; Discrete wavelet transform; Image (mathematics); Computer vision; Digital signal processing","score_opus":0.06308379760794391,"score_gpt":0.31623541155932366,"score_spread":0.25315161395137975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107676201","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05031899,0.0010892305,0.9442545,0.000103753315,0.00034922716,0.00018915723,0.0000026658322,0.00036059436,0.0033318736],"genre_scores_gemma":[0.72725207,0.0000017512367,0.27136794,0.00054335984,0.0006499796,0.00000409462,0.000004514596,0.00004944091,0.00012683585],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957044,0.00038078582,0.00068029034,0.0007136335,0.0010130189,0.0015078577],"domain_scores_gemma":[0.9981984,0.0002457655,0.00037180938,0.00046589645,0.00028358237,0.00043453608],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0015249916,0.00048821708,0.00050489366,0.000360879,0.0011505656,0.0011117301,0.0009418458,0.00017143169,0.0001584104],"category_scores_gemma":[0.000057079462,0.00047710846,0.00017712334,0.001107626,0.00016339756,0.0029296298,0.00053778506,0.00048353826,0.00007490839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004834252,0.00029884113,0.000858474,0.0001701192,0.00004166312,0.00011440287,0.0029120897,0.0022202956,0.40251523,0.0008104901,0.00020210491,0.5898079],"study_design_scores_gemma":[0.0017668362,0.0001317252,0.0041728537,0.000586964,0.00009804413,0.00080134685,0.00021693455,0.89616257,0.089629225,0.0021205333,0.002787038,0.0015259356],"about_ca_topic_score_codex":0.000038389808,"about_ca_topic_score_gemma":0.0000010006897,"teacher_disagreement_score":0.89394224,"about_ca_system_score_codex":0.00020074345,"about_ca_system_score_gemma":0.00023309671,"threshold_uncertainty_score":0.9999252},"labels":[],"label_agreement":null},{"id":"W2132315948","doi":"10.1049/iet-spr.2013.0354","title":"Time–frequency‐based instantaneous frequency estimation of sparse signals from incomplete set of samples","year":2014,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Department of National Defence","funders":"","keywords":"Time–frequency analysis; Algorithm; SIGNAL (programming language); Mathematics; Instantaneous phase; Signal reconstruction; Bilinear interpolation; Fourier transform; Set (abstract data type); Computer science; Spectral density estimation; Pattern recognition (psychology); Signal processing; Artificial intelligence; Statistics; Digital signal processing; Telecommunications; Mathematical analysis","score_opus":0.02559073078830124,"score_gpt":0.24140254109107587,"score_spread":0.21581181030277463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132315948","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57054454,0.0005024967,0.42585626,0.000019949814,0.0000425665,0.00016576039,0.00011092174,0.00050838204,0.002249111],"genre_scores_gemma":[0.9291756,0.0000037417688,0.070570484,0.000038778344,0.000071400806,0.0000061419937,0.00008504131,0.000046635367,0.0000021610217],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985752,0.00007008108,0.0005674084,0.00023295602,0.00031687692,0.00023748373],"domain_scores_gemma":[0.99900824,0.00024335968,0.00025485377,0.00026198768,0.0001688597,0.00006272308],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019295716,0.00024388357,0.00044785833,0.00016367612,0.000064361884,0.000034413224,0.00025059504,0.00012707266,0.00011569723],"category_scores_gemma":[0.000058108653,0.00024794834,0.00008091564,0.0002466102,0.00014301683,0.00020588271,0.000020111482,0.00015813124,0.000009884593],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021334097,0.000026987303,0.00015799144,0.00019625024,0.000035253768,0.000009408756,0.0003116541,0.15463236,0.80182546,0.00008057475,0.00015038575,0.0425523],"study_design_scores_gemma":[0.00018018989,0.000081881364,0.00010905756,0.0006852721,0.00003980667,0.0000050372087,0.000020275907,0.60419786,0.3820545,0.012379363,0.000034796365,0.00021193632],"about_ca_topic_score_codex":0.00018203504,"about_ca_topic_score_gemma":0.0000089758205,"teacher_disagreement_score":0.4495655,"about_ca_system_score_codex":0.000042297863,"about_ca_system_score_gemma":0.00006502318,"threshold_uncertainty_score":0.99999726},"labels":[],"label_agreement":null},{"id":"W2133933847","doi":"10.1049/iet-spr.2010.0032","title":"Multirate recovery scheme for wide-band global navigation satellite system signals","year":2011,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"GNSS positioning and interference","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Galileo (satellite navigation); Computer science; Satellite system; Satellite; Focus (optics); SIGNAL (programming language); Orbit (dynamics); Scheme (mathematics); Remote sensing; Satellite navigation; Phase (matter); Amplitude; Real-time computing; Global Positioning System; Algorithm; Telecommunications; Physics; Geology; Mathematics; Aerospace engineering; Optics; Engineering","score_opus":0.02980593206957237,"score_gpt":0.2368320720285291,"score_spread":0.20702613995895675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133933847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.363464,0.0022490073,0.6021504,0.000010826094,0.0003850529,0.00037179835,0.000051540104,0.00091520016,0.030402156],"genre_scores_gemma":[0.9926052,0.00000685272,0.007104795,0.000028069742,0.00009840434,0.000048420887,0.000029033117,0.000029235085,0.00004996615],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990583,0.000018194522,0.00029976192,0.0002159437,0.00012505043,0.00028272407],"domain_scores_gemma":[0.9995776,0.000034568933,0.00007722613,0.000092250026,0.00014370213,0.000074687916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017538128,0.00017905199,0.00017612473,0.000035252924,0.00013965474,0.00012806586,0.00012470869,0.00010508502,0.00001344446],"category_scores_gemma":[0.000010947057,0.00017998331,0.00006565855,0.00014412748,0.000030317815,0.0004478545,0.000008748802,0.00010429172,0.000041698626],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009083226,0.00027406059,0.008628579,0.012883832,0.0004627936,0.000050045262,0.0063494504,0.022843402,0.57749987,0.002048186,0.0023362846,0.36571515],"study_design_scores_gemma":[0.0012641627,0.0004426858,0.0020154105,0.0064960686,0.00012544228,0.00008090726,0.0007640067,0.49439067,0.48845372,0.0039951266,0.00080282264,0.0011689736],"about_ca_topic_score_codex":0.00001640608,"about_ca_topic_score_gemma":0.000001484405,"teacher_disagreement_score":0.6291412,"about_ca_system_score_codex":0.00013199821,"about_ca_system_score_gemma":0.000032122636,"threshold_uncertainty_score":0.7339507},"labels":[],"label_agreement":null},{"id":"W2158075440","doi":"10.1049/iet-spr.2012.0386","title":"Adaptive efficient sparse estimator achieving oracle properties","year":2013,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Compressed sensing; Oracle; Penalty method; Estimator; Algorithm; Context (archaeology); Lasso (programming language); Mathematical optimization; Computer science; SIGNAL (programming language); Mean squared error; Function (biology); Signal reconstruction; Mathematics; Signal processing; Statistics","score_opus":0.02499018661119706,"score_gpt":0.2120527107247888,"score_spread":0.18706252411359173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158075440","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73146373,0.0031984902,0.25535515,0.00009752672,0.00013392336,0.0004870604,0.0000017180915,0.0027244627,0.006537963],"genre_scores_gemma":[0.98612356,0.0000056710787,0.013546405,0.00005836103,0.000109101864,0.000047717353,0.0000012321633,0.000060040813,0.000047901685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896395,0.000021533082,0.00022669118,0.00022190699,0.0002107916,0.00035511758],"domain_scores_gemma":[0.99955624,0.00002148622,0.000050313574,0.00015453328,0.00012768326,0.000089769506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007910791,0.00022345249,0.00020428938,0.00008743973,0.00019176007,0.00020240621,0.00017223971,0.00007754667,0.00006659116],"category_scores_gemma":[0.000012420692,0.00019106884,0.000048891136,0.00017854667,0.0000737523,0.00029987455,0.000055075474,0.00021839725,0.00014080219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027627013,0.00010208284,0.00023638335,0.00020663391,0.000058675494,0.000031333708,0.0012779019,0.37315857,0.33708555,0.00009281848,0.0035701804,0.28415224],"study_design_scores_gemma":[0.000098210614,0.000036249337,0.00030438646,0.00050136645,0.000015339829,0.000016735694,0.00020282586,0.8853124,0.1127368,0.00026100065,0.00022243797,0.00029228482],"about_ca_topic_score_codex":0.000039148956,"about_ca_topic_score_gemma":9.416938e-7,"teacher_disagreement_score":0.5121538,"about_ca_system_score_codex":0.000055219407,"about_ca_system_score_gemma":0.00003184937,"threshold_uncertainty_score":0.77915615},"labels":[],"label_agreement":null},{"id":"W2163906842","doi":"10.1049/iet-spr.2009.0082","title":"Focusing inverse synthetic aperture radar images with higher-order motion error using the adaptive joint-time–frequency algorithm optimised with the genetic algorithm and the particle swarm optimisation algorithm – comparison and results","year":2010,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Department of National Defence","funders":"","keywords":"Algorithm; Inverse synthetic aperture radar; Particle swarm optimization; Computer science; Synthetic aperture radar; Focus (optics); Genetic algorithm; Search algorithm; Radar; Radar imaging; Computer vision; Optics; Physics","score_opus":0.014982158261256791,"score_gpt":0.23669857052539908,"score_spread":0.2217164122641423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163906842","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01749635,0.0012548786,0.97830504,0.0015351867,0.000051075487,0.0008224053,0.000024018405,0.00041301816,0.00009803426],"genre_scores_gemma":[0.343381,0.000021299897,0.65621984,0.00010919635,0.000121362325,0.000045103658,0.0000059498343,0.00007915512,0.00001709042],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980471,0.0001618407,0.00042404787,0.0004782667,0.00042878167,0.0004599613],"domain_scores_gemma":[0.9986722,0.00033945852,0.0002726956,0.0003708895,0.0002447073,0.00010006924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006427556,0.00043957884,0.0003681388,0.000056941903,0.00085393124,0.00039241058,0.0002383227,0.000121659155,0.0000077059385],"category_scores_gemma":[0.000046631227,0.00023013419,0.00003910056,0.000404696,0.0011639142,0.000633678,0.00007520321,0.0008025599,0.000001256349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002557579,0.00007565468,0.0000310966,0.000098865065,0.00017220875,0.0000378843,0.0059321513,0.1079158,0.058675185,0.000021372338,0.00013048193,0.82665354],"study_design_scores_gemma":[0.0015294002,0.00009320626,0.00011440102,0.00020051793,0.00019546045,0.00019824161,0.00087945245,0.9758681,0.019976618,0.0004701052,0.00009180355,0.00038266377],"about_ca_topic_score_codex":0.00008039194,"about_ca_topic_score_gemma":0.000008574151,"teacher_disagreement_score":0.86795235,"about_ca_system_score_codex":0.000073951756,"about_ca_system_score_gemma":0.00006661715,"threshold_uncertainty_score":0.93846},"labels":[],"label_agreement":null},{"id":"W2259805550","doi":"10.1049/iet-spr.2013.0392","title":"Optimisation of multiple feature stream weights for distributed speech processing in mobile environments","year":2015,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Discriminative model; Computer science; Speech recognition; Hidden Markov model; Word error rate; Mel-frequency cepstrum; Speech processing; Pattern recognition (psychology); Noise (video); Feature extraction; Artificial intelligence","score_opus":0.021694003042185557,"score_gpt":0.2583754798393787,"score_spread":0.23668147679719315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2259805550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20536362,0.002064088,0.7914725,0.00018461818,0.00007427476,0.0005537312,0.000020148585,0.00010930219,0.00015772134],"genre_scores_gemma":[0.8271569,0.0000047429903,0.17249383,0.00004673323,0.00008350323,0.000069843096,0.000054935204,0.00001999134,0.000069519876],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980593,0.000040896102,0.00042043388,0.00054850726,0.00047367153,0.00045719856],"domain_scores_gemma":[0.9990023,0.00007177074,0.000388092,0.00021873727,0.00015731831,0.00016179238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044941748,0.00025171885,0.00032353378,0.0001740801,0.00016216483,0.00023996431,0.000580223,0.00015670694,0.0000028014213],"category_scores_gemma":[0.00009474912,0.00023171249,0.000065642824,0.0006121338,0.000076105374,0.0014962993,0.000116727926,0.00019616522,0.000003224056],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000115735966,0.00041775368,0.005590632,0.00034982624,0.000009312313,0.000016831289,0.0026103696,0.008545315,0.040733382,0.000013103382,0.0002367478,0.941361],"study_design_scores_gemma":[0.0029310773,0.00032280112,0.0010098,0.00078409,0.000022838283,0.000029787565,0.00068190275,0.333689,0.6538183,0.0041544973,0.001989436,0.0005664714],"about_ca_topic_score_codex":0.00000792209,"about_ca_topic_score_gemma":0.0000067702126,"teacher_disagreement_score":0.9407945,"about_ca_system_score_codex":0.00016316069,"about_ca_system_score_gemma":0.0003045056,"threshold_uncertainty_score":0.9448961},"labels":[],"label_agreement":null},{"id":"W2289179757","doi":"10.1049/iet-spr.2014.0148","title":"Modified coherence‐based dictionary learning method for speech enhancement","year":2015,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Langley Research Center","keywords":"Computer science; Sparse approximation; Coherence (philosophical gambling strategy); K-SVD; Speech recognition; Noise (video); Speech enhancement; Artificial intelligence; Context (archaeology); Pattern recognition (psychology); Energy (signal processing); Noise reduction; Algorithm; Mathematics; Statistics","score_opus":0.0637031942681851,"score_gpt":0.33039009567193406,"score_spread":0.26668690140374895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2289179757","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002035801,0.0006147144,0.99254006,0.0006858992,0.00017273365,0.00025534944,0.0000010999837,0.0003434963,0.0033508725],"genre_scores_gemma":[0.4424323,8.272493e-7,0.5564316,0.0004664468,0.00016581401,0.00006376024,0.000008844396,0.000016267415,0.00041415115],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795306,0.00009256004,0.00034178828,0.00059371523,0.0005265794,0.00049231434],"domain_scores_gemma":[0.998751,0.00015333704,0.00025023398,0.00019125275,0.0004296724,0.0002245267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011233276,0.00022904633,0.00025022204,0.0001394107,0.0004479176,0.0004798004,0.0006015237,0.00009572688,0.000014886214],"category_scores_gemma":[0.000102386315,0.00021428216,0.00008068496,0.0005062636,0.000042979456,0.0009089024,0.000107441294,0.00025042336,0.00001990262],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000737881,0.00011312791,0.0001303745,0.00013720673,0.000015152284,0.000014908993,0.00063411077,0.018398229,0.038939327,0.00012252782,0.00094497786,0.94047624],"study_design_scores_gemma":[0.0008771948,0.00025340836,0.0000111769505,0.00013911801,0.0000136371045,0.000017987193,0.00010439717,0.67731124,0.31006128,0.007634817,0.0032788287,0.00029692485],"about_ca_topic_score_codex":0.000011348404,"about_ca_topic_score_gemma":0.0000010691225,"teacher_disagreement_score":0.94017935,"about_ca_system_score_codex":0.00011781,"about_ca_system_score_gemma":0.00064110564,"threshold_uncertainty_score":0.8738173},"labels":[],"label_agreement":null},{"id":"W2294780743","doi":"10.1049/iet-spr.2015.0360","title":"Unbiased, optimal, and in‐betweens: the trade‐off in discrete finite impulse response filtering","year":2016,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Royal Academy of Engineering","keywords":"Finite impulse response; Kalman filter; Mathematics; Robustness (evolution); Mean squared error; Gaussian; Algorithm; Control theory (sociology); Applied mathematics; Statistics; Computer science; Artificial intelligence","score_opus":0.019336170426854475,"score_gpt":0.25712154553305766,"score_spread":0.23778537510620318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294780743","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.587563,0.0011649283,0.40612522,0.004657808,0.000108282075,0.00016699001,0.000013605378,0.00012513876,0.000075019365],"genre_scores_gemma":[0.9918587,0.00006826867,0.0076066703,0.00031352622,0.00008106818,0.000008853795,0.0000014695278,0.000014805356,0.00004659364],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825287,0.0002499372,0.00035616753,0.0004762569,0.00023599732,0.0004287538],"domain_scores_gemma":[0.99830985,0.001156866,0.00010350403,0.00032898635,0.00001890273,0.00008186951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010657691,0.00018122989,0.00018988081,0.00014846322,0.00018295152,0.0003225211,0.0005939566,0.00008260263,0.000012857489],"category_scores_gemma":[0.00012853894,0.00010802528,0.00003307349,0.00048695298,0.00013324709,0.0008439983,0.0002538104,0.00025687227,0.0000062576605],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074747094,0.000065666005,0.005144082,0.00006134371,0.000008817068,0.00029425917,0.004492831,0.010122491,0.026227083,0.00015351883,0.0005592544,0.95212317],"study_design_scores_gemma":[0.00439629,0.0004185052,0.097764745,0.0046005766,0.000019879213,0.00020531125,0.0007076732,0.8639412,0.00904854,0.0031266278,0.014200134,0.0015705329],"about_ca_topic_score_codex":0.000013577523,"about_ca_topic_score_gemma":0.000012461419,"teacher_disagreement_score":0.95055264,"about_ca_system_score_codex":0.000034903816,"about_ca_system_score_gemma":0.000065452194,"threshold_uncertainty_score":0.4405143},"labels":[],"label_agreement":null},{"id":"W2298435078","doi":"10.1049/iet-spr.2014.0300","title":"Parallel‐computing‐based implementation of fast algorithms for discrete Gabor transform","year":2015,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Parallel computing; Parallel algorithm; Algorithm; Inter-process communication; Parallel processing; Signal processing; Block (permutation group theory); Process (computing); Overhead (engineering); Distributed computing; Digital signal processing; Computer hardware; Mathematics","score_opus":0.054553219577342424,"score_gpt":0.36766898654291214,"score_spread":0.3131157669655697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2298435078","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019668334,0.00024949017,0.996402,0.00054407015,0.00011304746,0.0003189852,0.000009068529,0.00009849972,0.0002980048],"genre_scores_gemma":[0.5278366,5.0315356e-7,0.47185618,0.00015718327,0.000087562876,0.00001218776,0.00000922777,0.000012064164,0.000028512417],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983813,0.000085476386,0.00042229943,0.0003485151,0.00040719257,0.00035520943],"domain_scores_gemma":[0.9990132,0.0001096648,0.00021651237,0.0001703914,0.00036314983,0.00012710426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001089876,0.0001804951,0.00026196116,0.000120080564,0.00015740648,0.00020625319,0.00048340028,0.000056323246,0.000005728066],"category_scores_gemma":[0.000017027372,0.0001544224,0.000111370784,0.00037409668,0.000053756048,0.0007113303,0.000039726816,0.000094425086,0.0000020468892],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007565208,0.000042433283,0.000059412007,0.00017147038,0.000014430455,0.000004928454,0.0031655063,0.002173524,0.0053206827,0.00037540635,0.00023457628,0.98836195],"study_design_scores_gemma":[0.003736135,0.0005357034,0.00017175883,0.0001359692,0.000036443715,0.000010264248,0.00063092815,0.86431134,0.11860846,0.0105589675,0.0009051831,0.00035882814],"about_ca_topic_score_codex":0.000036999485,"about_ca_topic_score_gemma":0.0000036341146,"teacher_disagreement_score":0.98800313,"about_ca_system_score_codex":0.000043245822,"about_ca_system_score_gemma":0.00041898398,"threshold_uncertainty_score":0.62971634},"labels":[],"label_agreement":null},{"id":"W2330995436","doi":"10.1049/iet-spr.2015.0279","title":"Iteratively reweighted correlation analysis method for robust parameter identification of multiple‐input multiple‐output discrete‐time systems","year":2016,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Control Systems and Identification","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Algorithm; Identification (biology); Estimation theory; Mathematics; Pattern recognition (psychology); Statistics; Artificial intelligence","score_opus":0.01765295537833075,"score_gpt":0.24675682589363532,"score_spread":0.22910387051530456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2330995436","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026883261,0.0005494746,0.9712019,0.000038826292,0.00019733129,0.00075878,0.00014135438,0.00018833736,0.000040721618],"genre_scores_gemma":[0.98706305,0.0000054943494,0.011625056,0.0000031584834,0.00015183189,0.00022363791,0.00012909452,0.000047772523,0.0007508849],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978278,0.0001381633,0.0010632674,0.0004073877,0.0003018848,0.0002614976],"domain_scores_gemma":[0.9978687,0.00076908944,0.00053349073,0.0002687401,0.0004878201,0.00007213992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086409773,0.00023855218,0.00050594,0.00038591473,0.00014865994,0.0002115975,0.00016757972,0.00015967122,0.000012947838],"category_scores_gemma":[0.00025897595,0.0001831669,0.00022866916,0.00059840357,0.00003556431,0.0007256166,0.000014023128,0.00007646835,0.000016771044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009707256,0.00003842499,0.0030586987,0.0004958435,0.00054379855,4.7121793e-7,0.0007023086,0.28772563,0.643014,0.000033382,0.00022922673,0.06406114],"study_design_scores_gemma":[0.000797931,0.000024315246,0.0054508573,0.00023899396,0.00043474056,0.0000016576297,0.00005840058,0.98083466,0.011668946,0.00006819071,0.0001693713,0.0002519207],"about_ca_topic_score_codex":0.000045014003,"about_ca_topic_score_gemma":0.000015293743,"teacher_disagreement_score":0.9601798,"about_ca_system_score_codex":0.00012655907,"about_ca_system_score_gemma":0.000025277786,"threshold_uncertainty_score":0.746933},"labels":[],"label_agreement":null},{"id":"W2338408058","doi":"10.1049/iet-spr.2015.0223","title":"Hierarchy precoder design for multi‐cell multiuser multiple‐input–multiple‐output wireless networks with interference alignment","year":2016,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Program for New Century Excellent Talents in University; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Precoding; Interference (communication); Base station; Zero-forcing precoding; Hierarchy; Transmitter power output; Interference alignment; Data stream mining; Key (lock); Data stream; Algorithm; MIMO; Transmitter; Telecommunications; Beamforming; Data mining; Channel (broadcasting)","score_opus":0.02972702995818889,"score_gpt":0.23510974660686154,"score_spread":0.20538271664867264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2338408058","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046599684,0.00062720664,0.9922998,0.000022967826,0.00019154784,0.0014671781,0.000022828794,0.0006432706,0.000065220105],"genre_scores_gemma":[0.7721205,0.00003118065,0.22656158,0.000029132818,0.00016808545,0.00046893142,0.000013997082,0.00017220069,0.00043442845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977383,0.000074634314,0.0005749784,0.0006196453,0.0002298258,0.00076262146],"domain_scores_gemma":[0.99851996,0.0005110297,0.00020791334,0.00029177484,0.00027180326,0.00019750079],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028377198,0.0005180289,0.0004304515,0.00013520708,0.00021652019,0.00012485358,0.00032806754,0.00021753169,0.000015084643],"category_scores_gemma":[0.000044951827,0.00037612222,0.000078822995,0.0002226552,0.000100298676,0.0008072748,0.000052360927,0.00020151508,0.000012636149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026432154,0.00007794172,0.001163612,0.00034307525,0.00004733147,0.000004744558,0.00068434945,0.89477044,0.028013714,0.0000018855839,0.00016852416,0.07446008],"study_design_scores_gemma":[0.0032502015,0.00014107916,0.0000495528,0.0010313267,0.00003622618,0.000008320011,0.00011147101,0.9435609,0.05099349,0.00001482447,0.0001881882,0.0006144149],"about_ca_topic_score_codex":0.0000063510734,"about_ca_topic_score_gemma":0.000023583956,"teacher_disagreement_score":0.7674605,"about_ca_system_score_codex":0.00026914652,"about_ca_system_score_gemma":0.0000659629,"threshold_uncertainty_score":0.99986905},"labels":[],"label_agreement":null},{"id":"W2344475046","doi":"10.1049/iet-spr.2015.0175","title":"Error‐free computation of 8‐point discrete cosine transform based on the Loeffler factorisation and algebraic integers","year":2016,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Virtual Materials Group (Canada); University of Calgary","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Discrete cosine transform; Algorithm; Gate array; Algebraic number; Computer science; Floating point; Factorization; Very-large-scale integration; Mathematics; Field-programmable gate array; Computer hardware; Artificial intelligence; Image (mathematics); Embedded system","score_opus":0.03235538358563826,"score_gpt":0.2714689809968635,"score_spread":0.23911359741122523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2344475046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01637046,0.000014257016,0.97770625,0.0045957807,0.0000376474,0.00020539071,0.000008827249,0.000048098133,0.0010132806],"genre_scores_gemma":[0.9974221,9.978401e-7,0.0021645976,0.00035337138,0.000019405457,0.0000093449235,0.000005955508,0.000008014375,0.000016211197],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990469,0.000051818388,0.00024290294,0.00021259529,0.0003005364,0.0001452016],"domain_scores_gemma":[0.99943346,0.00017320577,0.00013691085,0.00013432157,0.00007893773,0.00004317077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002862419,0.000116413736,0.00010389733,0.00008212717,0.000093518,0.00016864676,0.000248446,0.000029457717,0.000014772048],"category_scores_gemma":[0.000026418476,0.00006469165,0.000031181753,0.00018433048,0.00007766569,0.0010807303,0.00002890967,0.000054411896,0.000003202633],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000078888625,0.000057691577,0.00032324978,0.00009933246,0.000013418196,0.0000012751701,0.0021111968,0.00023072108,0.027303617,0.005922854,0.0004970009,0.9633607],"study_design_scores_gemma":[0.0026587944,0.0010845112,0.0063054436,0.0011432742,0.000025882875,0.0000074405393,0.00030915378,0.74151415,0.15892789,0.08727861,0.00024245074,0.00050239195],"about_ca_topic_score_codex":0.000007028009,"about_ca_topic_score_gemma":0.000001995299,"teacher_disagreement_score":0.9810516,"about_ca_system_score_codex":0.000039185303,"about_ca_system_score_gemma":0.000057394318,"threshold_uncertainty_score":0.2638049},"labels":[],"label_agreement":null},{"id":"W2551618634","doi":"10.1049/iet-spr.2016.0151","title":"Guest Editorial","year":2016,"lang":"en","type":"editorial","venue":"IET Signal Processing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Computer network; Provisioning; Software deployment; Wireless network; Heterogeneous network; Wireless; Mobile broadband; Cellular network; Interoperability; Radio resource management; Telecommunications; Distributed computing; World Wide Web","score_opus":0.005122501110519507,"score_gpt":0.23994722011821604,"score_spread":0.23482471900769653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2551618634","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.231411e-7,0.0010554615,0.14960337,0.000004275653,0.84539443,0.00016909628,0.00010145092,0.00086582126,0.0028057941],"genre_scores_gemma":[0.00051793555,0.00007872949,0.0008623509,0.0000022324937,0.9972059,0.0000596399,0.00021869966,0.00026319487,0.0007912894],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977707,0.00002884973,0.0005191377,0.00043292937,0.0007677172,0.00048064385],"domain_scores_gemma":[0.9987473,0.0002518107,0.0001949194,0.00024433236,0.00044120545,0.000120429526],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00023124952,0.00048637888,0.00049156597,0.00015066007,0.00013005997,0.00017502028,0.00034735363,0.0013264652,0.000035227175],"category_scores_gemma":[0.0001739365,0.0004367401,0.00008821419,0.00018265922,0.000050997467,0.0005856546,0.000046200992,0.0007908967,0.0001860576],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013941373,0.0000060947145,3.5466894e-7,0.00060530053,0.000023215835,0.0000055764385,0.00005556436,0.006359778,0.00081984355,7.797599e-7,0.98819923,0.003910318],"study_design_scores_gemma":[0.00042747657,0.000024565828,2.7761963e-8,0.0011238253,0.00003663585,0.0000012846172,0.000007477609,0.0021055902,0.00052342005,0.000108174296,0.99507934,0.0005621611],"about_ca_topic_score_codex":0.0000037970144,"about_ca_topic_score_gemma":0.000003026616,"teacher_disagreement_score":0.15181151,"about_ca_system_score_codex":0.00034656888,"about_ca_system_score_gemma":0.0002422968,"threshold_uncertainty_score":0.99997},"labels":[],"label_agreement":null},{"id":"W2595113344","doi":"10.1049/iet-spr.2016.0569","title":"Level crossing speech sampling and its sparsity promoting reconstruction using an iterative method with adaptive thresholding","year":2017,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Queen's University","funders":"Sharif University of Technology","keywords":"Thresholding; Computer science; Algorithm; Signal reconstruction; Iterative method; Iterative reconstruction; Gradient descent; Sampling (signal processing); Redundancy (engineering); Compressed sensing; Artificial intelligence; Mathematics; Computer vision; Signal processing; Image (mathematics); Artificial neural network; Filter (signal processing)","score_opus":0.16685648077491833,"score_gpt":0.35829541199184683,"score_spread":0.1914389312169285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595113344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42785242,0.00024230013,0.5713191,0.00007821408,0.000053808406,0.00014698606,0.000002491612,0.00011800505,0.00018664073],"genre_scores_gemma":[0.53632575,0.0000012301557,0.4634329,0.00004776845,0.00015615928,0.0000028491147,6.993379e-7,0.000020375788,0.00001227182],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972943,0.000119664,0.00038781017,0.0010909158,0.00044346927,0.00066385354],"domain_scores_gemma":[0.9979344,0.0000840343,0.00086758507,0.0003961199,0.00048043267,0.00023742407],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001235087,0.0003960823,0.0004195608,0.00018502229,0.0064236317,0.00739306,0.00077662314,0.00014157922,0.000004814335],"category_scores_gemma":[0.000100168356,0.00035482878,0.00004752066,0.0002843547,0.0002618231,0.00953362,0.00038480887,0.0004854629,0.0000013109245],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058391994,0.000048604037,0.0043323855,0.00014629487,0.000030676452,0.000060865223,0.0053461175,0.0010411585,0.16918622,0.00011375927,3.8852446e-7,0.81963515],"study_design_scores_gemma":[0.0005353694,0.00014552083,0.0017570093,0.001561185,0.00003469858,0.0010592294,0.00045355185,0.5778233,0.41244748,0.0036385008,0.0000048836796,0.0005392624],"about_ca_topic_score_codex":0.000064145526,"about_ca_topic_score_gemma":0.000030436197,"teacher_disagreement_score":0.81909585,"about_ca_system_score_codex":0.00012235092,"about_ca_system_score_gemma":0.0004614017,"threshold_uncertainty_score":0.9998904},"labels":[],"label_agreement":null},{"id":"W2738794741","doi":"10.1049/iet-spr.2017.0074","title":"Transformed cubature quadrature Kalman filter","year":2017,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Quadrature (astronomy); Kalman filter; Algorithm; Filter (signal processing); Covariance; Mathematics; Extended Kalman filter; Transformation (genetics); Computer science; Applied mathematics; Control theory (sociology); Statistics; Artificial intelligence; Computer vision","score_opus":0.02186514333046006,"score_gpt":0.2708588575557888,"score_spread":0.24899371422532876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2738794741","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069334446,0.0012888606,0.96467036,0.0045582876,0.00096601225,0.00019633079,0.000017561702,0.0005860372,0.020783113],"genre_scores_gemma":[0.9731136,0.000018113355,0.024985915,0.00068210775,0.00048991054,0.0000071144095,0.000013347251,0.000018884024,0.0006709947],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984025,0.000034326273,0.00022919729,0.00051251706,0.0003822176,0.00043924715],"domain_scores_gemma":[0.998687,0.0000429802,0.00019278249,0.00082361244,0.000107793785,0.0001458178],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00024567696,0.0002167665,0.00020388456,0.00005927871,0.0014264019,0.0017731384,0.0019011208,0.00020670821,0.00006679028],"category_scores_gemma":[0.000025903248,0.00017638366,0.00008982064,0.00012444134,0.00010640792,0.001790297,0.00016859491,0.0005297123,0.000047296227],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050748837,0.00011427522,0.0008062309,0.00016713177,0.000025499518,0.00018188049,0.002666577,0.00042427165,0.007257181,0.0049143033,0.037503492,0.9458884],"study_design_scores_gemma":[0.0036787204,0.0003928236,0.018246556,0.0021203957,0.000092993396,0.00045847386,0.00026517,0.43334708,0.034320634,0.037888743,0.46591276,0.0032756333],"about_ca_topic_score_codex":0.000011076924,"about_ca_topic_score_gemma":0.000009621506,"teacher_disagreement_score":0.96618015,"about_ca_system_score_codex":0.000016818893,"about_ca_system_score_gemma":0.000083551815,"threshold_uncertainty_score":0.9998736},"labels":[],"label_agreement":null},{"id":"W2798445441","doi":"10.1049/iet-spr.2018.5076","title":"Block sparse multi‐lead ECG compression exploiting between‐lead collaboration","year":2018,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Queen's University","funders":"","keywords":"Kernel (algebra); Computer science; Compression ratio; Wavelet; Estimator; Algorithm; Block (permutation group theory); Compression (physics); Compressed sensing; Discrete cosine transform; Pattern recognition (psychology); Gaussian; Daubechies wavelet; Artificial intelligence; Mathematics; Wavelet transform; Discrete wavelet transform; Statistics; Image (mathematics); Discrete mathematics","score_opus":0.05522897924781725,"score_gpt":0.32753154281563174,"score_spread":0.27230256356781446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2798445441","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07378044,0.00011379282,0.9227867,0.0006695961,0.00009681158,0.00024594684,0.0000028600398,0.00093026727,0.0013735777],"genre_scores_gemma":[0.7536656,0.0000021000756,0.24547912,0.00042056176,0.00026726548,0.000019021836,0.0000075920984,0.000020496247,0.00011822673],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795043,0.00017420643,0.00045835014,0.000559137,0.0004908971,0.00036695617],"domain_scores_gemma":[0.9985169,0.00009563279,0.00034954204,0.00033617785,0.0005947628,0.00010694532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062176946,0.00023172103,0.000250424,0.00019229323,0.00060432224,0.0007796935,0.0006765417,0.0001555616,0.000012378731],"category_scores_gemma":[0.000055475488,0.00022536596,0.00004720894,0.00084850425,0.00013010578,0.0019234408,0.00022590844,0.0002646115,0.00009137371],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045823424,0.0003275531,0.0045823413,0.00017575026,0.000032727756,0.000021669523,0.018967478,0.00052171765,0.24079825,0.0012349001,0.0049664923,0.7283253],"study_design_scores_gemma":[0.00048137736,0.00021059343,0.00051457336,0.0003779657,0.000012316174,0.000009563384,0.0003463772,0.6248396,0.36890835,0.0015281268,0.0023405347,0.00043061824],"about_ca_topic_score_codex":0.000013518454,"about_ca_topic_score_gemma":0.000012465766,"teacher_disagreement_score":0.72789466,"about_ca_system_score_codex":0.000064706765,"about_ca_system_score_gemma":0.00021491612,"threshold_uncertainty_score":0.9190157},"labels":[],"label_agreement":null},{"id":"W2799621666","doi":"10.1049/iet-spr.2017.0512","title":"Efficient blind source extraction of noisy mixture utilising a class of parallel linear predictor filters","year":2018,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Class (philosophy); Blind signal separation; Extraction (chemistry); Pattern recognition (psychology); Linear prediction; Artificial intelligence; Speech recognition; Algorithm; Chromatography; Telecommunications","score_opus":0.02264070401114699,"score_gpt":0.2959059034246326,"score_spread":0.2732651994134856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799621666","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20021522,0.00010185707,0.7983926,0.00018425778,0.00006673345,0.00020001686,0.0000020401571,0.00017429591,0.0006629955],"genre_scores_gemma":[0.8904224,0.0000015182218,0.1092135,0.00013428686,0.00012832016,0.000008247471,0.0000022884647,0.000014615675,0.00007484431],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983048,0.000096971984,0.00048885716,0.00036288248,0.0005138056,0.0002327074],"domain_scores_gemma":[0.99856645,0.00008945761,0.00051498634,0.00029767337,0.00045750133,0.000073950905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055600965,0.0001651739,0.00023646535,0.00020998303,0.00014036027,0.00008292672,0.00049761916,0.00014902845,0.000016655407],"category_scores_gemma":[0.000046546633,0.00015311483,0.00008417499,0.00056726584,0.00021347438,0.0003541805,0.00011412002,0.00022303313,0.0000046621544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016259517,0.0016966177,0.00089057966,0.001107741,0.00013125324,0.000014144321,0.053112827,0.09667304,0.66593504,0.0029027888,0.0022452944,0.17366475],"study_design_scores_gemma":[0.00072044134,0.0002698693,0.00017935722,0.00025452272,0.000013928839,0.0000143448815,0.00011422986,0.856919,0.14014582,0.0003694812,0.0008361027,0.00016288151],"about_ca_topic_score_codex":0.0000116156325,"about_ca_topic_score_gemma":0.0000021684366,"teacher_disagreement_score":0.760246,"about_ca_system_score_codex":0.000031595693,"about_ca_system_score_gemma":0.000191863,"threshold_uncertainty_score":0.6243841},"labels":[],"label_agreement":null},{"id":"W2888712767","doi":"10.1049/iet-spr.2018.5245","title":"Classification of Doppler radar reflections as preprocessing for breathing rate monitoring","year":2018,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Ottawa","funders":"","keywords":"Doppler effect; Preprocessor; Computer science; Doppler radar; Radar; Breathing; Remote sensing; Artificial intelligence; Geology; Medicine; Telecommunications; Physics","score_opus":0.044934519333246145,"score_gpt":0.3194929309296497,"score_spread":0.27455841159640354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888712767","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46931022,0.001005671,0.51880294,0.000050229788,0.00094249035,0.00050777174,0.000010386312,0.0008004144,0.008569838],"genre_scores_gemma":[0.9699011,0.000012483327,0.028075233,0.0000099124245,0.0017172795,0.000091507834,0.0000059133004,0.000105554136,0.00008105864],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99835086,0.000032075375,0.00050535565,0.00039975924,0.00026396278,0.00044801703],"domain_scores_gemma":[0.9988731,0.00015430168,0.00018708661,0.00022051971,0.00045615842,0.00010879464],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045930617,0.0002712037,0.00027654716,0.0002183127,0.00048562183,0.0001778591,0.00023508316,0.0001512813,0.00001717145],"category_scores_gemma":[0.000101558755,0.00029383347,0.000090691705,0.00057917094,0.00013504607,0.000894899,0.000037410562,0.00022522865,0.000017410519],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038768132,0.000022952187,0.0011613797,0.00057144265,0.00003458436,0.0000015679291,0.0012502504,0.0005638979,0.9685355,0.000047717025,0.000051597664,0.027720377],"study_design_scores_gemma":[0.00037542934,0.00011260649,0.0006456494,0.000825026,0.000056062145,0.000025801446,0.00064785377,0.006500517,0.98696476,0.0031268857,0.00036782882,0.0003516054],"about_ca_topic_score_codex":0.000017799834,"about_ca_topic_score_gemma":0.0000024958983,"teacher_disagreement_score":0.5005908,"about_ca_system_score_codex":0.00018987253,"about_ca_system_score_gemma":0.0001145867,"threshold_uncertainty_score":0.99995136},"labels":[],"label_agreement":null},{"id":"W2943747788","doi":"10.1049/iet-spr.2018.5400","title":"Non‐linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation","year":2019,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Agencia Nacional de Promoción Científica y Tecnológica; Universidad Nacional de Cuyo; Universidad Nacional de La Plata; Consejo Nacional de Investigaciones Científicas y Técnicas","keywords":"Kalman filter; Autoregressive model; Heteroscedasticity; Invariant extended Kalman filter; Extended Kalman filter; Autoregressive conditional heteroskedasticity; Clutter; Mathematics; Computer science; Algorithm; Statistics; Econometrics; Radar","score_opus":0.025137922278088427,"score_gpt":0.29206959005916755,"score_spread":0.26693166778107913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943747788","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06940697,0.000064409076,0.92917794,0.00025938,0.00046312582,0.00035056,0.000037073612,0.00017486673,0.00006567731],"genre_scores_gemma":[0.77733797,4.0626776e-7,0.22129688,0.0006974797,0.00020075588,0.000040885865,0.00026051412,0.000020336876,0.00014477316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813104,0.00004405002,0.00042659833,0.000590956,0.00039037576,0.00041698507],"domain_scores_gemma":[0.9988128,0.00028705332,0.00029578654,0.00031642162,0.00017425022,0.00011369435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020182751,0.00024118992,0.00029576317,0.000110387904,0.00028445164,0.0005049803,0.0005434939,0.00012618062,0.000059751972],"category_scores_gemma":[0.000027245205,0.0002169921,0.000108215725,0.00015677168,0.000066278946,0.0012125549,0.00010334067,0.00022000971,0.00008169291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019620547,0.0003097473,0.0045024287,0.0005251347,0.00008818555,0.000019176277,0.001760719,0.8554947,0.020383937,0.0019989582,0.027770363,0.08695044],"study_design_scores_gemma":[0.0007810908,0.0001690664,0.00087421935,0.00017187276,0.00001871605,0.000012598254,0.000014585818,0.9907811,0.0037404085,0.0022521594,0.0008815833,0.00030259843],"about_ca_topic_score_codex":0.0000045494053,"about_ca_topic_score_gemma":6.6753717e-7,"teacher_disagreement_score":0.707931,"about_ca_system_score_codex":0.00004092041,"about_ca_system_score_gemma":0.00007291753,"threshold_uncertainty_score":0.88486814},"labels":[],"label_agreement":null},{"id":"W2963378461","doi":"10.1049/iet-spr.2018.5037","title":"Sharp sufficient condition of block signal recovery via <i>l</i> <sub>2</sub> / <i>l</i> <sub>1</sub> ‐minimisation","year":2019,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Minimisation (clinical trials); Signal recovery; Signal processing; Computer science; Mathematics; Minification; Algorithm; Mathematical optimization; Telecommunications; Statistics; Compressed sensing","score_opus":0.008403849006667102,"score_gpt":0.20383061582777035,"score_spread":0.19542676682110324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963378461","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94637877,0.0004916504,0.050218023,0.00003537535,0.00026108333,0.00041796436,0.000027505142,0.00077513716,0.0013944777],"genre_scores_gemma":[0.9987002,0.0000650702,0.00055761874,0.0002305008,0.00022485436,0.000025563737,0.00008272125,0.000106912645,0.000006566063],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976639,0.00006457923,0.000679381,0.0004988512,0.0005695286,0.00052375917],"domain_scores_gemma":[0.99884456,0.00012296802,0.0003122438,0.00030949825,0.00028929938,0.00012139797],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029068041,0.00042098857,0.0004843322,0.00026551436,0.00014140598,0.00012934477,0.00025160165,0.0002713718,0.000026790816],"category_scores_gemma":[0.000014305757,0.00046052405,0.00017903247,0.00045887064,0.000092613736,0.00057305815,0.000069996306,0.00040528298,0.000091238435],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006110493,0.00008434668,0.00008835942,0.00024409774,0.000041185955,0.000010269397,0.0002352644,0.04280116,0.9131985,0.0000121540415,0.0016847748,0.04153879],"study_design_scores_gemma":[0.00038809676,0.00018729856,0.00020335156,0.0007016205,0.000063442734,0.000036709636,0.00006584174,0.112212904,0.88480324,0.0006900282,0.0001804316,0.0004670365],"about_ca_topic_score_codex":0.000005923216,"about_ca_topic_score_gemma":0.0000032855528,"teacher_disagreement_score":0.06941175,"about_ca_system_score_codex":0.00013924547,"about_ca_system_score_gemma":0.000099006254,"threshold_uncertainty_score":0.99978465},"labels":[],"label_agreement":null},{"id":"W3010044096","doi":"10.1049/iet-spr.2019.0247","title":"Joint beamforming and admission control for cache‐enabled Cloud‐RAN with limited fronthaul capacity","year":2020,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute of Population and Public Health; Engineering and Physical Sciences Research Council; King Saud University","keywords":"Computer science; C-RAN; Radio access network; Cache; Cloud computing; Telecommunications link; Beamforming; Admission control; Computer network; Power control; Integer programming; Quality of service; Optimization problem; Real-time computing; Power (physics); Base station; Telecommunications; Algorithm","score_opus":0.020051422122363834,"score_gpt":0.20852550136964096,"score_spread":0.18847407924727713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010044096","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032310538,0.0006363245,0.9656629,0.00016132611,0.00004349078,0.0005834635,0.000015218467,0.00037823681,0.00020850671],"genre_scores_gemma":[0.96057504,0.0000050766384,0.038876474,0.00014477142,0.00024000199,0.000058483423,0.000014643891,0.00006968599,0.000015811744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999012,0.000016221351,0.0002855506,0.00026309927,0.0001276024,0.00029552545],"domain_scores_gemma":[0.99946076,0.000052753097,0.00010046647,0.00007315028,0.00011186805,0.00020100581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012088135,0.00022135138,0.00031773458,0.000047174344,0.00018071222,0.00007852727,0.00006515078,0.00009675066,0.000008064641],"category_scores_gemma":[0.00006469199,0.00019248336,0.000032394724,0.00016030282,0.00003051412,0.00045116828,0.000011572342,0.00016472895,0.0000013370487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003069266,0.00001792217,0.00021060472,0.0017096134,0.0000628045,0.000006467606,0.0028031142,0.8371309,0.14361863,0.000020482825,0.00020229054,0.01391022],"study_design_scores_gemma":[0.0020549127,0.0001458173,0.000019852014,0.0003486418,0.000057032426,0.000013484435,0.00035554726,0.97507423,0.021039406,0.00008832564,0.00050551485,0.00029724126],"about_ca_topic_score_codex":0.0000059466524,"about_ca_topic_score_gemma":0.0000038958697,"teacher_disagreement_score":0.9282645,"about_ca_system_score_codex":0.00006377331,"about_ca_system_score_gemma":0.00003740579,"threshold_uncertainty_score":0.7849244},"labels":[],"label_agreement":null},{"id":"W3021477173","doi":"10.1049/iet-spr.2019.0180","title":"Multiwindow discrete Gabor transform using parallel lattice structures","year":2020,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Lattice (music); Gabor transform; Artificial intelligence; Gabor wavelet; Computer vision; Pattern recognition (psychology); Time–frequency analysis; Physics; Wavelet transform; Discrete wavelet transform; Acoustics; Wavelet","score_opus":0.04766600184260269,"score_gpt":0.31292647396022205,"score_spread":0.2652604721176194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021477173","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006225322,0.0007933883,0.98965997,0.0018381246,0.00009194321,0.00017975728,0.0000035189132,0.00026486503,0.0009431324],"genre_scores_gemma":[0.57668424,0.0000030246676,0.42141393,0.0016297305,0.00021701168,0.0000028946445,0.0000016485957,0.00002039636,0.000027112226],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979022,0.00014682362,0.00036940366,0.00059114845,0.00048595414,0.0005044805],"domain_scores_gemma":[0.9991655,0.00010263723,0.00014173535,0.00020849345,0.0001304485,0.00025119042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003342394,0.0002824832,0.0003187773,0.00007308907,0.00042228255,0.000671047,0.000863777,0.00010431633,0.000032683776],"category_scores_gemma":[0.000048710674,0.00024344701,0.00012183208,0.0006045111,0.000085766806,0.0015091104,0.0001170701,0.00035762286,0.000014296028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027578702,0.00005540832,0.00012779806,0.000525133,0.00006700991,0.00030738697,0.013385766,0.017393477,0.22840726,0.0030117175,0.00023378401,0.73620945],"study_design_scores_gemma":[0.0011928632,0.00011045999,0.00015636465,0.0000977564,0.000042322474,0.0000549543,0.00011774531,0.941265,0.044375185,0.010879104,0.0011945959,0.00051364495],"about_ca_topic_score_codex":0.000022835287,"about_ca_topic_score_gemma":8.661784e-7,"teacher_disagreement_score":0.9238715,"about_ca_system_score_codex":0.000033592813,"about_ca_system_score_gemma":0.00019693094,"threshold_uncertainty_score":0.9927481},"labels":[],"label_agreement":null},{"id":"W3032977126","doi":"10.1049/iet-spr.2019.0245","title":"Learning‐based design of random measurement matrix for compressed sensing with inter‐column correlation using copula function","year":2020,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Copula (linguistics); Computer science; Correlation; Pattern recognition (psychology); Compressed sensing; Random matrix; Artificial intelligence; Algorithm; Mathematics; Econometrics; Eigenvalues and eigenvectors; Physics","score_opus":0.058812487455528674,"score_gpt":0.2476509266920826,"score_spread":0.18883843923655394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3032977126","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011328414,0.00033939263,0.98706967,0.000026378388,0.000056087305,0.00056212523,0.0000015622578,0.0005720522,0.000044329772],"genre_scores_gemma":[0.9283716,0.0000014692142,0.07140587,0.000048818183,0.00009090292,0.000007445592,0.000008939648,0.00006297185,0.000001994296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988062,0.00008222033,0.00034018443,0.00022793475,0.0003270445,0.00021643484],"domain_scores_gemma":[0.99916404,0.00009211014,0.0001909722,0.000082758306,0.00040538947,0.00006473728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027902806,0.00021323789,0.0003293646,0.00008911343,0.00015443104,0.00008018045,0.0000762796,0.00008971592,0.0000061612623],"category_scores_gemma":[0.00004195468,0.00020896013,0.0000658147,0.00023162458,0.000043789314,0.00018375019,0.000012276574,0.0002042206,7.9254767e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084164017,0.000010404842,0.000053061445,0.00020962926,0.000038039176,0.000002073861,0.00019217301,0.73528624,0.25527385,0.0000010663864,0.00010505447,0.007986769],"study_design_scores_gemma":[0.0014620153,0.00033068145,0.000010849388,0.0007678077,0.00014350058,0.0000041077624,0.000091822374,0.86612165,0.13068621,0.000061052226,0.000109691675,0.000210593],"about_ca_topic_score_codex":0.00001063968,"about_ca_topic_score_gemma":0.0000011339895,"teacher_disagreement_score":0.91704315,"about_ca_system_score_codex":0.000080478276,"about_ca_system_score_gemma":0.00007869369,"threshold_uncertainty_score":0.8521147},"labels":[],"label_agreement":null},{"id":"W3126051646","doi":"10.1049/sil2.12011","title":"Robust Wiener filter‐based time gating method for detection of shallowly buried objects","year":2021,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Constant false alarm rate; Clutter; Wiener filter; Computer science; Gating; Filter (signal processing); Detector; Artificial intelligence; Object detection; Pattern recognition (psychology); Computer vision; Matched filter; Algorithm; Radar; Telecommunications","score_opus":0.028736012525713044,"score_gpt":0.27448226309363344,"score_spread":0.2457462505679204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126051646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023275282,0.00012487115,0.9755455,0.000044849985,0.000030408499,0.0001341834,0.0000102889435,0.00013410603,0.0007004843],"genre_scores_gemma":[0.61314344,2.8073546e-7,0.38658288,0.000028425377,0.00011144931,0.000057962156,0.0000091605325,0.000023714043,0.000042684493],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992344,0.000036984555,0.00022605988,0.00019889839,0.000115098264,0.00018856395],"domain_scores_gemma":[0.9993637,0.00024827034,0.00006701761,0.000109670706,0.00016190814,0.00004942406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000204467,0.000120020355,0.00019700397,0.000037020123,0.000104325736,0.00004184864,0.00007254759,0.00006592756,0.000030010564],"category_scores_gemma":[0.00006495854,0.00012332232,0.00008463686,0.000324429,0.000016750044,0.000102955375,0.0000133822405,0.000105360436,0.0000041225317],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051185043,0.00002159715,0.0000015738227,0.0002665689,0.000010030512,4.937483e-7,0.00007230133,0.069936186,0.8200083,0.000017236647,0.000010274865,0.10965031],"study_design_scores_gemma":[0.00014054093,0.000016022344,0.000043661355,0.000068707355,0.000021993666,0.0000010433279,0.00002861216,0.51979584,0.47873792,0.0009535316,0.000106101754,0.000086035485],"about_ca_topic_score_codex":0.000004023917,"about_ca_topic_score_gemma":0.0000015149014,"teacher_disagreement_score":0.5898682,"about_ca_system_score_codex":0.000021399725,"about_ca_system_score_gemma":0.000045189867,"threshold_uncertainty_score":0.5028938},"labels":[],"label_agreement":null},{"id":"W3129052808","doi":"10.1049/sil2.12012","title":"On optimum multi‐input multi‐output radar signal design: Ambiguity function, manifold structure and duration‐bandwidth","year":2021,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematics; Bandwidth (computing); Convex optimization; Mathematical optimization; Algorithm; Control theory (sociology); Regular polygon; Computer science; Telecommunications","score_opus":0.02201977373978155,"score_gpt":0.2281003762787936,"score_spread":0.20608060253901203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129052808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04715666,0.005131334,0.9460641,0.00007237093,0.00037548502,0.00031900418,0.000029822175,0.00050705654,0.00034414136],"genre_scores_gemma":[0.9680726,0.00001998581,0.030579438,0.00023390897,0.00039515176,0.000017124408,0.000039714516,0.00012037179,0.0005217237],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99732804,0.0001367788,0.00069502566,0.0006999415,0.0005510615,0.0005891492],"domain_scores_gemma":[0.998896,0.000117480966,0.0001822864,0.00024552704,0.00030424635,0.00025448407],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036423496,0.0005506983,0.000515722,0.00016870142,0.00068999594,0.0006797639,0.00019484402,0.00031420417,0.00016967859],"category_scores_gemma":[0.000043921842,0.00052860624,0.00010259152,0.0004739759,0.00007293049,0.0007518651,0.000055010565,0.00060384534,0.000019370576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000360509,0.00032981284,0.0009334268,0.0031445804,0.00042975848,0.0005403671,0.0028816785,0.3267518,0.35880396,0.00025548585,0.003537869,0.30203077],"study_design_scores_gemma":[0.0025459488,0.00022275785,0.0016490683,0.0008987025,0.00014927119,0.00027698954,0.00046015898,0.933872,0.05651095,0.0011023066,0.0010620882,0.0012497443],"about_ca_topic_score_codex":0.000017273616,"about_ca_topic_score_gemma":0.000019631765,"teacher_disagreement_score":0.9209159,"about_ca_system_score_codex":0.00013896583,"about_ca_system_score_gemma":0.0002095797,"threshold_uncertainty_score":0.9997165},"labels":[],"label_agreement":null},{"id":"W3134920422","doi":"10.1049/iet-spr.2020.0316","title":"Compact S‐transform for analysing local spectrum","year":2020,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Power Quality and Harmonics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computation; Interpolation (computer graphics); Series (stratigraphy); Spectrum (functional analysis); Fast Fourier transform; Plot (graphics); Fourier transform; Point (geometry); Algorithm; Discrete Fourier transform (general); Mathematics; Magnitude (astronomy); Computer science; Short-time Fourier transform; Mathematical analysis; Fourier analysis; Physics; Geometry; Artificial intelligence; Statistics; Image (mathematics)","score_opus":0.04089034815527034,"score_gpt":0.2658374649650737,"score_spread":0.22494711680980337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134920422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059353784,0.000934452,0.98768395,0.0017184903,0.00004757959,0.00012512859,0.0000177921,0.00039438574,0.0031428563],"genre_scores_gemma":[0.9983348,0.00000919021,0.00091682776,0.000451408,0.0002226367,0.0000029906803,0.000015362177,0.000035735364,0.000011020381],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991164,0.000008533942,0.00023762256,0.00016807271,0.00014881484,0.00032055491],"domain_scores_gemma":[0.9997101,0.00003646373,0.0000276607,0.000055627923,0.000022994382,0.00014716915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013038584,0.00015145518,0.00022525905,0.000037864644,0.00012734895,0.000105368636,0.00013197506,0.00006428084,0.00005358866],"category_scores_gemma":[0.0000054877037,0.00015526342,0.000104510575,0.00023430002,0.000040446626,0.00025908326,0.000006133469,0.00019245736,0.000018203204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026851686,0.00008199626,0.00013111887,0.0036608246,0.00031074934,0.000032508982,0.0095888525,0.5676731,0.03282321,0.0008372196,0.005347227,0.3792447],"study_design_scores_gemma":[0.00036512862,0.000058019898,0.00003091923,0.00006218046,0.00005358662,0.0000034271513,0.00026022637,0.9411876,0.043911602,0.0017826724,0.01203057,0.00025405182],"about_ca_topic_score_codex":0.000004337851,"about_ca_topic_score_gemma":0.000005164521,"teacher_disagreement_score":0.99239945,"about_ca_system_score_codex":0.000053590047,"about_ca_system_score_gemma":0.00004170627,"threshold_uncertainty_score":0.6331458},"labels":[],"label_agreement":null},{"id":"W3135360189","doi":"10.1049/iet-spr.2019.0587","title":"Design of <i>p</i> ‐norm linear phase FIR differentiators using adaptive modification rate artificial bee colony algorithm","year":2020,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Differentiator; Finite impulse response; Algorithm; Linear phase; Norm (philosophy); Adaptive filter; Computer science; Mathematics; Mathematical optimization; Filter (signal processing)","score_opus":0.14448299499383227,"score_gpt":0.3316646171062672,"score_spread":0.18718162211243494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135360189","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016249893,0.000052969648,0.9829705,0.00019170705,0.00004746021,0.00031918864,0.000017833245,0.000102378144,0.000048092475],"genre_scores_gemma":[0.8547019,0.0000016321187,0.1448569,0.00030084382,0.00009238763,0.0000105044965,0.000014612017,0.000015320738,0.0000059310073],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984166,0.000117483534,0.00047292176,0.00040498204,0.00032562012,0.0002623655],"domain_scores_gemma":[0.99915,0.00007056214,0.00034404322,0.000108269014,0.0001984007,0.0001287568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023603118,0.00018434267,0.00022557234,0.00008414063,0.00016522479,0.0002494242,0.00039356374,0.000053161057,0.000013337174],"category_scores_gemma":[0.000017801998,0.00018417565,0.000055842218,0.000573863,0.000056966473,0.0014528543,0.000087152104,0.00011000779,0.000010715395],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013897073,0.0002241619,0.00000679407,0.00005848972,0.00002696952,0.000008556932,0.0021449905,0.005311073,0.60242075,0.00041713804,0.00012281282,0.38911927],"study_design_scores_gemma":[0.0004431478,0.00036218154,0.00000959315,0.000034857407,0.000019062423,0.0000020183863,0.00007686219,0.7704779,0.22704193,0.0013501093,0.000025437797,0.00015693881],"about_ca_topic_score_codex":0.000007852126,"about_ca_topic_score_gemma":2.0296403e-7,"teacher_disagreement_score":0.838452,"about_ca_system_score_codex":0.000043503736,"about_ca_system_score_gemma":0.00020880283,"threshold_uncertainty_score":0.7510465},"labels":[],"label_agreement":null},{"id":"W3165838274","doi":"10.1049/sil2.12046","title":"Sensor fusion with high‐order moments constraints using projection‐based neural network","year":2021,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Sensor fusion; Gaussian; Moment (physics); Computer science; Wireless sensor network; Fusion; Artificial neural network; Projection (relational algebra); Algorithm; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.02525139603234954,"score_gpt":0.2572707327974981,"score_spread":0.23201933676514858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165838274","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078764975,0.00026386167,0.9192325,0.00034478612,0.00049011066,0.0001681197,0.00000682168,0.000354753,0.0003740569],"genre_scores_gemma":[0.70539767,0.000003101095,0.29343387,0.00068132975,0.00034469157,0.0000059757413,0.000027519522,0.000025121115,0.00008071313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975861,0.00015830844,0.00034382677,0.00073198066,0.0005822599,0.0005975695],"domain_scores_gemma":[0.9986696,0.000099857716,0.00021804699,0.00038069612,0.00048349242,0.00014830731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024872285,0.00027405442,0.00026470653,0.00008873116,0.00074687705,0.000630774,0.00036982106,0.0001196254,0.00011344605],"category_scores_gemma":[0.000019691217,0.00023526867,0.00005190592,0.0014298388,0.00014405526,0.00067389815,0.00017507472,0.00035683514,0.000010248966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010961972,0.00031868235,0.0038947042,0.0001842686,0.00005378001,0.0007967591,0.0004509109,0.68871,0.019076508,0.0003954163,0.0012230397,0.2847863],"study_design_scores_gemma":[0.0008433863,0.000082476814,0.00043923862,0.00048378677,0.000027485492,0.00027324446,0.00008586982,0.99191326,0.0044920743,0.00026001508,0.00067956967,0.00041958137],"about_ca_topic_score_codex":0.000023011879,"about_ca_topic_score_gemma":0.000005745119,"teacher_disagreement_score":0.6266327,"about_ca_system_score_codex":0.000057834186,"about_ca_system_score_gemma":0.00045798463,"threshold_uncertainty_score":0.9593978},"labels":[],"label_agreement":null},{"id":"W3217581544","doi":"10.1049/sil2.12080","title":"BCI‐control and monitoring system for smart home automation using wavelet classifiers","year":2021,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Brain–computer interface; Computer science; Electroencephalography; Artificial intelligence; Wavelet; Pattern recognition (psychology); Feature extraction; Data acquisition; Interface (matter); Signal processing; Speech recognition; Digital signal processing; Computer hardware","score_opus":0.04484829437629914,"score_gpt":0.28734292481552787,"score_spread":0.24249463043922873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217581544","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8165994,0.00030675295,0.18195054,0.00015312231,0.0004623873,0.00017719532,0.000010769674,0.0001766113,0.00016317678],"genre_scores_gemma":[0.9944003,0.0000020207274,0.0050653983,0.000119387405,0.00030845765,0.000013555456,0.0000011162085,0.000022956348,0.00006675658],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99871767,0.000082800216,0.00025548012,0.00042962545,0.00021739479,0.00029704554],"domain_scores_gemma":[0.99936366,0.00023062526,0.00014007855,0.00009238601,0.00009650918,0.00007673804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020943065,0.00015281915,0.00020730235,0.00007132687,0.00041552776,0.00045563257,0.00011499352,0.00007489596,0.0000019706397],"category_scores_gemma":[0.000062784704,0.00014525754,0.00005036143,0.00018765738,0.000053003634,0.0005062952,0.000041802858,0.00011958308,0.0000022795973],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030125813,0.000018235922,0.00052409124,0.00059584447,0.0000072636094,0.000026478652,0.00043081277,0.0005784237,0.9678111,0.00008642667,0.000018537576,0.029872635],"study_design_scores_gemma":[0.0005896532,0.000036286234,0.0002836918,0.00053356594,0.00002468543,0.00012331866,0.00040422607,0.46425083,0.53327733,0.00016765393,0.00013722754,0.00017151929],"about_ca_topic_score_codex":0.0000035396185,"about_ca_topic_score_gemma":3.8282036e-7,"teacher_disagreement_score":0.4636724,"about_ca_system_score_codex":0.000084081425,"about_ca_system_score_gemma":0.0001137655,"threshold_uncertainty_score":0.5923431},"labels":[],"label_agreement":null},{"id":"W4200592094","doi":"10.1049/sil2.12091","title":"Underwater source localization using time difference of arrival and frequency difference of arrival measurements based on an improved invasive weed optimization algorithm","year":2021,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Cramér–Rao bound; Algorithm; Computer science; Position (finance); Mean squared error; Time of arrival; Underwater; Noise (video); Gaussian; Gaussian noise; Upper and lower bounds; Mathematics; Control theory (sociology); Estimation theory; Artificial intelligence; Statistics; Physics; Telecommunications","score_opus":0.021243461222240797,"score_gpt":0.22510503022311631,"score_spread":0.20386156900087551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200592094","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09448423,0.000113140086,0.9049444,0.000006702066,0.000037389524,0.00015705207,0.000012784532,0.00016321761,0.000081054066],"genre_scores_gemma":[0.9696331,0.0000055324854,0.030190192,0.000031399417,0.00002361296,0.000007542504,0.000059483154,0.00003934821,0.000009801327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868315,0.00006825966,0.00040961802,0.00028458552,0.0003315317,0.00022287409],"domain_scores_gemma":[0.9991962,0.00004303062,0.00016515098,0.00017452883,0.00036330422,0.00005780904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012127446,0.00022750525,0.0003051508,0.00016359681,0.0001151403,0.00006547129,0.00014098702,0.0001694242,0.000036890066],"category_scores_gemma":[0.00007234547,0.00022142146,0.00003763166,0.00036340658,0.0001234028,0.00020954706,0.000033545046,0.00013236384,4.335876e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011690853,0.000044047756,0.0010125095,0.00024751233,0.000015202603,0.0000014033816,0.00023453376,0.6117644,0.37345782,0.0000032765172,4.1865437e-7,0.013207194],"study_design_scores_gemma":[0.00034622708,0.000068041474,0.00015723742,0.0002748795,0.000029490093,0.0000020566542,0.00012532024,0.64265543,0.3559415,0.00024976238,2.1243885e-7,0.00014980721],"about_ca_topic_score_codex":0.000022487742,"about_ca_topic_score_gemma":0.0000048441384,"teacher_disagreement_score":0.87514883,"about_ca_system_score_codex":0.00007440441,"about_ca_system_score_gemma":0.00013080356,"threshold_uncertainty_score":0.9029305},"labels":[],"label_agreement":null},{"id":"W4207075585","doi":"10.1049/sil2.12101","title":"Perfusion MRI in automatic classification of multiple sclerosis lesion subtypes","year":2022,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Lesion; Magnetic resonance imaging; Perfusion; Fluid-attenuated inversion recovery; Medicine; Segmentation; Multiple sclerosis; Pattern recognition (psychology); Hyperintensity; Artificial intelligence; Radiology; Computer science; Pathology","score_opus":0.09418724323760255,"score_gpt":0.27994551173246074,"score_spread":0.1857582684948582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4207075585","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9956069,0.000051746963,0.0027590499,0.0005625417,0.00008669868,0.00022002301,0.0000053130025,0.000110717236,0.0005970233],"genre_scores_gemma":[0.9994504,0.000010605192,0.00020180237,0.00014013376,0.000015940981,0.00007304016,0.0000046283117,0.000017065191,0.00008640971],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844235,0.0002562321,0.00035303176,0.00034752092,0.00043505477,0.00016583245],"domain_scores_gemma":[0.99935085,0.00015429649,0.00027936962,0.00014198634,0.000036703088,0.000036801917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042362502,0.00009944247,0.00013214149,0.00022476958,0.00041377064,0.000039672905,0.00020310972,0.000037331396,0.0002586084],"category_scores_gemma":[0.00012838235,0.00010278424,0.00004106488,0.0008501286,0.00007423645,0.00023738382,0.000067707035,0.00022486161,0.000012025037],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003475147,0.0001432211,0.0015246305,0.000051668223,2.8304316e-7,8.742281e-7,0.00060300855,0.00067830295,0.9108883,0.000078612895,0.000047417103,0.08594891],"study_design_scores_gemma":[0.00045750797,0.00008690731,0.07268651,0.000080956364,0.0000048883862,0.000009634925,0.001012345,0.54290366,0.38206172,0.00027931487,0.00027907823,0.00013750457],"about_ca_topic_score_codex":0.000017995457,"about_ca_topic_score_gemma":0.0000082198985,"teacher_disagreement_score":0.5422253,"about_ca_system_score_codex":0.0001334477,"about_ca_system_score_gemma":0.000087669345,"threshold_uncertainty_score":0.41914195},"labels":[],"label_agreement":null},{"id":"W4313563682","doi":"10.1049/sil2.12183","title":"A robust feedforward hybrid active noise control system with online secondary‐path modelling","year":2023,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Japan Society for the Promotion of Science; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Feed forward; Computer science; Active noise control; Control theory (sociology); Narrowband; Decoupling (probability); Finite impulse response; Band-pass filter; Electronic engineering; Noise reduction; Engineering; Algorithm; Telecommunications; Artificial intelligence; Control engineering","score_opus":0.022496848986273448,"score_gpt":0.21833312176947298,"score_spread":0.19583627278319954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313563682","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.079167336,0.0001764258,0.914959,0.000021327593,0.000051714916,0.00031977476,0.00017413961,0.004367756,0.00076251203],"genre_scores_gemma":[0.9602313,0.000011170871,0.03919227,0.000025500023,0.00018858127,0.00008311031,0.000055812816,0.00015403861,0.00005819258],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984387,0.000022997121,0.0003134228,0.00038780153,0.000299997,0.00053707033],"domain_scores_gemma":[0.99937207,0.000060601662,0.00010290061,0.00019346492,0.00014702244,0.00012397346],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000148599,0.0003576977,0.00040347595,0.00020691901,0.00018196054,0.00009492964,0.00022958081,0.00007600176,0.000010262098],"category_scores_gemma":[0.0000056824924,0.00032870896,0.000064928136,0.00043122927,0.000060962706,0.0005962233,0.000042828833,0.00046202127,0.000022959535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009572843,0.000014845655,0.000015823267,0.00060513325,0.000051747775,0.000121629724,0.00019829205,0.9747834,0.0077830786,0.00003828554,0.00007770942,0.01621432],"study_design_scores_gemma":[0.00060689874,0.00008757855,0.00005114366,0.0010585494,0.00003540353,0.000059036836,0.0003756946,0.9854287,0.011478712,0.00015983167,0.00023426149,0.00042418722],"about_ca_topic_score_codex":0.000009861201,"about_ca_topic_score_gemma":0.000003381758,"teacher_disagreement_score":0.881064,"about_ca_system_score_codex":0.00024526246,"about_ca_system_score_gemma":0.00007561082,"threshold_uncertainty_score":0.9999165},"labels":[],"label_agreement":null},{"id":"W4388662267","doi":"10.1049/2023/6610762","title":"Preset Conditional Generative Adversarial Network for Massive MIMO Detection","year":2023,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Fundamental Research Funds for the Central Universities","keywords":"Computer science; MIMO; Detector; Channel (broadcasting); Noise (video); SIGNAL (programming language); Detection theory; Artificial intelligence; Signal-to-noise ratio (imaging); Algorithm; Artificial neural network; Pattern recognition (psychology); Speech recognition; Telecommunications; Image (mathematics)","score_opus":0.03912166990310937,"score_gpt":0.2836421454314026,"score_spread":0.24452047552829323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388662267","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003978152,0.000038377693,0.99333805,0.0012302267,0.0003786386,0.0004215755,0.000025922207,0.0004240577,0.00016500625],"genre_scores_gemma":[0.97131574,0.0000012849846,0.026643531,0.00018241041,0.0012146826,0.0002505552,0.00017204735,0.000019135716,0.00020061352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983651,0.00008870299,0.000296268,0.0004949128,0.00039603907,0.0003589478],"domain_scores_gemma":[0.99891037,0.0002724358,0.00024022386,0.00016444507,0.00032817348,0.00008436968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000382052,0.00015869466,0.00015181844,0.00013952807,0.0006154059,0.00029175697,0.00035497657,0.00011021521,0.000017656692],"category_scores_gemma":[0.000060127633,0.00016327358,0.00007625884,0.00084526074,0.000059633487,0.0010084718,0.000078248326,0.00012793131,0.000061321814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001555034,0.00007253532,0.00020092503,0.00012581803,0.00007145741,0.0000111789805,0.0013610851,0.69220746,0.09856018,0.02861335,0.0149605675,0.16365992],"study_design_scores_gemma":[0.00047595327,0.00007671699,0.0015524582,0.00003198383,0.000010048947,0.0000033820897,0.000038427246,0.9284445,0.014952878,0.05268252,0.0015350453,0.00019610346],"about_ca_topic_score_codex":0.0000038596477,"about_ca_topic_score_gemma":0.000006292889,"teacher_disagreement_score":0.9673376,"about_ca_system_score_codex":0.00009958034,"about_ca_system_score_gemma":0.00019277583,"threshold_uncertainty_score":0.66581035},"labels":[],"label_agreement":null},{"id":"W4390966193","doi":"10.1049/2024/6666549","title":"MsDC‐DEQ‐Net: Deep Equilibrium Model (DEQ) with Multiscale Dilated Convolution for Image Compressive Sensing (CS)","year":2024,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Block (permutation group theory); Algorithm; Residual; Compressed sensing; Iterative reconstruction; Iterative method; Sampling (signal processing); Convolution (computer science); Artificial intelligence; Computation; Artificial neural network; Mathematics; Computer vision","score_opus":0.014471770583780349,"score_gpt":0.24269548610896902,"score_spread":0.22822371552518866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390966193","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.067188576,0.0028358044,0.9250036,0.000069496804,0.0001339114,0.00045610318,0.000027319898,0.0027123576,0.0015728287],"genre_scores_gemma":[0.9397081,0.0000126776085,0.059689634,0.000055658536,0.0002113094,0.00002284549,0.000051104857,0.00015716435,0.000091493705],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983614,0.000024105797,0.0003392951,0.0004750734,0.00025836262,0.0005417719],"domain_scores_gemma":[0.999282,0.00010153354,0.00006907947,0.00020401427,0.00023291583,0.00011044899],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000117300966,0.00038242605,0.00034669516,0.00016234748,0.00018134581,0.00041497988,0.00015995148,0.0001746589,0.000009326351],"category_scores_gemma":[0.0000098033115,0.00034875722,0.00010192746,0.00029069674,0.00013501784,0.000678453,0.00005216973,0.00034389316,0.000010502794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089856796,0.00002185709,0.000006785588,0.0005266417,0.000092197035,0.00009178968,0.0006751168,0.32061532,0.6399602,0.000047311572,0.0017005415,0.03617237],"study_design_scores_gemma":[0.0003028699,0.000054178578,0.00000778875,0.0010854818,0.00008673218,0.000065550725,0.000052821422,0.8110973,0.18488029,0.0017050023,0.000255777,0.00040615996],"about_ca_topic_score_codex":0.000015570266,"about_ca_topic_score_gemma":0.000008623009,"teacher_disagreement_score":0.87251955,"about_ca_system_score_codex":0.00011442,"about_ca_system_score_gemma":0.00007281731,"threshold_uncertainty_score":0.99989647},"labels":[],"label_agreement":null},{"id":"W4413061064","doi":"10.1049/sil2/7543401","title":"Automatic Epilepsy Seizure Classification Using EEG Signals Based on the CNN‐LSTM Model","year":2025,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Epilepsy; Electroencephalography; Computer science; Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Deep learning; Epileptic seizure; Gradient descent; Speech recognition; Artificial neural network; Neuroscience; Psychology","score_opus":0.07586318202429679,"score_gpt":0.3210713234522854,"score_spread":0.2452081414279886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413061064","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56302357,0.00007159377,0.42672604,0.0037695474,0.00014747928,0.00041545727,0.00000895429,0.00030716474,0.005530216],"genre_scores_gemma":[0.99014527,9.639122e-7,0.0022511287,0.007164428,0.00005555028,0.00002616407,0.0000016473101,0.00002438289,0.00033048273],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798214,0.00024069157,0.00039338163,0.0005692083,0.00045050867,0.00036409183],"domain_scores_gemma":[0.99860334,0.00071461446,0.00022549949,0.0003098661,0.00008860732,0.0000580976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004901706,0.00025243335,0.00022060577,0.00018343839,0.00068491907,0.00046145453,0.0005742646,0.000107647524,0.00007904064],"category_scores_gemma":[0.00018650087,0.00017676107,0.000092732225,0.0006590143,0.00016374304,0.0003174391,0.000066036075,0.00035697856,0.000029414952],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029399778,0.00012334074,0.000046119472,0.00017107851,0.0000050987314,0.0000058177757,0.0003640789,0.15381187,0.8242584,0.00090629735,0.0011095264,0.019168954],"study_design_scores_gemma":[0.00015821973,0.000032262797,0.00006709399,0.0005659613,0.000018579263,0.0000031872364,0.0000756495,0.7867569,0.20898409,0.0031090009,0.00007570365,0.00015339562],"about_ca_topic_score_codex":0.0000032900198,"about_ca_topic_score_gemma":4.877328e-7,"teacher_disagreement_score":0.632945,"about_ca_system_score_codex":0.00008927746,"about_ca_system_score_gemma":0.00033890942,"threshold_uncertainty_score":0.7208107},"labels":[],"label_agreement":null},{"id":"W654790286","doi":"10.1049/iet-spr.2014.0173","title":"Mean angle of arrival, angular and Doppler spreads estimation in multiple‐input multiple‐output system","year":2015,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Estimator; Transmitter; Algorithm; Angle of arrival; MIMO; Direction of arrival; Mathematics; Gaussian; Rayleigh fading; Doppler effect; Computer science; Channel (broadcasting); Statistics; Control theory (sociology); Fading; Telecommunications; Physics; Decoding methods","score_opus":0.03512724157525543,"score_gpt":0.2685694669163852,"score_spread":0.2334422253411298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W654790286","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15655941,0.00030053593,0.84185547,0.000048252026,0.000071151975,0.00027897005,0.000003828107,0.0003076056,0.00057479815],"genre_scores_gemma":[0.79561794,0.0000012359873,0.20430335,0.000012585196,0.000017619808,0.000019671132,0.000003638376,0.000013299095,0.000010636615],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983149,0.0001039909,0.0005547759,0.0003549602,0.0004664484,0.00020491821],"domain_scores_gemma":[0.99873513,0.00014201958,0.00039096345,0.0002674676,0.00035121493,0.00011321884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000785749,0.00017564523,0.00032241433,0.0003218285,0.00006577965,0.000108423745,0.00036975773,0.00010106428,0.0000011626473],"category_scores_gemma":[0.00021910563,0.00017252236,0.00004033044,0.000598938,0.000109450295,0.0011539527,0.00013720113,0.0001164597,0.0000023952714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021819294,0.0007500685,0.059277333,0.0044049774,0.00007467292,0.00005766733,0.029618226,0.05514958,0.073135935,0.0090870075,0.00077371276,0.7674526],"study_design_scores_gemma":[0.0005515191,0.00009684932,0.0019492178,0.0006240361,0.000009047059,0.000029408613,0.00021534332,0.9078103,0.08726633,0.0012471884,0.000025853986,0.00017494704],"about_ca_topic_score_codex":0.00023945533,"about_ca_topic_score_gemma":0.000020837018,"teacher_disagreement_score":0.85266066,"about_ca_system_score_codex":0.00010290299,"about_ca_system_score_gemma":0.0001537636,"threshold_uncertainty_score":0.7035257},"labels":[],"label_agreement":null}]}