{"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050557703,0.00020419911,0.99295557,0.00004485052,0.000025919739,0.000012162225,0.0000069066946,0.000050485356,0.0016442148],"genre_scores_gemma":[0.86693144,0.0010157453,0.12568055,0.00011019929,0.0001035501,0.00013837505,0.0000630508,0.000040964213,0.005916112],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892515,0.00034587798,0.000049660393,0.00024536607,0.0003208581,0.000113089554],"domain_scores_gemma":[0.9992054,0.0004619994,0.00013196454,0.00008803947,0.00009678637,0.00001572625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089332263,0.000813767,0.0008551324,0.0003292098,0.00033635806,0.0011437786,0.00061859254,0.0011949132,0.0017360548],"category_scores_gemma":[0.002361608,0.00026278244,0.0008007456,0.00043506848,0.001036215,0.001185997,0.0011666807,0.0010249184,0.0003969349],"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":[0.000094960924,0.000019214849,0.00014910869,0.0001386031,0.00003776557,0.0001259705,0.00009616078,0.8950462,0.012267751,0.04130791,0.0003621816,0.05035422],"study_design_scores_gemma":[0.000008841884,0.00008224484,0.00010161191,0.000017082604,0.000015752159,0.00007628969,0.00002396577,0.98191375,0.0048579006,0.011037446,0.0018508991,0.000014120721],"about_ca_topic_score_codex":0.0010455168,"about_ca_topic_score_gemma":0.00059918454,"teacher_disagreement_score":0.0017360548,"about_ca_system_score_codex":0.00049958646,"about_ca_system_score_gemma":0.0005668203,"threshold_uncertainty_score":0.005807638},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049762563,0.00019317263,0.9926186,0.000080404476,0.000056524907,0.00006496837,0.000058220507,0.0010013649,0.0009504799],"genre_scores_gemma":[0.08369028,0.000269899,0.9120468,0.00007255233,0.00008723151,0.00023469009,0.0005101774,0.00023141301,0.0028569899],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882454,0.00015175824,0.0000805756,0.00027827505,0.0005204081,0.00014435234],"domain_scores_gemma":[0.99789965,0.00063290494,0.00014154082,0.0003268147,0.00089377665,0.0001052949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010475712,0.001348727,0.0016106188,0.0029235973,0.0011040084,0.0014851424,0.0024619633,0.0014562766,0.005569497],"category_scores_gemma":[0.0059133894,0.0007740348,0.0014231055,0.0026498542,0.0009391185,0.0022446634,0.0021599743,0.0019423732,0.0026556787],"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":[0.00023700552,0.0000937102,0.0013641367,0.0001681809,0.000084377105,0.00016752665,0.00020227006,0.13838935,0.009996608,0.016601836,0.007986094,0.8247089],"study_design_scores_gemma":[0.0000397533,0.00009061561,0.00042300846,0.000013589963,0.000027181442,0.00017894403,0.000056173154,0.97538066,0.0046699937,0.01205443,0.0070335106,0.000032112406],"about_ca_topic_score_codex":0.005672293,"about_ca_topic_score_gemma":0.0054508257,"teacher_disagreement_score":0.005672293,"about_ca_system_score_codex":0.00072763144,"about_ca_system_score_gemma":0.0024774182,"threshold_uncertainty_score":0.018631816},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03357747,0.0005160138,0.9636342,0.0004455924,0.000053371026,0.000052456217,0.00006235309,0.00016604655,0.0014925383],"genre_scores_gemma":[0.5692482,0.0006715757,0.42636192,0.00017914621,0.00017313377,0.00013340329,0.00037462005,0.00009130376,0.0027666397],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988807,0.0004439856,0.000107457534,0.00022010409,0.00029142623,0.000056395267],"domain_scores_gemma":[0.9989542,0.00051387085,0.00011047758,0.00010293253,0.00027796396,0.000040566687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014631645,0.0006761266,0.0010725902,0.0013796929,0.0003810016,0.0010725647,0.000907289,0.0010294513,0.00092941243],"category_scores_gemma":[0.0048688175,0.000241453,0.0009811999,0.0007761739,0.00076933985,0.001151399,0.0008521772,0.00066941336,0.00052798016],"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":[0.00029168846,0.00017453115,0.0035483001,0.00028427388,0.00026647182,0.00026866043,0.0003120435,0.39502272,0.018677996,0.051410053,0.0036811603,0.5260621],"study_design_scores_gemma":[0.0000075347057,0.00006974424,0.0012378743,0.0000107913,0.000015303267,0.0000616346,0.000029703471,0.98158574,0.0015970891,0.014457326,0.0009059402,0.000021310183],"about_ca_topic_score_codex":0.0027828284,"about_ca_topic_score_gemma":0.0015571772,"teacher_disagreement_score":0.0027828284,"about_ca_system_score_codex":0.0010039036,"about_ca_system_score_gemma":0.00064947596,"threshold_uncertainty_score":0.007737994},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061375536,0.00039775838,0.98718864,0.00011198425,0.00007348955,0.000013892536,0.000011647057,0.00011356196,0.005951445],"genre_scores_gemma":[0.5743012,0.0017368103,0.41313487,0.00018248412,0.00024633444,0.0000852353,0.000048745504,0.00013778251,0.010126529],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999553,0.00015071969,0.00001942447,0.00004358651,0.00020630902,0.000026932375],"domain_scores_gemma":[0.9993569,0.00039470423,0.00004847048,0.00007591236,0.000102641265,0.000021280353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008149527,0.00040592093,0.00028735856,0.00095419335,0.00023846484,0.0010747813,0.0004628007,0.00045936106,0.0021716757],"category_scores_gemma":[0.002441637,0.00014731527,0.00034321146,0.00062513363,0.0010781453,0.0010385143,0.0005704682,0.0011108319,0.0005160022],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010302265,0.000027038823,0.00032420683,0.00012400573,0.000019363584,0.0002570103,0.000311047,0.06899444,0.023870341,0.78940517,0.0011921655,0.115372196],"study_design_scores_gemma":[0.000011377596,0.00012937392,0.00047758032,0.000032368596,0.000015523072,0.0003310002,0.00006583418,0.7423917,0.011102191,0.23179321,0.013608031,0.000041767453],"about_ca_topic_score_codex":0.0006091598,"about_ca_topic_score_gemma":0.0004749604,"teacher_disagreement_score":0.0021716757,"about_ca_system_score_codex":0.00050231855,"about_ca_system_score_gemma":0.0003148138,"threshold_uncertainty_score":0.0072649717},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035235516,0.0007052135,0.9943499,0.00019626354,0.000087258944,0.000026840387,0.000095297386,0.0003280664,0.000687639],"genre_scores_gemma":[0.4271577,0.0032449344,0.5539649,0.00046884245,0.00032067872,0.00045132387,0.0014608293,0.0002549016,0.012675844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989403,0.00039997674,0.000069662274,0.0002276049,0.00026571623,0.00009675155],"domain_scores_gemma":[0.99877864,0.0007009277,0.00012133168,0.00015872305,0.00020538447,0.000035035282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00172991,0.0008511684,0.0010412957,0.00086505443,0.00042063274,0.0010400495,0.0022422376,0.0016244774,0.0028035787],"category_scores_gemma":[0.004107155,0.0004844033,0.0017544084,0.0015250338,0.0007422836,0.0014192963,0.00088060286,0.001992193,0.0013204418],"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":[0.00026559958,0.00012267381,0.003005139,0.00031174696,0.000290625,0.00028033115,0.00018149597,0.53218955,0.0073915944,0.10133076,0.008558633,0.34607187],"study_design_scores_gemma":[0.0000049131186,0.000022007125,0.0002492991,0.000009581532,0.000015636822,0.00003876301,0.000006230923,0.98425496,0.00070182007,0.012976203,0.0017059442,0.000014552995],"about_ca_topic_score_codex":0.0069275936,"about_ca_topic_score_gemma":0.0060975016,"teacher_disagreement_score":0.0069275936,"about_ca_system_score_codex":0.0010234022,"about_ca_system_score_gemma":0.0013505316,"threshold_uncertainty_score":0.013774514},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018913236,0.0005231219,0.9781965,0.00015703072,0.00008050767,0.000039069586,0.000026224952,0.00018080624,0.0018835521],"genre_scores_gemma":[0.42417938,0.0014810424,0.56621635,0.00025707224,0.00024874246,0.00008621014,0.00015975167,0.00007750426,0.0072939214],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99963033,0.000119488635,0.000023237917,0.00007022941,0.00013186627,0.000024783838],"domain_scores_gemma":[0.9992441,0.0004254539,0.000066320616,0.00010125568,0.00013500584,0.000027888047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067393866,0.0008093073,0.000624171,0.0003916936,0.00023069888,0.00036316348,0.000575081,0.00072056224,0.000957314],"category_scores_gemma":[0.0018058151,0.00028430266,0.0005673803,0.0002999496,0.00069855986,0.00096247805,0.0008852438,0.00083387445,0.00041613413],"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":[0.0008308993,0.00016492791,0.0008279568,0.0005659758,0.00011636421,0.0003988309,0.0003724262,0.24989985,0.21370114,0.020162001,0.0023795883,0.51058006],"study_design_scores_gemma":[0.000026221314,0.00019964483,0.00048060337,0.000030339614,0.00003894662,0.0003409007,0.000050295228,0.9194175,0.07209276,0.003048847,0.0042449418,0.000028984794],"about_ca_topic_score_codex":0.0005962154,"about_ca_topic_score_gemma":0.0009857743,"teacher_disagreement_score":0.000957314,"about_ca_system_score_codex":0.00016635066,"about_ca_system_score_gemma":0.00038768543,"threshold_uncertainty_score":0.003564179},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006678797,0.00017053678,0.9924402,0.000020304487,0.00002679068,0.000012236096,0.000016050977,0.00024504797,0.00038990047],"genre_scores_gemma":[0.22099721,0.0006899427,0.7742841,0.00004709047,0.00007406633,0.00006807068,0.00022173485,0.00013582733,0.0034820195],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996958,0.000041401214,0.000020246462,0.00007926062,0.00013915211,0.000024169849],"domain_scores_gemma":[0.9995797,0.00020203156,0.00006103272,0.00004858482,0.00009886768,0.00000978204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048166464,0.0005636528,0.0006395491,0.000718411,0.00026958168,0.0005742178,0.0006417628,0.00068250357,0.0011496932],"category_scores_gemma":[0.0019176515,0.0002969101,0.0005852534,0.00045826833,0.0002654284,0.0008364788,0.00038047042,0.00069265784,0.00060597376],"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":[0.00016547772,0.000073743344,0.0022277602,0.0002914607,0.00008604156,0.00014782726,0.00020078238,0.15292482,0.05469596,0.009373113,0.0014551008,0.7783579],"study_design_scores_gemma":[0.0000137328625,0.000072687595,0.0022525308,0.000024126186,0.000030573465,0.00015843562,0.000023753888,0.97453487,0.015611338,0.0028453365,0.004406756,0.00002585991],"about_ca_topic_score_codex":0.0027545071,"about_ca_topic_score_gemma":0.0028560872,"teacher_disagreement_score":0.0027545071,"about_ca_system_score_codex":0.0003526246,"about_ca_system_score_gemma":0.0005355426,"threshold_uncertainty_score":0.0054769516},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032324065,0.0011938207,0.96391475,0.00010983313,0.0000728309,0.000025903679,0.000051838073,0.00040414004,0.0019027517],"genre_scores_gemma":[0.7494404,0.0006700289,0.2468996,0.00009265523,0.00004670506,0.000024826359,0.00008960613,0.00004707469,0.0026891148],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974483,0.000044192126,0.000018577433,0.000042366504,0.00012122434,0.000028838755],"domain_scores_gemma":[0.9997142,0.00010765474,0.00004411208,0.000057901234,0.000066730594,0.0000093651615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019581841,0.0003308633,0.00047877253,0.0003332251,0.00021221452,0.0005592331,0.0005870163,0.00039273547,0.0016500219],"category_scores_gemma":[0.0007551472,0.00019737537,0.0003139533,0.00035346538,0.00034424732,0.0011143305,0.00032544823,0.00031201972,0.00043711145],"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":[0.0008300788,0.000104888415,0.0009903015,0.0005782297,0.00012497512,0.0003373122,0.00018942803,0.21372204,0.2759359,0.06923123,0.0029399507,0.4350157],"study_design_scores_gemma":[0.000047268444,0.0003038172,0.0003339748,0.00002298444,0.0000618948,0.0005819535,0.000027978316,0.87417966,0.10888259,0.008250095,0.0072586494,0.00004907478],"about_ca_topic_score_codex":0.0004233723,"about_ca_topic_score_gemma":0.0006401684,"teacher_disagreement_score":0.0016500219,"about_ca_system_score_codex":0.0003040597,"about_ca_system_score_gemma":0.00027446594,"threshold_uncertainty_score":0.0055199265},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007687075,0.00009011827,0.99172103,0.000030286445,0.000008798807,0.000011066647,0.000019374545,0.00010013642,0.00033218344],"genre_scores_gemma":[0.19063672,0.00023061573,0.8068714,0.000071792674,0.000040778712,0.00010345207,0.00023102206,0.00008530642,0.0017289137],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992879,0.00024722234,0.000050028157,0.00009912588,0.00027136345,0.000044390683],"domain_scores_gemma":[0.9973847,0.0016761315,0.00021395145,0.0003223228,0.000328828,0.00007400052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091652374,0.00073241437,0.000877633,0.0009767574,0.00038464667,0.00074825576,0.00083134434,0.0007335439,0.0011874931],"category_scores_gemma":[0.004536601,0.0003774873,0.0005691924,0.000706966,0.0005911928,0.0020322353,0.0010760137,0.0007274013,0.0005088899],"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":[0.00066881353,0.00012288506,0.0021692428,0.00044287863,0.00012468614,0.00024853085,0.00031351516,0.43178046,0.07329687,0.08229715,0.0020132104,0.4065218],"study_design_scores_gemma":[0.000017883474,0.00006793108,0.00026114038,0.000013221114,0.000009392015,0.00009476043,0.000020332309,0.9714353,0.0134447785,0.013616864,0.0009966092,0.000021853632],"about_ca_topic_score_codex":0.0006050031,"about_ca_topic_score_gemma":0.0008252713,"teacher_disagreement_score":0.0011874931,"about_ca_system_score_codex":0.00041195963,"about_ca_system_score_gemma":0.00059503806,"threshold_uncertainty_score":0.0048471093},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017807415,0.00028307416,0.9755366,0.00012469687,0.00004020298,0.000023004739,0.000059554117,0.000058501348,0.006066985],"genre_scores_gemma":[0.83649534,0.00085931446,0.1574528,0.00016607445,0.000096525284,0.000119038785,0.00012620405,0.00004386532,0.0046407916],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998946,0.0002692931,0.000047543253,0.00013592336,0.00046366086,0.00013754972],"domain_scores_gemma":[0.99742377,0.0013162702,0.00025714067,0.00042170286,0.0005183765,0.00006270929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007476706,0.00068900327,0.0004304005,0.000616111,0.00041031817,0.0012320627,0.00051434635,0.00061113667,0.0014440439],"category_scores_gemma":[0.004339975,0.00019359305,0.00041992345,0.0010208669,0.0013400269,0.0011047486,0.0011821402,0.00090126466,0.00039721117],"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":[0.00015845061,0.000032788626,0.000925196,0.00016858024,0.00004196554,0.00024062303,0.00023372013,0.17939481,0.02922866,0.7017338,0.0011116697,0.08672983],"study_design_scores_gemma":[0.000023145712,0.0001515842,0.0006091983,0.000059618567,0.000028852155,0.00078976684,0.00007554807,0.7729667,0.036313202,0.18105312,0.007864539,0.00006465045],"about_ca_topic_score_codex":0.0008780784,"about_ca_topic_score_gemma":0.00073665945,"teacher_disagreement_score":0.0014440439,"about_ca_system_score_codex":0.0006017579,"about_ca_system_score_gemma":0.0010468418,"threshold_uncertainty_score":0.0048307776},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031737823,0.00023941103,0.9955499,0.000059205628,0.000025319407,0.000009282646,0.00003267351,0.00018650507,0.0007239895],"genre_scores_gemma":[0.44553638,0.0029230264,0.53757644,0.00024179368,0.00018808979,0.00021949968,0.00081804534,0.00036032032,0.012136401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993463,0.00018911717,0.000025847245,0.00015871605,0.0002201395,0.000059864633],"domain_scores_gemma":[0.99922514,0.00035915725,0.000076310906,0.000088939545,0.00022816756,0.000022344468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011410406,0.0011853944,0.0010384584,0.0011761754,0.00033658263,0.0009483389,0.0016421074,0.001281712,0.0018434708],"category_scores_gemma":[0.0032136329,0.000520618,0.0010567049,0.0018542574,0.00073766825,0.001929696,0.00070955645,0.0015088308,0.0011484289],"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":[0.00012659784,0.00005881044,0.0015090619,0.00019944804,0.00010347031,0.00026423932,0.00024694327,0.80199134,0.009907256,0.067504264,0.0036606595,0.11442795],"study_design_scores_gemma":[0.0000029438977,0.000008877415,0.00020816378,0.000007187724,0.000008988142,0.000057522222,0.000010870138,0.99274033,0.00049175025,0.005508676,0.00094516634,0.000009549132],"about_ca_topic_score_codex":0.005390442,"about_ca_topic_score_gemma":0.003898404,"teacher_disagreement_score":0.005390442,"about_ca_system_score_codex":0.0006397171,"about_ca_system_score_gemma":0.00080414687,"threshold_uncertainty_score":0.010718107},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038411722,0.00009515727,0.99559116,0.00004280534,0.000013179472,0.000011124402,0.000005773262,0.00008902658,0.0003106708],"genre_scores_gemma":[0.28046036,0.00036811156,0.7164633,0.0000968421,0.00006575877,0.00017843732,0.00010476681,0.000049011614,0.0022132928],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99952626,0.00015778953,0.00003221189,0.000099860474,0.00015256557,0.000031469932],"domain_scores_gemma":[0.99917895,0.00046519798,0.00010673249,0.000069721726,0.00016212146,0.000017324288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000588482,0.0006752457,0.00061357685,0.00035093806,0.0002945168,0.0004359255,0.00057533046,0.001074033,0.00094967487],"category_scores_gemma":[0.0032380004,0.00043408998,0.00030016215,0.0004204421,0.00054192665,0.00054114335,0.0003802994,0.0008685908,0.0003484109],"study_design_candidate":"theoretical_or_conceptual","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":[0.00025497717,0.000077542885,0.0008280712,0.0003025445,0.00006167583,0.00015037731,0.00021313346,0.45287132,0.0735598,0.02062992,0.0014913244,0.4495593],"study_design_scores_gemma":[0.000010475982,0.000056880526,0.00022413381,0.000013152977,0.000007655713,0.00007936073,0.000009439742,0.9863418,0.010408951,0.0019143273,0.0009235958,0.000010255943],"about_ca_topic_score_codex":0.0012274617,"about_ca_topic_score_gemma":0.0010317354,"teacher_disagreement_score":0.0012274617,"about_ca_system_score_codex":0.0002760279,"about_ca_system_score_gemma":0.0006376283,"threshold_uncertainty_score":0.0031769872},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.941307,0.00047801732,0.056082826,0.000086352564,0.000038346832,0.00001917485,0.0000469326,0.0003050922,0.0016362555],"genre_scores_gemma":[0.98912394,0.00018948886,0.009740711,0.000036983736,0.00001386196,0.000009087792,0.00004882277,0.000013755791,0.0008232112],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990941,0.00020034553,0.000044062184,0.00019660074,0.00025226013,0.00021261178],"domain_scores_gemma":[0.99761415,0.0013586764,0.00024140038,0.00022905055,0.00045256398,0.00010422749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009318268,0.00065132877,0.0006476951,0.00052684476,0.0003916906,0.0007072887,0.0006130782,0.001167773,0.00071686745],"category_scores_gemma":[0.005217526,0.00026011624,0.00031136448,0.000663983,0.00075008196,0.00078434905,0.0006094188,0.0004480791,0.00042319536],"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":[0.0060673007,0.00023917668,0.023623189,0.0006011519,0.00036186413,0.0021842981,0.00067871925,0.3185033,0.5055441,0.0037259085,0.00060676713,0.1378643],"study_design_scores_gemma":[0.00014478703,0.004788789,0.025181348,0.00007063864,0.00028414806,0.0035768815,0.00041399917,0.55225545,0.41031823,0.0014506548,0.00135862,0.00015649853],"about_ca_topic_score_codex":0.001207582,"about_ca_topic_score_gemma":0.00078253,"teacher_disagreement_score":0.001207582,"about_ca_system_score_codex":0.00041062772,"about_ca_system_score_gemma":0.0003926344,"threshold_uncertainty_score":0.0049280524},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029633138,0.00047661713,0.9684748,0.00006726121,0.00007858939,0.000022543267,0.00002247629,0.0001303409,0.0010941968],"genre_scores_gemma":[0.30192024,0.001479531,0.69317234,0.000106387764,0.00009372611,0.00005350324,0.00019586597,0.00013561819,0.0028428517],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963236,0.000058142774,0.000023589104,0.00007681891,0.0001880127,0.000020981843],"domain_scores_gemma":[0.9994803,0.0001626254,0.000066307126,0.00008054188,0.0001808534,0.000029340612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006954395,0.00048086297,0.0007271612,0.0008269015,0.0002766491,0.00076477067,0.0006682698,0.0008424906,0.0009442038],"category_scores_gemma":[0.002152366,0.00022750883,0.0007267943,0.00094310375,0.00052357104,0.0011600245,0.0005052663,0.00082271127,0.0005629666],"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":[0.0003379711,0.000103923536,0.0015705026,0.0003928335,0.00011528216,0.00038700903,0.00021041636,0.056775745,0.39585125,0.029196074,0.0016215143,0.51343745],"study_design_scores_gemma":[0.000028398515,0.00020945635,0.0022487112,0.000042192212,0.00010017664,0.00083068805,0.00006369467,0.869697,0.108452015,0.009212495,0.009064938,0.0000503076],"about_ca_topic_score_codex":0.00045841906,"about_ca_topic_score_gemma":0.00077687204,"teacher_disagreement_score":0.0009442038,"about_ca_system_score_codex":0.00018653783,"about_ca_system_score_gemma":0.00032257056,"threshold_uncertainty_score":0.003677845},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049100053,0.00012298713,0.9943498,0.00003855319,0.000016534761,0.00000777318,0.000032689783,0.000047122292,0.0004744858],"genre_scores_gemma":[0.30580738,0.0015712865,0.6893338,0.00008208483,0.00017376481,0.00007760963,0.0003943196,0.00006481857,0.0024949042],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99970204,0.00007201021,0.000017554476,0.000051449053,0.00013566633,0.000021335036],"domain_scores_gemma":[0.9991646,0.0004365305,0.00012419204,0.00013062156,0.0001232223,0.000020725201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007131416,0.0005711717,0.0005761456,0.0006225615,0.00019818894,0.0005627123,0.0006008341,0.00066175644,0.0009564133],"category_scores_gemma":[0.0029054487,0.00022600386,0.0004583887,0.0007670537,0.0006910425,0.0011170167,0.0005391689,0.0006681844,0.00034257976],"study_design_candidate":"theoretical_or_conceptual","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":[0.00029726236,0.00006564153,0.0010920523,0.0005434787,0.00009033117,0.00034674432,0.00030863294,0.48085198,0.08443315,0.09238965,0.0019876508,0.33759344],"study_design_scores_gemma":[0.0000046622126,0.00004484874,0.00036262945,0.00002690338,0.000014546541,0.00020314024,0.000022626242,0.9747242,0.012300979,0.010482954,0.0017934579,0.000019018207],"about_ca_topic_score_codex":0.00065572915,"about_ca_topic_score_gemma":0.00070729206,"teacher_disagreement_score":0.0009564133,"about_ca_system_score_codex":0.00027204305,"about_ca_system_score_gemma":0.00040959226,"threshold_uncertainty_score":0.003771484},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015378151,0.00028852368,0.9827663,0.000053543725,0.00005889355,0.000036230776,0.000023130535,0.00018927529,0.0012059747],"genre_scores_gemma":[0.305493,0.0007831399,0.6875611,0.00010416502,0.00015549047,0.00008952651,0.0001982686,0.00005373976,0.005561461],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993555,0.00011703363,0.00004568519,0.00009075037,0.00031655785,0.00007458731],"domain_scores_gemma":[0.9993999,0.00012392596,0.00009912686,0.00018410913,0.00016708154,0.000025915868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009277238,0.00065852416,0.0006029092,0.00075337756,0.00034770576,0.0007609363,0.00083781936,0.0006751656,0.0015089018],"category_scores_gemma":[0.0017920015,0.0002559154,0.00057422544,0.00047383015,0.0004065832,0.0011667162,0.0010450712,0.0007549601,0.00080392085],"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":[0.0007597874,0.00016746963,0.0012647993,0.00028431672,0.000092566785,0.00029067963,0.0003112228,0.14253978,0.16267988,0.0306157,0.0016690327,0.6593249],"study_design_scores_gemma":[0.000041422878,0.00023452468,0.0007409007,0.000059661295,0.000046611254,0.0004930246,0.00004864576,0.9310529,0.05651544,0.0041253306,0.0065863114,0.000055167046],"about_ca_topic_score_codex":0.0006172136,"about_ca_topic_score_gemma":0.0011798987,"teacher_disagreement_score":0.0015089018,"about_ca_system_score_codex":0.00024379356,"about_ca_system_score_gemma":0.0004492546,"threshold_uncertainty_score":0.0050477386},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049489355,0.00008117886,0.9941626,0.000061125094,0.000010021541,0.000009808195,0.000016252317,0.00008869518,0.0006214244],"genre_scores_gemma":[0.3904966,0.0005672186,0.6041603,0.00018794522,0.00011569575,0.00013381886,0.00024597437,0.00012089157,0.0039714817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994203,0.00021272035,0.000022437542,0.00006927588,0.00023104143,0.000044292232],"domain_scores_gemma":[0.9985921,0.00088520686,0.0001284049,0.00012396887,0.00022531468,0.000044913326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011300547,0.00068691536,0.00074128615,0.0003018461,0.00017263113,0.00058891124,0.0006153677,0.000860037,0.0012600039],"category_scores_gemma":[0.0043543847,0.00022569485,0.0004184842,0.00049595296,0.00051557005,0.00094091066,0.0011364488,0.0012199257,0.00043869705],"study_design_candidate":"theoretical_or_conceptual","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":[0.00026733786,0.00008548332,0.0010866794,0.00024427247,0.000061779894,0.00022752916,0.00012620621,0.7238594,0.038904447,0.066702075,0.0034730255,0.16496164],"study_design_scores_gemma":[0.000008009891,0.000032142852,0.00010514851,0.000004389791,0.0000032879582,0.00005846683,0.000005302486,0.9943857,0.0025693278,0.0023099342,0.000513327,0.0000050110993],"about_ca_topic_score_codex":0.0008001408,"about_ca_topic_score_gemma":0.00089033996,"teacher_disagreement_score":0.0012600039,"about_ca_system_score_codex":0.00022517446,"about_ca_system_score_gemma":0.0006756841,"threshold_uncertainty_score":0.005976379},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39496854,0.00042737543,0.5976116,0.00013385588,0.000043286644,0.00010946753,0.0000290436,0.0005127292,0.0061641154],"genre_scores_gemma":[0.5741898,0.00019690854,0.42315897,0.00003432877,0.00001114747,0.00007026121,0.0000519239,0.00004096924,0.0022457023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977857,0.000039006572,0.000009298819,0.000024416184,0.00013152495,0.000017197126],"domain_scores_gemma":[0.9995192,0.00025091603,0.000044618886,0.000038954044,0.00012984284,0.0000164248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071868015,0.00035951944,0.0004517951,0.00037375127,0.00012904895,0.0003616592,0.00030602535,0.0005424434,0.0007030251],"category_scores_gemma":[0.0011184308,0.00015319779,0.00033752268,0.0003656883,0.00026818787,0.0004334325,0.00018345244,0.00025697483,0.0001291271],"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":[0.00055466994,0.0002257536,0.00439408,0.0002734799,0.00014385844,0.00020585842,0.00028667747,0.43017337,0.23844005,0.003874166,0.0007331314,0.32069483],"study_design_scores_gemma":[0.00005525916,0.00033716857,0.004141709,0.000013578048,0.000042638505,0.00021715867,0.00004220892,0.8990379,0.09385041,0.00048156682,0.001750697,0.000029715473],"about_ca_topic_score_codex":0.0011209704,"about_ca_topic_score_gemma":0.0011986336,"teacher_disagreement_score":0.0011209704,"about_ca_system_score_codex":0.00032362586,"about_ca_system_score_gemma":0.0003319207,"threshold_uncertainty_score":0.0038008094},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031669874,0.00011193862,0.9673902,0.000030292062,0.0000128499005,0.000018513458,0.000016555538,0.0004258591,0.000323873],"genre_scores_gemma":[0.59262127,0.00012927639,0.4048163,0.000033696084,0.000027696347,0.00006301559,0.0001723913,0.00009266897,0.002043656],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997967,0.0000369912,0.000012204721,0.000053471576,0.00007990809,0.000020869358],"domain_scores_gemma":[0.99977046,0.00010089462,0.000026438576,0.000030454885,0.00006123527,0.000010463118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044450685,0.00070234376,0.00047477597,0.00028828578,0.00017103579,0.0003828736,0.0005242252,0.0004980255,0.001012752],"category_scores_gemma":[0.0011415374,0.00023764388,0.00038819693,0.00023083134,0.00021475603,0.00069319044,0.00042565772,0.00056836597,0.00047275168],"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":[0.00035488151,0.000202494,0.00074723235,0.000076324,0.000059530143,0.00009200095,0.00006249758,0.3220794,0.13672563,0.0024474359,0.0007541107,0.5363985],"study_design_scores_gemma":[0.000009374294,0.0000611874,0.00037688663,0.0000025946017,0.000008291821,0.00003457777,0.000008974409,0.9800602,0.01838117,0.00055560115,0.00049541565,0.0000056803287],"about_ca_topic_score_codex":0.0014912474,"about_ca_topic_score_gemma":0.0024575477,"teacher_disagreement_score":0.0014912474,"about_ca_system_score_codex":0.00024268824,"about_ca_system_score_gemma":0.0003374598,"threshold_uncertainty_score":0.0033879876},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032200662,0.00027105838,0.9954314,0.000044722863,0.000044558787,0.000020739239,0.000019213194,0.00020344536,0.00074486167],"genre_scores_gemma":[0.0957845,0.0007085189,0.8974856,0.00014414436,0.000111196256,0.000097548116,0.00021700648,0.00013301546,0.0053184167],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99958247,0.000080800244,0.000027098215,0.00008240457,0.0002038125,0.000023476277],"domain_scores_gemma":[0.9996147,0.0001227822,0.000030059331,0.000057536105,0.000156209,0.0000186625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004196345,0.00049667014,0.00047212368,0.00057943305,0.00024356332,0.00039380055,0.00060183334,0.00052758225,0.0023902364],"category_scores_gemma":[0.0009354442,0.00020724307,0.00054324593,0.0005419039,0.00028927805,0.00079942856,0.0006593761,0.000774936,0.0010802303],"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":[0.00027986645,0.000099552584,0.00048536406,0.00027810526,0.000100694284,0.00012528236,0.00014679939,0.042393606,0.1529077,0.012965687,0.0038089012,0.78640836],"study_design_scores_gemma":[0.000046503115,0.0002142659,0.000660157,0.000019963105,0.00004668686,0.00045167055,0.000030211362,0.918755,0.06169015,0.002651704,0.015397685,0.00003599419],"about_ca_topic_score_codex":0.00090987224,"about_ca_topic_score_gemma":0.0013945324,"teacher_disagreement_score":0.0023902364,"about_ca_system_score_codex":0.00022320302,"about_ca_system_score_gemma":0.00040312958,"threshold_uncertainty_score":0.007996142},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005676517,0.0054832897,0.98513454,0.00028454178,0.000068744994,0.000011405041,0.00002030353,0.00014318236,0.0031773457],"genre_scores_gemma":[0.49442765,0.018552754,0.47788534,0.0006171589,0.0007490487,0.00011635698,0.00021975616,0.0004680416,0.006963854],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966858,0.0011826191,0.0002983009,0.00060427573,0.000995717,0.00023328273],"domain_scores_gemma":[0.98857695,0.009504703,0.00039376208,0.0007873669,0.0006385505,0.000098766264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005767026,0.0011029526,0.0016978033,0.0011097277,0.0005407824,0.0029287557,0.0013095757,0.0031450207,0.0017098011],"category_scores_gemma":[0.01921873,0.0009983558,0.0009399864,0.0013318933,0.0018094408,0.0043919715,0.0011493126,0.0016343427,0.0006973729],"study_design_candidate":"theoretical_or_conceptual","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":[0.00024523353,0.00009063644,0.0015901458,0.00067702756,0.00018359437,0.00011513996,0.0002879607,0.23137058,0.0057379403,0.3210614,0.0027071561,0.43593317],"study_design_scores_gemma":[0.000023378461,0.00021666614,0.0009008147,0.00019030996,0.00009082623,0.00026947056,0.00010102997,0.7910108,0.00598247,0.19355516,0.0075683184,0.00009072026],"about_ca_topic_score_codex":0.0016268061,"about_ca_topic_score_gemma":0.0013327727,"teacher_disagreement_score":0.005767026,"about_ca_system_score_codex":0.0010038414,"about_ca_system_score_gemma":0.0009050377,"threshold_uncertainty_score":0.03049934},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051702266,0.00020703439,0.9913161,0.00006521725,0.000063262116,0.000033025288,0.000026872733,0.00073342095,0.0023847986],"genre_scores_gemma":[0.10821182,0.00042963962,0.88805616,0.000058027683,0.000062833664,0.00012090479,0.00021325573,0.00019148707,0.0026559625],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951065,0.00007936937,0.000036010628,0.00006663632,0.00025479618,0.000052658695],"domain_scores_gemma":[0.9992424,0.00023183653,0.00005601297,0.00019521457,0.00024948854,0.000025121293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052940455,0.0007385232,0.00058173254,0.0008231966,0.00050620316,0.0009841055,0.0009921849,0.00051932916,0.0035827903],"category_scores_gemma":[0.002016667,0.00031498104,0.0005629725,0.0011128798,0.00047979137,0.0012523559,0.00047995284,0.0008770779,0.0014843442],"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":[0.00043856833,0.00014064391,0.0009705976,0.00029134873,0.00008847296,0.00023493347,0.00017196118,0.14209327,0.07085898,0.059869003,0.0058965976,0.71894556],"study_design_scores_gemma":[0.00005166044,0.00010862028,0.00037746326,0.00001959097,0.000028096965,0.00025522764,0.00003144053,0.9365112,0.03766035,0.010117102,0.014806783,0.000032382122],"about_ca_topic_score_codex":0.0021804122,"about_ca_topic_score_gemma":0.002088023,"teacher_disagreement_score":0.0035827903,"about_ca_system_score_codex":0.0005883171,"about_ca_system_score_gemma":0.0011603793,"threshold_uncertainty_score":0.0119856},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013755902,0.000119348486,0.9979657,0.000029755623,0.000012184811,0.000009742932,0.000008760711,0.00012892531,0.0003499814],"genre_scores_gemma":[0.25025955,0.0008617565,0.7443831,0.00015824399,0.000107544394,0.00021221761,0.00020177408,0.00025347993,0.0035622849],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991792,0.0002589193,0.00004989931,0.00017178993,0.00028784655,0.000052290536],"domain_scores_gemma":[0.9988973,0.00049314374,0.00015401898,0.000094821306,0.00033691793,0.000023812228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011689144,0.0011366962,0.0008174435,0.00093706144,0.00047337168,0.00071229326,0.0008200696,0.00085184595,0.0016778567],"category_scores_gemma":[0.003628647,0.0004473824,0.0010538424,0.0009782452,0.0005182919,0.0010703156,0.0006500624,0.0013667445,0.0007112625],"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":[0.00016080418,0.000060249255,0.0008336077,0.00024808457,0.00018740127,0.00021180896,0.00020144624,0.7123256,0.022426829,0.026657758,0.0025821216,0.23410435],"study_design_scores_gemma":[0.0000042064735,0.000021325128,0.0001356997,0.000007077986,0.000011445508,0.0000310696,0.000006300607,0.9948856,0.0021537237,0.0019062001,0.0008240323,0.000013275123],"about_ca_topic_score_codex":0.0049783657,"about_ca_topic_score_gemma":0.004176314,"teacher_disagreement_score":0.0049783657,"about_ca_system_score_codex":0.00058327295,"about_ca_system_score_gemma":0.0016141789,"threshold_uncertainty_score":0.009898782},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00561704,0.0002049263,0.99146384,0.000062479805,0.000026024152,0.00002782309,0.000033226916,0.000072450195,0.0024921624],"genre_scores_gemma":[0.5905896,0.0011704132,0.4003253,0.00022868977,0.00014315026,0.00028792763,0.00023740274,0.00005360456,0.0069639436],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955493,0.00013157583,0.000017590535,0.00005360486,0.00017170119,0.000070576454],"domain_scores_gemma":[0.9997507,0.000074015305,0.000030711,0.000030764993,0.00009441394,0.000019428717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038824632,0.00073259725,0.0005091339,0.00026423758,0.00030637303,0.0005839048,0.00065313047,0.00047669874,0.001598649],"category_scores_gemma":[0.0007460233,0.00026358137,0.00033819053,0.0006300344,0.00040425308,0.0006228651,0.0007380233,0.0007201397,0.00059966155],"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":[0.0001310471,0.00008238965,0.0005184022,0.000218816,0.00007048552,0.00015294536,0.00017013508,0.7298325,0.042139947,0.047896918,0.0036069623,0.17517957],"study_design_scores_gemma":[0.000014468647,0.00011196099,0.00014775497,0.00001031763,0.000012716756,0.00005701462,0.000021363849,0.9879469,0.0036512415,0.005785451,0.0022289662,0.000011774185],"about_ca_topic_score_codex":0.002367287,"about_ca_topic_score_gemma":0.004581128,"teacher_disagreement_score":0.002367287,"about_ca_system_score_codex":0.00049490837,"about_ca_system_score_gemma":0.0011515658,"threshold_uncertainty_score":0.0053480268},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037360486,0.00031555071,0.95304847,0.000113105205,0.0001345383,0.000072451345,0.00006600829,0.0010093641,0.007880034],"genre_scores_gemma":[0.27825907,0.00032573906,0.7132837,0.00008224153,0.00008449169,0.00007661923,0.0002866009,0.000106347885,0.007495128],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997223,0.000029905235,0.000022628123,0.00004382286,0.00015319273,0.00002819625],"domain_scores_gemma":[0.9995828,0.0001265465,0.00004322585,0.00006537892,0.00016625167,0.000015761489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002966478,0.00044559606,0.00029036176,0.00058985385,0.0003170324,0.0008885099,0.0005696814,0.00033142013,0.0028077515],"category_scores_gemma":[0.0011715116,0.00015182166,0.00021678349,0.00053393224,0.00030449918,0.0008771707,0.00032126313,0.0004351792,0.00088224525],"study_design_candidate":"theoretical_or_conceptual","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":[0.00047179862,0.000082922605,0.0010984103,0.0002457921,0.00004694918,0.00031990343,0.00021508179,0.027583336,0.28927562,0.07944007,0.00351613,0.597704],"study_design_scores_gemma":[0.00013860596,0.00048080925,0.0011993042,0.00006635993,0.000055340828,0.0010972066,0.000084915715,0.48550063,0.4584402,0.01088616,0.041963954,0.000086575914],"about_ca_topic_score_codex":0.0011588753,"about_ca_topic_score_gemma":0.0021378421,"teacher_disagreement_score":0.0028077515,"about_ca_system_score_codex":0.00043488477,"about_ca_system_score_gemma":0.00088551844,"threshold_uncertainty_score":0.009392917},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00060781057,0.017055342,0.0012323165,0.07092063,0.759891,0.00014241053,0.0013170965,0.0008850591,0.14794837],"genre_scores_gemma":[0.008288834,0.021451782,0.0012093427,0.060728382,0.46233195,0.00016258445,0.0021145449,0.00082595233,0.44288665],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976318,0.00032344818,0.00018990562,0.00053931016,0.0010062768,0.00030928434],"domain_scores_gemma":[0.99183345,0.0012451179,0.0004576192,0.0007066865,0.0036029895,0.002154221],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002062881,0.0012371541,0.0010588983,0.0018206682,0.0016369091,0.006448778,0.0020461348,0.0044979313,0.34705004],"category_scores_gemma":[0.014258016,0.0004808632,0.0010162416,0.0008465996,0.0010855317,0.003653674,0.0024828766,0.0055953665,0.2213517],"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":[0.000033652814,0.000014620994,0.00008198964,0.00016419578,0.0000054333505,0.00016369842,0.000024543093,0.000025648877,0.00013443356,0.0015422907,0.9679593,0.029850064],"study_design_scores_gemma":[0.000008495846,0.000010540931,0.00010999049,0.00012468966,0.0000037781424,0.00029005844,0.00003196943,0.000022654527,0.00008243912,0.00072550995,0.99858534,0.0000044569947],"about_ca_topic_score_codex":0.0009004123,"about_ca_topic_score_gemma":0.0012461061,"teacher_disagreement_score":0.34705004,"about_ca_system_score_codex":0.0016353958,"about_ca_system_score_gemma":0.0029475286,"threshold_uncertainty_score":0.93135387},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01450924,0.000114316535,0.9842831,0.00004205173,0.000021106378,0.00002346575,0.000007631332,0.00012556305,0.00087344996],"genre_scores_gemma":[0.17995898,0.00020421021,0.8177572,0.00005533801,0.000028859606,0.000045675744,0.00003731161,0.000038894625,0.0018734852],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996711,0.000087011096,0.000026198046,0.000058633603,0.00013835618,0.000018724702],"domain_scores_gemma":[0.9995981,0.00017711968,0.000044769975,0.00006788678,0.00008878999,0.000023335904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005530469,0.00044854623,0.0003439247,0.00047385454,0.00021049817,0.0005664023,0.0006190508,0.0006468882,0.0009972639],"category_scores_gemma":[0.0012877948,0.00026231384,0.00058037875,0.00052506535,0.00042967434,0.0005402762,0.0005200655,0.0006028759,0.00029293713],"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":[0.00047029267,0.00014617266,0.0022456958,0.00025352114,0.00012412459,0.00030605504,0.00038628408,0.132781,0.22650039,0.036798287,0.0016267233,0.5983614],"study_design_scores_gemma":[0.000022686227,0.00015632164,0.00040250877,0.000009471088,0.000021742937,0.000270422,0.00001996032,0.95102316,0.04348704,0.0020591368,0.002505594,0.000021887112],"about_ca_topic_score_codex":0.0007585948,"about_ca_topic_score_gemma":0.00074164296,"teacher_disagreement_score":0.0009972639,"about_ca_system_score_codex":0.0002353812,"about_ca_system_score_gemma":0.00036789104,"threshold_uncertainty_score":0.0033361912},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022942123,0.00007895716,0.99653816,0.000030898318,0.000042339412,0.000013133211,0.000022579425,0.0002068874,0.0007728794],"genre_scores_gemma":[0.40420035,0.00080343726,0.58185166,0.00019369303,0.00014360972,0.00029732176,0.00046165317,0.00017902747,0.011869244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993237,0.00018001141,0.000029662013,0.00013368728,0.00027770136,0.0000552495],"domain_scores_gemma":[0.9994417,0.0001331413,0.000051715127,0.00007887104,0.00027906694,0.00001549502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008366789,0.00045096406,0.0010402387,0.00042120335,0.0003571936,0.0009831224,0.0008263178,0.0006900556,0.0022735244],"category_scores_gemma":[0.0017445774,0.00027276325,0.0006261414,0.0008284193,0.000487442,0.00068563444,0.000653311,0.0008507383,0.0007087988],"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":[0.0002761965,0.00006929165,0.0010253341,0.00022866265,0.00013618679,0.00011534876,0.00015149749,0.62287754,0.030691259,0.070186615,0.0037473163,0.27049482],"study_design_scores_gemma":[0.000010168285,0.000031883912,0.00013947973,0.000005158233,0.000009846497,0.000026443357,0.000004815367,0.9921308,0.0028716782,0.0023333735,0.002427984,0.000008375761],"about_ca_topic_score_codex":0.0048923697,"about_ca_topic_score_gemma":0.0023381184,"teacher_disagreement_score":0.0048923697,"about_ca_system_score_codex":0.0006313839,"about_ca_system_score_gemma":0.0010618238,"threshold_uncertainty_score":0.009727776},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030595927,0.00042096147,0.9672633,0.00014784165,0.000045766938,0.000031051386,0.00006211802,0.00025414865,0.0011789488],"genre_scores_gemma":[0.61475915,0.001109021,0.3802397,0.0001644415,0.00016443773,0.000094732975,0.00046098838,0.00007963573,0.0029279075],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965477,0.00008637175,0.000018995192,0.000051767114,0.00016190011,0.000026236898],"domain_scores_gemma":[0.9993636,0.0003034992,0.000080723104,0.00011405145,0.00011356175,0.000024566525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046359195,0.00053597643,0.00056498614,0.00046763287,0.00019251126,0.00042038382,0.00044167563,0.0005692288,0.00081627513],"category_scores_gemma":[0.0018256946,0.00016786516,0.00046613504,0.0007013184,0.00027161516,0.0007701475,0.00069668004,0.00054505473,0.00028674773],"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":[0.00077378243,0.00017001294,0.0032957338,0.00028843796,0.00016453787,0.00062953023,0.00017496149,0.29605404,0.09982489,0.01508685,0.003090602,0.58044654],"study_design_scores_gemma":[0.000020443542,0.00011456458,0.001014383,0.000014707795,0.000029725272,0.00039972708,0.00001940968,0.9735832,0.021179443,0.0017359793,0.00187398,0.0000144448495],"about_ca_topic_score_codex":0.0010074112,"about_ca_topic_score_gemma":0.0013379597,"teacher_disagreement_score":0.0010074112,"about_ca_system_score_codex":0.00015938551,"about_ca_system_score_gemma":0.00043451224,"threshold_uncertainty_score":0.0027307272},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049880445,0.000060605573,0.9944582,0.00001563162,0.00000943815,0.0000088144725,0.000007413362,0.00012711357,0.00032466487],"genre_scores_gemma":[0.2162147,0.00047228622,0.77929443,0.000046171215,0.000045527504,0.000106047606,0.00010361878,0.00007029348,0.003647028],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995921,0.00011858883,0.00002005671,0.000085716965,0.00015083775,0.000032646094],"domain_scores_gemma":[0.99948114,0.00027163967,0.000055432803,0.00006280413,0.00011213763,0.000016952676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007931552,0.00069204654,0.0007289919,0.00047208476,0.00035683112,0.0006440059,0.00061665534,0.00087819976,0.0011709917],"category_scores_gemma":[0.0016978363,0.0004043662,0.00067034445,0.0006147458,0.00054813403,0.0012323721,0.0007497897,0.0007924515,0.00074286957],"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":[0.00051629584,0.00014952195,0.001652461,0.00031909422,0.00016765708,0.00024321835,0.00017190653,0.295167,0.18334271,0.031415995,0.001173587,0.48568052],"study_design_scores_gemma":[0.000011818384,0.000069727444,0.00024554785,0.000008976979,0.000022333932,0.000119091754,0.000010852433,0.9650446,0.029810635,0.0030041928,0.0016356872,0.000016590286],"about_ca_topic_score_codex":0.00084260857,"about_ca_topic_score_gemma":0.00097389583,"teacher_disagreement_score":0.0011709917,"about_ca_system_score_codex":0.00023223246,"about_ca_system_score_gemma":0.00074433885,"threshold_uncertainty_score":0.004194677},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39384475,0.00075844495,0.60110545,0.00014503594,0.0002166921,0.00031957164,0.0002616826,0.00087690447,0.0024715008],"genre_scores_gemma":[0.6337491,0.0007128463,0.3624111,0.00008946758,0.00011352718,0.00023885093,0.0003984493,0.00006962164,0.002217032],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997004,0.00007499901,0.000024423962,0.00006701129,0.00009312554,0.000040053714],"domain_scores_gemma":[0.9994592,0.00028825225,0.000058076126,0.00005524129,0.00012229558,0.000016992122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005553846,0.00057650206,0.0005069692,0.00052188616,0.00016946149,0.00041662675,0.00027313747,0.0004449445,0.0009786955],"category_scores_gemma":[0.0015868116,0.00014260104,0.0003160079,0.00045312528,0.00015543924,0.00023792162,0.00019124558,0.00035769588,0.00072413223],"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":[0.0007534013,0.00036891433,0.0055657993,0.00023955686,0.000033368415,0.00016181458,0.00013596803,0.006838721,0.45218748,0.0006185654,0.0006287434,0.5324677],"study_design_scores_gemma":[0.00009156362,0.002238318,0.0933809,0.00008716505,0.00015474521,0.0012360453,0.00018401947,0.4791773,0.4147531,0.0012832213,0.007331292,0.0000822829],"about_ca_topic_score_codex":0.00039851002,"about_ca_topic_score_gemma":0.0006987646,"teacher_disagreement_score":0.0009786955,"about_ca_system_score_codex":0.00010939274,"about_ca_system_score_gemma":0.00020187424,"threshold_uncertainty_score":0.0032740831},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009140047,0.00094140135,0.9880987,0.00010572298,0.00007936475,0.000026681342,0.0000312308,0.00028080124,0.0012961526],"genre_scores_gemma":[0.54815936,0.0029643062,0.4407398,0.0001997931,0.0001441927,0.00018546094,0.00039451435,0.00020212776,0.0070104287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883336,0.00039280488,0.00010926622,0.0002134163,0.00037326178,0.000077862234],"domain_scores_gemma":[0.9960477,0.0028421553,0.00018294137,0.0002265836,0.0006640845,0.000036482772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024649082,0.0007974162,0.0008518971,0.00069219526,0.00034153103,0.0012014924,0.0006719765,0.0010305262,0.0022917935],"category_scores_gemma":[0.007900179,0.0003836204,0.0008555333,0.0006050887,0.000366294,0.0015613707,0.00065657526,0.00087508786,0.0005094638],"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":[0.0004152971,0.000091946284,0.0027718388,0.00039953858,0.00037378666,0.000100389436,0.00027425168,0.64482635,0.005771598,0.042226743,0.00129869,0.3014496],"study_design_scores_gemma":[0.000017264354,0.000061382816,0.0011007795,0.000022337092,0.000046660152,0.000034990888,0.000029105124,0.988839,0.0029076966,0.005157162,0.0017584235,0.000025279494],"about_ca_topic_score_codex":0.0084966365,"about_ca_topic_score_gemma":0.0065976586,"teacher_disagreement_score":0.0084966365,"about_ca_system_score_codex":0.00095422234,"about_ca_system_score_gemma":0.0011059896,"threshold_uncertainty_score":0.0168944},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021620918,0.00057335256,0.970539,0.00051100407,0.00009593954,0.00007020245,0.0001715412,0.00018310915,0.0062349383],"genre_scores_gemma":[0.7998358,0.0012243742,0.19273233,0.00065112684,0.00025647445,0.0003703418,0.00054113526,0.00018240834,0.0042060884],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99876887,0.0003052131,0.00010764222,0.0002783944,0.00039848883,0.00014141937],"domain_scores_gemma":[0.9965252,0.0018898832,0.0004541303,0.00027032333,0.0007193273,0.00014111526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015877716,0.001052356,0.00083526096,0.0008742952,0.0003524724,0.0010346047,0.0007607612,0.0013661764,0.0037741994],"category_scores_gemma":[0.010032814,0.00039999495,0.0007231464,0.00044906922,0.0018692039,0.0021859922,0.0017574087,0.0020770086,0.00093430484],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008847049,0.00015198819,0.0023601379,0.0017772914,0.00012564582,0.0014301573,0.00095392345,0.16981268,0.29226196,0.42820746,0.010597482,0.09143666],"study_design_scores_gemma":[0.00009389943,0.0005216742,0.0013550202,0.00013737097,0.00003743313,0.0010370912,0.00019716518,0.8167265,0.08431384,0.08789498,0.0075632264,0.00012172739],"about_ca_topic_score_codex":0.0005545137,"about_ca_topic_score_gemma":0.0003366659,"teacher_disagreement_score":0.0037741994,"about_ca_system_score_codex":0.00038160174,"about_ca_system_score_gemma":0.00097319094,"threshold_uncertainty_score":0.012625933},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034768544,0.0005912918,0.9581433,0.00028075822,0.00006756375,0.000085386426,0.000058307116,0.0002504891,0.00575447],"genre_scores_gemma":[0.9250895,0.00036544382,0.07090668,0.000077283636,0.000056650788,0.00010537019,0.000058320948,0.00003333537,0.003307417],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937314,0.00017928872,0.000020280155,0.000093921626,0.0001531236,0.00018027611],"domain_scores_gemma":[0.9992481,0.00040172014,0.00010553688,0.000034884244,0.00015031533,0.00005940312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092906185,0.0011493663,0.0009993169,0.00035838282,0.00054418575,0.0012056609,0.00093568803,0.0007823384,0.00188576],"category_scores_gemma":[0.0015269904,0.0003202479,0.0005255505,0.00067196816,0.0008379068,0.0007951183,0.00091710204,0.0011310256,0.00025805438],"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":[0.00015386863,0.00008760852,0.00030231813,0.000056784505,0.000029505833,0.00008870211,0.000032406657,0.9521133,0.005841827,0.008954004,0.000877772,0.03146196],"study_design_scores_gemma":[0.0000066932125,0.000029082446,0.000058310663,0.0000025983768,0.000004359945,0.000008782353,0.000006893315,0.99814093,0.00061057,0.00097328576,0.00015428502,0.000004229803],"about_ca_topic_score_codex":0.011913742,"about_ca_topic_score_gemma":0.011566897,"teacher_disagreement_score":0.011913742,"about_ca_system_score_codex":0.0011610466,"about_ca_system_score_gemma":0.0018089458,"threshold_uncertainty_score":0.023688793},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017861806,0.00014442876,0.97959614,0.000074599884,0.00004999671,0.000028174047,0.000035399935,0.0003685375,0.0018408924],"genre_scores_gemma":[0.13817523,0.00028762582,0.8584798,0.00005919683,0.00004301165,0.00007242359,0.00016049681,0.0000673211,0.0026550188],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969506,0.00005017394,0.000019377898,0.00005015691,0.00015519293,0.000030076657],"domain_scores_gemma":[0.9997131,0.000091349095,0.000032520373,0.000072009156,0.00006989438,0.000021088237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040835052,0.00046222867,0.00052413804,0.00067431,0.00031890886,0.0009299408,0.00054199353,0.00056523015,0.001899408],"category_scores_gemma":[0.00096639374,0.0002891617,0.0005717523,0.00091417064,0.00048604986,0.00124981,0.00064324134,0.0006223257,0.0009363693],"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":[0.0005036478,0.00014480708,0.0012808053,0.00017643224,0.00006617528,0.0002901833,0.00014307935,0.14311816,0.1292968,0.067772314,0.0025950873,0.6546124],"study_design_scores_gemma":[0.00005479934,0.00011218528,0.000418098,0.000016483365,0.000021769521,0.00029286847,0.000031074036,0.94715136,0.031880282,0.012889563,0.0071071205,0.000024389663],"about_ca_topic_score_codex":0.0013442042,"about_ca_topic_score_gemma":0.001899483,"teacher_disagreement_score":0.001899408,"about_ca_system_score_codex":0.0003794873,"about_ca_system_score_gemma":0.0008562931,"threshold_uncertainty_score":0.0063542128},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013085302,0.0000764917,0.998251,0.000041912954,0.000010065787,0.00001910457,0.0000146541,0.00007594726,0.00020223806],"genre_scores_gemma":[0.25209224,0.0005799704,0.74451756,0.0003065664,0.000110721805,0.0003406459,0.00034014758,0.00012744367,0.0015847285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986916,0.0004964899,0.000060823764,0.00029346766,0.0003724199,0.00008522072],"domain_scores_gemma":[0.9980003,0.001099476,0.00024039017,0.00018265784,0.00041080386,0.00006630436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015302021,0.0012003868,0.00096557965,0.0006018534,0.00034857486,0.00069717417,0.0010443124,0.0009448896,0.0015357463],"category_scores_gemma":[0.0058692712,0.0005276501,0.00063137914,0.0007643244,0.0008445583,0.0013506254,0.0010937366,0.0014427966,0.00072204805],"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":[0.0002757718,0.00014970834,0.0010644656,0.00046391835,0.00015023339,0.00020034642,0.00017856195,0.6277053,0.035185374,0.055821523,0.0039650155,0.27483976],"study_design_scores_gemma":[0.0000102121485,0.00005167451,0.0001184764,0.000011490591,0.000010860724,0.000059062393,0.0000076332,0.9921645,0.0030985377,0.0035577426,0.0008960201,0.000013853136],"about_ca_topic_score_codex":0.0014283349,"about_ca_topic_score_gemma":0.0018518972,"teacher_disagreement_score":0.0015357463,"about_ca_system_score_codex":0.0005347701,"about_ca_system_score_gemma":0.0013148616,"threshold_uncertainty_score":0.008092582},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010538656,0.00012662311,0.98870426,0.000018761957,0.00002131398,0.000014347025,0.000013145473,0.00027537806,0.0002874236],"genre_scores_gemma":[0.24671502,0.0003893082,0.74940616,0.00007032904,0.00004736109,0.000072102805,0.00013842042,0.0000792078,0.0030821525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996425,0.00005593006,0.000023220213,0.00007844254,0.00016466892,0.00003518202],"domain_scores_gemma":[0.999666,0.00009377763,0.00006465978,0.000041241954,0.00010810923,0.000026160538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047201142,0.00066829426,0.00069129973,0.0006717655,0.00017741529,0.00051788596,0.00056212425,0.0005468198,0.0012031376],"category_scores_gemma":[0.0008745123,0.000279105,0.0005267416,0.00038209444,0.00031700215,0.0006113144,0.00055540714,0.0005030041,0.00064620085],"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":[0.0003181564,0.00008851495,0.0008170972,0.00012312159,0.00006599065,0.00014470416,0.0001122373,0.041562475,0.47195303,0.008729073,0.00082432455,0.4752613],"study_design_scores_gemma":[0.000021775428,0.00023273399,0.001600563,0.00001816056,0.00004871512,0.0003267936,0.000018316508,0.8625963,0.12940255,0.002418317,0.003267863,0.000047935122],"about_ca_topic_score_codex":0.00062957586,"about_ca_topic_score_gemma":0.00068159844,"teacher_disagreement_score":0.0012031376,"about_ca_system_score_codex":0.00026349368,"about_ca_system_score_gemma":0.00060216844,"threshold_uncertainty_score":0.004024923},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008359841,0.00012365851,0.9903841,0.00006365989,0.0000064971,0.000016138612,0.0000068172644,0.000038130845,0.0010011317],"genre_scores_gemma":[0.5315231,0.00038972168,0.4654034,0.00010435018,0.000039152485,0.00017195087,0.000056339213,0.00007786453,0.0022341395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993901,0.00028101815,0.000024251616,0.00009101163,0.0001687633,0.000044683296],"domain_scores_gemma":[0.9987331,0.00088696333,0.00011996001,0.0000604202,0.00017199396,0.000027486196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013220129,0.00086113985,0.00089893874,0.00041995625,0.00021518256,0.0005630463,0.0004903266,0.0011379366,0.0014034958],"category_scores_gemma":[0.0034772344,0.00045728713,0.00055748515,0.0004487453,0.0007527185,0.0008503509,0.0006664482,0.00071300706,0.00027226828],"study_design_candidate":"theoretical_or_conceptual","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":[0.00007174675,0.000036145168,0.00018861893,0.00010114923,0.000024858255,0.00003445363,0.000057964917,0.9408001,0.009764013,0.016267879,0.00030048515,0.032352593],"study_design_scores_gemma":[0.000009452978,0.0000868229,0.000094099974,0.000009466789,0.000004893554,0.00001518895,0.0000073884676,0.994058,0.0023664914,0.0029122524,0.00042906535,0.000006728399],"about_ca_topic_score_codex":0.00061533845,"about_ca_topic_score_gemma":0.0005273683,"teacher_disagreement_score":0.0014034958,"about_ca_system_score_codex":0.000481917,"about_ca_system_score_gemma":0.0005536067,"threshold_uncertainty_score":0.006991565},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010858721,0.00032749117,0.9820962,0.00008066076,0.00008653653,0.000033081713,0.00018423171,0.0016437253,0.004689305],"genre_scores_gemma":[0.30935922,0.0009487044,0.6768615,0.00013702032,0.00018135563,0.00017318249,0.0011814014,0.0010260846,0.010131497],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968886,0.000048906397,0.000022795377,0.00004696304,0.00016789089,0.000024634355],"domain_scores_gemma":[0.9993837,0.00028000455,0.00006785849,0.00012037587,0.00012172457,0.000026243746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045985638,0.00074623554,0.0003597548,0.0012369149,0.0002691754,0.001129408,0.0005096935,0.00048296875,0.014411477],"category_scores_gemma":[0.0021282053,0.00020161089,0.0005457849,0.0014440694,0.0005097847,0.001257298,0.0007618266,0.0006681545,0.0039048698],"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":[0.00038842874,0.00007727783,0.0013039734,0.0005385149,0.000065290595,0.0006679151,0.0004468339,0.10371519,0.14706759,0.088583805,0.012390726,0.64475447],"study_design_scores_gemma":[0.000026567363,0.00018090718,0.0016927295,0.00007452835,0.000030339694,0.0009201658,0.0002498684,0.8875168,0.039789215,0.034043383,0.0354324,0.00004325336],"about_ca_topic_score_codex":0.0010585358,"about_ca_topic_score_gemma":0.00092954753,"teacher_disagreement_score":0.014411477,"about_ca_system_score_codex":0.00027458422,"about_ca_system_score_gemma":0.00037844808,"threshold_uncertainty_score":0.048211217},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011639053,0.00010341419,0.9859293,0.00003957885,0.000021085903,0.000034156285,0.0000058092564,0.00013827636,0.002089304],"genre_scores_gemma":[0.38065866,0.00014505103,0.61555994,0.000094598196,0.000018233486,0.00017531119,0.000035949313,0.00004473401,0.0032673809],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977916,0.00004739166,0.000017117238,0.000042970565,0.00009632742,0.000017005572],"domain_scores_gemma":[0.99960583,0.00014053195,0.0000606716,0.000027223337,0.0001513336,0.000014481913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004954058,0.00041777332,0.00035449362,0.00028494958,0.00018290785,0.00038323025,0.00076168316,0.0006907149,0.0010804515],"category_scores_gemma":[0.0013676416,0.00020491621,0.00030926193,0.00024035417,0.00028631417,0.0003681245,0.00023216894,0.00048197352,0.00028763953],"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":[0.00012582437,0.0000876798,0.0010378255,0.00026607595,0.0000634817,0.00013022668,0.00016357498,0.60600096,0.06610192,0.014782323,0.0014100722,0.30983007],"study_design_scores_gemma":[0.000013642643,0.00007022996,0.00012266055,0.000007618531,0.000007899446,0.00003967944,0.000006289849,0.9921314,0.0055924305,0.0006443054,0.0013576717,0.0000061281103],"about_ca_topic_score_codex":0.00094548124,"about_ca_topic_score_gemma":0.0010343258,"teacher_disagreement_score":0.0010804515,"about_ca_system_score_codex":0.00030929584,"about_ca_system_score_gemma":0.00044255122,"threshold_uncertainty_score":0.0036144853},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011784456,0.00021126962,0.98658425,0.00011629802,0.000034657598,0.000028078734,0.000035414167,0.0001958867,0.001009726],"genre_scores_gemma":[0.77611274,0.00040113335,0.22030528,0.00016095159,0.00008835321,0.00016089847,0.00021011311,0.000056529825,0.0025040298],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988695,0.00031666562,0.00007165333,0.00025846556,0.0003771178,0.0001065526],"domain_scores_gemma":[0.99896896,0.00050313934,0.000121036486,0.00007367247,0.0002937401,0.00003949926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013704775,0.0010242283,0.0013825597,0.00051267387,0.0005346761,0.0009733772,0.0010010559,0.0009472932,0.001190292],"category_scores_gemma":[0.002989841,0.00066370267,0.00085425284,0.00090350147,0.000796583,0.002064349,0.0014187915,0.0016207071,0.00020952839],"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":[0.00017141487,0.000053544216,0.0004496009,0.00008396378,0.00007818011,0.000054286575,0.000057870977,0.9116227,0.0050984686,0.0060433373,0.0008303758,0.0754562],"study_design_scores_gemma":[0.0000028212996,0.000009534018,0.00006305554,0.0000021041114,0.000003084287,0.000004503783,0.0000021119235,0.998161,0.0007097267,0.00094671256,0.000091326336,0.0000040679874],"about_ca_topic_score_codex":0.006912632,"about_ca_topic_score_gemma":0.004957616,"teacher_disagreement_score":0.006912632,"about_ca_system_score_codex":0.0010578819,"about_ca_system_score_gemma":0.0015354567,"threshold_uncertainty_score":0.0137447715},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19634052,0.00039004133,0.78757286,0.00030824545,0.00016069363,0.00028537476,0.00037748984,0.008074287,0.0064904178],"genre_scores_gemma":[0.8688701,0.00020788753,0.12630239,0.00013403538,0.000036881393,0.00019056775,0.00040164567,0.00008734676,0.0037692077],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997539,0.000028929646,0.000019670217,0.000058425,0.000107846456,0.000031231222],"domain_scores_gemma":[0.99981934,0.00002528252,0.000022992988,0.000027306123,0.000091690126,0.0000134376805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026184085,0.00035873355,0.00037410916,0.00042175516,0.0001797827,0.00040075224,0.00044509326,0.000333757,0.002331605],"category_scores_gemma":[0.00060499506,0.00012447673,0.0002349794,0.00041800019,0.00010917515,0.0003914961,0.00033505645,0.00034459258,0.0008780633],"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":[0.00064884045,0.00039416022,0.004558737,0.00027179683,0.0001161815,0.00042468504,0.00013136708,0.033308268,0.25991023,0.0022871455,0.010159431,0.68778914],"study_design_scores_gemma":[0.00007611781,0.0004209416,0.008799563,0.000034343848,0.000073347066,0.0003818448,0.000031681007,0.8658023,0.115513176,0.0010787358,0.007752279,0.00003559516],"about_ca_topic_score_codex":0.0012179026,"about_ca_topic_score_gemma":0.00069449935,"teacher_disagreement_score":0.002331605,"about_ca_system_score_codex":0.00026657165,"about_ca_system_score_gemma":0.000365463,"threshold_uncertainty_score":0.0078000426},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015312631,0.00006431165,0.98371744,0.000051865154,0.000017770086,0.0000106594025,0.000009214385,0.00018387321,0.0006321392],"genre_scores_gemma":[0.5412226,0.00015635123,0.4556749,0.0000690774,0.00003401372,0.0000884601,0.00007659439,0.000050338913,0.0026277474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997371,0.000057504305,0.0000131882925,0.000057669502,0.00011307585,0.000021346083],"domain_scores_gemma":[0.9997795,0.00007413676,0.000044479322,0.000020032958,0.00007089402,0.000010895366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000357559,0.0005728629,0.00056254823,0.0004981452,0.00022506286,0.00033212552,0.0007026507,0.00045218997,0.00058902544],"category_scores_gemma":[0.0008965053,0.00021641278,0.00042354,0.0005383011,0.00034192653,0.00069324166,0.0005891366,0.00048637987,0.00016955797],"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":[0.00013906191,0.00006185464,0.0016859429,0.000096721,0.00006965304,0.00015704025,0.00014896331,0.7264077,0.049411356,0.010388467,0.0011002754,0.21033293],"study_design_scores_gemma":[0.000008052614,0.000023494427,0.00017420469,0.0000022693955,0.000005791409,0.000021600594,0.000006116305,0.9969392,0.001849753,0.00059691194,0.00036667308,0.000005946177],"about_ca_topic_score_codex":0.0027932527,"about_ca_topic_score_gemma":0.0019842235,"teacher_disagreement_score":0.0027932527,"about_ca_system_score_codex":0.00031789648,"about_ca_system_score_gemma":0.0006699294,"threshold_uncertainty_score":0.005553961},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98884064,0.0010938001,0.008554388,0.00007231793,0.000023295066,0.00006339246,0.00023520361,0.00010822705,0.0010086673],"genre_scores_gemma":[0.9962049,0.00017497514,0.003224851,0.000022043909,0.000015280295,0.00002271836,0.00012285798,0.000014101161,0.00019824409],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872655,0.00069863943,0.00007689407,0.00021409265,0.00020438666,0.00007932776],"domain_scores_gemma":[0.9980605,0.00097480684,0.00030212424,0.00021219807,0.00037853548,0.00007183825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041177026,0.00046768764,0.0004021324,0.0017911069,0.00016945373,0.00060284976,0.00036107923,0.0005718586,0.0005801791],"category_scores_gemma":[0.0075840433,0.00023969372,0.0002568073,0.00033572936,0.00038185337,0.00069462974,0.0003139623,0.00029162908,0.0004534768],"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":[0.00280257,0.00017198555,0.7945706,0.00015657372,0.0001752979,0.000997021,0.00031975415,0.004290775,0.03566991,0.0003336998,0.00078265514,0.15972908],"study_design_scores_gemma":[0.00006580825,0.0015797617,0.8858992,0.00009675437,0.00025639986,0.0045729754,0.00030977066,0.07839605,0.02589997,0.00074723037,0.002117186,0.00005881144],"about_ca_topic_score_codex":0.0008781374,"about_ca_topic_score_gemma":0.0009795825,"teacher_disagreement_score":0.0041177026,"about_ca_system_score_codex":0.00021604786,"about_ca_system_score_gemma":0.00020813617,"threshold_uncertainty_score":0.021776736},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020104958,0.00021334724,0.9746702,0.00010372676,0.00012696172,0.00004904273,0.000034172655,0.0007990003,0.0038986374],"genre_scores_gemma":[0.93934363,0.00012093383,0.054312054,0.000103544895,0.000055149576,0.00010733492,0.000071066344,0.000021211383,0.0058650267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993844,0.000106669766,0.000037016413,0.00019897356,0.00021619703,0.000056652214],"domain_scores_gemma":[0.99960977,0.00009849111,0.00006092255,0.00004003897,0.00017356427,0.000017234608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060356525,0.0006977145,0.00059804117,0.00022037573,0.0004393313,0.0007099447,0.0008862659,0.00074040785,0.0020992216],"category_scores_gemma":[0.0006426445,0.0002388765,0.00042808417,0.0001677159,0.0004400438,0.0005721963,0.0006597422,0.0006146336,0.00045310127],"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":[0.0007315784,0.0002085036,0.0016304576,0.0005309636,0.00015766289,0.0006982501,0.00032611235,0.4977339,0.16852891,0.01552061,0.0034442015,0.31048888],"study_design_scores_gemma":[0.000033373173,0.0002992472,0.00042917684,0.000021574666,0.0000348033,0.000112181966,0.000015055211,0.9814649,0.013397657,0.001071763,0.0030982941,0.000021988315],"about_ca_topic_score_codex":0.002315862,"about_ca_topic_score_gemma":0.0024040008,"teacher_disagreement_score":0.002315862,"about_ca_system_score_codex":0.00031141404,"about_ca_system_score_gemma":0.00058104703,"threshold_uncertainty_score":0.0070225596},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0111833885,0.0003879937,0.9862053,0.0002558707,0.000048137692,0.000027791311,0.00007591489,0.00042582388,0.0013897651],"genre_scores_gemma":[0.8452396,0.00065220956,0.14659834,0.0007167846,0.00010477963,0.0001484156,0.0005020201,0.00015122035,0.005886764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992105,0.0003126789,0.000028981502,0.00020251857,0.00015846702,0.00008697482],"domain_scores_gemma":[0.99786985,0.0015875886,0.0001363835,0.00014997437,0.0001927381,0.000063428335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014614877,0.0014954475,0.0009885874,0.00044605506,0.00030007746,0.00062876573,0.0012982021,0.001118639,0.001719938],"category_scores_gemma":[0.0037330377,0.0005741078,0.0006818362,0.00039826718,0.0012651562,0.001071955,0.0015193339,0.0024543037,0.00040450596],"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":[0.000090489666,0.00003429354,0.0007648899,0.00006048082,0.000048029604,0.000076580436,0.000037859812,0.9464021,0.0020617894,0.010179283,0.0015389883,0.038705286],"study_design_scores_gemma":[0.0000016564436,0.000009541561,0.000050996397,0.000002691462,0.0000031041943,0.000010793574,0.0000018955175,0.9971879,0.00044633733,0.0021430608,0.00013888045,0.0000031590225],"about_ca_topic_score_codex":0.003050779,"about_ca_topic_score_gemma":0.003446954,"teacher_disagreement_score":0.003050779,"about_ca_system_score_codex":0.0008662534,"about_ca_system_score_gemma":0.00061727746,"threshold_uncertainty_score":0.0077291727},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014660868,0.00024951727,0.9811662,0.00023664632,0.00005090873,0.000026952875,0.00012651154,0.00061664777,0.002865706],"genre_scores_gemma":[0.65899014,0.0005892505,0.3224139,0.00045155495,0.000066087676,0.00022408512,0.00069339,0.00023809237,0.016333487],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988866,0.000018748327,0.0000055055734,0.000033237706,0.000037239734,0.000016686372],"domain_scores_gemma":[0.9998198,0.00006461997,0.000019781244,0.000027754244,0.000050570008,0.000017489065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032595376,0.00062363676,0.00045873446,0.00020474396,0.00021645735,0.00051527354,0.001335226,0.0008504904,0.0024825896],"category_scores_gemma":[0.00078517385,0.0003061876,0.00040419545,0.0002444068,0.00058808393,0.00092554756,0.0007849707,0.0012240602,0.00059224013],"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":[0.0000954077,0.00006536203,0.00055966055,0.000073489915,0.000035742214,0.000089681605,0.000038763043,0.8928958,0.012786213,0.024601618,0.0030777943,0.06568045],"study_design_scores_gemma":[0.0000024494104,0.000011808951,0.000025520401,0.0000018156495,0.0000021684789,0.000010763469,0.0000016098148,0.99652267,0.0012072456,0.0016487349,0.0005629861,0.0000022647662],"about_ca_topic_score_codex":0.0053204913,"about_ca_topic_score_gemma":0.009502399,"teacher_disagreement_score":0.0053204913,"about_ca_system_score_codex":0.0007092059,"about_ca_system_score_gemma":0.0010350711,"threshold_uncertainty_score":0.01057905},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37650493,0.0020465706,0.6064512,0.0006411405,0.00037939975,0.00012086235,0.0010187599,0.00538064,0.0074563716],"genre_scores_gemma":[0.9554943,0.00043644552,0.03918255,0.00009702393,0.00004769565,0.000050958468,0.00079842785,0.00004866583,0.003843938],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992025,0.00001076547,0.0000052609503,0.000026332364,0.000017747196,0.000019613186],"domain_scores_gemma":[0.99991846,0.000025070702,0.000010945614,0.000008330073,0.000031720945,0.000005464637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018391009,0.00077298377,0.00034182792,0.00042791426,0.00012799574,0.00034724118,0.00041552525,0.00039199262,0.0014020163],"category_scores_gemma":[0.00050673133,0.00018390639,0.00043247416,0.00035558353,0.000121837664,0.0004293601,0.00026063883,0.00043170335,0.000484963],"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":[0.00047355812,0.00019634701,0.0048327046,0.00013203944,0.00015054835,0.0003477532,0.0000509476,0.36077625,0.059649058,0.0015434318,0.005811656,0.56603575],"study_design_scores_gemma":[0.00000299225,0.000021619924,0.000872427,0.0000038801036,0.000009603239,0.000026390317,0.0000037275825,0.99543613,0.0031345345,0.0002854765,0.00019928446,0.000003968165],"about_ca_topic_score_codex":0.0065787616,"about_ca_topic_score_gemma":0.0071150763,"teacher_disagreement_score":0.0065787616,"about_ca_system_score_codex":0.0003826658,"about_ca_system_score_gemma":0.0004087398,"threshold_uncertainty_score":0.013080955},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02136238,0.00046015432,0.9769817,0.00007283272,0.00005453051,0.0000117898335,0.000029186102,0.00027936438,0.0007481018],"genre_scores_gemma":[0.488144,0.000662411,0.5077534,0.000086674074,0.00015105029,0.00005996699,0.00014175632,0.00006761568,0.002933174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995473,0.00008774021,0.000029271085,0.00008983129,0.00020532665,0.00004044089],"domain_scores_gemma":[0.9992273,0.0003300997,0.000097441894,0.00007441987,0.00024281,0.000027994885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056235737,0.0007507163,0.0007478444,0.00084402366,0.00029699501,0.000720473,0.00046877758,0.000639618,0.00052782847],"category_scores_gemma":[0.0022599527,0.00037122596,0.00049227546,0.00070197566,0.00023576844,0.0010311357,0.0006417986,0.0007151755,0.0003147733],"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":[0.00037958848,0.00007889725,0.0041538933,0.00019099464,0.00014552572,0.0001719164,0.00018722526,0.27657592,0.044078268,0.0095409565,0.0016341507,0.66286266],"study_design_scores_gemma":[0.000022275584,0.00007926695,0.0015269957,0.000015416123,0.000036953857,0.00023129204,0.00003429198,0.9787884,0.014372061,0.0029995898,0.0018579576,0.00003557005],"about_ca_topic_score_codex":0.0010145004,"about_ca_topic_score_gemma":0.0012316345,"teacher_disagreement_score":0.0010145004,"about_ca_system_score_codex":0.00026131512,"about_ca_system_score_gemma":0.00056230323,"threshold_uncertainty_score":0.0029740334},"labels":[],"label_agreement":null}]}