{"meta":{"query_hash":"65fb48c233b2","filters":{"venue":"IEEE Signal Processing Letters"},"cohort_total":157,"direct_labels_cover":0,"predictions_cover":157,"exported":157,"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/65fb48c233b2","api":"https://metacan.xera.ac/api/v1/cohort?venue=IEEE+Signal+Processing+Letters"},"results":[{"id":"W1550510559","doi":"10.1109/lsp.2015.2447279","title":"An Implicit Contour Morphing Framework Applied to Computer-Aided Severe Weather Forecasting","year":2015,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Vision and Imaging","field":"Computer Science","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":"Environment and Climate Change Canada","funders":"","keywords":"Morphing; Computer science; Artificial intelligence; Representation (politics); Computer vision; Matching (statistics); Active contour model; Pattern recognition (psychology); Image (mathematics); Mathematics; Image segmentation","score_opus":0.038967805837775205,"score_gpt":0.28749968803431003,"score_spread":0.24853188219653483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1550510559","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":[0.0031289475,0.00008242786,0.99574274,0.00005580056,0.000018305627,0.000018621262,0.000024147856,0.0003987529,0.0005302767],"genre_scores_gemma":[0.20440081,0.00035476108,0.79278487,0.000054858217,0.000040943083,0.00008276616,0.00015479131,0.00030219305,0.0018240075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998524,0.00003227905,0.000008209839,0.000029978035,0.00006440987,0.000012728653],"domain_scores_gemma":[0.99979275,0.00007903083,0.00002538012,0.000044270684,0.00004738896,0.000011079488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045948723,0.00049566256,0.00045119235,0.0007266675,0.00027840337,0.0007046165,0.0010265103,0.0008459371,0.0017961852],"category_scores_gemma":[0.0012023872,0.00039896125,0.0005500559,0.0005676659,0.00048992643,0.0006948784,0.0008253177,0.00088764634,0.00032581924],"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":[0.000048318794,0.000031322765,0.000414241,0.00007607604,0.000034331584,0.00015355155,0.000090118796,0.65972465,0.021576853,0.023439018,0.0016898339,0.29272178],"study_design_scores_gemma":[0.0000020224136,0.000008094733,0.00006878974,0.000003217416,0.0000028446366,0.00003277095,0.0000028559498,0.9949675,0.0016784447,0.001755104,0.001473741,0.0000046613413],"about_ca_topic_score_codex":0.00286612,"about_ca_topic_score_gemma":0.0023636522,"teacher_disagreement_score":0.00286612,"about_ca_system_score_codex":0.00026184958,"about_ca_system_score_gemma":0.00054848945,"threshold_uncertainty_score":0.0060088634},"labels":[],"label_agreement":null},{"id":"W1644173899","doi":"10.1109/lsp.2015.2487369","title":"A Patch-Structure Representation Method for Quality Assessment of Contrast Changed Images","year":2015,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":426,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Contrast (vision); Artificial intelligence; Computer science; Image quality; Representation (politics); Quality (philosophy); Pattern recognition (psychology); Computer vision; Perception; Feature (linguistics); Image (mathematics)","score_opus":0.11060641191341014,"score_gpt":0.4258384142188925,"score_spread":0.3152320023054824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1644173899","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":[0.01914457,0.0002528875,0.9789533,0.000051333674,0.000032359258,0.00008459183,0.00012919704,0.0006078499,0.0007439085],"genre_scores_gemma":[0.27033204,0.00040896953,0.7266211,0.00006675712,0.000055603676,0.00014939724,0.00048642032,0.0001816704,0.0016980198],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99955255,0.00006284978,0.000027295715,0.000104882936,0.00022296657,0.000029476294],"domain_scores_gemma":[0.99914,0.00018059953,0.000119494965,0.00015447731,0.0003659352,0.000039525396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007082644,0.00049551396,0.00053818926,0.0018101801,0.00023070327,0.0007109257,0.00064954156,0.0005210504,0.0019755955],"category_scores_gemma":[0.0026598792,0.00020640083,0.00068021653,0.0011799234,0.00036794893,0.0009827188,0.00054763193,0.0007934043,0.0006114758],"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":[0.00035391943,0.00012811898,0.0038558298,0.00029105914,0.00016878401,0.00014988815,0.00017064008,0.046580013,0.17342833,0.007127689,0.003643974,0.7641017],"study_design_scores_gemma":[0.000038041886,0.00029293576,0.0101428,0.000023855277,0.00011493342,0.0006381632,0.00005228361,0.91838,0.0625629,0.0028587554,0.0048276037,0.00006768389],"about_ca_topic_score_codex":0.0017268716,"about_ca_topic_score_gemma":0.0017741667,"teacher_disagreement_score":0.0019755955,"about_ca_system_score_codex":0.00044841925,"about_ca_system_score_gemma":0.00040498519,"threshold_uncertainty_score":0.0066090226},"labels":[],"label_agreement":null},{"id":"W1851080149","doi":"10.1109/lsp.2015.2461026","title":"Robust Texture Classification by Aggregating Pixel-Based LBP Statistics","year":2015,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":12,"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 Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Local binary patterns; Discriminative model; Artificial intelligence; Histogram; Pixel; Pattern recognition (psychology); Robustness (evolution); Contextual image classification; Image texture; Computer science; Mathematics; Computer vision; Image segmentation; Image (mathematics)","score_opus":0.05884349942968492,"score_gpt":0.26493486036786246,"score_spread":0.20609136093817754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1851080149","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":[0.080660276,0.00042315995,0.9144018,0.00017307539,0.0001362374,0.000054728935,0.00017119983,0.0019086724,0.0020707825],"genre_scores_gemma":[0.65204644,0.0006109601,0.34332228,0.00015768006,0.00028625183,0.0000747676,0.00071705395,0.0002885543,0.0024959522],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926335,0.00008613546,0.000042251544,0.00013385678,0.00038934732,0.0000850195],"domain_scores_gemma":[0.9990031,0.00020047462,0.00016475958,0.00021601873,0.00036678874,0.000048970032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073057436,0.0005038487,0.0009952106,0.0022785217,0.00030626496,0.0010492852,0.00068842765,0.00044564856,0.0012181376],"category_scores_gemma":[0.0026732222,0.00028186847,0.0005134455,0.0017012804,0.0004803226,0.0012818486,0.00076816115,0.0005893307,0.0010994765],"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":[0.0004634825,0.00013290177,0.0034059677,0.00012005815,0.00006792737,0.00013650325,0.000089365334,0.05547589,0.16441444,0.0040443977,0.0039089634,0.76774013],"study_design_scores_gemma":[0.0000208439,0.00015651497,0.005452635,0.00001559306,0.00006109082,0.00024603851,0.00006295579,0.9414264,0.043746486,0.004955518,0.0038140728,0.000041891868],"about_ca_topic_score_codex":0.0012119348,"about_ca_topic_score_gemma":0.0014082077,"teacher_disagreement_score":0.0022785217,"about_ca_system_score_codex":0.00043829804,"about_ca_system_score_gemma":0.00042804846,"threshold_uncertainty_score":0.00407511},"labels":[],"label_agreement":null},{"id":"W1966102612","doi":"10.1109/lsp.2011.2177086","title":"Compressed Sensing With Nonlinear Analog Mapping in a Noisy Environment","year":2011,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","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":"Queen's University","funders":"","keywords":"Compressed sensing; Computer science; Nonlinear system; Nonlinear distortion; Distortion (music); SIGNAL (programming language); Noise (video); Noise measurement; Signal reconstruction; Analog signal; Signal-to-noise ratio (imaging); Channel (broadcasting); Algorithm; Signal processing; Artificial intelligence; Noise reduction; Digital signal processing; Telecommunications; Bandwidth (computing); Image (mathematics)","score_opus":0.02492894682055673,"score_gpt":0.18574376289286823,"score_spread":0.1608148160723115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966102612","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":[0.10459878,0.0006325217,0.88910395,0.0007352609,0.00013064855,0.000059831807,0.00005618288,0.00023434506,0.0044485196],"genre_scores_gemma":[0.84962875,0.0003309568,0.14768857,0.00021367504,0.00011156213,0.00004811057,0.000038228616,0.00001185533,0.0019283948],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997243,0.0000933668,0.000010809982,0.00003874601,0.000113997885,0.0000186309],"domain_scores_gemma":[0.99971884,0.00013435082,0.00004939941,0.000035923313,0.000044630262,0.00001679711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002488832,0.00035848108,0.00033395083,0.0001653676,0.00021670827,0.00035081146,0.00046320877,0.00047046726,0.0005946811],"category_scores_gemma":[0.00092410244,0.00012685033,0.00013187261,0.00023948721,0.0005449083,0.0007525087,0.00069304515,0.0003603088,0.00014233316],"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":[0.0007974538,0.00017841159,0.0018514056,0.00045533024,0.000081670165,0.0006664882,0.00027904555,0.28297773,0.36306927,0.08636282,0.00238843,0.26089194],"study_design_scores_gemma":[0.000044492095,0.0002632458,0.00034920961,0.000012481311,0.000016524962,0.00028414352,0.000020420164,0.958675,0.031015255,0.007228191,0.0020613165,0.00002970108],"about_ca_topic_score_codex":0.00047064875,"about_ca_topic_score_gemma":0.00063520635,"teacher_disagreement_score":0.0005946811,"about_ca_system_score_codex":0.00022859318,"about_ca_system_score_gemma":0.00025321625,"threshold_uncertainty_score":0.0019893646},"labels":[],"label_agreement":null},{"id":"W1970225942","doi":"10.1109/lsp.2013.2260823","title":"An Approximate ML Estimator for the Location Parameter of the Generalized Gaussian Distribution With <formula formulatype=\"inline\"> <tex Notation=\"TeX\">$p=5$</tex></formula>","year":2013,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","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 Alberta","funders":"","keywords":"Estimator; Mathematics; Estimation theory; Applied mathematics; Gaussian; Notation; Distribution (mathematics); Statistics; Combinatorics; Algorithm; Discrete mathematics; Mathematical analysis; Arithmetic; Physics","score_opus":0.0176814643939917,"score_gpt":0.2643202399608761,"score_spread":0.2466387755668844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970225942","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":[0.0021612854,0.0001832294,0.99682933,0.00009904054,0.000027544089,0.000016294123,0.00006053817,0.000321098,0.0003016586],"genre_scores_gemma":[0.111918196,0.0006121736,0.8824759,0.00023002463,0.00016286707,0.0001834574,0.0008998965,0.00025712204,0.0032603564],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99886143,0.0004392088,0.000053417178,0.000284325,0.00027727894,0.00008427989],"domain_scores_gemma":[0.9964083,0.0020782806,0.00036257735,0.000504014,0.0005569493,0.00008987634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022769384,0.0013268611,0.0017323642,0.001148583,0.0004885342,0.0015036823,0.0018538426,0.0021066663,0.0027782894],"category_scores_gemma":[0.013253294,0.0007012068,0.000934727,0.0016018852,0.0011878093,0.002393315,0.0016989391,0.0018776755,0.0022640259],"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":[0.0005600307,0.00018088911,0.004050986,0.000760256,0.00033961568,0.00040752912,0.00029225738,0.3715281,0.038445517,0.07282803,0.012918202,0.4976886],"study_design_scores_gemma":[0.000051739455,0.000060347866,0.0007709513,0.000042551757,0.000047561407,0.00035833046,0.000044648034,0.97170854,0.0060803434,0.01756191,0.0032234844,0.000049505492],"about_ca_topic_score_codex":0.002633935,"about_ca_topic_score_gemma":0.0037044904,"teacher_disagreement_score":0.0027782894,"about_ca_system_score_codex":0.0007121969,"about_ca_system_score_gemma":0.0024225519,"threshold_uncertainty_score":0.012041748},"labels":[],"label_agreement":null},{"id":"W1972011722","doi":"10.1109/lsp.2010.2092427","title":"Performance Analysis of Relay Selection With Feedback Delay and Channel Estimation Errors","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Relay; Channel state information; Computer science; Relay channel; Selection (genetic algorithm); Channel (broadcasting); Bit error rate; Cooperative diversity; Control theory (sociology); Telecommunications; Fading; Wireless; Artificial intelligence; Power (physics)","score_opus":0.016057965101815973,"score_gpt":0.24338001800869338,"score_spread":0.2273220529068774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972011722","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":[0.6670543,0.003158247,0.32342255,0.00064634177,0.000071697534,0.00007409617,0.00017656587,0.00034732977,0.0050487993],"genre_scores_gemma":[0.9939732,0.0004036395,0.0050456724,0.00002820853,0.000022748089,0.000018112127,0.000029505167,0.000013584206,0.00046532296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99723357,0.001353056,0.00009303786,0.0003148694,0.0005852314,0.0004202439],"domain_scores_gemma":[0.97219276,0.022941943,0.0019759203,0.0007163418,0.0019465317,0.00022649253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004110435,0.0014398249,0.0011902903,0.00078341377,0.000554357,0.0011272851,0.0008831624,0.0012903055,0.00059345644],"category_scores_gemma":[0.0241134,0.00044031884,0.0003590668,0.00092385773,0.0015009185,0.0015290042,0.0011379517,0.0006894758,0.00013666718],"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":[0.00038076934,0.00003748659,0.0019633076,0.000066228335,0.00005347685,0.000266863,0.000081617145,0.9827124,0.003986273,0.0037912955,0.00012857947,0.006531726],"study_design_scores_gemma":[0.000021576026,0.00025040915,0.0007567758,0.000011146826,0.000047168196,0.00020619028,0.000051069994,0.9935231,0.0036048912,0.001396383,0.00011118867,0.000020125977],"about_ca_topic_score_codex":0.0036103053,"about_ca_topic_score_gemma":0.0016414846,"teacher_disagreement_score":0.004110435,"about_ca_system_score_codex":0.001864603,"about_ca_system_score_gemma":0.001066548,"threshold_uncertainty_score":0.02173835},"labels":[],"label_agreement":null},{"id":"W1981028967","doi":"10.1109/lsp.2015.2426654","title":"Compressed Sensing with Non-Gaussian Noise and Partial Support Information","year":2015,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","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":"Gaussian noise; Computer science; Noise (video); Compressed sensing; Gaussian; Noise measurement; Artificial intelligence; Noise reduction; Physics; Image (mathematics)","score_opus":0.01373653777517439,"score_gpt":0.20786002746354335,"score_spread":0.19412348968836896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981028967","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":[0.055256266,0.001416434,0.93784606,0.0017751276,0.00010169411,0.00003479431,0.00031170287,0.00014948807,0.0031085103],"genre_scores_gemma":[0.79260033,0.0031281929,0.19169928,0.0005364103,0.00050447515,0.00018387532,0.0010800337,0.000114642346,0.010152802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895775,0.00033594941,0.000038265534,0.00021873387,0.00033630172,0.000113031776],"domain_scores_gemma":[0.9959692,0.0032294502,0.00031744118,0.0001885617,0.0002095047,0.000085851825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015659365,0.0011407759,0.0010930815,0.0005859989,0.00027151988,0.0011220083,0.0013117316,0.0019527253,0.0012112106],"category_scores_gemma":[0.008432221,0.00045328704,0.0006595524,0.0011984691,0.001736288,0.0020883519,0.0013212948,0.0015751332,0.00035776565],"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":[0.00039373775,0.00008158226,0.0010234128,0.00055535114,0.0001087823,0.00056872756,0.00020021721,0.8128468,0.0075195567,0.13746583,0.0025085635,0.036727376],"study_design_scores_gemma":[0.000019064932,0.00003784203,0.00017771768,0.000014883469,0.000011618039,0.000059079022,0.000017359596,0.9743804,0.0012592286,0.023519829,0.0004911672,0.000011882986],"about_ca_topic_score_codex":0.0042826086,"about_ca_topic_score_gemma":0.0028396863,"teacher_disagreement_score":0.0042826086,"about_ca_system_score_codex":0.0006939734,"about_ca_system_score_gemma":0.0008060126,"threshold_uncertainty_score":0.008515358},"labels":[],"label_agreement":null},{"id":"W1981093646","doi":"10.1109/lsp.2010.2096466","title":"Joint Relay Selection and Power Allocation for Two-Way Relay Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":140,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Ontario Tech University","funders":"","keywords":"Relay; Maximization; Transceiver; Selection (genetic algorithm); Computer science; Transmitter power output; Relay channel; Signal-to-noise ratio (imaging); Power (physics); Channel (broadcasting); Mathematical optimization; Joint (building); Control theory (sociology); Mathematics; Topology (electrical circuits); Telecommunications; Wireless; Transmitter; Engineering","score_opus":0.022019181504354182,"score_gpt":0.2610998891457004,"score_spread":0.23908070764134626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981093646","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":[0.03515134,0.00061796035,0.96244806,0.00012263775,0.000026298212,0.00004614996,0.000020777923,0.00013417467,0.0014326066],"genre_scores_gemma":[0.88041157,0.00038393063,0.11726656,0.00005814426,0.00004261933,0.00010010831,0.000028181585,0.000026541346,0.0016824328],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833834,0.000943603,0.000056573164,0.00018359938,0.00030676374,0.00017113538],"domain_scores_gemma":[0.9986634,0.00088245975,0.00014680575,0.0001469838,0.00010882389,0.0000514731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016447094,0.001110534,0.0011307122,0.00043318883,0.00037103193,0.00084692513,0.0010104304,0.00087809225,0.00094513356],"category_scores_gemma":[0.0030419219,0.0004117458,0.00037147783,0.0007617351,0.0011248427,0.0014502034,0.0013514123,0.0005523486,0.00029153124],"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":[0.00032880544,0.00010168535,0.0005766158,0.00015609137,0.00008408485,0.00027710412,0.000207374,0.8793172,0.016179476,0.031774227,0.0008986093,0.07009869],"study_design_scores_gemma":[0.00004917801,0.00013137405,0.00013896989,0.0000058339565,0.000017014894,0.00010913501,0.000024160841,0.98486555,0.003074098,0.010810391,0.0007562024,0.000018065888],"about_ca_topic_score_codex":0.0003991248,"about_ca_topic_score_gemma":0.00050192245,"teacher_disagreement_score":0.0016447094,"about_ca_system_score_codex":0.00055928243,"about_ca_system_score_gemma":0.00038692742,"threshold_uncertainty_score":0.008698165},"labels":[],"label_agreement":null},{"id":"W1983890061","doi":"10.1109/lsp.2012.2235430","title":"Decentralized Conditional Posterior Cramér–Rao Lower Bound for Nonlinear Distributed Estimation","year":2012,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","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":"York University","funders":"","keywords":"Estimator; Computer science; Upper and lower bounds; Algorithm; Representation (politics); Mathematics; Mathematical optimization; Statistics","score_opus":0.020006486035621746,"score_gpt":0.2711232236546598,"score_spread":0.2511167376190381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983890061","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":[0.001714816,0.00021736728,0.99618953,0.000115245326,0.000023198294,0.000013064854,0.000036734218,0.00019645254,0.0014936535],"genre_scores_gemma":[0.4771021,0.0015743239,0.5126347,0.00034804258,0.0003246704,0.00032363928,0.0004873794,0.00029964428,0.0069055064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99834836,0.00047177743,0.000060287184,0.00026425137,0.00071816385,0.00013711862],"domain_scores_gemma":[0.9961039,0.0023389873,0.00022660483,0.00058607163,0.0006841538,0.00006029621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00215723,0.0007958118,0.0009569816,0.00068957836,0.00048332333,0.0015084645,0.0011289595,0.0009522789,0.0030785364],"category_scores_gemma":[0.012120672,0.00047157495,0.0005709469,0.0011788927,0.0011735471,0.0015492493,0.0011722489,0.0018067987,0.0011466634],"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":[0.000075360105,0.000040954725,0.00038880968,0.00013633503,0.00003886141,0.00006760018,0.00007276724,0.78993225,0.0047185356,0.13070624,0.004614696,0.06920758],"study_design_scores_gemma":[0.0000055899977,0.000013188771,0.00009911315,0.000008734729,0.000004767141,0.000019892794,0.000003927571,0.9821683,0.00094430946,0.01570349,0.0010199096,0.000008699614],"about_ca_topic_score_codex":0.004382403,"about_ca_topic_score_gemma":0.00608818,"teacher_disagreement_score":0.004382403,"about_ca_system_score_codex":0.0017449138,"about_ca_system_score_gemma":0.0026001958,"threshold_uncertainty_score":0.012660265},"labels":[],"label_agreement":null},{"id":"W1984640775","doi":"10.1109/lsp.2007.896162","title":"Two Novel Channel-Augmentation Schemes for MIMO Systems","year":2007,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Wireless Communication Techniques","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":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"MIMO; Computer science; Fading; Channel (broadcasting); Scheme (mathematics); Antenna (radio); Distributed antenna system; Algorithm; Topology (electrical circuits); Mathematics; Telecommunications","score_opus":0.025598043320981836,"score_gpt":0.2888996111643366,"score_spread":0.2633015678433548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984640775","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":[0.031256113,0.0006968115,0.9612796,0.00035709355,0.00020621176,0.0000940334,0.00008379719,0.0006544693,0.005371948],"genre_scores_gemma":[0.6696675,0.00051752053,0.32512704,0.0002722773,0.00027974148,0.00018868178,0.0001068618,0.000038909508,0.003801501],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918467,0.00028669642,0.00004665475,0.00011900446,0.00024916694,0.000113734204],"domain_scores_gemma":[0.9985227,0.00049116125,0.0001799826,0.0005143884,0.00019367646,0.00009808403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065134146,0.00081159634,0.00066921016,0.00038125974,0.00044101177,0.00065553794,0.00088449375,0.00078899786,0.0016571489],"category_scores_gemma":[0.0022767554,0.00032234585,0.00047481808,0.00052286335,0.0007167711,0.0010570664,0.0013983839,0.0010492117,0.00054472755],"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":[0.0008658317,0.00027130297,0.0009567883,0.00025488407,0.000095379786,0.00044521067,0.0003418841,0.20400558,0.16489951,0.17447187,0.0042934124,0.44909832],"study_design_scores_gemma":[0.00012587532,0.0006098786,0.0007352602,0.000036463105,0.000045390974,0.0010214056,0.00003800589,0.91259086,0.038215365,0.03230465,0.014164682,0.00011215288],"about_ca_topic_score_codex":0.00023290457,"about_ca_topic_score_gemma":0.00042950828,"teacher_disagreement_score":0.0016571489,"about_ca_system_score_codex":0.00034183316,"about_ca_system_score_gemma":0.0005501534,"threshold_uncertainty_score":0.0055437684},"labels":[],"label_agreement":null},{"id":"W1991456552","doi":"10.1109/lsp.2013.2280394","title":"A Blind AM/PM Estimation Method for Power Amplifier Linearization","year":2013,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Power Amplifier Design","field":"Engineering","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 Calgary; Communications Research Centre Canada","funders":"","keywords":"Linearization; Amplifier; Adjacent channel power ratio; Control theory (sociology); Nonlinear distortion; Phase distortion; Amplitude; Power (physics); SIGNAL (programming language); Distortion (music); Function (biology); Mathematics; Phase (matter); Nonlinear system; Computer science; Predistortion; Telecommunications; Physics; Bandwidth (computing); Optics; Artificial intelligence","score_opus":0.01800534508840123,"score_gpt":0.2737738232214601,"score_spread":0.25576847813305886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991456552","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":[0.0017350724,0.00006102526,0.99770916,0.000016140442,0.000015447624,0.000010177513,0.0000107593805,0.00020902399,0.00023322625],"genre_scores_gemma":[0.17733283,0.0002763199,0.8185492,0.00010205268,0.00012931439,0.00011456094,0.00013904163,0.000080625214,0.003276014],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995085,0.00013722233,0.000027469576,0.00009145346,0.00020640827,0.000028954006],"domain_scores_gemma":[0.999432,0.00020699171,0.000091672795,0.00008217841,0.00016519651,0.000021968133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045246465,0.00076042133,0.00051667425,0.0006620365,0.00038201286,0.00044582743,0.0005321086,0.000528323,0.0012337473],"category_scores_gemma":[0.0013180005,0.00033336552,0.0005132767,0.00044656583,0.00051205605,0.0009240399,0.0006151865,0.0007872401,0.00087220717],"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":[0.00031271466,0.000105053405,0.00096467306,0.00023663767,0.00013536148,0.00012056361,0.00014914804,0.18660033,0.15128392,0.028047834,0.003018218,0.6290256],"study_design_scores_gemma":[0.000012402498,0.00007023928,0.00038575608,0.000009166534,0.000020587071,0.00012963137,0.000010011229,0.9625307,0.029622795,0.004111046,0.0030568975,0.000040915813],"about_ca_topic_score_codex":0.0012298743,"about_ca_topic_score_gemma":0.0017382734,"teacher_disagreement_score":0.0012337473,"about_ca_system_score_codex":0.00035504068,"about_ca_system_score_gemma":0.0008283202,"threshold_uncertainty_score":0.0041273236},"labels":[],"label_agreement":null},{"id":"W2001936727","doi":"10.1109/lsp.2007.905088","title":"Combining Gaussianized/Non-Gaussianized Features to Improve Speaker Diarization of Telephone Conversations","year":2007,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Speaker diarisation; Viterbi algorithm; Computer science; Cluster analysis; Speech recognition; Mixture model; Pattern recognition (psychology); Segmentation; Bayesian information criterion; Mel-frequency cepstrum; Feature (linguistics); Artificial intelligence; Speaker recognition; Word error rate; Test set; Hierarchical clustering; Feature extraction; Hidden Markov model","score_opus":0.010488642288494734,"score_gpt":0.23965041366761372,"score_spread":0.22916177137911897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001936727","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":[0.42100483,0.0018578353,0.55633134,0.00041483017,0.00052808714,0.00025051803,0.0011283911,0.012799383,0.005684873],"genre_scores_gemma":[0.5103622,0.0005093376,0.47566497,0.00020772146,0.00016601734,0.00011969338,0.0046015717,0.0010861781,0.0072823665],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99822587,0.000404059,0.00008204378,0.0005288808,0.00047423723,0.00028500578],"domain_scores_gemma":[0.9977894,0.00082044984,0.00008715813,0.0003185443,0.000881544,0.000102956845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025713376,0.0016832832,0.0013813924,0.0014384517,0.0005034486,0.00086831144,0.0007453482,0.00077781134,0.0027964476],"category_scores_gemma":[0.003909823,0.00035502884,0.0012569855,0.0010903255,0.00032160454,0.0011614071,0.0010631011,0.00096314895,0.002641727],"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":[0.0015778615,0.0004636273,0.0039891014,0.00022794509,0.0003140681,0.00019916585,0.0002724604,0.025355158,0.11958639,0.000465259,0.0045161094,0.8430328],"study_design_scores_gemma":[0.00035106947,0.0015301376,0.035325654,0.000038228438,0.0008766636,0.0009208083,0.0003017982,0.5734382,0.3676856,0.0014717912,0.0177698,0.0002901788],"about_ca_topic_score_codex":0.004744414,"about_ca_topic_score_gemma":0.0066714836,"teacher_disagreement_score":0.004744414,"about_ca_system_score_codex":0.0003748898,"about_ca_system_score_gemma":0.0005937288,"threshold_uncertainty_score":0.01359874},"labels":[],"label_agreement":null},{"id":"W2002330609","doi":"10.1109/lsp.2005.843775","title":"Ramanujan sums and discrete Fourier transforms","year":2005,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Fractal and DNA sequence analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Arithmetic function; Mathematics; Ramanujan's sum; Discrete Fourier transform (general); Computation; Fourier transform; Discrete mathematics; Integer (computer science); Fourier analysis; Algebra over a field; Algorithm; Fractional Fourier transform; Pure mathematics; Mathematical analysis; Computer science","score_opus":0.0066716420776136015,"score_gpt":0.23015481363450868,"score_spread":0.22348317155689507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002330609","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":[0.09217261,0.03970095,0.6082781,0.0026314172,0.0022457887,0.00010110164,0.0005801041,0.00041677814,0.2538732],"genre_scores_gemma":[0.7613491,0.017810183,0.15756539,0.00084723916,0.0031074947,0.00018980296,0.00047312313,0.00022129688,0.058436267],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99941945,0.00013324298,0.000027604576,0.00010389759,0.00024522404,0.00007058809],"domain_scores_gemma":[0.99953604,0.00020920273,0.00006307686,0.000058504476,0.00009206628,0.000041167674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007104855,0.00062313053,0.00065812515,0.0021019743,0.0007196129,0.0019459736,0.00074410555,0.00081337243,0.0045746504],"category_scores_gemma":[0.0021632905,0.0002224361,0.0004482408,0.0014014549,0.002223208,0.0023011346,0.0010401312,0.001599992,0.0014002769],"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":[0.0000063818725,0.0000047044637,0.000034452532,0.0000254744,0.0000030114566,0.000038665123,0.000051937597,0.002086583,0.00052695285,0.9872706,0.00084838254,0.009102789],"study_design_scores_gemma":[0.0000031057943,0.000010029262,0.00008940645,0.000016853888,0.0000019984227,0.00014117658,0.000031759508,0.012012737,0.00040207326,0.97401506,0.013262137,0.00001368365],"about_ca_topic_score_codex":0.00058538566,"about_ca_topic_score_gemma":0.0004527701,"teacher_disagreement_score":0.0045746504,"about_ca_system_score_codex":0.00076071225,"about_ca_system_score_gemma":0.00042315098,"threshold_uncertainty_score":0.015303731},"labels":[],"label_agreement":null},{"id":"W2002428115","doi":"10.1109/lsp.2010.2090869","title":"A Testbed and Methodology for Comparing Live Video Frame Rate Control Methods","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Video Coding and Compression Technologies","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":"Simon Fraser University","funders":"","keywords":"Testbed; Computer science; Frame (networking); Real-time computing; Frame rate; Control (management); Mobile telephony; Simple (philosophy); Computer vision; Artificial intelligence; Computer network; Mobile radio","score_opus":0.08701103915539984,"score_gpt":0.3555977499668551,"score_spread":0.26858671081145524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002428115","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":[0.046030354,0.00042050134,0.94206804,0.00014344478,0.00035447977,0.0042157765,0.0008297296,0.0014454342,0.00449228],"genre_scores_gemma":[0.22505414,0.0004960226,0.76167357,0.000097751006,0.00008863248,0.009236033,0.000930296,0.00020976955,0.0022136914],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9955134,0.0016998202,0.0005475741,0.00044119783,0.0015298624,0.00026816383],"domain_scores_gemma":[0.99225664,0.0024959424,0.0010530626,0.0016418991,0.002271026,0.00028144632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005494903,0.00064324494,0.0006307759,0.0014190213,0.0007055303,0.00090647204,0.0019072373,0.0010929306,0.003683661],"category_scores_gemma":[0.013934415,0.0002683398,0.0004559696,0.0009809602,0.00087161316,0.001127536,0.0012292273,0.0010437185,0.0006980371],"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":[0.0032349315,0.0046956334,0.008023995,0.0023841301,0.0002957903,0.0010080356,0.0010204721,0.1531711,0.29726142,0.14928178,0.01362,0.3660027],"study_design_scores_gemma":[0.00092791294,0.015728641,0.0116151245,0.00046488032,0.00023370255,0.0014494422,0.0009121284,0.44721034,0.41655776,0.021314006,0.08321943,0.00036656816],"about_ca_topic_score_codex":0.0011579462,"about_ca_topic_score_gemma":0.0006409749,"teacher_disagreement_score":0.005494903,"about_ca_system_score_codex":0.0010321956,"about_ca_system_score_gemma":0.0010505865,"threshold_uncertainty_score":0.029060185},"labels":[],"label_agreement":null},{"id":"W2004118907","doi":"10.1109/lsp.2010.2081977","title":"Novel Rotated Quasi-Orthogonal Space-Time Block Codes With the Fixed Nearest Neighbor Number","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Wireless Communication 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":"McMaster University","funders":"","keywords":"Space–time block code; Coding gain; Diversity gain; Algorithm; Block code; Transmitter; Constellation diagram; k-nearest neighbors algorithm; Decoding methods; Coding (social sciences); Computer science; Constellation; Mathematics; Topology (electrical circuits); Telecommunications; Fading; Bit error rate; Combinatorics; Statistics; Artificial intelligence; Physics","score_opus":0.007424528874151641,"score_gpt":0.2213551860971476,"score_spread":0.21393065722299595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004118907","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":[0.07540789,0.001298446,0.91588056,0.0003653759,0.00019775113,0.00007683722,0.00015528858,0.0001836656,0.006434155],"genre_scores_gemma":[0.6072286,0.0011818703,0.3861916,0.00023604449,0.00015409211,0.0001548549,0.0002682981,0.000045619752,0.004539057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999253,0.0002498874,0.00003447662,0.00008510371,0.0002903418,0.00008725925],"domain_scores_gemma":[0.99909604,0.00019752483,0.00017266105,0.00015807907,0.0003183142,0.000057314952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004685225,0.0005634873,0.0005651803,0.00042763737,0.00035213365,0.0007024133,0.0006058862,0.00063248555,0.00073370576],"category_scores_gemma":[0.0018802286,0.00025761337,0.00028407937,0.0008459827,0.00061528647,0.000980999,0.0005762233,0.0005345326,0.00046024623],"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":[0.00063541,0.00008414224,0.0018726856,0.0002635796,0.00012197034,0.00062124745,0.0002775135,0.41308954,0.08862581,0.35559857,0.0035638567,0.13524556],"study_design_scores_gemma":[0.00005115003,0.00013181358,0.0002753983,0.00002116977,0.000028001827,0.00044544102,0.000027822998,0.95993435,0.011970723,0.020944262,0.0061183698,0.00005150373],"about_ca_topic_score_codex":0.0016662199,"about_ca_topic_score_gemma":0.0019828188,"teacher_disagreement_score":0.0016662199,"about_ca_system_score_codex":0.0005812545,"about_ca_system_score_gemma":0.0011373087,"threshold_uncertainty_score":0.004217267},"labels":[],"label_agreement":null},{"id":"W2004626593","doi":"10.1109/lsp.2014.2361459","title":"Complex-Valued Gaussian Sum Filter for Nonlinear Filtering of Non-Gaussian/Non-Circular Noise","year":2014,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":29,"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","funders":"","keywords":"Gaussian; Gaussian filter; Gaussian noise; Mathematics; Gaussian random field; Algorithm; Gauss sum; Adaptive filter; Noise (video); Gaussian process; Filter (signal processing); Computer science; Pattern recognition (psychology); Artificial intelligence; Discrete mathematics; Computer vision; Physics; Image (mathematics)","score_opus":0.02411567965468811,"score_gpt":0.2585075381105583,"score_spread":0.2343918584558702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004626593","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":[0.0024870532,0.0001477836,0.9967597,0.000060498507,0.00003446743,0.000005332835,0.0000059429353,0.00006006464,0.00043918026],"genre_scores_gemma":[0.340171,0.0013759605,0.65150714,0.00033295673,0.00017994622,0.00007791088,0.00011345993,0.00006113161,0.006180422],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950016,0.00012703004,0.000021314501,0.00010399362,0.00021624002,0.000031201325],"domain_scores_gemma":[0.99945575,0.00027000127,0.000048145484,0.00007922587,0.00012815406,0.000018690094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008430451,0.0005555985,0.0004903128,0.0003893384,0.0003325174,0.0006730151,0.00066403364,0.00093118474,0.0010397072],"category_scores_gemma":[0.0022611937,0.00018582631,0.0005111831,0.00065925013,0.00057222275,0.0007936811,0.0005293571,0.0010823297,0.00045858845],"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":[0.0003644113,0.00011051684,0.0011563475,0.00028149827,0.0001233829,0.00030675533,0.00025813808,0.28616643,0.06750298,0.17527962,0.0035799467,0.46487],"study_design_scores_gemma":[0.000007471752,0.000052000738,0.00022963677,0.000009626676,0.000014853375,0.00011182598,0.000009956358,0.97829825,0.008819032,0.0092929,0.0031405606,0.000013863101],"about_ca_topic_score_codex":0.0014071628,"about_ca_topic_score_gemma":0.0016749136,"teacher_disagreement_score":0.0014071628,"about_ca_system_score_codex":0.0005294495,"about_ca_system_score_gemma":0.00056862953,"threshold_uncertainty_score":0.0044585466},"labels":[],"label_agreement":null},{"id":"W2010985693","doi":"10.1109/lsp.2013.2244882","title":"Selection Combining for Differential Amplify-and-Forward Relaying Over Rayleigh-Fading Channels","year":2013,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Cooperative Communication and Network Coding","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":"University of Saskatchewan","funders":"","keywords":"Maximal-ratio combining; Rayleigh fading; Fading; Selection (genetic algorithm); Channel (broadcasting); Bit error rate; Algorithm; Differential (mechanical device); Channel state information; Computer science; Signal-to-noise ratio (imaging); Mathematics; Telecommunications; Statistics; Topology (electrical circuits); Wireless; Engineering; Artificial intelligence; Combinatorics","score_opus":0.0307345730189498,"score_gpt":0.26614186653302313,"score_spread":0.23540729351407333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010985693","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":[0.050301,0.002122637,0.94288826,0.0001470482,0.000037246824,0.000031870597,0.000019108347,0.00007875165,0.0043741385],"genre_scores_gemma":[0.90209574,0.0040319366,0.09143826,0.000073879826,0.00015338462,0.000064576074,0.00003960276,0.000027284645,0.0020754086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957126,0.00015282888,0.000014000906,0.000032585293,0.00019829803,0.000031144446],"domain_scores_gemma":[0.9994727,0.0003542358,0.000059335838,0.00003535497,0.00007018827,0.00000828476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005233682,0.00072343147,0.0003811598,0.00035239055,0.00025960413,0.00063905923,0.00033244197,0.00041213873,0.00039408167],"category_scores_gemma":[0.0011861101,0.00022163332,0.00043156033,0.00049267587,0.0005310286,0.00049724616,0.00041476544,0.0003220715,0.00018671756],"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":[0.00017770512,0.00003831995,0.0019523558,0.0002494577,0.00012571148,0.0006965403,0.00024991465,0.7453802,0.0814234,0.071897745,0.0008683064,0.09694032],"study_design_scores_gemma":[0.000008403255,0.00013621755,0.0004465198,0.000017033064,0.00004595157,0.0003274352,0.000022066126,0.97962934,0.0073423753,0.009985682,0.0020235775,0.000015483378],"about_ca_topic_score_codex":0.00046321453,"about_ca_topic_score_gemma":0.00068115134,"teacher_disagreement_score":0.00072343147,"about_ca_system_score_codex":0.00052247226,"about_ca_system_score_gemma":0.00028623093,"threshold_uncertainty_score":0.0037908554},"labels":[],"label_agreement":null},{"id":"W2014316832","doi":"10.1109/lsp.2007.913136","title":"New Design of Robust $H_{\\infty}$ Filters for 2-D Systems","year":2008,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Stability and Control of Uncertain Systems","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Filtering theory; Mathematics; Applied mathematics; Matrix (chemical analysis); Matrix algebra; Linear matrix inequality; Computer science; Algorithm; Control theory (sociology); Mathematical optimization; Artificial intelligence; Eigenvalues and eigenvectors; Control (management)","score_opus":0.04310504232231796,"score_gpt":0.2107964113959364,"score_spread":0.16769136907361845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014316832","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":[0.0016275606,0.00015241292,0.99719113,0.000031887765,0.000033305587,0.000015315829,0.000015165368,0.0000840323,0.0008491139],"genre_scores_gemma":[0.33377978,0.0011758155,0.65726495,0.0002646858,0.000265646,0.0003405349,0.00021036385,0.00009145954,0.006606855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99959654,0.000063503794,0.000032533324,0.00011559757,0.00015516195,0.00003672697],"domain_scores_gemma":[0.9996619,0.00013009702,0.000076148084,0.000030888732,0.000087029715,0.00001399366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074572297,0.0009231485,0.00089922576,0.00043841818,0.00024511016,0.0008781361,0.0010400524,0.0015468694,0.0024207076],"category_scores_gemma":[0.0010846546,0.00043862997,0.000793835,0.0002975403,0.0004794514,0.0008338033,0.0006664622,0.00077338197,0.000602529],"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":[0.00032001475,0.00009996161,0.00046511367,0.00065806886,0.0002125998,0.00032094392,0.00025642008,0.54287434,0.10615299,0.06991322,0.0032384305,0.27548787],"study_design_scores_gemma":[0.00003666479,0.00014736704,0.00015337972,0.000024022456,0.000027285598,0.00008108469,0.00001171086,0.9796973,0.0091978535,0.0037641828,0.006834645,0.000024499832],"about_ca_topic_score_codex":0.0008948364,"about_ca_topic_score_gemma":0.0011615731,"teacher_disagreement_score":0.0024207076,"about_ca_system_score_codex":0.0005255019,"about_ca_system_score_gemma":0.0005871729,"threshold_uncertainty_score":0.008098006},"labels":[],"label_agreement":null},{"id":"W2018214963","doi":"10.1109/lsp.2013.2281597","title":"Deterministic Construction of Real-Valued Ternary Sensing Matrices Using Optical Orthogonal Codes","year":2013,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Sparse and Compressive Sensing 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":"Lakehead University","funders":"","keywords":"Golomb coding; Hadamard transform; Normalization (sociology); Binary number; Algorithm; Matrix (chemical analysis); Hadamard matrix; Computer science; Complex Hadamard matrix; Ternary operation; Sparse matrix; Mathematics; Theoretical computer science; Arithmetic; Image (mathematics); Image processing; Image compression; Artificial intelligence","score_opus":0.01800034238972296,"score_gpt":0.2396329377314017,"score_spread":0.22163259534167876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018214963","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":[0.06444027,0.0004403458,0.92518,0.00023528609,0.00017655254,0.000089556816,0.00009745677,0.0002422415,0.009098279],"genre_scores_gemma":[0.6106858,0.00046412353,0.3852158,0.00021014265,0.000102930295,0.00015960773,0.00015255652,0.000055061526,0.0029539973],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99951017,0.00009384165,0.000028333765,0.00008283794,0.00022582033,0.000059059395],"domain_scores_gemma":[0.9993431,0.00019048626,0.00014033231,0.0001347645,0.00014140854,0.00004989731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004116318,0.0004181239,0.00028992884,0.00057394913,0.0003366959,0.000617798,0.00043082167,0.00042377244,0.0011877624],"category_scores_gemma":[0.0015120619,0.00020712178,0.00026826534,0.0004637997,0.00069851754,0.0007524642,0.00068788783,0.0005816899,0.00034322636],"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":[0.00021641812,0.00008190369,0.00056538073,0.0001987916,0.000026714424,0.00030908198,0.00021690784,0.048322517,0.16121916,0.66058975,0.0016270896,0.12662624],"study_design_scores_gemma":[0.00009107414,0.00050421694,0.0005535079,0.00006999511,0.000029339306,0.0009903716,0.00011512432,0.6042405,0.25093016,0.11463164,0.027711622,0.00013246092],"about_ca_topic_score_codex":0.000339399,"about_ca_topic_score_gemma":0.0005156956,"teacher_disagreement_score":0.0011877624,"about_ca_system_score_codex":0.00043512712,"about_ca_system_score_gemma":0.00062520365,"threshold_uncertainty_score":0.003973484},"labels":[],"label_agreement":null},{"id":"W2020513142","doi":"10.1109/lsp.2007.913625","title":"A New Model for Image Segmentation","year":2008,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"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":"Image segmentation; Segmentation; Computer science; Scale-space segmentation; Artificial intelligence; Segmentation-based object categorization; Image (mathematics); Computer vision; Set (abstract data type); Pattern recognition (psychology); Variable (mathematics); Mathematics","score_opus":0.03619138375877132,"score_gpt":0.2935405548139096,"score_spread":0.2573491710551383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020513142","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":[0.000983073,0.0003313756,0.99558336,0.000258065,0.00009930134,0.000021342079,0.00008662621,0.00024306997,0.0023938646],"genre_scores_gemma":[0.13951051,0.0026280365,0.8270183,0.00081134477,0.0006674802,0.00053081283,0.0010121745,0.00059382094,0.027227405],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991574,0.00014854695,0.00004505771,0.00023571568,0.00034789907,0.00006531383],"domain_scores_gemma":[0.9995546,0.00013889567,0.000046363217,0.000099414974,0.00012095081,0.000039758692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006903865,0.00084620964,0.0009823809,0.0011766192,0.0005039628,0.0020952388,0.0021683779,0.0023970103,0.0038106402],"category_scores_gemma":[0.0017071993,0.0005925207,0.0014909443,0.0014238579,0.0012049921,0.0029295455,0.0016948307,0.0020274268,0.0024016418],"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":[0.00011078128,0.000052018393,0.0005197783,0.00027515643,0.000086550725,0.00036711883,0.00026653096,0.35528457,0.02361763,0.48123935,0.014238355,0.12394216],"study_design_scores_gemma":[0.000011007644,0.000027990898,0.0001030187,0.000017563061,0.000016833419,0.00021513799,0.000015066687,0.9116366,0.0015771514,0.06328583,0.023072086,0.000021679918],"about_ca_topic_score_codex":0.0021746226,"about_ca_topic_score_gemma":0.0021609354,"teacher_disagreement_score":0.0038106402,"about_ca_system_score_codex":0.0012110671,"about_ca_system_score_gemma":0.0010203078,"threshold_uncertainty_score":0.012747884},"labels":[],"label_agreement":null},{"id":"W2022215515","doi":"10.1109/lsp.2007.898329","title":"On Crosstalk Cancellation and Equalization With Multiple Loudspeakers for 3-D Sound Reproduction","year":2007,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":27,"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":"Loudspeaker; Crosstalk; Binaural recording; Acoustics; Reverberation; Computer science; Equalization (audio); Impulse (physics); Impulse response; Directional sound; Sound recording and reproduction; Speech recognition; Physics; Mathematics; Telecommunications; Electronic engineering; Engineering; Decoding methods","score_opus":0.018706615015836137,"score_gpt":0.2603135747922342,"score_spread":0.24160695977639807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022215515","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":[0.009166961,0.0015650167,0.98507047,0.0001764488,0.00008386262,0.00003908404,0.000017192559,0.00021112117,0.003669782],"genre_scores_gemma":[0.29782906,0.006411342,0.6850332,0.0004181675,0.00049806025,0.00012528012,0.00014473288,0.00013013635,0.009410056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993975,0.00021501203,0.000033962402,0.00005841643,0.00025141024,0.00004368455],"domain_scores_gemma":[0.9990212,0.00077606714,0.000034816207,0.00004988704,0.00010260483,0.000015424306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006488709,0.0009022872,0.00052321906,0.00051458617,0.00034345812,0.000579245,0.00052954233,0.0012373173,0.0023466707],"category_scores_gemma":[0.0021395262,0.0004679729,0.00063878676,0.0006828776,0.0007661369,0.0010634987,0.0007755126,0.00072292145,0.00088710774],"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":[0.00035534715,0.00019483862,0.0008794381,0.00060699554,0.00015208354,0.00061295216,0.00038934164,0.32035232,0.1505926,0.035140987,0.0051115635,0.48561153],"study_design_scores_gemma":[0.000035971523,0.00015958287,0.00042562268,0.00003796702,0.00003638475,0.00045183956,0.00003933319,0.93948513,0.0425036,0.009772108,0.006982555,0.0000698451],"about_ca_topic_score_codex":0.001583339,"about_ca_topic_score_gemma":0.0021537573,"teacher_disagreement_score":0.0023466707,"about_ca_system_score_codex":0.0004143732,"about_ca_system_score_gemma":0.000391576,"threshold_uncertainty_score":0.007850349},"labels":[],"label_agreement":null},{"id":"W2022401652","doi":"10.1109/lsp.2012.2205142","title":"Tractable Bound for Spherical Section Property in the Presence of Side-Information","year":2012,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Sparse and Compressive Sensing Techniques","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 Toronto","funders":"","keywords":"Semidefinite programming; Relaxation (psychology); Upper and lower bounds; Compressed sensing; Semidefinite embedding; Linear programming; Constraint (computer-aided design); Dual (grammatical number); Property (philosophy); Mathematics; Computer science; Algorithm; Mathematical optimization; Computational complexity theory; Combinatorics; Quadratically constrained quadratic program; Quadratic programming; Mathematical analysis; Geometry","score_opus":0.021915080156452174,"score_gpt":0.23723496350879497,"score_spread":0.2153198833523428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022401652","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":[0.015302681,0.0006206626,0.9701662,0.0008656756,0.00006396788,0.00006545623,0.00032245895,0.00018506525,0.012407813],"genre_scores_gemma":[0.767493,0.0023326117,0.21728249,0.0008777638,0.00039842282,0.0004749997,0.0009750629,0.00045324786,0.009712369],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979425,0.0006704588,0.000066803696,0.0003222122,0.00068680674,0.00031118395],"domain_scores_gemma":[0.98540765,0.011567077,0.00072936364,0.0008821173,0.0010365226,0.0003771582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033193978,0.0018214701,0.0012977151,0.00083700067,0.0006824616,0.002255527,0.0014799908,0.0014105372,0.0088848155],"category_scores_gemma":[0.015443637,0.0006318655,0.00087875384,0.00097369595,0.0024486692,0.0049113655,0.0028334807,0.0044562295,0.0012806138],"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":[0.0003628465,0.00014256366,0.0008906304,0.00052480964,0.00006961196,0.0008344566,0.0003102451,0.44224912,0.015416925,0.4919404,0.010064609,0.037193812],"study_design_scores_gemma":[0.000021296028,0.00008648745,0.00021790458,0.000075748416,0.000017233455,0.0002763529,0.00009102866,0.85484344,0.0036034256,0.13824692,0.0024890779,0.00003096306],"about_ca_topic_score_codex":0.0013563436,"about_ca_topic_score_gemma":0.0015937808,"teacher_disagreement_score":0.0088848155,"about_ca_system_score_codex":0.001773102,"about_ca_system_score_gemma":0.0017383595,"threshold_uncertainty_score":0.02972269},"labels":[],"label_agreement":null},{"id":"W2024165348","doi":"10.1109/lsp.2010.2102017","title":"Relay Selection in Dual-Hop Vehicular Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Relay; Selection (genetic algorithm); Computer science; Rayleigh fading; Channel (broadcasting); Algorithm; Artificial intelligence; Fading; Computer network; Physics","score_opus":0.016658020982744527,"score_gpt":0.25113018062649,"score_spread":0.2344721596437455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024165348","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":[0.4038653,0.0032307643,0.5791321,0.0007049814,0.00017689339,0.000090568974,0.000109564324,0.0001547321,0.012535049],"genre_scores_gemma":[0.9923665,0.0005321212,0.0053035747,0.00005185052,0.00003694168,0.000030645006,0.000025255878,0.000006649803,0.0016464669],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902034,0.000354474,0.000021555314,0.00013061041,0.00020932902,0.0002636064],"domain_scores_gemma":[0.99823254,0.00105726,0.00021671594,0.00009126967,0.00029262443,0.000109608234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011477163,0.0007376,0.00071726623,0.00081729237,0.0005602308,0.0010330314,0.0010455218,0.00091908115,0.0006945666],"category_scores_gemma":[0.0034688357,0.00039119803,0.0004145781,0.00085513113,0.0012181585,0.0010682885,0.0011994615,0.00047565094,0.00018959606],"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":[0.00015648233,0.000037019418,0.0014482945,0.000057976984,0.000047567017,0.0003542065,0.00012582765,0.95963126,0.0032782077,0.026012518,0.0005052608,0.00834543],"study_design_scores_gemma":[0.000012183873,0.00007134269,0.0002620968,0.000007326799,0.000014875653,0.00007439855,0.00004718743,0.99224347,0.0005386169,0.0062425914,0.0004741915,0.000011677637],"about_ca_topic_score_codex":0.0037102483,"about_ca_topic_score_gemma":0.0020114025,"teacher_disagreement_score":0.0037102483,"about_ca_system_score_codex":0.0012198804,"about_ca_system_score_gemma":0.0006050887,"threshold_uncertainty_score":0.008850932},"labels":[],"label_agreement":null},{"id":"W2025445694","doi":"10.1109/lsp.2010.2091405","title":"Wideband Spectrum Sensing for Cognitive Radios With Correlated Subband Occupancy","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Wideband; Cognitive radio; Estimator; Detector; Statistic; Algorithm; Energy (signal processing); Maximum a posteriori estimation; Mathematics; Binary number; Statistics; Computer science; Telecommunications; Wireless; Electronic engineering; Engineering; Maximum likelihood","score_opus":0.009559461532204896,"score_gpt":0.22465536200219627,"score_spread":0.21509590046999139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025445694","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":[0.06653166,0.00023899031,0.93202424,0.00011413658,0.000021867585,0.000016700584,0.000017070623,0.000058933012,0.00097634125],"genre_scores_gemma":[0.93358016,0.00019887788,0.06547901,0.00007430609,0.00004149458,0.000035185778,0.000018329045,0.000010660888,0.0005619142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991685,0.00030487895,0.00002637744,0.00014126457,0.00026798382,0.00009109372],"domain_scores_gemma":[0.998692,0.0008614973,0.00016570115,0.000113034796,0.00012918375,0.000038603557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009529972,0.00058210304,0.00074807537,0.00023386319,0.00029344193,0.0008536735,0.00066909567,0.0007311503,0.00044292808],"category_scores_gemma":[0.0044096755,0.00039949192,0.0003881414,0.00034984827,0.0009043174,0.0012292957,0.00089298346,0.00067169,0.00009836055],"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":[0.00039466107,0.00008207644,0.0021622581,0.00015593547,0.00012486335,0.00044888863,0.00018822345,0.8619933,0.02488754,0.06271715,0.0004353754,0.04640976],"study_design_scores_gemma":[0.000008892187,0.000030081854,0.00020970328,0.0000049717514,0.000011212266,0.000040414216,0.000013277427,0.9921766,0.0011443745,0.0062168567,0.00013726928,0.000006430943],"about_ca_topic_score_codex":0.001363891,"about_ca_topic_score_gemma":0.0017665008,"teacher_disagreement_score":0.001363891,"about_ca_system_score_codex":0.00045062494,"about_ca_system_score_gemma":0.0006848361,"threshold_uncertainty_score":0.0050400496},"labels":[],"label_agreement":null},{"id":"W2033400894","doi":"10.1109/lsp.2003.811586","title":"Multiwavelets denoising using neighboring coefficients","year":2003,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":171,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Noise reduction; Thresholding; Wavelet; Pattern recognition (psychology); Artificial intelligence; Video denoising; Computer science; Extension (predicate logic); Term (time); Image denoising; Wavelet transform; Mathematics; Step detection; Algorithm; Computer vision; Image (mathematics)","score_opus":0.03591050733372826,"score_gpt":0.28826437517229503,"score_spread":0.2523538678385668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033400894","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":[0.07718746,0.00090114947,0.9193593,0.00014402764,0.00016608005,0.000034898134,0.000045430264,0.00032305755,0.0018384235],"genre_scores_gemma":[0.35645527,0.0016200246,0.63592,0.00016579212,0.00017397564,0.000055413362,0.0002963088,0.00034666446,0.004966585],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999446,0.00009418222,0.00003440047,0.00012971973,0.00025383744,0.000041768722],"domain_scores_gemma":[0.9987325,0.00049963716,0.00012313388,0.00026004846,0.0003232771,0.0000614247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012710206,0.000847332,0.0010438865,0.0008944993,0.00026861907,0.00075292616,0.0005286688,0.00109419,0.0016468483],"category_scores_gemma":[0.0028896115,0.00041142607,0.001033689,0.0009861022,0.000497442,0.0020659526,0.0006975613,0.0011071913,0.00084734085],"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":[0.0005737396,0.00031975502,0.0024811414,0.0004350647,0.00022159601,0.00029941535,0.00017966746,0.08508535,0.407887,0.009518589,0.0022374047,0.49076128],"study_design_scores_gemma":[0.00003878652,0.00029221745,0.002018177,0.00003225491,0.0001352661,0.0005468788,0.000048026643,0.76421213,0.22356445,0.005429519,0.0036459558,0.000036472826],"about_ca_topic_score_codex":0.00044583468,"about_ca_topic_score_gemma":0.0011497167,"teacher_disagreement_score":0.0016468483,"about_ca_system_score_codex":0.00020941613,"about_ca_system_score_gemma":0.000273795,"threshold_uncertainty_score":0.006721914},"labels":[],"label_agreement":null},{"id":"W2039911522","doi":"10.1109/lsp.2014.2378773","title":"Slow Adaptive Power Control and Outage Avoidance in Composite Fading Wireless Channels","year":2014,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Wireless Communication Networks Research","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":"Polytechnique Montréal","funders":"","keywords":"Fading; Computer science; Fading distribution; Power control; Transmitter power output; Phase-shift keying; Wireless; Electronic engineering; Channel state information; Power (physics); Control theory (sociology); Telecommunications; Bit error rate; Channel (broadcasting); Engineering; Transmitter; Rayleigh fading; Physics; Control (management)","score_opus":0.016468949652246496,"score_gpt":0.24837610074508942,"score_spread":0.23190715109284293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039911522","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":[0.108259484,0.00030547436,0.8892458,0.00010599221,0.000027609403,0.000013449591,0.000015362024,0.00011396296,0.0019128552],"genre_scores_gemma":[0.98412794,0.00017737948,0.015030304,0.000022052373,0.00002164214,0.000012912098,0.0000075127505,0.0000097444245,0.0005905659],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997795,0.00005507895,0.000008530026,0.00004295296,0.000075874814,0.00003801544],"domain_scores_gemma":[0.99943644,0.00031399433,0.000099266625,0.00005049646,0.0000752947,0.0000245626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032297705,0.000374589,0.0003126021,0.00023966447,0.0002283442,0.00045189264,0.0003541879,0.00026559772,0.00046624325],"category_scores_gemma":[0.0014581785,0.00015372003,0.00013441399,0.00032116793,0.00074306456,0.00055545534,0.0005987247,0.0005492535,0.00009352848],"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":[0.00023237227,0.000040277708,0.0010590669,0.000082701335,0.000029746372,0.0002710597,0.00016083772,0.85176903,0.046618327,0.033191647,0.0006233202,0.065921605],"study_design_scores_gemma":[0.000009206562,0.00007110194,0.00029247894,0.0000032880369,0.0000047969534,0.00004752627,0.000014506268,0.99069995,0.0025485859,0.005991548,0.0003102263,0.0000067785504],"about_ca_topic_score_codex":0.00054655096,"about_ca_topic_score_gemma":0.00052326766,"teacher_disagreement_score":0.00054655096,"about_ca_system_score_codex":0.00024505504,"about_ca_system_score_gemma":0.0002617148,"threshold_uncertainty_score":0.001777947},"labels":[],"label_agreement":null},{"id":"W2043089841","doi":"10.1109/lsp.2015.2396650","title":"Compressive Sensing Time Reversal MIMO Radar: Joint Direction and Doppler Frequency Estimation","year":2015,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Microwave Imaging and Scattering Analysis","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":"Concordia University; York University","funders":"","keywords":"MIMO; Clutter; Doppler effect; Computer science; Multipath propagation; Radar; Compressed sensing; Algorithm; Joint (building); Monte Carlo method; Acoustics; Control theory (sociology); Telecommunications; Physics; Mathematics; Engineering; Statistics; Artificial intelligence; Beamforming","score_opus":0.014592450303728532,"score_gpt":0.2070477329729977,"score_spread":0.19245528266926917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043089841","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":[0.011596445,0.00019481327,0.98586106,0.00021785573,0.00006741246,0.000019901276,0.000024557108,0.00014091346,0.001877032],"genre_scores_gemma":[0.3849014,0.0005269221,0.61148506,0.0002935769,0.00020144442,0.000046621913,0.00011678773,0.000044541026,0.0023836456],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996724,0.000088292305,0.0000150118885,0.000047610323,0.00015078012,0.00002587778],"domain_scores_gemma":[0.9995136,0.0001946798,0.00007349105,0.00009371069,0.000098301374,0.000026180758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005013069,0.00039705524,0.00036194615,0.00019599586,0.00015143953,0.0004995115,0.00048596723,0.00063232635,0.0008023599],"category_scores_gemma":[0.0017319476,0.00020876582,0.00025541545,0.00030741084,0.00040222192,0.00071343355,0.00054784416,0.0006978233,0.00036821037],"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":[0.00031756918,0.00012742382,0.0017556445,0.00020033526,0.00007919383,0.00027169558,0.00009998577,0.45566022,0.12557392,0.11230726,0.0037665872,0.29984018],"study_design_scores_gemma":[0.000013512844,0.00007196626,0.00015577184,0.000007627253,0.000007729123,0.00020559295,0.0000091848915,0.9798004,0.012581579,0.0051773232,0.0019575635,0.000011720383],"about_ca_topic_score_codex":0.00055053947,"about_ca_topic_score_gemma":0.0008813891,"teacher_disagreement_score":0.0008023599,"about_ca_system_score_codex":0.00022676126,"about_ca_system_score_gemma":0.0005847572,"threshold_uncertainty_score":0.002684176},"labels":[],"label_agreement":null},{"id":"W2044943815","doi":"10.1109/lsp.2012.2221711","title":"Afriat's Test for Detecting Malicious Agents","year":2012,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":20,"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 British Columbia","funders":"","keywords":"Computer science; Construct (python library); Set (abstract data type); Test (biology); Artificial intelligence; Mathematical optimization; Machine learning; Data mining; Algorithm; Mathematics","score_opus":0.025365773182734093,"score_gpt":0.25587131966948873,"score_spread":0.23050554648675464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044943815","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":[0.047111563,0.00028529656,0.9457678,0.0020717806,0.00013903911,0.00012528141,0.00015894903,0.0005119153,0.003828369],"genre_scores_gemma":[0.8341578,0.00020731644,0.16119744,0.00092281954,0.00025235882,0.00034276638,0.00030410907,0.00009121649,0.0025241356],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98630315,0.0060927127,0.0006522165,0.0023164665,0.0035803518,0.0010551417],"domain_scores_gemma":[0.9098998,0.075462185,0.0034640443,0.004454762,0.005334823,0.0013844686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014774686,0.0013667574,0.0024319664,0.0026730187,0.0011440864,0.0022435766,0.0030965174,0.0043862704,0.0016449428],"category_scores_gemma":[0.12509514,0.0004925346,0.0016479847,0.00123102,0.0052747824,0.0049523874,0.0030827394,0.0034982553,0.00060813327],"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":[0.0022853573,0.0002829701,0.025786884,0.00031690186,0.0004685605,0.00089695264,0.00062018586,0.26477954,0.01120931,0.502246,0.009021229,0.182086],"study_design_scores_gemma":[0.00012478323,0.0004517662,0.001413309,0.000034739292,0.000039288865,0.00048257853,0.00005694014,0.8828421,0.006538173,0.106605925,0.0013533424,0.00005707443],"about_ca_topic_score_codex":0.0014504712,"about_ca_topic_score_gemma":0.0005493624,"teacher_disagreement_score":0.014774686,"about_ca_system_score_codex":0.0014967276,"about_ca_system_score_gemma":0.002151633,"threshold_uncertainty_score":0.07813692},"labels":[],"label_agreement":null},{"id":"W2045069106","doi":"10.1109/lsp.2010.2097590","title":"Mean Square Error Estimation in Thresholding","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image and Signal Denoising Methods","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":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto; Toronto Metropolitan University","funders":"","keywords":"Mean squared error; Thresholding; Computer science; Probabilistic logic; Algorithm; Mathematics; Statistics; Mean square; Pattern recognition (psychology); Artificial intelligence; Image (mathematics)","score_opus":0.025875996023772743,"score_gpt":0.2967279493003013,"score_spread":0.27085195327652856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045069106","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":[0.0020487106,0.00046686037,0.9967809,0.00007445094,0.000045072797,0.000007293267,0.000008440094,0.000077375174,0.0004909408],"genre_scores_gemma":[0.16079862,0.0015204985,0.8337349,0.00018445635,0.00028522743,0.00008777864,0.00008831982,0.00019673909,0.0031034416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974025,0.0006721765,0.00015521463,0.0004930115,0.0011759307,0.000101238664],"domain_scores_gemma":[0.99495727,0.0032328092,0.0003659452,0.0005114589,0.0008633816,0.000069168804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028132016,0.0008540566,0.0013776348,0.0010736571,0.00032775642,0.0014586361,0.0016135974,0.002058813,0.0008484388],"category_scores_gemma":[0.015619995,0.00058785867,0.00065150444,0.0011226032,0.0012508592,0.0019903583,0.0014071078,0.0020671228,0.0005312309],"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":[0.00028628923,0.00006138091,0.0013469448,0.0004531434,0.00018760483,0.00023683878,0.00023353612,0.58534014,0.048637126,0.081796095,0.0025445307,0.2788764],"study_design_scores_gemma":[0.000010998806,0.00005438833,0.0002908696,0.000026497177,0.000023241011,0.00015232449,0.0000112477765,0.9635578,0.015606158,0.018002352,0.002237345,0.000026755548],"about_ca_topic_score_codex":0.0011255613,"about_ca_topic_score_gemma":0.0008178362,"teacher_disagreement_score":0.0028132016,"about_ca_system_score_codex":0.0006653031,"about_ca_system_score_gemma":0.0006076521,"threshold_uncertainty_score":0.014877856},"labels":[],"label_agreement":null},{"id":"W2052628454","doi":"10.1109/97.855449","title":"GMDF for noise reduction and echo cancellation","year":2000,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Echo (communications protocol); Noise reduction; Reduction (mathematics); Microphone; Noise (video); Frequency domain; Telephony; Algorithm; Speech enhancement; Computer science; Computation; Block (permutation group theory); Signal-to-noise ratio (imaging); Return loss; Speech recognition; Telecommunications; Mathematics; Artificial intelligence; Antenna (radio); Computer network","score_opus":0.012919708862018786,"score_gpt":0.23510887816552795,"score_spread":0.22218916930350915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052628454","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":[0.0052418774,0.00030703438,0.9925216,0.0000624421,0.00005475372,0.000018144308,0.000024989835,0.00054317445,0.0012259756],"genre_scores_gemma":[0.053689525,0.00025916708,0.94231766,0.000085816166,0.00003494014,0.000049883045,0.0001183632,0.000113163864,0.0033313453],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995808,0.0001006654,0.000013943742,0.000048125938,0.00022556249,0.00003095139],"domain_scores_gemma":[0.99961096,0.00014366693,0.000028105005,0.000076672055,0.00012510445,0.000015449685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000372975,0.00055002363,0.0003442524,0.000388619,0.00019384464,0.00038149316,0.00062402437,0.0007318503,0.0042520245],"category_scores_gemma":[0.0015803581,0.0001599949,0.00039407585,0.0005600725,0.00032861403,0.00060704956,0.0005730538,0.00072338147,0.0019638024],"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":[0.00031326787,0.000057551362,0.00042029354,0.00031312538,0.000052435957,0.00011008173,0.000077932884,0.04989589,0.16663061,0.019104704,0.0040288065,0.7589953],"study_design_scores_gemma":[0.00008532645,0.0002948327,0.0006652254,0.000060350263,0.000030889994,0.0009872188,0.000040694376,0.7960678,0.13954772,0.01073457,0.051434845,0.00005048275],"about_ca_topic_score_codex":0.0008231233,"about_ca_topic_score_gemma":0.001108623,"teacher_disagreement_score":0.0042520245,"about_ca_system_score_codex":0.00029742005,"about_ca_system_score_gemma":0.00045525472,"threshold_uncertainty_score":0.01422441},"labels":[],"label_agreement":null},{"id":"W2054040518","doi":"10.1109/97.889636","title":"Wavelet speech enhancement based on the Teager energy operator","year":2001,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":194,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Energy operator; Speech enhancement; Wavelet; Speech recognition; Energy (signal processing); Computer science; Noise (video); A priori and a posteriori; Artificial intelligence; Pattern recognition (psychology); Noise measurement; Wavelet transform; Noise reduction; Mathematics; Statistics","score_opus":0.015494806076616208,"score_gpt":0.22468159980515479,"score_spread":0.20918679372853857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054040518","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":[0.015985496,0.0003612407,0.9826985,0.000043369906,0.00004997581,0.000018557103,0.000010617474,0.00024454214,0.00058768917],"genre_scores_gemma":[0.21539852,0.0011426808,0.7784564,0.00011329162,0.000113368675,0.00006239688,0.00006989052,0.00013217509,0.0045112967],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998041,0.00004343163,0.000011170101,0.000040133767,0.00008467466,0.000016607253],"domain_scores_gemma":[0.9996697,0.00013620278,0.00003856687,0.00004886993,0.00008418478,0.000022579508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049110845,0.0004622421,0.00047356758,0.00035022153,0.00012416141,0.0004567311,0.00048113897,0.0005047904,0.001341107],"category_scores_gemma":[0.00075642054,0.00021617726,0.00048542206,0.0002931595,0.0003330428,0.00091375184,0.00052836025,0.0007329663,0.00058398355],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004160865,0.000078651094,0.00033399035,0.00018468325,0.000048614387,0.00020755109,0.000114817296,0.01944851,0.6221696,0.0131194405,0.0007589355,0.34311914],"study_design_scores_gemma":[0.0000579626,0.0005250849,0.0015311468,0.000028343055,0.000069736314,0.00082392694,0.000032836902,0.6664359,0.31676832,0.0049604336,0.008708481,0.000057801026],"about_ca_topic_score_codex":0.00019282429,"about_ca_topic_score_gemma":0.00025539377,"teacher_disagreement_score":0.001341107,"about_ca_system_score_codex":0.00011392754,"about_ca_system_score_gemma":0.0001772648,"threshold_uncertainty_score":0.0044864416},"labels":[],"label_agreement":null},{"id":"W2056426543","doi":"10.1109/lsp.2013.2295919","title":"Optimal Non-regenerative Linear MIMO Relay for Orthogonal Space Time Codes","year":2014,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Cooperative Communication and Network Coding","field":"Computer Science","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":"Queen's University","funders":"","keywords":"MIMO; Relay; Computer science; Block code; Node (physics); Algorithm; Transmission (telecommunications); Constraint (computer-aided design); Decoding methods; Mathematical optimization; Power (physics); Mathematics; Telecommunications; Beamforming; Engineering","score_opus":0.023744280591426612,"score_gpt":0.26893856541071093,"score_spread":0.24519428481928432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056426543","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":[0.07651384,0.0006370152,0.91395557,0.00030028092,0.00002972061,0.000044800538,0.000059404643,0.00012293688,0.008336449],"genre_scores_gemma":[0.9309997,0.0004817373,0.064901486,0.000056741934,0.000021981366,0.000051013638,0.00004110574,0.000021141186,0.0034250834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996766,0.00014588842,0.000009537028,0.00004044639,0.00009141285,0.000036150534],"domain_scores_gemma":[0.99962234,0.0002338476,0.00005744816,0.000025916494,0.00004752579,0.000012882953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004553719,0.00052986067,0.0004615645,0.00016976957,0.0002422063,0.0006130624,0.00042277825,0.00053539686,0.0015083611],"category_scores_gemma":[0.0010934107,0.00021476382,0.00033111288,0.00029817072,0.0006470577,0.00055012165,0.0004509567,0.0004340372,0.00033312853],"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":[0.00008330548,0.000026170766,0.00017830862,0.00009372147,0.000019952475,0.0001838221,0.000094875264,0.9155568,0.010654226,0.06415382,0.0004166239,0.008538298],"study_design_scores_gemma":[0.000009682007,0.000024318564,0.000037915193,0.000003828459,0.0000053752774,0.000034496145,0.000015558271,0.99193126,0.001370067,0.0063529797,0.00020927688,0.0000052001183],"about_ca_topic_score_codex":0.0018405775,"about_ca_topic_score_gemma":0.0028131318,"teacher_disagreement_score":0.0018405775,"about_ca_system_score_codex":0.0007483827,"about_ca_system_score_gemma":0.00089327287,"threshold_uncertainty_score":0.0054299235},"labels":[],"label_agreement":null},{"id":"W2058687912","doi":"10.1109/lsp.2012.2194142","title":"Time-Frequency Analysis via Ramanujan Sums","year":2012,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Fractal and DNA sequence analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Time–frequency analysis; Kernel (algebra); Signal processing; Algorithm; Ramanujan's sum; Mathematics; Computer science; Discrete mathematics; Digital signal processing; Pure mathematics","score_opus":0.007960043356324654,"score_gpt":0.23251962637157922,"score_spread":0.22455958301525455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058687912","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":[0.024343107,0.00063037465,0.9649343,0.00013324627,0.0000807728,0.000031597887,0.00005289581,0.00016259684,0.009631069],"genre_scores_gemma":[0.599825,0.0023492922,0.38267562,0.00017412563,0.0002763218,0.00018314968,0.00020287558,0.00025545308,0.014058287],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99963665,0.00011395919,0.000016857928,0.000046055666,0.0001601889,0.000026402913],"domain_scores_gemma":[0.9996542,0.0001733417,0.000035151777,0.000045178585,0.00007280881,0.000019326113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007202173,0.000713475,0.0004991336,0.0016866832,0.0003817882,0.0010624374,0.00074496795,0.0005126401,0.0029107304],"category_scores_gemma":[0.0017877445,0.00026062565,0.000624425,0.00096107234,0.0009859862,0.0012869592,0.0007216044,0.0009621099,0.0010300227],"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":[0.00005899315,0.000045092074,0.0002902546,0.00015835321,0.00004315895,0.00015661107,0.00020459993,0.11884808,0.022993162,0.761943,0.0012472669,0.09401153],"study_design_scores_gemma":[0.0000068201725,0.000035798625,0.0002302668,0.000022052558,0.000009925081,0.00016665435,0.00003168021,0.7431903,0.004398182,0.24667026,0.0051991036,0.000038990896],"about_ca_topic_score_codex":0.00059027027,"about_ca_topic_score_gemma":0.0005141561,"teacher_disagreement_score":0.0029107304,"about_ca_system_score_codex":0.00035922154,"about_ca_system_score_gemma":0.00038838788,"threshold_uncertainty_score":0.009737372},"labels":[],"label_agreement":null},{"id":"W2064878266","doi":"10.1109/lsp.2013.2262939","title":"Sum-Rate Maximization for Active Channels","year":2013,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","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":"Ontario Tech University","funders":"","keywords":"Maximization; Channel (broadcasting); Mathematical optimization; Power (physics); Computer science; Power gain; Signal-to-noise ratio (imaging); Mathematics; Telecommunications; Physics; Amplifier; Bandwidth (computing)","score_opus":0.012454201381007416,"score_gpt":0.20368526409614515,"score_spread":0.19123106271513773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064878266","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":[0.0164974,0.0010826216,0.9691232,0.0004240086,0.00007557217,0.00004369063,0.00011586767,0.00008947209,0.0125481915],"genre_scores_gemma":[0.8296887,0.0034559285,0.14671952,0.00043586484,0.00036194202,0.00030426035,0.00029365206,0.00017882245,0.018561272],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986896,0.00053152075,0.00004046627,0.00017576758,0.0003500428,0.00021266539],"domain_scores_gemma":[0.99744344,0.0018304616,0.00018598669,0.00014449573,0.0003256429,0.000069932634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001770272,0.0016215993,0.0010298156,0.00046677733,0.0004565548,0.0017672419,0.0011477935,0.00097322237,0.0027837271],"category_scores_gemma":[0.0042994944,0.0005190357,0.00052892236,0.00088584755,0.0015009458,0.002243628,0.0013368801,0.0014537777,0.00072129804],"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":[0.0002205525,0.0001382996,0.00043843553,0.00052984874,0.000101772384,0.00028839387,0.00024039092,0.6313554,0.015447531,0.30930123,0.0053083654,0.036629744],"study_design_scores_gemma":[0.000033889854,0.00007854733,0.00015684534,0.000028542583,0.000030695926,0.00013211532,0.00006200937,0.89857495,0.003933779,0.0928923,0.004057404,0.000018900666],"about_ca_topic_score_codex":0.0011133426,"about_ca_topic_score_gemma":0.0010015072,"teacher_disagreement_score":0.0027837271,"about_ca_system_score_codex":0.0011149102,"about_ca_system_score_gemma":0.000850913,"threshold_uncertainty_score":0.009362221},"labels":[],"label_agreement":null},{"id":"W2068355928","doi":"10.1109/lsp.2007.896176","title":"Receive Antenna Selection Based on Union-Bound Minimization Using Convex Optimization","year":2007,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Wireless Communication Techniques","field":"Engineering","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 Alberta","funders":"","keywords":"Transmitter; Mathematical optimization; MIMO; Antenna (radio); Selection (genetic algorithm); Convex optimization; Computer science; Optimization problem; Computational complexity theory; Minification; Algorithm; Mathematics; Upper and lower bounds; Interior point method; Regular polygon; Telecommunications; Beamforming","score_opus":0.01640996327632522,"score_gpt":0.25594514161115084,"score_spread":0.23953517833482563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068355928","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":[0.0016709862,0.000058246602,0.9973839,0.00006051925,0.000007439073,0.000009453081,0.00000943191,0.000094490475,0.00070553547],"genre_scores_gemma":[0.19608527,0.0004044389,0.799317,0.00017195259,0.00011934743,0.00022925645,0.00018969268,0.00023086336,0.0032521882],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997577,0.0012331383,0.00009039556,0.00024329554,0.00069696136,0.00015914527],"domain_scores_gemma":[0.9975777,0.0016291399,0.00022223,0.00020744084,0.00030336564,0.000060086135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021128221,0.0013208564,0.0025579408,0.0006545849,0.00048552084,0.0016724677,0.0013014723,0.0011044971,0.0017911198],"category_scores_gemma":[0.0048879753,0.0006084301,0.00075263914,0.0011880078,0.0012070197,0.001576504,0.0015535428,0.0014383154,0.0009907462],"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":[0.00017212186,0.00007654318,0.00027043573,0.00007732052,0.00005336044,0.000056248387,0.00006738114,0.8849309,0.0043159947,0.030902367,0.0027844452,0.07629288],"study_design_scores_gemma":[0.000010346572,0.000020155087,0.000030627536,0.000003925464,0.0000049762557,0.000015806743,0.0000050770177,0.99468875,0.0014395341,0.0034501043,0.00032667562,0.000004123817],"about_ca_topic_score_codex":0.00078985916,"about_ca_topic_score_gemma":0.00057842187,"teacher_disagreement_score":0.0025579408,"about_ca_system_score_codex":0.0008381867,"about_ca_system_score_gemma":0.0008585528,"threshold_uncertainty_score":0.011173785},"labels":[],"label_agreement":null},{"id":"W2068403421","doi":"10.1109/lsp.2014.2372333","title":"No-Reference Quality Assessment of Contrast-Distorted Images Based on Natural Scene Statistics","year":2014,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":340,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Scene statistics; Contrast (vision); Artificial intelligence; Computer science; Image quality; Support vector machine; Distortion (music); Mean opinion score; Entropy (arrow of time); Pattern recognition (psychology); Quality Score; Quality (philosophy); Regression; Computer vision; Statistics; Image (mathematics); Mathematics; Perception; Metric (unit)","score_opus":0.03557197115822961,"score_gpt":0.3340860052783217,"score_spread":0.2985140341200921,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068403421","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":[0.35897064,0.0009073425,0.6363453,0.00013895187,0.000057560534,0.00010614541,0.0002582047,0.00088344456,0.002332415],"genre_scores_gemma":[0.90690005,0.00044109309,0.09137179,0.000044114546,0.000034391916,0.000032346597,0.00040674114,0.00006946145,0.0006999834],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999129,0.00016733838,0.000057492132,0.0001558974,0.0004412022,0.00004903425],"domain_scores_gemma":[0.99807656,0.0003754301,0.00038286165,0.0002738695,0.0008190765,0.00007214369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010316024,0.00047776575,0.0005971936,0.0012702836,0.00016637571,0.0006932103,0.00050658593,0.00035581595,0.00065690884],"category_scores_gemma":[0.00423525,0.0001562637,0.00049194245,0.00050116115,0.0004994503,0.0008987829,0.0005863388,0.00039110018,0.00031946358],"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":[0.0016173051,0.00022133542,0.023643995,0.0006376578,0.0003010325,0.00049542636,0.0003101715,0.07528714,0.30237234,0.0034701556,0.002252243,0.5893912],"study_design_scores_gemma":[0.000031799835,0.0006296857,0.045029677,0.00004213939,0.00013871449,0.0009670694,0.00017093183,0.7893651,0.15930793,0.0020104814,0.0021973161,0.00010915418],"about_ca_topic_score_codex":0.0013410622,"about_ca_topic_score_gemma":0.0019481889,"teacher_disagreement_score":0.0013410622,"about_ca_system_score_codex":0.00035375496,"about_ca_system_score_gemma":0.00030370854,"threshold_uncertainty_score":0.0054557323},"labels":[],"label_agreement":null},{"id":"W2074905719","doi":"10.1109/lsp.2015.2407196","title":"Structure-Induced Complex Kalman Filter for Decentralized Sequential Bayesian Estimation","year":2015,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Kalman filter; Computer science; Overhead (engineering); Computation; Filter (signal processing); Covariance; Algorithm; Artificial intelligence; Mathematics; Statistics; Computer vision","score_opus":0.0630893052736087,"score_gpt":0.29759018526909914,"score_spread":0.23450087999549044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074905719","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":[0.0025824492,0.00004015938,0.99668044,0.000053889245,0.000015343327,0.000009827607,0.0000123888185,0.00007468286,0.0005307508],"genre_scores_gemma":[0.68184835,0.00047235945,0.31345022,0.00015201741,0.00011216567,0.0001983528,0.000199669,0.000071322414,0.0034955137],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945766,0.00015824991,0.000023209055,0.00011651747,0.00020168484,0.000042666703],"domain_scores_gemma":[0.99908555,0.00041284045,0.00012130358,0.00016174282,0.00019430288,0.00002436271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007182905,0.00043360773,0.00051925017,0.00030319332,0.00031930313,0.00056517276,0.0006611021,0.0006626548,0.001124316],"category_scores_gemma":[0.0037565415,0.0003222721,0.00047393586,0.0005392501,0.00063298846,0.00094842695,0.00081722654,0.00097005453,0.00032896257],"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":[0.00008298606,0.000029851511,0.00053611543,0.00006785765,0.000039275797,0.000074508636,0.00007911165,0.8507086,0.0058634095,0.08057419,0.0016518951,0.060292214],"study_design_scores_gemma":[0.000005589324,0.000014655144,0.000071280745,0.0000023808539,0.0000039127467,0.000013098455,0.0000028232544,0.990265,0.00060946867,0.0084369285,0.00057058915,0.000004282044],"about_ca_topic_score_codex":0.0030137585,"about_ca_topic_score_gemma":0.0029525443,"teacher_disagreement_score":0.0030137585,"about_ca_system_score_codex":0.0009481587,"about_ca_system_score_gemma":0.0015771459,"threshold_uncertainty_score":0.006879449},"labels":[],"label_agreement":null},{"id":"W2085825224","doi":"10.1109/lsp.2012.2190280","title":"Simultaneous Feature and Model Selection for Continuous Hidden Markov Models","year":2012,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":16,"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":"Computer science; Hidden Markov model; Feature selection; Artificial intelligence; Markov model; Pattern recognition (psychology); Model selection; Selection (genetic algorithm); Maximum-entropy Markov model; Feature (linguistics); Markov process; Data modeling; Markov chain; Machine learning; Variable-order Markov model; Mathematics; Statistics","score_opus":0.017139816809225668,"score_gpt":0.2578869165525539,"score_spread":0.24074709974332825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085825224","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":[0.0037357626,0.00015886212,0.9956768,0.00006730804,0.000016455157,0.000014058006,0.000025305351,0.00015618964,0.000149282],"genre_scores_gemma":[0.52035034,0.0004824226,0.47596368,0.00023111343,0.0001734514,0.00030707225,0.0006013533,0.00017569294,0.001714909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786794,0.0008981256,0.00009947285,0.00041150252,0.00056035153,0.00016253578],"domain_scores_gemma":[0.996618,0.0025484683,0.00021506895,0.00026391918,0.00026668535,0.00008785832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024558546,0.0012955564,0.0016819623,0.0011083252,0.000668928,0.0010463174,0.0018441185,0.001192977,0.0013817692],"category_scores_gemma":[0.0068707736,0.00091463677,0.001567088,0.001192698,0.0008214497,0.0015297047,0.0018387405,0.0017089222,0.00047227467],"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":[0.0003313288,0.00014387614,0.002639516,0.00025727018,0.00040992757,0.0005089082,0.0002729735,0.6217093,0.01163481,0.055987153,0.0028991448,0.30320588],"study_design_scores_gemma":[0.000010246101,0.000026542193,0.00015464108,0.0000043635505,0.000016038643,0.000042635245,0.00000559121,0.98638713,0.0008507243,0.012045739,0.00044439104,0.000011951451],"about_ca_topic_score_codex":0.00308538,"about_ca_topic_score_gemma":0.0031452242,"teacher_disagreement_score":0.00308538,"about_ca_system_score_codex":0.0006984486,"about_ca_system_score_gemma":0.0010940577,"threshold_uncertainty_score":0.012987971},"labels":[],"label_agreement":null},{"id":"W2087364536","doi":"10.1109/lsp.2014.2362932","title":"Constant Modulus Blind Adaptive Beamforming Based on Unscented Kalman Filtering","year":2014,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Kalman filter; Constant (computer programming); Algorithm; Beamforming; Control theory (sociology); Noise (video); Extended Kalman filter; Adaptive filter; Covariance matrix; Covariance; Mathematics; Computer science; Artificial intelligence; Statistics","score_opus":0.022403969720660527,"score_gpt":0.24993786564012105,"score_spread":0.22753389591946052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087364536","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":[0.0008433996,0.000095103416,0.9985039,0.000027340377,0.000021408769,0.000008782714,0.000006943076,0.00014502935,0.00034809634],"genre_scores_gemma":[0.14899378,0.00073844974,0.8457473,0.00014275718,0.00009530779,0.00017095073,0.0001419719,0.00010585529,0.0038636543],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994154,0.00014064876,0.000037398382,0.00014665704,0.00022230926,0.00003753451],"domain_scores_gemma":[0.99937505,0.0002551443,0.00008573545,0.00006949096,0.0001940408,0.00002045887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066463574,0.00083898375,0.00064317876,0.00046707242,0.00037609861,0.00069015427,0.0011382684,0.0008090494,0.0017313112],"category_scores_gemma":[0.0026627064,0.00034851453,0.00059676287,0.00087161234,0.00063927914,0.0015683261,0.00083891413,0.0011562037,0.00087479356],"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":[0.00019905978,0.00006541429,0.00090342906,0.00024407396,0.00009499297,0.000111629095,0.00016512381,0.29369354,0.056221582,0.06724947,0.0035720477,0.57747966],"study_design_scores_gemma":[0.000016826927,0.000072877,0.0002100819,0.000015164939,0.0000144826345,0.000115730596,0.000008434347,0.98212093,0.008925374,0.0047967904,0.003673762,0.000029581957],"about_ca_topic_score_codex":0.0024803989,"about_ca_topic_score_gemma":0.0024292378,"teacher_disagreement_score":0.0024803989,"about_ca_system_score_codex":0.00052696984,"about_ca_system_score_gemma":0.001251089,"threshold_uncertainty_score":0.0057917833},"labels":[],"label_agreement":null},{"id":"W2088550858","doi":"10.1109/lsp.2007.905096","title":"Optimal Soft Slicer Design for MMSE-DFE","year":2007,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Wireless Communication Techniques","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":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université Laval","funders":"","keywords":"Equalizer; Noise (video); Mean squared error; Computer science; Optimal design; Gaussian; Gaussian noise; Minimum mean square error; Control theory (sociology); Algorithm; Electronic engineering; Engineering; Mathematics; Artificial intelligence; Statistics; Telecommunications","score_opus":0.026195379593097517,"score_gpt":0.27298755300769906,"score_spread":0.24679217341460155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088550858","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":[0.008899819,0.0003109973,0.9893156,0.000065346176,0.00003126121,0.000021014223,0.000028643899,0.00012988702,0.0011973269],"genre_scores_gemma":[0.46698928,0.0005993797,0.52978814,0.0001954913,0.00009631577,0.0001030609,0.000106903935,0.000040692765,0.002080749],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922466,0.00028298981,0.000050389128,0.00012204936,0.0002518673,0.0000681204],"domain_scores_gemma":[0.9994802,0.00019485012,0.00007581934,0.00006893936,0.00016263206,0.000017674607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006695837,0.00091576605,0.0006306653,0.00037834962,0.000355254,0.00060346635,0.00045813905,0.00069567247,0.0015663861],"category_scores_gemma":[0.0019577732,0.00035689474,0.00036229947,0.0003342088,0.0004330501,0.000782051,0.00060252607,0.0005229184,0.00048685342],"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":[0.0008520718,0.00008339517,0.0011509005,0.00039731842,0.00017397273,0.00038854787,0.00029167504,0.23362897,0.23528183,0.11012767,0.0034712849,0.4141524],"study_design_scores_gemma":[0.00007767402,0.00029685727,0.0004758417,0.00004906268,0.000063641324,0.00052632287,0.000034967212,0.90915257,0.06769932,0.015724288,0.005840964,0.000058443013],"about_ca_topic_score_codex":0.00055991235,"about_ca_topic_score_gemma":0.0011245479,"teacher_disagreement_score":0.0015663861,"about_ca_system_score_codex":0.0003334593,"about_ca_system_score_gemma":0.0005726369,"threshold_uncertainty_score":0.0052400827},"labels":[],"label_agreement":null},{"id":"W2092305139","doi":"10.1109/lsp.2014.2381458","title":"FSITM: A Feature Similarity Index For Tone-Mapped Images","year":2014,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Pattern recognition (psychology); Feature (linguistics); Similarity (geometry); Feature extraction; Range (aeronautics); Image (mathematics)","score_opus":0.01226481585074939,"score_gpt":0.2747091463863644,"score_spread":0.262444330535615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092305139","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":[0.14434882,0.00213616,0.8475963,0.00011970531,0.00019593617,0.00024286317,0.00061569584,0.0009846019,0.003759937],"genre_scores_gemma":[0.6681269,0.0010958469,0.32604587,0.00012223954,0.00021870856,0.00027282236,0.0015136957,0.00020225595,0.0024016807],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99904245,0.0001231903,0.00008798843,0.00013066016,0.0005664701,0.000049305527],"domain_scores_gemma":[0.9984806,0.00043902732,0.0002652039,0.00017420219,0.00056842266,0.00007244195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081752177,0.00058165187,0.0006569514,0.0031876597,0.00026565092,0.00107639,0.00054745446,0.00052785163,0.0013883606],"category_scores_gemma":[0.003979107,0.00012445668,0.00051303854,0.0016619453,0.00034355425,0.001989259,0.0007423253,0.00046554735,0.00044843953],"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":[0.000987825,0.00020308683,0.010269992,0.00059365813,0.0002783752,0.00018554046,0.0001924925,0.015868917,0.14516254,0.0053337137,0.003907442,0.8170165],"study_design_scores_gemma":[0.000120935074,0.0021155141,0.06259669,0.00014306606,0.00048492415,0.0031420432,0.00048803858,0.69984925,0.20099716,0.01020565,0.019585893,0.00027082267],"about_ca_topic_score_codex":0.0007234235,"about_ca_topic_score_gemma":0.0007761806,"teacher_disagreement_score":0.0031876597,"about_ca_system_score_codex":0.00040764964,"about_ca_system_score_gemma":0.00035106976,"threshold_uncertainty_score":0.0046445727},"labels":[],"label_agreement":null},{"id":"W2096084339","doi":"10.1109/lsp.2010.2051574","title":"A Template Matching Procedure for Automatic Target Recognition in Synthetic Aperture Sonar Imagery","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Sonar; Synthetic aperture sonar; Artificial intelligence; Computer science; Computer vision; Pattern recognition (psychology); Template matching; Automatic target recognition; Matching (statistics); Shadow (psychology); Synthetic aperture radar; Receiver operating characteristic; Image (mathematics); Mathematics","score_opus":0.02077882598449221,"score_gpt":0.2480856596607653,"score_spread":0.22730683367627308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096084339","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":[0.0022766097,0.00007071846,0.9968368,0.000011731819,0.000022830907,0.000037357637,0.000033394175,0.0005491445,0.00016151332],"genre_scores_gemma":[0.03344198,0.00013181478,0.9652464,0.00003087873,0.000035718367,0.00015072597,0.00023510632,0.00010093433,0.00062643737],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884135,0.00024126253,0.00009001198,0.0002308083,0.00051493396,0.00008155398],"domain_scores_gemma":[0.99886,0.00034542574,0.00014761275,0.00023180174,0.0003700206,0.00004500136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010239522,0.0007807074,0.0010049891,0.0015737982,0.00046954348,0.00068209483,0.0015238603,0.0009254541,0.0017386061],"category_scores_gemma":[0.0029761586,0.0004900311,0.0010469586,0.001644639,0.00063421397,0.0009176321,0.00075944397,0.0010183452,0.002101886],"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":[0.00018072905,0.00011278392,0.0006772765,0.00019583521,0.000111233145,0.0001953983,0.00010619499,0.024338525,0.17002136,0.006757419,0.0029226441,0.79438066],"study_design_scores_gemma":[0.000033993936,0.0003666768,0.003021081,0.000028142265,0.00007503803,0.0014960906,0.000055412158,0.80684537,0.1705173,0.0072848364,0.010176861,0.000099244506],"about_ca_topic_score_codex":0.0012681622,"about_ca_topic_score_gemma":0.0013289789,"teacher_disagreement_score":0.0017386061,"about_ca_system_score_codex":0.00037383626,"about_ca_system_score_gemma":0.0008630436,"threshold_uncertainty_score":0.0058162212},"labels":[],"label_agreement":null},{"id":"W2097993571","doi":"10.1109/lsp.2004.833486","title":"Subsampling Matrices for Wavelet Decompositions on Body Centered Cubic Lattices","year":2004,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Mathematics; Wavelet; Lattice (music); Filter bank; Upsampling; Matrix (chemical analysis); Filter (signal processing); Algorithm; Applied mathematics; Computer science; Physics; Artificial intelligence","score_opus":0.036516394162294456,"score_gpt":0.3082803783564526,"score_spread":0.27176398419415815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097993571","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":[0.03755223,0.00021435012,0.9564763,0.00011258334,0.00006384795,0.000027411286,0.000057749075,0.00019280797,0.005302776],"genre_scores_gemma":[0.40269533,0.000739259,0.5909131,0.00013011975,0.00011052497,0.00010389357,0.00022859072,0.00019429285,0.0048848595],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996457,0.00006913837,0.000014940262,0.000033712902,0.00020403428,0.00003252615],"domain_scores_gemma":[0.9994438,0.00017014248,0.00009034371,0.00008460484,0.0001483474,0.00006279664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040336617,0.0003757705,0.00028896492,0.00058413524,0.00032763832,0.0006572683,0.00027558688,0.0003037352,0.0020302883],"category_scores_gemma":[0.0016126159,0.00020794338,0.00027269643,0.00045976677,0.0005443422,0.00059884886,0.00036411872,0.0007036927,0.00061624637],"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":[0.00018493403,0.00006100937,0.000495575,0.00016017258,0.000016905491,0.00028244636,0.00027448943,0.098941386,0.08778208,0.60745865,0.0045195557,0.19982286],"study_design_scores_gemma":[0.000026932958,0.00014229011,0.00053540646,0.000032980388,0.000009471494,0.00048481402,0.00012817004,0.79087186,0.039633784,0.14816512,0.019917186,0.00005194752],"about_ca_topic_score_codex":0.0009182758,"about_ca_topic_score_gemma":0.0013418276,"teacher_disagreement_score":0.0020302883,"about_ca_system_score_codex":0.00035873576,"about_ca_system_score_gemma":0.00038435546,"threshold_uncertainty_score":0.0067919493},"labels":[],"label_agreement":null},{"id":"W2098010864","doi":"10.1109/lsp.2004.831721","title":"Maximal Gap of a Sampling Set for the Exact Iterative Reconstruction Algorithm in Shift Invariant Spaces","year":2004,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Mathematical Analysis and Transform Methods","field":"Mathematics","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":"University of Alberta","funders":"","keywords":"Bandlimiting; Invariant (physics); Nyquist–Shannon sampling theorem; Nyquist rate; Algorithm; Mathematics; Sampling (signal processing); Nonuniform sampling; Iterative method; Signal reconstruction; Filter (signal processing); Computer science; Signal processing; Fourier transform; Digital signal processing; Mathematical analysis; Computer vision","score_opus":0.10290560255561343,"score_gpt":0.3516640625095064,"score_spread":0.24875845995389295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098010864","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":[0.089686215,0.00028664648,0.9021024,0.00022087104,0.000041057374,0.000028573311,0.00006615423,0.0002243576,0.0073437574],"genre_scores_gemma":[0.7613317,0.0003089287,0.23483449,0.00013494815,0.00008372475,0.00015857589,0.00016635036,0.000303522,0.0026778472],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987953,0.00038451934,0.00008209104,0.00015203119,0.00047950575,0.000106694875],"domain_scores_gemma":[0.99648166,0.0023399002,0.0002128381,0.00031444695,0.00040036067,0.00025082903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020594816,0.00061397575,0.00070188206,0.0010301159,0.0006638467,0.001320962,0.0009216487,0.0007036059,0.0024498738],"category_scores_gemma":[0.008245182,0.00027787327,0.0005853379,0.00044059785,0.0019450976,0.0026209175,0.0025588693,0.0016376482,0.0004510715],"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":[0.00019469176,0.00004459381,0.0005413079,0.000107974,0.000021540562,0.00011398076,0.00034644545,0.02708739,0.010618721,0.9235288,0.00071066065,0.036683924],"study_design_scores_gemma":[0.000019756124,0.00012963866,0.0003245657,0.000040454175,0.0000112131775,0.0002554662,0.000076776734,0.3823907,0.013310444,0.60104805,0.0023661272,0.000026767126],"about_ca_topic_score_codex":0.0001797606,"about_ca_topic_score_gemma":0.00013259069,"teacher_disagreement_score":0.0024498738,"about_ca_system_score_codex":0.0008111263,"about_ca_system_score_gemma":0.0006343821,"threshold_uncertainty_score":0.010891676},"labels":[],"label_agreement":null},{"id":"W2098087097","doi":"10.1109/lsp.2002.804133","title":"Natural gradient learning neural networks for adaptive inversion of Hammerstein systems","year":2002,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Neural Networks and Applications","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":"Gradient descent; Backpropagation; Adaptive filter; Artificial neural network; Kernel adaptive filter; Computer science; Multilayer perceptron; Nonlinear system; Activation function; Adaptive system; Inversion (geology); Perceptron; Algorithm; Adaptive algorithm; Adaptive learning; Filter (signal processing); Control theory (sociology); Artificial intelligence; Filter design","score_opus":0.02519008230801976,"score_gpt":0.2214355908204638,"score_spread":0.19624550851244404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098087097","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":[0.009122817,0.0018724215,0.9823887,0.0005578432,0.00019791399,0.000032116972,0.000027083684,0.0002783775,0.0055227797],"genre_scores_gemma":[0.54487884,0.0025518844,0.44012892,0.00041822487,0.0002585553,0.000195186,0.00011287878,0.00011037904,0.011345149],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999856,0.0000519784,0.000008471672,0.000019421741,0.000053592787,0.0000104306555],"domain_scores_gemma":[0.99975115,0.00013593928,0.000019197816,0.000026628883,0.00005975259,0.0000074403197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053060206,0.00036155782,0.00029553304,0.00018957835,0.00018919806,0.00038798343,0.0005085238,0.000663666,0.0017127596],"category_scores_gemma":[0.0018392,0.0001461693,0.0002088817,0.0003140994,0.00042247705,0.0008750436,0.00038045822,0.0009729958,0.0004561739],"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":[0.00010456907,0.0000539424,0.00048354507,0.0003185075,0.00004200145,0.00013620597,0.000121715115,0.50603986,0.009817835,0.24844494,0.0062406696,0.22819619],"study_design_scores_gemma":[0.0000060502593,0.000016426247,0.00007055498,0.000009432008,0.000003591473,0.000024819834,0.000003542857,0.9596953,0.0012838136,0.03552421,0.003356585,0.000005767519],"about_ca_topic_score_codex":0.0012882407,"about_ca_topic_score_gemma":0.001983404,"teacher_disagreement_score":0.0017127596,"about_ca_system_score_codex":0.00047911066,"about_ca_system_score_gemma":0.00040876458,"threshold_uncertainty_score":0.0057297945},"labels":[],"label_agreement":null},{"id":"W2099508202","doi":"10.1109/lsp.2011.2109711","title":"Minimizing the Effect of Sampling Jitters in Wireless Sensor Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Sampling (signal processing); Wireless sensor network; Discretization; Computer science; Invariant (physics); Algorithm; Jitter; Synchronization (alternating current); Mathematical optimization; Control theory (sociology); Filter (signal processing); Mathematics; Artificial intelligence; Computer vision; Channel (broadcasting); Telecommunications; Computer network; Mathematical analysis","score_opus":0.022081012154578642,"score_gpt":0.2285486826656723,"score_spread":0.20646767051109366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099508202","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":[0.04601576,0.00042408804,0.9524364,0.00010885963,0.000026997608,0.000013989008,0.000014437348,0.000117900134,0.0008415334],"genre_scores_gemma":[0.9377874,0.0006060722,0.060332004,0.000049817947,0.000033620447,0.00004017732,0.000025491054,0.000040727755,0.0010845778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994134,0.0002131628,0.00003291368,0.00008051061,0.00020371255,0.00005641732],"domain_scores_gemma":[0.99885094,0.0007878208,0.00016766795,0.00007603727,0.00008632634,0.000031160253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013772137,0.00069586514,0.00046827542,0.00030968618,0.00033102036,0.0005828475,0.0005294414,0.000676204,0.00034796976],"category_scores_gemma":[0.0037747303,0.00030398316,0.00033486646,0.0004682281,0.00054613745,0.0010281638,0.0005424158,0.0004577545,0.000078097844],"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":[0.00007142592,0.000015886533,0.00032325633,0.000049778948,0.000013717596,0.00004675345,0.0000331057,0.9716771,0.004927294,0.0068417084,0.000106889354,0.015893104],"study_design_scores_gemma":[0.0000047937133,0.000050931845,0.00011797922,0.0000041766634,0.000008131008,0.00001517002,0.000009145791,0.99353796,0.0027151597,0.0033218816,0.00021106233,0.000003581154],"about_ca_topic_score_codex":0.0021098806,"about_ca_topic_score_gemma":0.0013263292,"teacher_disagreement_score":0.0021098806,"about_ca_system_score_codex":0.00069833786,"about_ca_system_score_gemma":0.000695828,"threshold_uncertainty_score":0.007283449},"labels":[],"label_agreement":null},{"id":"W2101773947","doi":"10.1109/lsp.2005.849497","title":"A novel chaos-based high-resolution imaging technique and its application to through-the-wall imaging","year":2005,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Radar imaging; Computer science; Image resolution; Radar; Modulation (music); Noise (video); Signal-to-noise ratio (imaging); Resolution (logic); Doppler effect; Medical imaging; Artificial intelligence; Physics; Image (mathematics); Telecommunications; Acoustics","score_opus":0.007447983199257703,"score_gpt":0.21970498307951536,"score_spread":0.21225699988025765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101773947","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":[0.1110162,0.0018932169,0.8815288,0.000506011,0.00013377331,0.000063090854,0.000047039077,0.00034322325,0.0044686096],"genre_scores_gemma":[0.45632634,0.0011964539,0.5394233,0.00015016837,0.00009561208,0.00006983164,0.000036332982,0.000033745102,0.0026682764],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999908,0.000018850129,0.00000467771,0.000013167796,0.000046436522,0.000008876582],"domain_scores_gemma":[0.9998178,0.000066232424,0.000043517306,0.000024021494,0.000033545828,0.000014974847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017630399,0.00023939682,0.000257536,0.0002571324,0.00020349234,0.00024954186,0.0003469121,0.00046534656,0.00068205973],"category_scores_gemma":[0.00041456404,0.00014837073,0.0001802274,0.00022398029,0.00033615116,0.0006943498,0.00038020898,0.00045499048,0.00023723503],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008780647,0.00003218909,0.00038861483,0.00016185298,0.000018474348,0.00038136012,0.00014338968,0.0051794336,0.90472895,0.013460296,0.0005121323,0.07490558],"study_design_scores_gemma":[0.00006109818,0.0005468281,0.0010612009,0.000039196253,0.00004216818,0.00426186,0.000064869695,0.22134036,0.7518246,0.0050716805,0.015612069,0.000074049676],"about_ca_topic_score_codex":0.00011009178,"about_ca_topic_score_gemma":0.00020746331,"teacher_disagreement_score":0.00068205973,"about_ca_system_score_codex":0.00014261273,"about_ca_system_score_gemma":0.00019954336,"threshold_uncertainty_score":0.0022817254},"labels":[],"label_agreement":null},{"id":"W2102842504","doi":"10.1109/lsp.2004.827917","title":"Exact Fractional-Order Differentiators for Polynomial Signals","year":2004,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Control Systems Design","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Differentiator; Mathematics; Fractional calculus; Polynomial; Integer (computer science); Applied mathematics; Impulse response; Impulse (physics); Mathematical analysis; Computer science; Bandwidth (computing)","score_opus":0.011040532937078962,"score_gpt":0.2291901033403009,"score_spread":0.21814957040322194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102842504","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":[0.013197845,0.0007199184,0.9804258,0.000101318794,0.00008349826,0.000016178417,0.00003287737,0.00026764354,0.005154951],"genre_scores_gemma":[0.67495793,0.0013694357,0.3131403,0.00013862738,0.00010702349,0.00004807494,0.000120767756,0.00008638211,0.010031449],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995894,0.00006127925,0.000026302621,0.00007774548,0.00020523503,0.000040083996],"domain_scores_gemma":[0.99970883,0.00010447224,0.00003918961,0.0000733019,0.00006205953,0.000012076643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005757402,0.00038046244,0.00045810302,0.00034159547,0.00028149222,0.0008986018,0.0005263007,0.00065164606,0.0019584498],"category_scores_gemma":[0.0017301925,0.00020787041,0.0003567184,0.00040175667,0.0005425872,0.0012285619,0.00047623052,0.0008927802,0.0005948554],"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":[0.00020515184,0.00004307144,0.00042222883,0.0003072788,0.000026226497,0.00021115458,0.0002922484,0.27986902,0.06177239,0.4381821,0.0015499599,0.21711913],"study_design_scores_gemma":[0.000019042995,0.00007350251,0.00021131035,0.000024432109,0.000010941272,0.0001526266,0.000019899682,0.89324486,0.014975422,0.07914353,0.012102561,0.000021906319],"about_ca_topic_score_codex":0.0008614368,"about_ca_topic_score_gemma":0.00086323475,"teacher_disagreement_score":0.0019584498,"about_ca_system_score_codex":0.00075440423,"about_ca_system_score_gemma":0.00047179146,"threshold_uncertainty_score":0.006551683},"labels":[],"label_agreement":null},{"id":"W2102868031","doi":"10.1109/lsp.2005.851259","title":"Concurrent data transmission through analog speech channel using data hiding","year":2005,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Steganography and Watermarking Techniques","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":"University of Calgary","funders":"","keywords":"Computer science; Decoding methods; Data transmission; Speech coding; Information hiding; Bit error rate; Transmission (telecommunications); Speech recognition; Embedding; Voice activity detection; Channel (broadcasting); Set (abstract data type); SIGNAL (programming language); Speech processing; Computer hardware; Computer network; Artificial intelligence; Algorithm; Telecommunications","score_opus":0.11715877038861656,"score_gpt":0.3357297328575816,"score_spread":0.21857096246896507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102868031","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":[0.4323714,0.001024725,0.5607932,0.00018055708,0.00015941267,0.00011359174,0.000048044818,0.00045783175,0.004851138],"genre_scores_gemma":[0.89703083,0.00037343605,0.09944001,0.00007267674,0.00006834774,0.000045333447,0.000043717366,0.00002306896,0.002902478],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997131,0.00004359882,0.000019108335,0.00005495378,0.00012990482,0.000039354156],"domain_scores_gemma":[0.9994118,0.00022840049,0.000093287956,0.00013055661,0.00010458363,0.000031383825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025211507,0.00044936533,0.00032808626,0.0002713592,0.00029132393,0.0004090216,0.0005468443,0.00045434342,0.001199733],"category_scores_gemma":[0.0007260968,0.00019306951,0.00027427392,0.00025517374,0.00044944524,0.0012353149,0.00068458583,0.00046128148,0.00033657637],"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":[0.0005636972,0.00013715326,0.0013606399,0.00033578367,0.000053151874,0.00060101674,0.00035508414,0.007614443,0.83335364,0.008612369,0.00035956985,0.1466534],"study_design_scores_gemma":[0.00008835613,0.0010250697,0.0009482208,0.000041797382,0.00010737624,0.001779741,0.00010462753,0.13006416,0.85352504,0.0025198348,0.009731088,0.000064677995],"about_ca_topic_score_codex":0.00019211261,"about_ca_topic_score_gemma":0.00030054385,"teacher_disagreement_score":0.001199733,"about_ca_system_score_codex":0.00015196245,"about_ca_system_score_gemma":0.00021891919,"threshold_uncertainty_score":0.0040134788},"labels":[],"label_agreement":null},{"id":"W2103636088","doi":"10.1109/lsp.2003.813679","title":"Speech probability distribution","year":2003,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":298,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Decorrelation; Generalized gamma distribution; Distribution (mathematics); Marginal distribution; Mathematics; Generalized integer gamma distribution; Speech processing; Speech recognition; Probability distribution; Multivariate statistics; Gaussian; Gamma distribution; Multivariate normal distribution; Joint probability distribution; Discrete cosine transform; Computer science; Random variable; Statistics; Artificial intelligence; Mathematical analysis; Physics","score_opus":0.018041832663961418,"score_gpt":0.23248197373314894,"score_spread":0.21444014106918752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103636088","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":[0.029235719,0.0006026632,0.95653653,0.00047088298,0.0000895088,0.00007363439,0.0007941393,0.0009978932,0.011199116],"genre_scores_gemma":[0.88321453,0.0023283106,0.0896928,0.00031412626,0.00037466103,0.00025834786,0.0023062755,0.00036754162,0.021143502],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990433,0.00014034027,0.00004309495,0.00033983792,0.0003420341,0.00009137394],"domain_scores_gemma":[0.9966762,0.0019230634,0.0002227849,0.0004380555,0.0006662111,0.00007375969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010966152,0.0006513598,0.00064505235,0.001471011,0.0005070217,0.0016742336,0.0009298908,0.0010349551,0.0092855515],"category_scores_gemma":[0.007683611,0.00025684453,0.00061180687,0.0011027729,0.0011350033,0.0019540845,0.00083232805,0.0009036603,0.0038533371],"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":[0.0004071935,0.00010716681,0.010178856,0.00040118842,0.000112481146,0.0019619577,0.0006869553,0.28050846,0.039288864,0.43603268,0.011680153,0.21863404],"study_design_scores_gemma":[0.000022833809,0.00011036018,0.0062928763,0.000046290854,0.000048704504,0.0022536246,0.00014754606,0.83884865,0.008904944,0.13146816,0.01176033,0.0000956923],"about_ca_topic_score_codex":0.0022809554,"about_ca_topic_score_gemma":0.0009478241,"teacher_disagreement_score":0.0092855515,"about_ca_system_score_codex":0.0007090711,"about_ca_system_score_gemma":0.0006507108,"threshold_uncertainty_score":0.031063199},"labels":[],"label_agreement":null},{"id":"W2105476049","doi":"10.1109/lsp.2010.2043152","title":"A Widely Linear Distortionless Filter for Single-Channel Noise Reduction","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Noise reduction; Distortion (music); Filter (signal processing); Computer science; Speech recognition; Noise (video); Reduction (mathematics); Noise measurement; Speech enhancement; Signal-to-noise ratio (imaging); Algorithm; Short-time Fourier transform; Mathematics; Fourier transform; Artificial intelligence; Telecommunications; Fourier analysis; Amplifier; Computer vision; Bandwidth (computing)","score_opus":0.026199003219751712,"score_gpt":0.24661025636581593,"score_spread":0.22041125314606422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105476049","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":[0.0021118417,0.00036381095,0.9966012,0.00008099327,0.00004196741,0.000011035655,0.000012002074,0.00012983683,0.00064723345],"genre_scores_gemma":[0.14633621,0.0012888813,0.84230506,0.00036999932,0.00019002536,0.00009964553,0.00014239925,0.000087225,0.009180582],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994412,0.000110007735,0.000029903642,0.00015967702,0.0002298746,0.000029338738],"domain_scores_gemma":[0.99967134,0.00011663689,0.000039794824,0.000048126167,0.000109568864,0.000014559832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000539368,0.00066619006,0.000523761,0.0003589744,0.00031719534,0.00077802263,0.0007102238,0.00101381,0.0024608804],"category_scores_gemma":[0.0011030361,0.00026826662,0.00044091593,0.00060746307,0.00044072306,0.0010669357,0.00047232222,0.00090146094,0.0016014042],"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":[0.0003562456,0.000097878365,0.0006732701,0.00032797764,0.000095699324,0.00028481986,0.00021855168,0.05576596,0.19975235,0.06322059,0.005059065,0.67414755],"study_design_scores_gemma":[0.000056043795,0.0003451127,0.0006638552,0.00005013066,0.000077696546,0.0007602903,0.00005255126,0.85473883,0.09857111,0.011464018,0.03315177,0.00006860594],"about_ca_topic_score_codex":0.0011201339,"about_ca_topic_score_gemma":0.0016876471,"teacher_disagreement_score":0.0024608804,"about_ca_system_score_codex":0.0004927954,"about_ca_system_score_gemma":0.00057085167,"threshold_uncertainty_score":0.008232415},"labels":[],"label_agreement":null},{"id":"W2106188774","doi":"10.1109/lsp.2011.2110644","title":"Performance Analysis of Joint User Scheduling and Antenna Selection Over MIMO Fading Channels","year":2011,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Rayleigh fading; MIMO; Computer science; Fading; Scheduling (production processes); Block code; 3G MIMO; Coding (social sciences); Electronic engineering; Telecommunications; Algorithm; Mathematical optimization; Decoding methods; Mathematics; Engineering; Channel (broadcasting); Statistics","score_opus":0.017963922635838483,"score_gpt":0.20701836204880925,"score_spread":0.18905443941297076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106188774","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":[0.30508173,0.0015305064,0.68357563,0.0002832493,0.00006513082,0.00006593697,0.00011575588,0.00037897506,0.00890294],"genre_scores_gemma":[0.98917204,0.00022319921,0.009903238,0.000020368107,0.000032833963,0.000015060022,0.000027610658,0.00001778606,0.00058791647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978103,0.0010148354,0.00005460826,0.00015171665,0.00058155396,0.00038701107],"domain_scores_gemma":[0.9958223,0.0026836959,0.00042832454,0.00035838605,0.0005644498,0.0001427892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020272906,0.0009059458,0.00087654777,0.00041350586,0.00038949633,0.0010597046,0.0004794578,0.0005893607,0.0012036334],"category_scores_gemma":[0.0057335617,0.00025720696,0.00032399324,0.0008564191,0.00064834143,0.00067499705,0.0008055757,0.00038656278,0.00027821923],"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":[0.00026492577,0.000030099272,0.0012840587,0.000048614907,0.00005167524,0.00010318311,0.00003121974,0.96743,0.009611341,0.006547669,0.00026016313,0.01433703],"study_design_scores_gemma":[0.0000057826546,0.00009511913,0.00044700858,0.0000030968176,0.00001793955,0.00004996833,0.0000132693,0.9957218,0.00262107,0.000901825,0.00011656962,0.0000064662927],"about_ca_topic_score_codex":0.0021356905,"about_ca_topic_score_gemma":0.0017478251,"teacher_disagreement_score":0.0021356905,"about_ca_system_score_codex":0.0009572068,"about_ca_system_score_gemma":0.0011248662,"threshold_uncertainty_score":0.010721445},"labels":[],"label_agreement":null},{"id":"W2107363208","doi":"10.1109/lsp.2004.842286","title":"Optimal step size of the adaptive multichannel LMS algorithm for blind SIMO identification","year":2005,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"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":"Algorithm; Computer science; Adaptive filter; System identification; Adaptive system; Norm (philosophy); Least mean squares filter; Computational complexity theory; Adaptive algorithm; Control theory (sociology); Mathematics; Artificial intelligence; Data modeling","score_opus":0.023243235674970608,"score_gpt":0.2721660751831672,"score_spread":0.2489228395081966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107363208","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":[0.007357407,0.00030938638,0.99059147,0.00013660033,0.000050489853,0.000024376908,0.000012719339,0.00023470414,0.0012827604],"genre_scores_gemma":[0.2720355,0.0004459127,0.72541493,0.00010929573,0.000059803617,0.00018894552,0.00005896709,0.000092589355,0.0015940903],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995704,0.00016452865,0.000023547793,0.00006396627,0.00015556299,0.000021822992],"domain_scores_gemma":[0.99913627,0.00054472924,0.00004783604,0.000069133785,0.0001787118,0.000023396728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077848166,0.000554551,0.000355846,0.00039453313,0.0002510879,0.0004659515,0.00052056066,0.0008490943,0.0015936533],"category_scores_gemma":[0.00483803,0.0002062002,0.00017981853,0.00036467714,0.00051134103,0.00079411233,0.00044881995,0.0007938051,0.0005853533],"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":[0.0006108209,0.00014658248,0.000998537,0.00029808463,0.000054727916,0.00012715814,0.00018605628,0.2884098,0.08919974,0.07990445,0.0049024,0.5351617],"study_design_scores_gemma":[0.000053215805,0.00007777666,0.00025714285,0.000023680019,0.000010252573,0.00007519539,0.000016627799,0.97075033,0.016916838,0.00960345,0.0021947073,0.00002071442],"about_ca_topic_score_codex":0.00053234014,"about_ca_topic_score_gemma":0.0007922171,"teacher_disagreement_score":0.0015936533,"about_ca_system_score_codex":0.00031179402,"about_ca_system_score_gemma":0.0007898253,"threshold_uncertainty_score":0.005331278},"labels":[],"label_agreement":null},{"id":"W2107815709","doi":"10.1109/lsp.2009.2026207","title":"A New Iterative Joint Detection and Decoding Algorithm for V-BLAST Architecture","year":2009,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Wireless Communication Techniques","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":"Queen's University","funders":"","keywords":"Decoding methods; Algorithm; Computer science; Detector; Computational complexity theory; Variable (mathematics); Reduction (mathematics); Joint (building); List decoding; Iterative method; Theoretical computer science; Mathematics; Telecommunications; Engineering; Concatenated error correction code","score_opus":0.012303446691002674,"score_gpt":0.240217113792134,"score_spread":0.22791366710113134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107815709","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":[0.001922972,0.000095615236,0.9961784,0.00006636699,0.0000318126,0.00002513859,0.000030861454,0.00055471587,0.0010940674],"genre_scores_gemma":[0.05642475,0.00020731612,0.9380687,0.000118349664,0.00004575881,0.00012599879,0.00017910002,0.000050837956,0.0047790534],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935526,0.00014344222,0.000040090737,0.00008148272,0.00032515472,0.000054566262],"domain_scores_gemma":[0.99962914,0.00009272623,0.000036485606,0.00006648158,0.00015637251,0.000018781575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006013726,0.0005607832,0.0005531779,0.0005746618,0.00037237688,0.0007296775,0.00089763437,0.0008727026,0.00258778],"category_scores_gemma":[0.0014567877,0.0002615125,0.00034974454,0.00070314744,0.00035524214,0.00097644154,0.0007658274,0.0008713811,0.0018620702],"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":[0.00031668803,0.0000973317,0.00072500994,0.00014792774,0.000092105765,0.00015360824,0.0001557859,0.12094948,0.07012827,0.06411351,0.008540891,0.7345793],"study_design_scores_gemma":[0.00007469171,0.0002534859,0.00031265232,0.000020604808,0.000032642965,0.00043271555,0.000022749118,0.93411773,0.031111836,0.016247127,0.017331313,0.00004255858],"about_ca_topic_score_codex":0.0015072295,"about_ca_topic_score_gemma":0.0034924985,"teacher_disagreement_score":0.00258778,"about_ca_system_score_codex":0.00053187355,"about_ca_system_score_gemma":0.0014042752,"threshold_uncertainty_score":0.008656919},"labels":[],"label_agreement":null},{"id":"W2109018632","doi":"10.1109/lsp.2009.2030856","title":"Adaptive Noise Variance Estimation in BayesShrink","year":2009,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Variance (accounting); Noise (video); Computer science; Autocorrelation; Noise measurement; Residual; Statistics; Noise reduction; Image denoising; Pattern recognition (psychology); Estimation; Algorithm; Gradient noise; Artificial intelligence; Mathematics; Image (mathematics); Noise floor","score_opus":0.019474039657864314,"score_gpt":0.27247710685081483,"score_spread":0.2530030671929505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109018632","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":[0.003559058,0.00025950692,0.9953571,0.000047210175,0.000030603165,0.00001637729,0.00001509076,0.00023123503,0.00048377254],"genre_scores_gemma":[0.110215515,0.00042820344,0.88580537,0.00009715482,0.00007092466,0.00007637728,0.00009451909,0.0001950059,0.0030169955],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816567,0.00044224842,0.00009738368,0.00030548117,0.0008987363,0.00009050688],"domain_scores_gemma":[0.99902916,0.00042587874,0.00008691261,0.0001458202,0.0002811273,0.000030977706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020526072,0.00067929266,0.0011874444,0.0010745698,0.00058387144,0.0010237801,0.0010762325,0.0010836506,0.0015977845],"category_scores_gemma":[0.004004414,0.0005726822,0.00062571035,0.0006597033,0.00091107166,0.0010856765,0.0012099134,0.001221478,0.00078541925],"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":[0.0004911507,0.00009035113,0.0015241908,0.00026782876,0.00013840932,0.00021348507,0.00021083697,0.08905684,0.0780482,0.039363597,0.002923768,0.7876713],"study_design_scores_gemma":[0.00002633296,0.000077258526,0.0008973063,0.00003738013,0.000045926066,0.00033399864,0.000021680577,0.93563473,0.045151263,0.012821544,0.004896165,0.00005639212],"about_ca_topic_score_codex":0.0021674123,"about_ca_topic_score_gemma":0.002677929,"teacher_disagreement_score":0.0021674123,"about_ca_system_score_codex":0.00070607447,"about_ca_system_score_gemma":0.00086413336,"threshold_uncertainty_score":0.010855317},"labels":[],"label_agreement":null},{"id":"W2109753595","doi":"10.1109/lsp.2010.2048940","title":"Self-Organizing Maps for Topic Trend Discovery","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Mitacs","keywords":"Latent Dirichlet allocation; Computer science; Topic model; Visualization; Dimensionality reduction; Data mining; Information retrieval; Set (abstract data type); Data visualization; Process (computing); Latent semantic analysis; Curse of dimensionality; Artificial intelligence; Machine learning","score_opus":0.01012035866637828,"score_gpt":0.25203025302561993,"score_spread":0.24190989435924165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109753595","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":[0.018854033,0.0012495394,0.97082,0.00041879143,0.00013655052,0.0001406884,0.0013974893,0.0042327163,0.0027502063],"genre_scores_gemma":[0.28762653,0.001143065,0.70323855,0.00010747635,0.00031823808,0.00064026954,0.0031424295,0.0004510318,0.0033323576],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897206,0.00039329167,0.00005543385,0.00019789323,0.00030355796,0.00007780097],"domain_scores_gemma":[0.9968953,0.0018181613,0.00029109424,0.00040193845,0.00051224796,0.000081340004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017848595,0.00091269845,0.00091753027,0.0058499384,0.0009168875,0.0019006929,0.0012494511,0.0009607089,0.0028993424],"category_scores_gemma":[0.008497552,0.0005529106,0.0011475579,0.0050193043,0.00048349774,0.0018477328,0.0012153103,0.0010304811,0.0013256972],"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":[0.00033558873,0.00020007334,0.0057226443,0.0005551165,0.0003877735,0.0004312099,0.0010263415,0.21231395,0.00548469,0.05372649,0.030754024,0.68906206],"study_design_scores_gemma":[0.000016667673,0.000021043767,0.0014671191,0.000025115742,0.00002418199,0.0001013023,0.00015657241,0.9414755,0.0019387491,0.046638858,0.0080969455,0.00003792335],"about_ca_topic_score_codex":0.004405148,"about_ca_topic_score_gemma":0.004498892,"teacher_disagreement_score":0.0058499384,"about_ca_system_score_codex":0.0006842669,"about_ca_system_score_gemma":0.0009030539,"threshold_uncertainty_score":0.009699285},"labels":[],"label_agreement":null},{"id":"W2110429185","doi":"10.1109/lsp.2004.824054","title":"Competitive Splitting for Codebook Initialization","year":2004,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":23,"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":"Codebook; Initialization; Linde–Buzo–Gray algorithm; Vector quantization; Computer science; Code (set theory); Algorithm; Scheme (mathematics); Code word; Quantization (signal processing); Artificial intelligence; Pattern recognition (psychology); Mathematics; Decoding methods","score_opus":0.022905612856052852,"score_gpt":0.2849617544417169,"score_spread":0.26205614158566404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110429185","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":[0.005527023,0.00023642032,0.99117106,0.000052703526,0.000053468226,0.000068798756,0.000018211715,0.00034090367,0.00253142],"genre_scores_gemma":[0.37375173,0.00036146992,0.620335,0.00024660456,0.0001085607,0.00023475604,0.00026629798,0.00020328302,0.004492378],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998365,0.00047043542,0.00009116546,0.00021761916,0.00070568407,0.00015014554],"domain_scores_gemma":[0.9969849,0.0011290774,0.0001503792,0.0006731916,0.0008992883,0.00016313423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018373101,0.00072005705,0.0007638147,0.0007165266,0.00074292475,0.0010078773,0.0017303539,0.001105613,0.0032872485],"category_scores_gemma":[0.008304587,0.00040722592,0.000458456,0.0010425042,0.0012573968,0.0018410158,0.0018087097,0.0015747336,0.0018969752],"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":[0.00070897926,0.00013054103,0.00086994865,0.00021706073,0.00006283715,0.00019194126,0.0003066704,0.19861335,0.055822488,0.16646665,0.008611721,0.5679979],"study_design_scores_gemma":[0.00005321625,0.00020061337,0.00018483451,0.000021391088,0.000014505135,0.00026274638,0.000028501669,0.92721003,0.03251026,0.03253189,0.006942233,0.000039840103],"about_ca_topic_score_codex":0.0018598312,"about_ca_topic_score_gemma":0.001921805,"teacher_disagreement_score":0.0032872485,"about_ca_system_score_codex":0.0010060531,"about_ca_system_score_gemma":0.0011164189,"threshold_uncertainty_score":0.010996938},"labels":[],"label_agreement":null},{"id":"W2110440191","doi":"10.1109/lsp.2005.845604","title":"Nonintrusive speech quality evaluation using an adaptive neurofuzzy inference system","year":2005,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":23,"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":"Oticon Fonden","keywords":"Adaptive neuro fuzzy inference system; Inference; Computer science; Fuzzy inference system; Quality (philosophy); Speech recognition; Standard error; Correlation; Standard system; Artificial intelligence; Pattern recognition (psychology); Data mining; Machine learning; Fuzzy logic; Fuzzy control system; Statistics; Mathematics; Engineering","score_opus":0.08097163783514222,"score_gpt":0.33252862390904736,"score_spread":0.25155698607390514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110440191","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":[0.1391454,0.00055413344,0.85698664,0.000092844195,0.000097284385,0.000071679126,0.00005595482,0.0007368515,0.0022592114],"genre_scores_gemma":[0.8128633,0.00022299432,0.18520837,0.00004781315,0.000054804827,0.00005227828,0.000063370055,0.00002795635,0.0014589945],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990995,0.00014361614,0.000055684137,0.0001266067,0.0005487416,0.000025844853],"domain_scores_gemma":[0.9991523,0.00034919064,0.0001056339,0.00006123395,0.0003103394,0.000021197233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007860089,0.00046579348,0.00039473892,0.00054310175,0.00021738786,0.00045239518,0.00053884904,0.00042044497,0.00061719597],"category_scores_gemma":[0.0023639423,0.00017072036,0.00029313212,0.00022627186,0.0001975842,0.00056298886,0.00030259407,0.0003916983,0.00018466884],"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":[0.000572735,0.000164335,0.0047218474,0.0002693936,0.00012418824,0.00018906436,0.0002238723,0.036972206,0.30257738,0.0010236633,0.0007409681,0.6524203],"study_design_scores_gemma":[0.00004268568,0.00058642094,0.013031726,0.00003769639,0.00011301754,0.00042773943,0.00005676065,0.88515484,0.098056994,0.0006011192,0.0018143238,0.00007667735],"about_ca_topic_score_codex":0.0014584839,"about_ca_topic_score_gemma":0.0026843704,"teacher_disagreement_score":0.0014584839,"about_ca_system_score_codex":0.00029946072,"about_ca_system_score_gemma":0.00020566749,"threshold_uncertainty_score":0.004156828},"labels":[],"label_agreement":null},{"id":"W2113242314","doi":"10.1109/lsp.2009.2016486","title":"Near-Optimum Pilot and Data Symbols Power Allocation for MIMO Spatial Multiplexing System With Zero-Forcing Receiver","year":2009,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced MIMO Systems Optimization","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":"NovAtel (Canada)","funders":"","keywords":"MIMO; Channel (broadcasting); Fading; Phase-shift keying; Algorithm; Computer science; Spatial multiplexing; Minimum mean square error; Multiplexing; Signal-to-noise ratio (imaging); Quadrature amplitude modulation; Mathematics; Bit error rate; Electronic engineering; Control theory (sociology); Telecommunications; Statistics; Engineering; Estimator","score_opus":0.022508672688732006,"score_gpt":0.24420718359586885,"score_spread":0.22169851090713685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113242314","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":[0.10272878,0.00057402154,0.89248526,0.00021722463,0.00003821094,0.00003723097,0.000031898497,0.00016915698,0.00371822],"genre_scores_gemma":[0.9190614,0.0003396218,0.079514235,0.00006248361,0.00004875217,0.00004002141,0.000025336327,0.000016529184,0.0008916197],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934703,0.00032414738,0.000021304366,0.00007321872,0.00014196905,0.00009230185],"domain_scores_gemma":[0.99917394,0.00049017544,0.00010388039,0.00005542697,0.00014831698,0.000028219674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074668584,0.00078971847,0.00057572866,0.00022496372,0.0003926529,0.00047773973,0.0004970874,0.0006048745,0.0011282547],"category_scores_gemma":[0.0016945839,0.0003650107,0.00029493604,0.000366988,0.0005605128,0.0005908346,0.0004680335,0.00032755933,0.0002686941],"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":[0.001135653,0.0001385994,0.003065639,0.00040723456,0.00015934597,0.0005115668,0.00045645988,0.6914045,0.12954499,0.03716137,0.0017201055,0.13429454],"study_design_scores_gemma":[0.000031113716,0.00019505603,0.00048013942,0.000015067035,0.000031162977,0.00017172314,0.000037379035,0.9854821,0.009490818,0.0036372812,0.0004114469,0.000016715008],"about_ca_topic_score_codex":0.0007314337,"about_ca_topic_score_gemma":0.0016953972,"teacher_disagreement_score":0.0011282547,"about_ca_system_score_codex":0.0003997729,"about_ca_system_score_gemma":0.00082540023,"threshold_uncertainty_score":0.0039488673},"labels":[],"label_agreement":null},{"id":"W2113988735","doi":"10.1109/97.855448","title":"Moment-based fast discrete sine transforms","year":2000,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","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":"Concordia University","funders":"","keywords":"Sine; Adder; Modular design; Computer science; Moment (physics); Discrete sine transform; Algorithm; Sequence (biology); Mathematics; Topology (electrical circuits); Applied mathematics; Arithmetic; Mathematical analysis; Physics; Fourier transform; Telecommunications","score_opus":0.01573902928794182,"score_gpt":0.24535829382182922,"score_spread":0.2296192645338874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113988735","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":[0.019345574,0.00037474438,0.97325754,0.000115058305,0.00016435272,0.000023829143,0.000057941194,0.00067069376,0.005990329],"genre_scores_gemma":[0.4274766,0.0008184527,0.5631523,0.00011214004,0.00026572534,0.000051517774,0.0002679658,0.00018724448,0.0076680346],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99979824,0.0000249581,0.000009867163,0.000023541885,0.00012663449,0.000016651482],"domain_scores_gemma":[0.99969506,0.00012228887,0.000028776496,0.000050466388,0.00008355663,0.000019970876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025699174,0.0004283605,0.00032371812,0.000851207,0.00023334978,0.00067970034,0.000529559,0.00039622132,0.0037928086],"category_scores_gemma":[0.0014322285,0.00018823358,0.00036502638,0.0006367502,0.00040068096,0.0010621267,0.00054379296,0.0005664269,0.001094729],"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":[0.00057254726,0.00005056966,0.0007545439,0.00020654402,0.000040940962,0.0003451251,0.00017098998,0.11261213,0.1176823,0.24597922,0.003589863,0.5179952],"study_design_scores_gemma":[0.000052228293,0.00018081066,0.00045760005,0.000035569457,0.000023510871,0.0004751512,0.000038073795,0.89408827,0.044735745,0.03967418,0.020199558,0.000039280887],"about_ca_topic_score_codex":0.000389187,"about_ca_topic_score_gemma":0.0006662555,"teacher_disagreement_score":0.0037928086,"about_ca_system_score_codex":0.00030004597,"about_ca_system_score_gemma":0.00025818776,"threshold_uncertainty_score":0.012688279},"labels":[],"label_agreement":null},{"id":"W2116096293","doi":"10.1109/lsp.2003.821661","title":"Quadtree-Based Multiregion Multiquality Image Coding","year":2004,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Quadtree; Computer science; JPEG 2000; JPEG; Context-adaptive binary arithmetic coding; Artificial intelligence; Computer vision; Image compression; Coding (social sciences); Data compression; Coding tree unit; Image resolution; Pixel; Fidelity; Image processing; Algorithm; Decoding methods; Image (mathematics); Mathematics","score_opus":0.027739680469587417,"score_gpt":0.29771743022956054,"score_spread":0.26997774975997313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116096293","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":[0.029093977,0.0005009576,0.96831256,0.000066379485,0.000019092857,0.000056601566,0.00006178587,0.00021527453,0.0016734297],"genre_scores_gemma":[0.42532998,0.00075028726,0.5704661,0.000103054415,0.000023445971,0.000060363007,0.00019951307,0.000092724076,0.0029745682],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998209,0.00003808901,0.000009421175,0.000021786054,0.00009195043,0.000017912927],"domain_scores_gemma":[0.99949574,0.00013500417,0.00006934173,0.0001300821,0.00015003675,0.000019779493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035135,0.00027172084,0.000306404,0.00038126027,0.00013005675,0.00034531922,0.0006107038,0.00030607847,0.0015021539],"category_scores_gemma":[0.00096208876,0.00013519527,0.00024513237,0.00048482828,0.00027037502,0.0006821163,0.0004797758,0.0004421268,0.00038300015],"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":[0.00021333102,0.000082713195,0.0006896649,0.00020985043,0.000038969247,0.00030650076,0.00018361717,0.12178584,0.51849186,0.051993534,0.002220189,0.30378398],"study_design_scores_gemma":[0.00002181554,0.00023645758,0.0007543723,0.000031705025,0.000019454541,0.0006632262,0.000036181802,0.85435015,0.1302017,0.0058878064,0.0077670924,0.00003014259],"about_ca_topic_score_codex":0.0006754861,"about_ca_topic_score_gemma":0.0011240521,"teacher_disagreement_score":0.0015021539,"about_ca_system_score_codex":0.00023014634,"about_ca_system_score_gemma":0.00023336268,"threshold_uncertainty_score":0.005025208},"labels":[],"label_agreement":null},{"id":"W2116394319","doi":"10.1109/lsp.2004.842263","title":"Approximate ML detection for MIMO systems using multistage sphere decoding","year":2005,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Quadrature amplitude modulation; MIMO; Decoding methods; Constellation diagram; Algorithm; Signal-to-noise ratio (imaging); Computer science; Constellation; Generalization; QAM; SIGNAL (programming language); Mathematics; Telecommunications; Bit error rate; Physics; Channel (broadcasting)","score_opus":0.025362257043798294,"score_gpt":0.26590775703555997,"score_spread":0.24054549999176167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116394319","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":[0.0031451108,0.00012940016,0.9960402,0.00003566315,0.0000090958765,0.000007480865,0.000018023977,0.00016450233,0.00045036274],"genre_scores_gemma":[0.20142783,0.00047490426,0.7956288,0.00009577958,0.000059554204,0.000070310874,0.00022239302,0.00008558134,0.0019347924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909675,0.00030834682,0.00005718325,0.0000903773,0.00038363328,0.0000637508],"domain_scores_gemma":[0.99854326,0.00086639094,0.00010270755,0.00023385996,0.00021935636,0.000034451183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095505425,0.000864904,0.00086652784,0.00061239465,0.00023282575,0.00087425276,0.0006721027,0.00075644546,0.0013626023],"category_scores_gemma":[0.003424077,0.00043118634,0.00054807134,0.00078885426,0.0006870067,0.0013996104,0.0012260642,0.00080361246,0.0009516072],"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":[0.00019167336,0.000042193806,0.0008609691,0.00015044358,0.00008453175,0.00014062898,0.00013622003,0.7270157,0.02460327,0.06083987,0.0018823192,0.18405217],"study_design_scores_gemma":[0.0000069783723,0.0000160392,0.000060228944,0.000004039003,0.0000033609542,0.000039105442,0.0000032030803,0.98712337,0.0050902795,0.00692341,0.00072368566,0.000006344186],"about_ca_topic_score_codex":0.0010450777,"about_ca_topic_score_gemma":0.0015911305,"teacher_disagreement_score":0.0013626023,"about_ca_system_score_codex":0.00064199005,"about_ca_system_score_gemma":0.0007378412,"threshold_uncertainty_score":0.0050508976},"labels":[],"label_agreement":null},{"id":"W2119544044","doi":"10.1109/lsp.2002.805312","title":"A new NLMS algorithm for slow noise magnitude variation","year":2002,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Control Systems and Identification","field":"Engineering","cited_by":23,"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":"Magnitude (astronomy); Algorithm; Noise (video); Bounding overwatch; A priori and a posteriori; Ellipsoid; Computer science; Noise measurement; Set (abstract data type); Variation (astronomy); Mathematics; Artificial intelligence; Noise reduction; Image (mathematics)","score_opus":0.0121263314737242,"score_gpt":0.19745691652166175,"score_spread":0.18533058504793756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119544044","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":[0.00060742744,0.000051125837,0.9987375,0.000029930161,0.000027799817,0.000010504071,0.000009287613,0.00016093266,0.00036551093],"genre_scores_gemma":[0.034129,0.00014251738,0.9614606,0.00008102287,0.00006510966,0.00016615017,0.00014884645,0.00009290026,0.0037137803],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934524,0.00013205427,0.000041897718,0.00014378523,0.00030325667,0.000033702574],"domain_scores_gemma":[0.99927896,0.00025998638,0.00006623651,0.000076042,0.0002926927,0.000026165128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070241606,0.00078199175,0.0007307805,0.0006102511,0.00041112772,0.00079051085,0.0012816357,0.0011143151,0.0034835534],"category_scores_gemma":[0.0030574952,0.00037202702,0.00047207932,0.000641638,0.0005303063,0.0013473507,0.0010278646,0.0013313612,0.002061213],"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":[0.00011847359,0.00003984903,0.0003431655,0.00015663545,0.00004271676,0.00007155335,0.00016602053,0.22310905,0.027593197,0.0378699,0.005917342,0.704572],"study_design_scores_gemma":[0.000016726297,0.000026251118,0.000081774604,0.000012627838,0.0000056289196,0.00005744055,0.000011081014,0.98256713,0.004612671,0.004799489,0.007794772,0.000014485068],"about_ca_topic_score_codex":0.001654465,"about_ca_topic_score_gemma":0.0018486495,"teacher_disagreement_score":0.0034835534,"about_ca_system_score_codex":0.0004880934,"about_ca_system_score_gemma":0.0011516009,"threshold_uncertainty_score":0.011653662},"labels":[],"label_agreement":null},{"id":"W2124479510","doi":"10.1109/lsp.2006.874462","title":"Linear Dynamic Models With Mixture of Experts Architecture for Recognition of Speech Under Additive Noise Conditions","year":2006,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","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":"University of Ottawa","funders":"","keywords":"Computer science; Speech recognition; Feature (linguistics); Noise (video); Pattern recognition (psychology); Dynamic Bayesian network; Speech processing; Kalman filter; Mixture model; Linear predictive coding; Speech enhancement; Bayesian probability; Artificial intelligence; Noise reduction","score_opus":0.014179992862331516,"score_gpt":0.24019827539021027,"score_spread":0.22601828252787876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124479510","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":[0.008374721,0.0003880148,0.9904665,0.00005729772,0.000022743798,0.0000093778635,0.000013172078,0.00020753154,0.00046055097],"genre_scores_gemma":[0.6722317,0.0009550906,0.32021692,0.00014038716,0.00011295227,0.00013102018,0.00023503632,0.00008803848,0.005888828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993901,0.00022954016,0.000027690747,0.00012876834,0.00017063363,0.000053295505],"domain_scores_gemma":[0.999511,0.00028262878,0.00004128388,0.000048858168,0.00010061154,0.000015755486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008484009,0.0007113086,0.000734738,0.00037132492,0.00019651008,0.0005560319,0.0008600693,0.0010389235,0.00090803485],"category_scores_gemma":[0.0024498436,0.0006074944,0.00077320583,0.0004343029,0.0003540323,0.0011864551,0.0006402715,0.0013622326,0.0006371745],"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":[0.00028211655,0.00006457456,0.00056033523,0.00012640192,0.00017706487,0.00009707214,0.00016833839,0.8024803,0.017156135,0.015372145,0.001087299,0.1624283],"study_design_scores_gemma":[0.0000030041745,0.000019200266,0.00009355637,0.0000026490973,0.000009869736,0.000015408628,0.0000031179866,0.9965373,0.0010262044,0.0018375898,0.00044425717,0.00000782668],"about_ca_topic_score_codex":0.003174446,"about_ca_topic_score_gemma":0.0031765623,"teacher_disagreement_score":0.003174446,"about_ca_system_score_codex":0.00035542014,"about_ca_system_score_gemma":0.00036745204,"threshold_uncertainty_score":0.006311953},"labels":[],"label_agreement":null},{"id":"W2124541692","doi":"10.1109/lsp.2010.2047958","title":"OFDM Transmission for Time-Based Range Estimation","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Cramér–Rao bound; Orthogonal frequency-division multiplexing; Upper and lower bounds; Estimator; Maximum likelihood; Transmission (telecommunications); Channel (broadcasting); Algorithm; Estimation theory; Statistics; Range (aeronautics); Signal-to-noise ratio (imaging); Mathematics; Computer science; Telecommunications; Engineering","score_opus":0.006878368925534709,"score_gpt":0.21307243780885174,"score_spread":0.20619406888331704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124541692","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":[0.006978085,0.0013867886,0.9882718,0.00014507103,0.00009753132,0.00002180826,0.000036586807,0.00019157103,0.0028706717],"genre_scores_gemma":[0.5284916,0.0037199433,0.46128148,0.00039326193,0.00047183063,0.00015163478,0.00017350628,0.000068048175,0.005248776],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999534,0.00013132911,0.000022466134,0.00007397944,0.0001851958,0.000053068943],"domain_scores_gemma":[0.9991738,0.0004144996,0.000119120334,0.0001320374,0.00014227907,0.000018164765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053735194,0.0006375472,0.00052397343,0.00048631473,0.00040423957,0.00065109326,0.00071289425,0.00076276186,0.0013829008],"category_scores_gemma":[0.0026356117,0.00020281225,0.00030675475,0.00059090555,0.0004558933,0.0008880757,0.00078475225,0.0006350321,0.00058100524],"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":[0.00030226327,0.00007664529,0.0029290987,0.0007214895,0.00014532049,0.0013555392,0.0002998176,0.13683651,0.107443325,0.15797433,0.005317866,0.58659774],"study_design_scores_gemma":[0.000025191186,0.0003464142,0.0011365487,0.00010628707,0.00011170406,0.0029987984,0.00007414325,0.91546947,0.0438687,0.012806372,0.022994274,0.000062092855],"about_ca_topic_score_codex":0.00043195792,"about_ca_topic_score_gemma":0.0006273817,"teacher_disagreement_score":0.0013829008,"about_ca_system_score_codex":0.00028442277,"about_ca_system_score_gemma":0.00033707713,"threshold_uncertainty_score":0.0046262145},"labels":[],"label_agreement":null},{"id":"W2127670824","doi":"10.1109/lsp.2004.824062","title":"Design of Optimal Camera Apertures Adapted to Display Devices Over Arbitrary Sampling Lattices","year":2004,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image and Signal Denoising Methods","field":"Computer Science","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 Ottawa","funders":"","keywords":"Cathode ray tube; Computer vision; Computer science; Aperture (computer memory); Sampling (signal processing); Artificial intelligence; Lattice (music); Computer graphics (images); Physics","score_opus":0.039213536791458524,"score_gpt":0.2974968647435631,"score_spread":0.2582833279521046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127670824","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":[0.081315584,0.0004782056,0.91149205,0.00017321012,0.000055284247,0.000099490106,0.00012934394,0.00030263513,0.0059542465],"genre_scores_gemma":[0.47095403,0.00045467942,0.52709126,0.0000780339,0.000030142299,0.00017574348,0.000091117494,0.00014014618,0.0009849148],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951947,0.00013749985,0.000041566665,0.000082218525,0.00016121665,0.000057971556],"domain_scores_gemma":[0.9989386,0.00038691828,0.00022903379,0.00010921189,0.00023718507,0.000099087534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065701717,0.0005576743,0.00058824534,0.0004437499,0.0001830017,0.0009259815,0.00058402226,0.00049192883,0.0009443198],"category_scores_gemma":[0.002604649,0.00043017723,0.0002813192,0.00037927355,0.0004883766,0.00072683586,0.0007185733,0.0004904832,0.0003591659],"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":[0.0007353329,0.00022740077,0.0017983746,0.00047949265,0.00009662349,0.00032448367,0.00032489176,0.27179164,0.49655432,0.09527581,0.0029443637,0.1294473],"study_design_scores_gemma":[0.00012810474,0.00022450557,0.00063408253,0.00004271048,0.00002613749,0.00031141093,0.000082937804,0.88828546,0.092375115,0.012355252,0.0054597612,0.00007448491],"about_ca_topic_score_codex":0.0003055312,"about_ca_topic_score_gemma":0.00035388905,"teacher_disagreement_score":0.0009443198,"about_ca_system_score_codex":0.00072961807,"about_ca_system_score_gemma":0.00037900335,"threshold_uncertainty_score":0.0052937865},"labels":[],"label_agreement":null},{"id":"W2129688096","doi":"10.1109/lsp.2011.2140396","title":"Enhanced Seam Carving via Integration of Energy Gradient Functionals","year":2011,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Michigan State University","keywords":"Seam carving; Retargeting; Maxima and minima; Computer science; Energy (signal processing); Carving; Artificial intelligence; Computer vision; Process (computing); Image (mathematics); Pattern recognition (psychology); Mathematics; Engineering; Statistics","score_opus":0.033064214020486586,"score_gpt":0.24607507287474523,"score_spread":0.21301085885425863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129688096","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":[0.020395992,0.00020377686,0.97794676,0.000037890855,0.000027507935,0.000016674958,0.000010250107,0.00043689224,0.0009242735],"genre_scores_gemma":[0.38891777,0.000252147,0.60624176,0.0000789387,0.00004104564,0.00003660628,0.00007527126,0.0002768716,0.004079561],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998016,0.00002567969,0.000008470881,0.00003165568,0.00011167054,0.000021011489],"domain_scores_gemma":[0.9997626,0.00006826499,0.000019333003,0.000048142312,0.00008625208,0.000015392625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000383165,0.00050452584,0.00055500626,0.0007159157,0.00019771405,0.0005190181,0.0007565193,0.0005979831,0.0013734853],"category_scores_gemma":[0.001004357,0.00029729647,0.00047945854,0.0003839713,0.00023108369,0.00090867234,0.0007159543,0.00051144214,0.0004124003],"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":[0.00022732465,0.000105233354,0.00081622374,0.0001869048,0.000095853255,0.00020791525,0.00022118345,0.19149943,0.17581017,0.014865251,0.0021524,0.6138121],"study_design_scores_gemma":[0.000007766904,0.00005611933,0.00039919486,0.0000064398428,0.000016404867,0.00017677998,0.000017978122,0.9675399,0.02711435,0.0023944634,0.0022543962,0.000016151766],"about_ca_topic_score_codex":0.001142344,"about_ca_topic_score_gemma":0.001259942,"teacher_disagreement_score":0.0013734853,"about_ca_system_score_codex":0.00023613693,"about_ca_system_score_gemma":0.00029416406,"threshold_uncertainty_score":0.004594803},"labels":[],"label_agreement":null},{"id":"W2130071862","doi":"10.1109/lsp.2008.2009656","title":"Message From the New Editor-In-Chief","year":2008,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Academic Writing and Publishing","field":"Arts and Humanities","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Editor in chief; Management","score_opus":0.02734552893838506,"score_gpt":0.21913824626788342,"score_spread":0.19179271732949835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130071862","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":[0.00035499164,0.0029816406,0.00036413924,0.3688275,0.6212158,0.000041492338,0.000236394,0.00015630442,0.005821746],"genre_scores_gemma":[0.004087841,0.004036335,0.0005337594,0.43359485,0.45582715,0.00009087735,0.00024501444,0.00016883088,0.10141532],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99793434,0.00023074105,0.00022280314,0.00032911813,0.001088846,0.00019414304],"domain_scores_gemma":[0.9893406,0.002369583,0.0008201611,0.00033302023,0.0050189407,0.0021176506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025733623,0.0011821515,0.002278296,0.0010292149,0.0016072233,0.0042338343,0.0019706301,0.0120383045,0.03336497],"category_scores_gemma":[0.01697248,0.000744662,0.0010397562,0.00063530874,0.0009677546,0.002571509,0.0010298402,0.0148808565,0.04599599],"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":[0.000069871865,0.000013932827,0.00009942744,0.00006146501,0.000008533105,0.0001542461,0.000013876456,0.000021218357,0.00012043118,0.00016281041,0.9938783,0.005395959],"study_design_scores_gemma":[0.000059216327,0.00006215786,0.0009761189,0.00009521319,0.000023008304,0.0004625087,0.00008980923,0.00018322236,0.0003609683,0.00046912214,0.9971885,0.000030271201],"about_ca_topic_score_codex":0.0009059639,"about_ca_topic_score_gemma":0.0021136287,"teacher_disagreement_score":0.03336497,"about_ca_system_score_codex":0.0010633164,"about_ca_system_score_gemma":0.0021530215,"threshold_uncertainty_score":0.11161697},"labels":[],"label_agreement":null},{"id":"W2134724842","doi":"10.1109/lsp.2004.842289","title":"A note on \"Split vector-radix-2/8 2-D Fast Fourier Transform\"","year":2005,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Control Systems and Identification","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":false,"ca_institutions":"Concordia University","funders":"","keywords":"Radix (gastropod); Fast Fourier transform; Algorithm; Signal processing; Computer science; Fourier transform; Mathematics; Electronic circuit; Arithmetic; Discrete mathematics; Digital signal processing; Electrical engineering; Engineering; Mathematical analysis; Computer hardware","score_opus":0.00816124069315376,"score_gpt":0.20891596752542543,"score_spread":0.20075472683227166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134724842","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":[0.0028858816,0.04219827,0.6295206,0.04888086,0.24089764,0.0003214897,0.00097411283,0.0030329507,0.03128827],"genre_scores_gemma":[0.032587975,0.05197516,0.68888557,0.054003805,0.061389867,0.00056321285,0.0016088085,0.0022738522,0.10671164],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985851,0.00037686527,0.00014157443,0.00022137276,0.0005841859,0.00009094994],"domain_scores_gemma":[0.998114,0.0007821412,0.00009749692,0.00032753745,0.0005936777,0.00008519979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025572036,0.0016322472,0.00072149193,0.0011133421,0.000936787,0.0016019105,0.0019893376,0.002041108,0.010940088],"category_scores_gemma":[0.0054039164,0.0005365374,0.0009259966,0.0014599318,0.0025000945,0.0027768181,0.0012992251,0.005305397,0.011466678],"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":[0.00050352636,0.00007830704,0.00043703115,0.0011165102,0.000107436055,0.001148028,0.00025651965,0.0020255677,0.021656599,0.12137666,0.60446113,0.24683268],"study_design_scores_gemma":[0.000027816219,0.00013174214,0.00085344573,0.00017825562,0.000026711821,0.0009167158,0.000033618104,0.004567754,0.0139236655,0.031356297,0.9478957,0.000088209694],"about_ca_topic_score_codex":0.0013560898,"about_ca_topic_score_gemma":0.0029254504,"teacher_disagreement_score":0.010940088,"about_ca_system_score_codex":0.00073329394,"about_ca_system_score_gemma":0.00073435105,"threshold_uncertainty_score":0.036598206},"labels":[],"label_agreement":null},{"id":"W2135090566","doi":"10.1109/lsp.2006.871715","title":"Improved structured least squares for the application of unitary ESPRIT to cross arrays","year":2006,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Unitary state; Algorithm; Least-squares function approximation; Minification; Rotational invariance; Total least squares; Set (abstract data type); Unitary transformation; Non-linear least squares; Mathematics; Computer science; Mathematical optimization; Estimation theory; Statistics; Physics","score_opus":0.010858717955748885,"score_gpt":0.27006486049824974,"score_spread":0.25920614254250085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135090566","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":[0.0022733565,0.000060113107,0.99717975,0.000029293073,0.000010188268,0.0000072311395,0.000008376948,0.0001264769,0.00030524988],"genre_scores_gemma":[0.06329871,0.00017228542,0.9354991,0.00006144649,0.00003201234,0.00005174116,0.00008346973,0.000069048074,0.00073230907],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992824,0.00027222955,0.00003356315,0.000105152605,0.0002789674,0.000027642198],"domain_scores_gemma":[0.9985258,0.0008597446,0.00009948015,0.00022759466,0.00026616175,0.000021230177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001068231,0.00080141815,0.00047820542,0.00037866228,0.0001934493,0.00040809158,0.00066518167,0.0008122764,0.0014318915],"category_scores_gemma":[0.0045126965,0.00036160898,0.00053057325,0.00063510623,0.00045510763,0.0009501491,0.0008459997,0.0011159313,0.000992069],"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":[0.0002239685,0.00008886039,0.0006638515,0.00022293582,0.000112628695,0.00021759598,0.00020919318,0.40966752,0.12284466,0.03992608,0.0029410697,0.42288166],"study_design_scores_gemma":[0.000011855391,0.000055281347,0.00018987413,0.000007187861,0.000009559373,0.000087065535,0.0000092658365,0.98179984,0.011987719,0.003764364,0.0020658548,0.000012163347],"about_ca_topic_score_codex":0.0005052925,"about_ca_topic_score_gemma":0.00085382315,"teacher_disagreement_score":0.0014318915,"about_ca_system_score_codex":0.00016712699,"about_ca_system_score_gemma":0.0004038088,"threshold_uncertainty_score":0.0056494474},"labels":[],"label_agreement":null},{"id":"W2141019968","doi":"10.1109/lsp.2013.2285284","title":"LED Backlight Adjustment for Backward-Compatible Stereoscopic Display","year":2013,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Optical Imaging Technologies","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":"McMaster University","funders":"","keywords":"Backlight; Stereoscopy; Liquid-crystal display; Computer science; Computer vision; Image quality; Artificial intelligence; SIGNAL (programming language); Display device; Stereo display; Modulation (music); Computer graphics (images); Image (mathematics); Acoustics; Physics","score_opus":0.011491927451348536,"score_gpt":0.2338641614197815,"score_spread":0.22237223396843298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141019968","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":[0.4266194,0.0006489937,0.5579201,0.00020800553,0.00016425052,0.00009744264,0.00018753277,0.0019940452,0.012160176],"genre_scores_gemma":[0.82072526,0.00039441156,0.17375919,0.00011964003,0.000034506214,0.000060772916,0.00012467211,0.00015568655,0.0046258275],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998704,0.000018732258,0.0000061559313,0.000022452059,0.00006458394,0.000017547172],"domain_scores_gemma":[0.9998561,0.000035088335,0.000021236616,0.000023520783,0.00005226721,0.000011710359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014976726,0.00031913028,0.00017491454,0.00025681587,0.00016448647,0.00023119521,0.0005352147,0.00027767415,0.0024573198],"category_scores_gemma":[0.00042423684,0.00018417998,0.00017653713,0.0002004973,0.00012592571,0.0002453047,0.0004347486,0.00030930317,0.0005390047],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001090246,0.000018519719,0.00022835271,0.000054873737,0.0000035542635,0.00007607856,0.000048159196,0.00083539344,0.97061026,0.00066509336,0.00036323516,0.026987445],"study_design_scores_gemma":[0.00003893027,0.00013983995,0.0019429312,0.000014232082,0.00002006077,0.00037232623,0.000019172136,0.03759477,0.95194846,0.00036464666,0.007513458,0.000031150095],"about_ca_topic_score_codex":0.00044329872,"about_ca_topic_score_gemma":0.000638299,"teacher_disagreement_score":0.0024573198,"about_ca_system_score_codex":0.00026812573,"about_ca_system_score_gemma":0.00022839037,"threshold_uncertainty_score":0.008220553},"labels":[],"label_agreement":null},{"id":"W2142162273","doi":"10.1109/lsp.2009.2016460","title":"Detection for OFDM Systems With Channel Estimation Errors Using Variational Inference","year":2009,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":6,"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":"University of Toronto","keywords":"Orthogonal frequency-division multiplexing; Channel (broadcasting); Computer science; Algorithm; Inference; Channel state information; Monte Carlo method; Impulse (physics); Mathematics; Artificial intelligence; Telecommunications; Statistics; Wireless","score_opus":0.023215834574185207,"score_gpt":0.26723691973974906,"score_spread":0.24402108516556384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142162273","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":[0.014945558,0.00018441277,0.9838164,0.00024253484,0.000014558557,0.000021969623,0.000031819258,0.000079396,0.0006633148],"genre_scores_gemma":[0.7310844,0.0004168432,0.2653665,0.00017640257,0.00008573675,0.00012522421,0.00016072577,0.00008125943,0.0025028787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990189,0.00048717708,0.000036759346,0.00012960013,0.00023052748,0.00009692466],"domain_scores_gemma":[0.9953939,0.003886948,0.00025345772,0.00016484834,0.00024165091,0.00005933946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00302283,0.0004938052,0.00082903175,0.00050305907,0.00047720908,0.00117543,0.0010571092,0.00094536634,0.0011096399],"category_scores_gemma":[0.010123514,0.0007098253,0.0006251429,0.00063724804,0.0012992907,0.0014815497,0.0012736571,0.0014613436,0.00016171935],"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":[0.000094072195,0.00002776092,0.0010599997,0.000058431586,0.00007868621,0.0000637151,0.00010153564,0.9154071,0.0010024902,0.051142927,0.0005840471,0.030379148],"study_design_scores_gemma":[0.000005754799,0.000004903739,0.00008405342,0.0000021586118,0.0000027967617,0.0000086712425,0.0000030248136,0.99257576,0.00022762486,0.006983176,0.00009826944,0.0000038221365],"about_ca_topic_score_codex":0.0071918634,"about_ca_topic_score_gemma":0.0060583027,"teacher_disagreement_score":0.0071918634,"about_ca_system_score_codex":0.0015466993,"about_ca_system_score_gemma":0.0017260261,"threshold_uncertainty_score":0.015986443},"labels":[],"label_agreement":null},{"id":"W2143309866","doi":"10.1109/97.988716","title":"Robust array interpolation using second-order cone programming","year":2002,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Interpolation (computer graphics); Robustness (evolution); Mathematical optimization; Second-order cone programming; Computer science; Multivariate interpolation; Convex optimization; Algorithm; Stairstep interpolation; Mathematics; Bilinear interpolation; Nearest-neighbor interpolation; Regular polygon; Artificial intelligence; Computer vision; Geometry","score_opus":0.04750596592998896,"score_gpt":0.2604011387072404,"score_spread":0.21289517277725145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143309866","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":[0.0017094142,0.000037822378,0.9974674,0.000021249294,0.000007573982,0.0000093997705,0.000014272417,0.000050383856,0.0006824006],"genre_scores_gemma":[0.22160284,0.00034212237,0.7735696,0.000082117826,0.000043190634,0.00015877746,0.0001886218,0.00011980671,0.0038929542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994394,0.00017763453,0.00002089001,0.00008276671,0.00022780703,0.00005153501],"domain_scores_gemma":[0.9991736,0.0004334009,0.00009453039,0.00008253757,0.00018936819,0.000026443664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012008151,0.0011027618,0.0009256265,0.00046074862,0.00028169385,0.0008433986,0.0008455914,0.00054799585,0.0013964203],"category_scores_gemma":[0.0020328658,0.00039396915,0.0007300639,0.00068373914,0.00052871165,0.0009111744,0.0007815429,0.0011993113,0.00044157926],"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":[0.00005573265,0.000045720866,0.00016776969,0.00006009906,0.000023295124,0.00005943951,0.00003679146,0.9086978,0.0061801015,0.038930435,0.0008316076,0.044911265],"study_design_scores_gemma":[0.000003670972,0.000019310995,0.000017964141,0.0000023986568,0.0000016012959,0.000011973217,0.000002424796,0.99427795,0.0011702183,0.004024109,0.00046379224,0.0000045892534],"about_ca_topic_score_codex":0.0017867491,"about_ca_topic_score_gemma":0.0015714593,"teacher_disagreement_score":0.0017867491,"about_ca_system_score_codex":0.0004689014,"about_ca_system_score_gemma":0.0010170756,"threshold_uncertainty_score":0.006350577},"labels":[],"label_agreement":null},{"id":"W2145296449","doi":"10.1109/lsp.2009.2016449","title":"Proportional Fair Multiuser Scheduling in LTE","year":2009,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":249,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Telecommunications link; Computer science; Scheduling (production processes); Maximum throughput scheduling; Proportionally fair; Fairness measure; Throughput; Computer network; Mathematical optimization; Round-robin scheduling; Real-time computing; Fair-share scheduling; Distributed computing; Mathematics; Telecommunications; Quality of service; Wireless","score_opus":0.008065413057944063,"score_gpt":0.21871355088665861,"score_spread":0.21064813782871455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145296449","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":[0.027009822,0.0011831279,0.96509093,0.0003051257,0.00018402655,0.00006641789,0.00006304563,0.00036938104,0.005728158],"genre_scores_gemma":[0.91779584,0.0004272934,0.07871036,0.00012250907,0.00010609519,0.00006795365,0.000038834198,0.00002740788,0.0027035715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987143,0.00045623738,0.000054140295,0.00013470926,0.00045719033,0.0001834504],"domain_scores_gemma":[0.9991974,0.0003614643,0.00006556977,0.00012794184,0.00019515572,0.000052598785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019497391,0.00040717542,0.00073046715,0.0003291919,0.00083595933,0.00097352837,0.0011188943,0.00064665766,0.0011845957],"category_scores_gemma":[0.0041806595,0.00026262464,0.00030022653,0.0006432713,0.000648652,0.0009018256,0.0006372711,0.0005237011,0.00023480383],"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":[0.00020367607,0.000047158195,0.0005776843,0.00007007538,0.000033826862,0.00016921788,0.00008726979,0.8632328,0.003974962,0.062498532,0.002430258,0.066674404],"study_design_scores_gemma":[0.000013648219,0.000023764424,0.000078918485,0.0000026605667,0.000005509863,0.00002677122,0.000007401064,0.9849096,0.0006331534,0.013466032,0.0008260454,0.0000065239396],"about_ca_topic_score_codex":0.0072801155,"about_ca_topic_score_gemma":0.005586562,"teacher_disagreement_score":0.0072801155,"about_ca_system_score_codex":0.0013665499,"about_ca_system_score_gemma":0.0016786461,"threshold_uncertainty_score":0.014475465},"labels":[],"label_agreement":null},{"id":"W2145313093","doi":"10.1109/lsp.2009.2026119","title":"Robust Affine Invariant Region-Based Shape Descriptors: The ICA Zernike Moment Shape Descriptor and the Whitening Zernike Moment Shape Descriptor","year":2009,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Zernike polynomials; Affine transformation; Artificial intelligence; Invariant (physics); Pattern recognition (psychology); Moment (physics); Mathematics; Velocity Moments; Feature extraction; Shape analysis (program analysis); Computer vision; Image retrieval; Computer science; Image (mathematics); Geometry; Wavefront; Optics; Physics","score_opus":0.03754018332489837,"score_gpt":0.22499904654516623,"score_spread":0.18745886322026786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145313093","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":[0.02939952,0.0033845967,0.96184874,0.00019275011,0.0002959285,0.00011784241,0.0005278848,0.0014146621,0.0028180182],"genre_scores_gemma":[0.41544402,0.00357449,0.5652956,0.0005438203,0.0006961776,0.00032157724,0.0043646377,0.0003596305,0.009400024],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992137,0.00008704911,0.00005326628,0.00014549427,0.00044286775,0.00005766152],"domain_scores_gemma":[0.998841,0.00019872127,0.00016401925,0.00021730205,0.0005344279,0.000044607023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075133215,0.0005506712,0.001018304,0.0023208184,0.0001859167,0.00093944254,0.0010339337,0.00072778296,0.0017218974],"category_scores_gemma":[0.0019778288,0.0002601991,0.0011533155,0.002207518,0.0005093956,0.0014637015,0.0006168925,0.0006140241,0.0018003337],"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":[0.0003546208,0.00011852725,0.0021443875,0.00036579973,0.00018966339,0.00021184243,0.000052263942,0.033171028,0.116622575,0.011322204,0.00735225,0.8280947],"study_design_scores_gemma":[0.000112512666,0.0007313548,0.012148977,0.00007908385,0.00028571684,0.0021471896,0.00013338015,0.77900195,0.15069382,0.010333935,0.043984003,0.00034807948],"about_ca_topic_score_codex":0.0014325326,"about_ca_topic_score_gemma":0.0013229853,"teacher_disagreement_score":0.0023208184,"about_ca_system_score_codex":0.00042532975,"about_ca_system_score_gemma":0.00057679176,"threshold_uncertainty_score":0.005760312},"labels":[],"label_agreement":null},{"id":"W2145473440","doi":"10.1109/lsp.2009.2036879","title":"Motion Vector Outlier Rejection Cascade for Global Motion Estimation","year":2009,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Outlier; Cascade; Computer science; Artificial intelligence; Motion vector; Anomaly detection; Motion estimation; Noise (video); Pattern recognition (psychology); Computer vision; Image (mathematics); Engineering","score_opus":0.01720391461006942,"score_gpt":0.2899850123693512,"score_spread":0.2727810977592818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145473440","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":[0.020343572,0.000328102,0.97759676,0.000060356313,0.000039808183,0.00004403806,0.000059094276,0.00063135964,0.0008968916],"genre_scores_gemma":[0.35629746,0.0004970493,0.6378331,0.00009582617,0.00008948547,0.00009353282,0.0004632618,0.00012483614,0.0045055123],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991936,0.00013979975,0.000029388055,0.00011331548,0.00045650356,0.00006730041],"domain_scores_gemma":[0.9992743,0.00021658448,0.00008830631,0.0001372471,0.0002467147,0.000036940895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091622514,0.0009869434,0.000924106,0.00071635423,0.00033532968,0.00044280651,0.0011965898,0.00080022775,0.0021762475],"category_scores_gemma":[0.0024180836,0.00043193097,0.00052655087,0.0006405321,0.000286719,0.0010389958,0.0009570281,0.00092448946,0.0010281389],"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":[0.0007894575,0.00014482306,0.0021285033,0.00017983378,0.00016244523,0.00033665885,0.00011625634,0.07878889,0.21612269,0.005200114,0.0037292559,0.69230103],"study_design_scores_gemma":[0.000029393663,0.00024827966,0.0022324377,0.000017681119,0.000055993834,0.00038553154,0.000023453047,0.9033102,0.087368965,0.0018857028,0.0043988456,0.00004344404],"about_ca_topic_score_codex":0.0016811155,"about_ca_topic_score_gemma":0.0035197039,"teacher_disagreement_score":0.0021762475,"about_ca_system_score_codex":0.00026657927,"about_ca_system_score_gemma":0.00040949308,"threshold_uncertainty_score":0.00728029},"labels":[],"label_agreement":null},{"id":"W2148197013","doi":"10.1109/lsp.2003.819855","title":"A Fast Adaptive Algorithm for MMSE Receivers in DS-CDMA Systems","year":2004,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Wireless Communication Networks Research","field":"Computer Science","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":"Queen's University; Concordia University","funders":"","keywords":"Code division multiple access; Computer science; Algorithm; Single antenna interference cancellation; Minimum mean square error; Interference (communication); Convergence (economics); Iterative method; Overhead (engineering); Base station; Least mean squares filter; Adaptive filter; Filter (signal processing); Multiuser detection; Adaptive algorithm; Mathematics; Channel (broadcasting); Telecommunications; Decoding methods; Statistics","score_opus":0.033853186115505564,"score_gpt":0.2775606841244963,"score_spread":0.24370749800899075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148197013","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":[0.0024131988,0.000283008,0.99619544,0.00004510659,0.000031385858,0.000015550499,0.0000078851945,0.00012979608,0.0008786388],"genre_scores_gemma":[0.13964501,0.00081857346,0.85302365,0.0000837571,0.00009666954,0.00015389797,0.000070954295,0.000060295657,0.006047167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996147,0.00011029367,0.000018372044,0.00004696291,0.0001864559,0.000023161667],"domain_scores_gemma":[0.99959,0.0001883439,0.000028212584,0.000040019622,0.00014399721,0.000009431404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072656956,0.00059360376,0.0003746786,0.00030382324,0.0002773426,0.0005496075,0.0005153615,0.00067851436,0.0015181871],"category_scores_gemma":[0.002416475,0.00028063392,0.000260648,0.00033068887,0.00039692994,0.0006526333,0.00042911601,0.0008467682,0.0009789369],"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":[0.0002024413,0.000045755463,0.00070271373,0.00021693019,0.00005718005,0.00012429063,0.0002014051,0.457392,0.032072373,0.14168909,0.0028212161,0.3644746],"study_design_scores_gemma":[0.000020224143,0.00006484843,0.00012842192,0.000017398559,0.000008950119,0.000059561113,0.000009949978,0.98085886,0.0064914194,0.0066820085,0.0056463284,0.000011904068],"about_ca_topic_score_codex":0.0012893,"about_ca_topic_score_gemma":0.0016229122,"teacher_disagreement_score":0.0015181871,"about_ca_system_score_codex":0.0004205628,"about_ca_system_score_gemma":0.00071705366,"threshold_uncertainty_score":0.005078852},"labels":[],"label_agreement":null},{"id":"W2148310803","doi":"10.1109/lsp.2009.2036654","title":"KPAC: A Kernel-Based Parametric Active Contour Method for Fast Image Segmentation","year":2009,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","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":"University of Waterloo","funders":"","keywords":"Active contour model; Artificial intelligence; Kernel (algebra); Computer vision; Computer science; Image segmentation; Parametric statistics; Segmentation; Scale-space segmentation; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.020273126132299764,"score_gpt":0.32887448648951567,"score_spread":0.3086013603572159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148310803","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":[0.0011448982,0.00015153663,0.9977228,0.000052195002,0.000021261181,0.00002392724,0.000018056973,0.0005849659,0.0002803912],"genre_scores_gemma":[0.07174472,0.0004393123,0.9251567,0.00010900343,0.000065511646,0.00015906361,0.00019078043,0.0003904974,0.0017443559],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992718,0.00014379167,0.000040624884,0.000110395566,0.00038624366,0.000047163732],"domain_scores_gemma":[0.99843544,0.00071800087,0.00013800644,0.00024388488,0.00040414114,0.000060642185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010274828,0.0009175849,0.0011743646,0.0016313002,0.0005043446,0.0012631412,0.0020890953,0.001971219,0.0020992274],"category_scores_gemma":[0.0039106365,0.00056792214,0.0008217886,0.0018363029,0.00097048905,0.0019692369,0.0014520267,0.0016352611,0.0012745115],"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":[0.0002004858,0.00010014347,0.00060566765,0.0003186009,0.00008435082,0.00023907365,0.00022728527,0.1343642,0.063786246,0.01791004,0.007688851,0.77447504],"study_design_scores_gemma":[0.00001212897,0.00003367925,0.00019996957,0.00001050497,0.000011902306,0.00024046312,0.00001344206,0.9786395,0.011615751,0.0034165655,0.0057747266,0.000031427586],"about_ca_topic_score_codex":0.0017298696,"about_ca_topic_score_gemma":0.0017386872,"teacher_disagreement_score":0.0020992274,"about_ca_system_score_codex":0.0005277367,"about_ca_system_score_gemma":0.0010377914,"threshold_uncertainty_score":0.0070225596},"labels":[],"label_agreement":null},{"id":"W2152487627","doi":"10.1109/lsp.2005.847859","title":"Analytically derived TOA-DOA distributions of uplink/downlink wireless-cellular multipaths arisen from scatterers with an inverted-parabolic spatial distribution around the mobile","year":2005,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Telecommunications link; Omnidirectional antenna; Azimuth; Base station; Computer science; Physics; Telecommunications; Antenna (radio); Optics","score_opus":0.013164368132865307,"score_gpt":0.22122602214297002,"score_spread":0.2080616540101047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152487627","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":[0.03640216,0.00031325576,0.9560708,0.0001410139,0.00003394117,0.00002676184,0.0002729929,0.00015525751,0.006583902],"genre_scores_gemma":[0.82203126,0.0015716862,0.16907495,0.00013021145,0.00010639597,0.00010984886,0.0006435435,0.0001435351,0.006188502],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998173,0.000041888015,0.000009347166,0.000024495263,0.00008312681,0.000023941939],"domain_scores_gemma":[0.9993772,0.00029046982,0.000073056326,0.00009486361,0.00014675452,0.000017684586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048033267,0.0006003747,0.00040728183,0.0005826139,0.0002831114,0.00079333707,0.0006745668,0.00042628936,0.002237241],"category_scores_gemma":[0.0025713914,0.0003012217,0.00058069,0.0005500606,0.00064450974,0.0009441157,0.0005908222,0.00059650047,0.00084729283],"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":[0.00006214432,0.0000421042,0.0034407903,0.0001429718,0.000048280806,0.0002656995,0.00025966315,0.71648866,0.0124741895,0.21700507,0.0020937286,0.047676608],"study_design_scores_gemma":[0.0000055387145,0.00001313942,0.0006929405,0.00001485112,0.000010657007,0.0002873768,0.000048441867,0.96484184,0.0017818151,0.030723749,0.001563244,0.000016453248],"about_ca_topic_score_codex":0.001558968,"about_ca_topic_score_gemma":0.0027888878,"teacher_disagreement_score":0.002237241,"about_ca_system_score_codex":0.00067487225,"about_ca_system_score_gemma":0.00074478803,"threshold_uncertainty_score":0.007484257},"labels":[],"label_agreement":null},{"id":"W2153200902","doi":"10.1109/lsp.2011.2106119","title":"Robust Recursive Least-Squares Adaptive-Filtering Algorithm for Impulsive-Noise Environments","year":2011,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Recursive least squares filter; Algorithm; Adaptive filter; A priori and a posteriori; Computer science; Noise (video); Signal processing; Mathematics; Artificial intelligence; Digital signal processing","score_opus":0.044550366222394215,"score_gpt":0.2249580311770806,"score_spread":0.18040766495468638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153200902","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":[0.0020927696,0.00007033029,0.9972385,0.000027141386,0.000017125925,0.0000067401625,0.0000070785186,0.00021861434,0.00032152433],"genre_scores_gemma":[0.19852294,0.00036073365,0.7960641,0.000092330054,0.00005123107,0.00012107125,0.00016639313,0.00010295762,0.004518281],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996276,0.00006822512,0.00002495841,0.000081053025,0.00016793744,0.000030356907],"domain_scores_gemma":[0.9995783,0.00017972683,0.000055112836,0.000044845703,0.00013033897,0.000011648525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006044524,0.00057301525,0.00072288257,0.00036922126,0.00027707568,0.0005674566,0.0010701647,0.0008832449,0.0011749229],"category_scores_gemma":[0.0018290587,0.0003219392,0.00055884366,0.00052769214,0.00036354587,0.0006561899,0.0004750492,0.00083416735,0.00070813246],"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":[0.00015275148,0.00005715559,0.00083860307,0.00014465544,0.00010734359,0.0001551947,0.00014210142,0.4728916,0.029910255,0.01947313,0.0026306768,0.47349644],"study_design_scores_gemma":[0.000009653091,0.00002142825,0.00015356822,0.0000047397575,0.000010192459,0.0000445393,0.0000043439286,0.99401253,0.0030643279,0.0012734404,0.0013907183,0.000010496354],"about_ca_topic_score_codex":0.002434661,"about_ca_topic_score_gemma":0.0028622716,"teacher_disagreement_score":0.002434661,"about_ca_system_score_codex":0.00037427756,"about_ca_system_score_gemma":0.00077530864,"threshold_uncertainty_score":0.00484097},"labels":[],"label_agreement":null},{"id":"W2153435651","doi":"10.1109/lsp.2005.847886","title":"A simplified clipping and filtering technique for PAR reduction in OFDM systems","year":2005,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":245,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Clipping (morphology); Fast Fourier transform; Algorithm; Computer science; Iterative method; Reduction (mathematics); Noise (video); Orthogonal frequency-division multiplexing; Mathematics; Telecommunications; Artificial intelligence; Channel (broadcasting)","score_opus":0.01683630079010304,"score_gpt":0.24054252699816017,"score_spread":0.22370622620805714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153435651","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":[0.010456172,0.0002952914,0.98632646,0.00009450716,0.000039742637,0.00003608848,0.000025028437,0.00029438848,0.0024322956],"genre_scores_gemma":[0.25451958,0.0010663987,0.73859155,0.00015541178,0.00017718648,0.00009232006,0.00011796311,0.00013127046,0.005148296],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995803,0.00006986492,0.0000148354,0.00005106422,0.00025043386,0.000033407807],"domain_scores_gemma":[0.9996805,0.000092552706,0.000033237,0.00009384421,0.00008935586,0.000010494424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003604285,0.0006238014,0.0004426529,0.00041198358,0.0004177317,0.0005457774,0.00050709397,0.0005059617,0.001746225],"category_scores_gemma":[0.0011315871,0.00019158376,0.0006378818,0.00065194856,0.00043815083,0.00077566813,0.00026763944,0.0007318687,0.00073769956],"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":[0.00031329197,0.00010295392,0.00078850205,0.00027582658,0.00011909424,0.00052483694,0.00040389437,0.11905262,0.3364553,0.05683301,0.0053640176,0.47976667],"study_design_scores_gemma":[0.000020500189,0.00029784665,0.001924737,0.000021748025,0.000065474975,0.001274477,0.00004138223,0.8467891,0.12681371,0.0057736,0.016898714,0.00007883507],"about_ca_topic_score_codex":0.0014193685,"about_ca_topic_score_gemma":0.0016283775,"teacher_disagreement_score":0.001746225,"about_ca_system_score_codex":0.00039671527,"about_ca_system_score_gemma":0.0005144348,"threshold_uncertainty_score":0.005841732},"labels":[],"label_agreement":null},{"id":"W2154465879","doi":"10.1109/97.911475","title":"On alias-component matrices of discrete-time linear periodically time-varying systems","year":2001,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Control Systems and Identification","field":"Engineering","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 Alberta","funders":"","keywords":"Alias; Fourier series; Discrete Fourier transform (general); Control theory (sociology); Component (thermodynamics); Discrete-time Fourier transform; Discrete time and continuous time; Mathematics; Fourier transform; Linear system; Series (stratigraphy); Discrete Fourier series; Matrix (chemical analysis); Applied mathematics; Computer science; Mathematical analysis; Fourier analysis; Fractional Fourier transform; Physics; Materials science; Statistics","score_opus":0.007800231753352081,"score_gpt":0.20796301496409428,"score_spread":0.20016278321074218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154465879","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":[0.021735726,0.0008301926,0.97055805,0.0001712528,0.0001087724,0.000029930585,0.00006173785,0.00012372104,0.0063804956],"genre_scores_gemma":[0.84879667,0.004479943,0.13296498,0.00023471385,0.00036858823,0.00014019507,0.0003254049,0.00014907928,0.012540375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99966097,0.000077305136,0.000015849817,0.00006289983,0.00015734298,0.00002568252],"domain_scores_gemma":[0.99921215,0.00044734386,0.00011351779,0.00006973282,0.00013670561,0.000020513517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006208032,0.0007724517,0.0003661765,0.0005347711,0.00023608471,0.0006930709,0.0005113432,0.00057850155,0.0022915483],"category_scores_gemma":[0.0027779352,0.00024383565,0.00040024368,0.0007276949,0.0007205595,0.001004927,0.0003790993,0.0010250361,0.00045425483],"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":[0.00009612017,0.000062818945,0.00068760425,0.00034383274,0.000050191593,0.00040088402,0.00047730192,0.568836,0.037362467,0.3377158,0.0015150502,0.052451994],"study_design_scores_gemma":[0.0000035225826,0.000035272064,0.00025378552,0.00001609381,0.000006053568,0.00007391936,0.000020653672,0.9630373,0.0020557619,0.032923616,0.0015597267,0.000014167944],"about_ca_topic_score_codex":0.0034818621,"about_ca_topic_score_gemma":0.0024044102,"teacher_disagreement_score":0.0034818621,"about_ca_system_score_codex":0.00037308398,"about_ca_system_score_gemma":0.00032335665,"threshold_uncertainty_score":0.0076660514},"labels":[],"label_agreement":null},{"id":"W2155213659","doi":"10.1109/lsp.2006.884038","title":"Time Delay Estimation via Minimum Entropy","year":2007,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":88,"is_retracted":false,"has_abstract":true,"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":"Multilateration; Computer science; Algorithm; Reverberation; Entropy (arrow of time); Additive white Gaussian noise; Gaussian; Speech recognition; White noise; Mathematics; Telecommunications; Acoustics","score_opus":0.008847824756960781,"score_gpt":0.2346610019047672,"score_spread":0.22581317714780644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155213659","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":[0.0067447275,0.00017272381,0.99209374,0.00007888838,0.000014780141,0.000009194219,0.000035024,0.00011471909,0.00073625235],"genre_scores_gemma":[0.52482903,0.0008897785,0.46895462,0.000080867554,0.00018132993,0.00011243098,0.0004468184,0.00016082432,0.0043443176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964714,0.00010527509,0.000021017142,0.00007439663,0.000122217,0.000029967965],"domain_scores_gemma":[0.99901044,0.0006898823,0.000106403226,0.00006122012,0.00010334644,0.000028707631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061579165,0.0005832913,0.00081484596,0.00094285357,0.00026692668,0.0007909521,0.00055247947,0.0006331079,0.0015723731],"category_scores_gemma":[0.003616367,0.00042164163,0.00043327015,0.0007222143,0.0005029296,0.0014518773,0.00093279197,0.00063808885,0.0004226938],"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":[0.00015607207,0.000029650833,0.00096422865,0.00014447265,0.000055942506,0.00013443711,0.00006521768,0.7700758,0.011120495,0.057290893,0.0016642766,0.15829849],"study_design_scores_gemma":[0.00000650831,0.000012625402,0.00020854844,0.0000062917343,0.0000044413214,0.000035842026,0.0000032587247,0.98327833,0.0018042676,0.014163258,0.0004672246,0.000009423149],"about_ca_topic_score_codex":0.0010534524,"about_ca_topic_score_gemma":0.0007639141,"teacher_disagreement_score":0.0015723731,"about_ca_system_score_codex":0.00048817674,"about_ca_system_score_gemma":0.00056307873,"threshold_uncertainty_score":0.00526011},"labels":[],"label_agreement":null},{"id":"W2156333708","doi":"10.1109/lsp.2009.2018225","title":"Generalized Bayesian Estimators of the Spectral Amplitude for Speech Enhancement","year":2009,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":22,"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":"Estimator; Speech enhancement; Bayesian probability; Wiener filter; Mathematics; Noise (video); Computer science; Algorithm; Filter (signal processing); Speech recognition; Statistics; Artificial intelligence","score_opus":0.015077704216672452,"score_gpt":0.25900691157439654,"score_spread":0.24392920735772408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156333708","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":[0.0029253894,0.00033254523,0.9961708,0.000040364248,0.000011215087,0.000008601366,0.00001008551,0.000059292914,0.0004416317],"genre_scores_gemma":[0.22598892,0.0017864215,0.7682338,0.00017741125,0.00015742473,0.00016739071,0.0001721974,0.000128124,0.0031883768],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922585,0.00030045674,0.000033435983,0.00009032634,0.00031596218,0.000033831246],"domain_scores_gemma":[0.9987748,0.00074836303,0.00011587977,0.00013151628,0.00020507365,0.000024452718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020895218,0.0007889632,0.000659758,0.00056702265,0.00017703812,0.0007302879,0.0008205512,0.0009409789,0.0011786406],"category_scores_gemma":[0.0061852355,0.00035216907,0.00053044886,0.00042367654,0.00047502044,0.0014616033,0.0009214489,0.00087923714,0.00059837213],"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":[0.00020100927,0.00008039071,0.0009505389,0.0002926696,0.00015777639,0.00009126505,0.000108101616,0.4030823,0.051564094,0.13751543,0.00182358,0.40413284],"study_design_scores_gemma":[0.000011537011,0.0000365539,0.00031247895,0.000021161446,0.000026175245,0.00006689746,0.0000076498845,0.9709272,0.0053566326,0.020836595,0.0023774912,0.000019570281],"about_ca_topic_score_codex":0.0005247702,"about_ca_topic_score_gemma":0.0007706885,"teacher_disagreement_score":0.0020895218,"about_ca_system_score_codex":0.00028484745,"about_ca_system_score_gemma":0.00046376034,"threshold_uncertainty_score":0.011050522},"labels":[],"label_agreement":null},{"id":"W2158370950","doi":"10.1109/lsp.2009.2036875","title":"Filtering, Sampling, and Reconstruction With Time-Varying Bandwidths","year":2009,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Mathematical Analysis and Transform Methods","field":"Mathematics","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":"University of Waterloo","funders":"","keywords":"Computer science; Signal reconstruction; Sampling (signal processing); Algorithm; Artificial intelligence; Signal processing; Computer vision; Filter (signal processing); Telecommunications; Radar","score_opus":0.042576538966375255,"score_gpt":0.306846019093904,"score_spread":0.26426948012752877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158370950","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":[0.013079086,0.0006817971,0.9842221,0.0001304738,0.00006149167,0.000009339292,0.000013978537,0.000083758845,0.001717881],"genre_scores_gemma":[0.40509385,0.003019059,0.5859598,0.00018714907,0.00029663957,0.000079086945,0.00009379518,0.00009784747,0.005172803],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99948776,0.00016596545,0.0000253371,0.00008440595,0.0001928193,0.000043667293],"domain_scores_gemma":[0.99935514,0.00031361,0.00007148357,0.000148621,0.00008677911,0.00002439158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009883614,0.0004615042,0.00051715533,0.000584113,0.00024704484,0.00073378475,0.0005892605,0.0010225426,0.0008297853],"category_scores_gemma":[0.0027939728,0.00022793071,0.0005413319,0.0006964788,0.0011289831,0.0017435114,0.00067625556,0.0008781725,0.00025109557],"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":[0.00029427712,0.00003706535,0.0006320516,0.0002758714,0.000054350203,0.00041253344,0.00027037165,0.07462087,0.08509398,0.65107775,0.0013000428,0.18593077],"study_design_scores_gemma":[0.000034818862,0.00009974035,0.00082192547,0.00004524066,0.00003557529,0.00077341934,0.0000640987,0.67051536,0.028572386,0.2890621,0.009919784,0.000055482007],"about_ca_topic_score_codex":0.00067790004,"about_ca_topic_score_gemma":0.00054066203,"teacher_disagreement_score":0.0010225426,"about_ca_system_score_codex":0.00039284537,"about_ca_system_score_gemma":0.0002664639,"threshold_uncertainty_score":0.0052270293},"labels":[],"label_agreement":null},{"id":"W2158386631","doi":"10.1109/lsp.2008.2008482","title":"A Robust Adaptive Dimension Reduction Technique With Application to Array Processing","year":2008,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Robustness (evolution); Orthogonality; Algorithm; Computer science; Preprocessor; Adaptive filter; Dimension (graph theory); Matrix (chemical analysis); Reduction (mathematics); Signal processing; Noise reduction; Dimensionality reduction; Mathematics; Digital signal processing; Artificial intelligence; Computer hardware","score_opus":0.022742001599102276,"score_gpt":0.2385570505773691,"score_spread":0.2158150489782668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158386631","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":[0.0012399977,0.00006796859,0.998348,0.000032966345,0.00001471151,0.000008255415,0.000007791444,0.000118013464,0.00016234917],"genre_scores_gemma":[0.050319966,0.0002026704,0.94804204,0.00007890937,0.00005446639,0.00009906989,0.00006327715,0.00004733074,0.0010921791],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99954396,0.00010316347,0.000028419347,0.00008181787,0.00021485945,0.000027735952],"domain_scores_gemma":[0.99947864,0.00020011322,0.000058953145,0.00010026499,0.0001454392,0.000016542846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048564092,0.00063802407,0.0005120385,0.00043413855,0.00028340417,0.00048623292,0.00056250347,0.0005707382,0.0011659397],"category_scores_gemma":[0.001650923,0.00035589837,0.000601044,0.00067728234,0.00047294144,0.00072320225,0.00078224664,0.0010424168,0.0006786282],"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":[0.00017480082,0.00006699315,0.00055344775,0.00018144802,0.00011248831,0.00014453872,0.00016720686,0.27840313,0.12199597,0.03474711,0.003518272,0.5599346],"study_design_scores_gemma":[0.000019181587,0.00009820444,0.000227974,0.000010332564,0.000017238119,0.0001994538,0.000010691179,0.95668256,0.031762443,0.004519662,0.0064195916,0.000032672135],"about_ca_topic_score_codex":0.00079522846,"about_ca_topic_score_gemma":0.0007471706,"teacher_disagreement_score":0.0011659397,"about_ca_system_score_codex":0.00023836664,"about_ca_system_score_gemma":0.0005014758,"threshold_uncertainty_score":0.0039004087},"labels":[],"label_agreement":null},{"id":"W2159693415","doi":"10.1109/lsp.2003.817852","title":"Adaptive beamforming with sidelobe control: a second-order cone programming approach","year":2003,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Adaptive beamformer; Beamforming; Second-order cone programming; Minimum-variance unbiased estimator; Control theory (sociology); Cone (formal languages); Computer science; Mathematics; Convex optimization; Mathematical optimization; Regular polygon; Algorithm; Control (management); Telecommunications; Statistics","score_opus":0.014256741831875482,"score_gpt":0.22759183949744605,"score_spread":0.21333509766557057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159693415","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":[0.0002477591,0.00004522087,0.998906,0.000033177846,0.000009891167,0.000008158948,0.000007743259,0.00002659904,0.00071552023],"genre_scores_gemma":[0.07761523,0.0010217789,0.91436297,0.00022413078,0.00016547058,0.0003060263,0.00013992305,0.00017942862,0.0059850058],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934655,0.00020265995,0.000024736877,0.000075004355,0.0003071673,0.000043917284],"domain_scores_gemma":[0.99933136,0.0002788781,0.000070237336,0.000044269244,0.00022952033,0.000045631055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090681715,0.0013216578,0.0008983839,0.0005579779,0.00024439656,0.0011278866,0.0013182178,0.0007623092,0.002526137],"category_scores_gemma":[0.0014074825,0.0005650314,0.0006657293,0.0008053439,0.00077304005,0.0009661093,0.00093374075,0.0019947952,0.0008938864],"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":[0.00006219332,0.00011142288,0.0003542147,0.00021434044,0.000054696673,0.00016558495,0.00011428056,0.6511742,0.017392531,0.18058085,0.0056326455,0.14414313],"study_design_scores_gemma":[0.0000074448812,0.000042522395,0.000040752027,0.000010541826,0.000004312697,0.000047187415,0.0000071623153,0.9792701,0.0014228883,0.016122507,0.0030147603,0.000009811933],"about_ca_topic_score_codex":0.0011604064,"about_ca_topic_score_gemma":0.0014259252,"teacher_disagreement_score":0.002526137,"about_ca_system_score_codex":0.0004916232,"about_ca_system_score_gemma":0.0010474086,"threshold_uncertainty_score":0.008450747},"labels":[],"label_agreement":null},{"id":"W2161770374","doi":"10.1109/lsp.2005.860544","title":"A novel radius-adjusted approach for blind adaptive equalization","year":2005,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Blind Source Separation Techniques","field":"Computer Science","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":"University of Windsor","funders":"","keywords":"Quadrature amplitude modulation; QAM; Algorithm; Blind equalization; Adaptive equalizer; Weighting; Constellation diagram; Mean squared error; Equalization (audio); Constellation; Computer science; Mathematics; Convergence (economics); RADIUS; Control theory (sociology); Bit error rate; Statistics; Decoding methods; Artificial intelligence","score_opus":0.06706403537024434,"score_gpt":0.289431842851911,"score_spread":0.22236780748166662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161770374","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":[0.0008835827,0.00016404719,0.9981073,0.000022356815,0.000028492803,0.00001039393,0.000008142813,0.00019826402,0.0005775345],"genre_scores_gemma":[0.051816903,0.00033142176,0.9446057,0.00008829497,0.00007329731,0.00007440544,0.00006258293,0.00009496746,0.002852474],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933213,0.00015496033,0.000044912504,0.00013195814,0.00029819467,0.000037839498],"domain_scores_gemma":[0.9995372,0.0001314512,0.000038583723,0.00009607935,0.00017509378,0.000021599772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047828173,0.0007082661,0.000625527,0.0007077078,0.0004019695,0.0007602662,0.0011663379,0.0008768414,0.0018933483],"category_scores_gemma":[0.0017614437,0.00031175508,0.00054794387,0.0006592752,0.00047500798,0.0013420391,0.00090266083,0.00079541566,0.0012710359],"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":[0.00030698933,0.000057455556,0.0003645126,0.00019255711,0.00011270674,0.00012708436,0.0001309505,0.126423,0.13604307,0.08948036,0.004776516,0.6419848],"study_design_scores_gemma":[0.000026091167,0.000084287356,0.00021689758,0.0000129894515,0.000025819409,0.00033590486,0.00001041282,0.94043535,0.029603135,0.013213594,0.015978968,0.00005667497],"about_ca_topic_score_codex":0.00074556697,"about_ca_topic_score_gemma":0.0009388976,"teacher_disagreement_score":0.0018933483,"about_ca_system_score_codex":0.00044858642,"about_ca_system_score_gemma":0.0005225106,"threshold_uncertainty_score":0.0063338876},"labels":[],"label_agreement":null},{"id":"W2165644958","doi":"10.1109/lsp.2009.2017477","title":"Snake Validation: A PCA-Based Outlier Detection Method","year":2009,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Initialization; Principal component analysis; Artificial intelligence; Outlier; Pattern recognition (psychology); Anomaly detection; Computer science; Object detection; Computer vision; Object (grammar); Image (mathematics)","score_opus":0.015075720332213735,"score_gpt":0.26648741657391545,"score_spread":0.25141169624170173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165644958","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":[0.006331195,0.000046843477,0.99203414,0.000034375214,0.000023119599,0.00003052978,0.000019336152,0.0011733127,0.00030716966],"genre_scores_gemma":[0.19776382,0.00013029297,0.79893607,0.00008821203,0.000038386155,0.00014931512,0.0002651556,0.0005603359,0.0020684656],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99735934,0.0006094743,0.00013304458,0.00037311533,0.001335204,0.0001898831],"domain_scores_gemma":[0.99668175,0.00081744906,0.00049057073,0.0005422092,0.0013589096,0.00010911624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003635161,0.0010765509,0.0013797001,0.002700841,0.00076138275,0.0013758425,0.0023964916,0.0016709714,0.0016783511],"category_scores_gemma":[0.0070512984,0.0006390166,0.0012098444,0.0015852065,0.0011306729,0.001015792,0.0017497028,0.0015025731,0.0009432179],"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":[0.00037399112,0.00017990003,0.005600856,0.0002275946,0.00022171195,0.00041858462,0.0002962734,0.17759185,0.10614744,0.015928201,0.006060702,0.68695295],"study_design_scores_gemma":[0.000019823437,0.00010120213,0.001382191,0.000021515594,0.000026816198,0.00026941742,0.000024374176,0.95002097,0.04175657,0.002862818,0.0034597379,0.00005458466],"about_ca_topic_score_codex":0.0010972824,"about_ca_topic_score_gemma":0.00087207084,"teacher_disagreement_score":0.003635161,"about_ca_system_score_codex":0.00050545775,"about_ca_system_score_gemma":0.0012016922,"threshold_uncertainty_score":0.019224823},"labels":[],"label_agreement":null},{"id":"W2166218055","doi":"10.1109/lsp.2010.2042520","title":"On the Global Output SNR of the Parameterized Frequency-Domain Multichannel Noise Reduction Wiener Filter","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","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":"Institut National de la Recherche Scientifique","funders":"","keywords":"Wiener filter; Filter (signal processing); Metric (unit); Mathematics; Reduction (mathematics); Noise reduction; Algorithm; Parameterized complexity; Distortion (music); Noise (video); Signal-to-noise ratio (imaging); Computer science; Control theory (sociology); Speech recognition; Telecommunications; Statistics; Artificial intelligence; Amplifier; Engineering","score_opus":0.015608696853521037,"score_gpt":0.22788214736407178,"score_spread":0.21227345051055074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166218055","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":[0.03024617,0.0016634112,0.95745546,0.00019154265,0.000059787242,0.000017691966,0.000083784995,0.00023406425,0.010048089],"genre_scores_gemma":[0.82937557,0.0063229343,0.15456961,0.00038580372,0.0004821204,0.00013293116,0.00022408967,0.00050818344,0.00799884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989619,0.00031045047,0.00004573519,0.00024168802,0.00034848705,0.000091796035],"domain_scores_gemma":[0.9968978,0.0020057175,0.00025301237,0.00027757543,0.0005089055,0.000057031444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001834666,0.001413688,0.0006853016,0.0006333128,0.00018831706,0.0009794902,0.00046606504,0.0008225639,0.0025407001],"category_scores_gemma":[0.0060102292,0.00029519456,0.00053879287,0.000506339,0.0015150685,0.0018345698,0.0011913996,0.0015370058,0.0009212128],"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":[0.00061727373,0.00006722368,0.0014792483,0.00071749237,0.000110754285,0.0007421688,0.00025841244,0.52190155,0.1640687,0.18340704,0.002465342,0.12416476],"study_design_scores_gemma":[0.000015615535,0.00031172627,0.0018594217,0.00012065835,0.00006615985,0.00050351897,0.00004809038,0.90070647,0.04297591,0.04992233,0.0034038448,0.000066180735],"about_ca_topic_score_codex":0.00033988862,"about_ca_topic_score_gemma":0.00024742057,"teacher_disagreement_score":0.0025407001,"about_ca_system_score_codex":0.0005661416,"about_ca_system_score_gemma":0.00030466268,"threshold_uncertainty_score":0.009702742},"labels":[],"label_agreement":null},{"id":"W2167012018","doi":"10.1109/lsp.2005.861598","title":"Nonintrusive speech quality estimation using Gaussian mixture models","year":2006,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Mixture model; Computer science; Consistency (knowledge bases); Speech recognition; Pattern recognition (psychology); Gaussian; Quality (philosophy); Estimation theory; Artificial intelligence; Expectation–maximization algorithm; Speech processing; Algorithm; Mathematics; Statistics; Maximum likelihood","score_opus":0.025695192229035557,"score_gpt":0.2754719879494821,"score_spread":0.24977679572044653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167012018","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":[0.0025149207,0.00016596846,0.9964185,0.000015072616,0.000014709396,0.000009848345,0.000013591649,0.00070133025,0.0001461948],"genre_scores_gemma":[0.14768057,0.00052131986,0.84870124,0.000043996915,0.00004834365,0.00006882822,0.0003118999,0.00032891604,0.0022948761],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987483,0.00024195547,0.00006615162,0.00026915895,0.0006027227,0.00007165209],"domain_scores_gemma":[0.9991873,0.00033456305,0.00008850264,0.00011436535,0.00024981354,0.000025518139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012774799,0.0009510326,0.0009525796,0.0010513389,0.00032389996,0.0010159531,0.0012111957,0.0007153064,0.0011639922],"category_scores_gemma":[0.0038589328,0.00056812493,0.0008482165,0.00081571104,0.00033483838,0.0010626388,0.0011465204,0.0013302541,0.0012812897],"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":[0.00027331075,0.00007179094,0.0015116017,0.00011441447,0.00011700381,0.00009677854,0.00012636032,0.11875239,0.039889954,0.006066044,0.0019257608,0.83105457],"study_design_scores_gemma":[0.000011873393,0.00005362438,0.0013964956,0.000013491849,0.000028419352,0.0001413666,0.00001241043,0.9762417,0.01655668,0.0032335015,0.0022763014,0.000034134137],"about_ca_topic_score_codex":0.003733295,"about_ca_topic_score_gemma":0.0034466314,"teacher_disagreement_score":0.003733295,"about_ca_system_score_codex":0.00046091553,"about_ca_system_score_gemma":0.00055873476,"threshold_uncertainty_score":0.0074231625},"labels":[],"label_agreement":null},{"id":"W2167583366","doi":"10.1109/lsp.2010.2046191","title":"Blind Unique Identification of MIMO Channels Using Signal Designs and High-Order Moments","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Wireless Communication 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":"McMaster University","funders":"","keywords":"MIMO; Channel (broadcasting); Mathematics; Phase-shift keying; Fading; Algorithm; Transmission (telecommunications); SIGNAL (programming language); Diophantine equation; Closed-form expression; Control theory (sociology); Applied mathematics; Topology (electrical circuits); Computer science; Telecommunications; Bit error rate; Mathematical analysis; Discrete mathematics; Combinatorics","score_opus":0.026768261866324018,"score_gpt":0.2734679694600586,"score_spread":0.24669970759373455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167583366","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":[0.009666016,0.000077699886,0.9895732,0.0000622264,0.000013570829,0.000019056035,0.000022367281,0.0000691974,0.000496714],"genre_scores_gemma":[0.5742768,0.00038663938,0.42335832,0.00011494414,0.00007967265,0.00020296995,0.00013380824,0.00003665531,0.0014101813],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997787,0.00084069965,0.0001462502,0.0003695082,0.00067114626,0.00018534184],"domain_scores_gemma":[0.9935643,0.0041494775,0.0009811135,0.00059921044,0.0005839384,0.000122065685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001982581,0.0008505086,0.0013105245,0.0007034078,0.000639818,0.0011646531,0.00068991754,0.0011515932,0.001266889],"category_scores_gemma":[0.014207465,0.00062681607,0.0005743886,0.0006645977,0.0016241766,0.0026523126,0.0014086921,0.001229318,0.00040310976],"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":[0.0003955517,0.0000814444,0.0014457976,0.00041125788,0.00011007191,0.0003921048,0.0003670467,0.34920081,0.040671594,0.4914653,0.0012849267,0.11417413],"study_design_scores_gemma":[0.000043856624,0.0001710233,0.00020430723,0.000029810059,0.000019302995,0.0002541103,0.000043285698,0.89741486,0.021170193,0.079385586,0.0012062297,0.00005734396],"about_ca_topic_score_codex":0.00031654222,"about_ca_topic_score_gemma":0.00045880617,"teacher_disagreement_score":0.001982581,"about_ca_system_score_codex":0.000586915,"about_ca_system_score_gemma":0.0014199762,"threshold_uncertainty_score":0.010485053},"labels":[],"label_agreement":null},{"id":"W2168134888","doi":"10.1109/lsp.2008.917801","title":"A Windowed Eigenspectrum Method for Multivariate sEMG Classification During Reaching Movements","year":2008,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Muscle activation and electromyography studies","field":"Engineering","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 British Columbia","funders":"","keywords":"Pattern recognition (psychology); Univariate; Support vector machine; Feature extraction; Artificial intelligence; Multivariate statistics; Computer science; Classifier (UML); Covariance; Eigenvalues and eigenvectors; Feature vector; Speech recognition; Machine learning; Mathematics; Statistics","score_opus":0.029171371936648656,"score_gpt":0.2592598645913635,"score_spread":0.23008849265471487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168134888","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":[0.008886989,0.00024567955,0.98984104,0.00003978127,0.000039803388,0.0000252462,0.000080785605,0.0005358745,0.00030469737],"genre_scores_gemma":[0.1430403,0.00046879303,0.8539349,0.000050082024,0.00009202959,0.00013994814,0.00031184024,0.00013082812,0.001831299],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997986,0.000042810116,0.000016676358,0.00004892828,0.00007876441,0.000014204059],"domain_scores_gemma":[0.99965703,0.0001418048,0.00003433395,0.000043677686,0.00010937604,0.000013801903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045135236,0.00050855504,0.00040778768,0.0007732854,0.00020727054,0.00031340332,0.0003245596,0.00043635975,0.0019748227],"category_scores_gemma":[0.0010244553,0.00016909138,0.0004349345,0.0006936206,0.00021966982,0.00052844564,0.00020522652,0.00037828108,0.00087762595],"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":[0.00015951222,0.00006140572,0.0009392164,0.0001373401,0.000053944663,0.00011618282,0.000074190044,0.011802178,0.14466736,0.0031544499,0.0019462203,0.8368879],"study_design_scores_gemma":[0.000034528148,0.00025958222,0.010143704,0.00004343978,0.00006469935,0.0009542082,0.00005894922,0.90193915,0.06784589,0.0051745023,0.013398599,0.00008274668],"about_ca_topic_score_codex":0.0005209394,"about_ca_topic_score_gemma":0.0009800288,"teacher_disagreement_score":0.0019748227,"about_ca_system_score_codex":0.000112509595,"about_ca_system_score_gemma":0.0002780983,"threshold_uncertainty_score":0.0066064596},"labels":[],"label_agreement":null},{"id":"W2168375507","doi":"10.1109/lsp.2011.2152393","title":"Joint DOD and DOA Estimation for MIMO Array With Velocity Receive Sensors","year":2011,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"MIMO; Direction of arrival; Joint (building); Computer science; Sensor array; Constraint (computer-aided design); Algorithm; Pairing; Key (lock); Mathematics; Telecommunications; Engineering; Channel (broadcasting); Antenna (radio); Physics","score_opus":0.032979630456559494,"score_gpt":0.2480927040236721,"score_spread":0.2151130735671126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168375507","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":[0.010253378,0.00022128546,0.98877174,0.000052881383,0.000028296972,0.000006993546,0.000022353282,0.000093891824,0.0005491269],"genre_scores_gemma":[0.38448426,0.00075676903,0.6118048,0.000078757934,0.00014621443,0.000052346997,0.00022393571,0.00005130681,0.0024016665],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995696,0.00011689025,0.00003194389,0.00010007082,0.00014133609,0.00004014977],"domain_scores_gemma":[0.99902487,0.00055387424,0.00013678912,0.000094114796,0.00015809316,0.0000322117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005499998,0.00068214786,0.00063068536,0.00047969737,0.00026559216,0.0005868258,0.00034121957,0.0004556283,0.00062047614],"category_scores_gemma":[0.0032841878,0.00040043026,0.0003699303,0.00053022645,0.00034367133,0.0009227106,0.00065954926,0.0005293059,0.00034008923],"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":[0.00036716243,0.00006381105,0.0032134508,0.00022189476,0.00011951811,0.00017013798,0.00019620146,0.43124762,0.0455382,0.028100759,0.0016294591,0.48913184],"study_design_scores_gemma":[0.00002263107,0.000095347445,0.0009473095,0.000015555723,0.000024275403,0.00017245083,0.000043221968,0.98013216,0.0089599015,0.007179797,0.0023807478,0.000026578478],"about_ca_topic_score_codex":0.00090542965,"about_ca_topic_score_gemma":0.0012729936,"teacher_disagreement_score":0.00090542965,"about_ca_system_score_codex":0.0002139404,"about_ca_system_score_gemma":0.00045985045,"threshold_uncertainty_score":0.0029087067},"labels":[],"label_agreement":null},{"id":"W2407561938","doi":"10.1109/lsp.2015.2438008","title":"Median Filtering Forensics Based on Convolutional Neural Networks","year":2015,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":418,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; National Science Foundation","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Pooling; Image (mathematics); Pattern recognition (psychology); Filter (signal processing); Median filter; Residual; Computer vision; Image processing; Algorithm","score_opus":0.026587484609427624,"score_gpt":0.2257852914226731,"score_spread":0.19919780681324548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2407561938","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":[0.057565954,0.00070003496,0.9358151,0.00027057997,0.00006181001,0.000046864363,0.00013063932,0.0027532396,0.0026556992],"genre_scores_gemma":[0.7034728,0.00077384175,0.29037935,0.00019383052,0.00007833142,0.000056710927,0.00041212048,0.00010875706,0.0045243013],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997061,0.000033035976,0.000013371337,0.00007343835,0.00011769195,0.000056280078],"domain_scores_gemma":[0.99951375,0.00016534005,0.000084005085,0.00007205958,0.00014218231,0.000022726552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006215421,0.0007548231,0.0007003896,0.0011539464,0.0003174534,0.0006379183,0.0010433646,0.0007803113,0.0014608358],"category_scores_gemma":[0.0014695002,0.0003777245,0.0006912121,0.00071410596,0.000501608,0.0011333822,0.0006220855,0.00069239025,0.00051896763],"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":[0.00033559016,0.00009178843,0.002500992,0.0001056416,0.000106235355,0.00020106282,0.00006303301,0.2245762,0.03588784,0.005126958,0.0026378005,0.72836673],"study_design_scores_gemma":[0.0000033968704,0.000023381052,0.00050784287,0.0000073469587,0.000013032803,0.000079983445,0.0000063193384,0.9853476,0.012071717,0.0014096048,0.0005211727,0.000008583651],"about_ca_topic_score_codex":0.0060822507,"about_ca_topic_score_gemma":0.006418267,"teacher_disagreement_score":0.0060822507,"about_ca_system_score_codex":0.000921921,"about_ca_system_score_gemma":0.0007213923,"threshold_uncertainty_score":0.012093663},"labels":[],"label_agreement":null},{"id":"W2420110223","doi":"10.1109/lsp.2016.2572666","title":"Fold-based Kolmogorov–Smirnov Modulation Classifier","year":2016,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Rail Traffic Control and Safety; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Classifier (UML); Computer science; Pattern recognition (psychology); Robustness (evolution); Artificial intelligence; Speech recognition; Phase-shift keying; Machine learning; Algorithm; Bit error rate; Decoding methods","score_opus":0.02862253564090129,"score_gpt":0.24333215340247819,"score_spread":0.2147096177615769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2420110223","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":[0.07351123,0.0004398108,0.9218792,0.00026160874,0.00013926663,0.000117779855,0.00016389319,0.00060800335,0.0028792995],"genre_scores_gemma":[0.7890119,0.0002992818,0.20618133,0.00019030449,0.00017075891,0.00021153505,0.0007223677,0.000053832722,0.0031586292],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984547,0.00031906876,0.0001647659,0.00022976927,0.00069872243,0.00013310484],"domain_scores_gemma":[0.9975745,0.001115638,0.00022508552,0.0002663958,0.0007396857,0.00007863868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021117537,0.0007311926,0.0011459433,0.0015117618,0.00071712607,0.0015487988,0.00080790883,0.0012415892,0.0020156193],"category_scores_gemma":[0.008042491,0.00020766676,0.0006865469,0.000758188,0.000621333,0.0021221319,0.0008571898,0.0010352214,0.0013417643],"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":[0.0008384548,0.0002720937,0.009373473,0.00016684018,0.00011173573,0.0002703016,0.000110571076,0.07251179,0.048771523,0.020584976,0.0044244165,0.84256387],"study_design_scores_gemma":[0.000019578427,0.0001999021,0.0025431481,0.000017342547,0.000025687103,0.0003143013,0.000028451663,0.9707072,0.017094776,0.007557697,0.0014536186,0.000038318223],"about_ca_topic_score_codex":0.00070426974,"about_ca_topic_score_gemma":0.0008039875,"teacher_disagreement_score":0.0021117537,"about_ca_system_score_codex":0.0006894367,"about_ca_system_score_gemma":0.0009212357,"threshold_uncertainty_score":0.011168122},"labels":[],"label_agreement":null},{"id":"W2474721870","doi":"10.1109/lsp.2016.2584780","title":"Low-Complexity Design of Noninteger Fractionally Spaced Adaptive Equalizers for Coherent Optical Receivers","year":2016,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Optical Network Technologies","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":"Huawei Technologies (Canada)","funders":"","keywords":"Adaptive equalizer; Equalization (audio); Decoupling (probability); Equalizer; Computer science; Distortion (music); Sampling (signal processing); Channel (broadcasting); Algorithm; Computational complexity theory; Joint (building); Electronic engineering; Control theory (sociology); Telecommunications; Bandwidth (computing); Artificial intelligence; Engineering","score_opus":0.047880550093600274,"score_gpt":0.2523412639975562,"score_spread":0.20446071390395593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2474721870","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":[0.023925357,0.00016059475,0.97440505,0.000059125163,0.000020961621,0.000027894492,0.000009887773,0.00010707297,0.0012841327],"genre_scores_gemma":[0.5558006,0.00021852033,0.4415262,0.000082515566,0.000046326426,0.0000848915,0.000036223275,0.000024843332,0.0021798222],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998136,0.00004616814,0.000010717807,0.000035116707,0.000070416536,0.000023952034],"domain_scores_gemma":[0.99979466,0.000077724,0.00004384965,0.000023113622,0.000050914,0.000009722799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034058632,0.00042109273,0.00028960672,0.00019709404,0.00020965569,0.0005589022,0.0005667355,0.00035942896,0.00097447267],"category_scores_gemma":[0.00085965224,0.00027094374,0.0001545158,0.0001579064,0.00027838984,0.0006321887,0.00026856578,0.00047567475,0.00026182662],"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":[0.00027074778,0.00015909711,0.0012344214,0.00015458238,0.000103373,0.00014338129,0.00017183364,0.38331532,0.2585723,0.07371868,0.0011482417,0.28100798],"study_design_scores_gemma":[0.000023011055,0.000097066964,0.00026730902,0.0000082260685,0.000013775809,0.00006244121,0.000010902739,0.9623439,0.031170743,0.00404033,0.0019522115,0.0000101784735],"about_ca_topic_score_codex":0.0005191492,"about_ca_topic_score_gemma":0.0018576928,"teacher_disagreement_score":0.00097447267,"about_ca_system_score_codex":0.00047268768,"about_ca_system_score_gemma":0.00055024197,"threshold_uncertainty_score":0.0034296513},"labels":[],"label_agreement":null},{"id":"W2588196171","doi":"10.1109/lsp.2017.2669333","title":"Light-Field Image Super-Resolution Using Convolutional Neural Network","year":2017,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":193,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Ministère de l'Éducation, du Loisir et du Sport Québec","keywords":"Convolutional neural network; Image resolution; Computer science; Light field; Artificial intelligence; Angular resolution (graph drawing); Computer vision; Field (mathematics); Image quality; Sample (material); Artificial neural network; Resolution (logic); Image (mathematics); Mathematics; Physics","score_opus":0.027469422306649876,"score_gpt":0.2939887329418525,"score_spread":0.2665193106352026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588196171","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":[0.043024678,0.0011733432,0.9484583,0.0003090671,0.00006594309,0.000057194513,0.0003611755,0.003380131,0.00317023],"genre_scores_gemma":[0.38610658,0.00083930866,0.6070703,0.00029219498,0.000061064355,0.00006026062,0.00096141174,0.00021058052,0.00439821],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974006,0.00003318988,0.000009603238,0.00007199771,0.000108793465,0.000036321613],"domain_scores_gemma":[0.99966633,0.00010263154,0.000041947766,0.0000763275,0.00009015681,0.000022590635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000495221,0.00089263474,0.0006397034,0.00068383536,0.0002209617,0.00053248834,0.0011249803,0.0006706377,0.0017068798],"category_scores_gemma":[0.0010502287,0.0004697033,0.0005903637,0.00061294966,0.00033170782,0.0012359216,0.00095451565,0.0011489268,0.00062290486],"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":[0.00041177598,0.00014122333,0.0014430736,0.000244476,0.00021619647,0.00026698,0.00009726828,0.30476156,0.14461036,0.005729571,0.006582524,0.53549504],"study_design_scores_gemma":[0.0000062538215,0.00001681296,0.00024384742,0.000006365956,0.000013998327,0.000055984015,0.0000045621873,0.98404413,0.013424131,0.0012921944,0.0008833191,0.000008427475],"about_ca_topic_score_codex":0.0064328886,"about_ca_topic_score_gemma":0.011213489,"teacher_disagreement_score":0.0064328886,"about_ca_system_score_codex":0.00079205143,"about_ca_system_score_gemma":0.0005775823,"threshold_uncertainty_score":0.012790918},"labels":[],"label_agreement":null},{"id":"W2590427356","doi":"10.1109/lsp.2017.2674960","title":"Unsupervized Image Clustering With SIFT-Based Soft-Matching Affinity Propagation","year":2017,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Image and Video Retrieval Techniques","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":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Scale-invariant feature transform; Pattern recognition (psychology); Artificial intelligence; Cluster analysis; Affinity propagation; Matching (statistics); Similarity (geometry); Computer science; Feature extraction; Image (mathematics); Set (abstract data type); Mathematics; Computer vision; Correlation clustering; Canopy clustering algorithm","score_opus":0.02159910978089052,"score_gpt":0.2776652470225455,"score_spread":0.256066137241655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2590427356","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":[0.008556892,0.00009102082,0.9892268,0.000076245,0.000030191553,0.000060635135,0.000026950234,0.00096293184,0.00096832187],"genre_scores_gemma":[0.33050308,0.0001782661,0.6615364,0.00023029979,0.00007035572,0.00020544541,0.0003694362,0.00021755826,0.0066891024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979019,0.00030413727,0.0001237159,0.0004333433,0.0010276152,0.00020935148],"domain_scores_gemma":[0.997329,0.00057259103,0.0002480052,0.00077634095,0.000939507,0.0001345908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014744118,0.0010379648,0.0014352779,0.0021453786,0.0011313118,0.0016525994,0.004430956,0.002015064,0.0029079432],"category_scores_gemma":[0.0050614867,0.00063040503,0.0011121539,0.0031296902,0.0011907473,0.0022664992,0.0028991525,0.0013943147,0.0016908258],"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":[0.00034701594,0.0002953547,0.0011073503,0.00018587895,0.00016772148,0.0001701909,0.0002452975,0.3349725,0.028408471,0.01704345,0.0053244545,0.6117322],"study_design_scores_gemma":[0.000012071751,0.000033092205,0.00016365659,0.0000035341259,0.0000116364035,0.000058468755,0.000015285936,0.9877955,0.006727409,0.0044365176,0.0007303672,0.000012497832],"about_ca_topic_score_codex":0.00783444,"about_ca_topic_score_gemma":0.0077323285,"teacher_disagreement_score":0.00783444,"about_ca_system_score_codex":0.0015262157,"about_ca_system_score_gemma":0.0014062364,"threshold_uncertainty_score":0.015577674},"labels":[],"label_agreement":null},{"id":"W2593553812","doi":"10.1109/lsp.2017.2679208","title":"Adaptive Metric Learning and Probe-Specific Reranking for Person Reidentification","year":2017,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Metric (unit); Computer science; Artificial intelligence; Machine learning","score_opus":0.07155596924491908,"score_gpt":0.3122934657408728,"score_spread":0.24073749649595372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593553812","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":[0.008196592,0.00037745555,0.9887687,0.000109478744,0.000062092375,0.000080149926,0.00010446246,0.001602278,0.0006988124],"genre_scores_gemma":[0.2516613,0.00038214592,0.7417258,0.00025465546,0.0002232226,0.00025673036,0.0010077686,0.000391772,0.004096673],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99623173,0.0012552509,0.00017721104,0.0011733657,0.00094253646,0.0002199806],"domain_scores_gemma":[0.9965996,0.0008362118,0.00045560143,0.0011975303,0.00078370527,0.00012735545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019490931,0.0019441609,0.0023993968,0.0031684027,0.00095129036,0.0010884451,0.0031045852,0.0018714871,0.0019898545],"category_scores_gemma":[0.010021752,0.00051024894,0.0010835795,0.003143767,0.001002119,0.003294844,0.0018617751,0.0015464525,0.001919167],"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":[0.00020639936,0.00015195411,0.0024405709,0.000176696,0.0001723765,0.00015492631,0.00022102403,0.09132407,0.015703961,0.01057109,0.008923186,0.86995363],"study_design_scores_gemma":[0.000032000953,0.00020210187,0.0014256244,0.00001277992,0.000052233972,0.00049108715,0.00007718157,0.953992,0.013950105,0.021070067,0.008619543,0.000075191245],"about_ca_topic_score_codex":0.003552158,"about_ca_topic_score_gemma":0.004994269,"teacher_disagreement_score":0.003552158,"about_ca_system_score_codex":0.00092593784,"about_ca_system_score_gemma":0.00092675386,"threshold_uncertainty_score":0.010307908},"labels":[],"label_agreement":null},{"id":"W2596774547","doi":"10.1109/lsp.2017.2682857","title":"Track-to-Track Association by Coherent Point Drift","year":2017,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":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":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Track (disk drive); Expectation–maximization algorithm; Probabilistic logic; Computer science; Centroid; Point (geometry); Gaussian; Algorithm; Artificial intelligence; Maximization; Association (psychology); Computer vision; Maximum likelihood; Mathematics; Mathematical optimization; Statistics","score_opus":0.0147413015517044,"score_gpt":0.25258489859027616,"score_spread":0.23784359703857177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2596774547","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":[0.005974146,0.0002609009,0.9929597,0.000072221424,0.00006626368,0.000018797748,0.00001865157,0.00018835154,0.0004408936],"genre_scores_gemma":[0.35683206,0.00068262836,0.6369898,0.00030294366,0.0002751605,0.00015517388,0.00041393083,0.00017425317,0.004174191],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99679875,0.0005582047,0.00013082469,0.0008854881,0.0014178604,0.00020885834],"domain_scores_gemma":[0.9961134,0.0011262523,0.0005644781,0.00089255045,0.0011692042,0.00013411022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030422849,0.0008296386,0.0013740904,0.0011365511,0.0011300849,0.0014448748,0.0023174225,0.0014326437,0.000936252],"category_scores_gemma":[0.009944444,0.00069982524,0.0007628174,0.0028849866,0.00093729765,0.0033639881,0.0032792015,0.0016983456,0.0010210148],"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":[0.0004538106,0.00013448573,0.0061842734,0.00016661994,0.00026716542,0.00021632518,0.000364297,0.2839925,0.022297328,0.050298218,0.0043169647,0.6313081],"study_design_scores_gemma":[0.000030434054,0.00011832524,0.00079326367,0.000009521741,0.000043206986,0.00026079797,0.000024315965,0.9733709,0.010009157,0.010617426,0.0046808855,0.00004174797],"about_ca_topic_score_codex":0.0016647975,"about_ca_topic_score_gemma":0.0014706308,"teacher_disagreement_score":0.0030422849,"about_ca_system_score_codex":0.00071704853,"about_ca_system_score_gemma":0.001613867,"threshold_uncertainty_score":0.01608932},"labels":[],"label_agreement":null},{"id":"W2717930533","doi":"10.1109/lsp.2017.2717946","title":"Saliency-Guided Just Noticeable Distortion Estimation Using the Normalized Laplacian Pyramid","year":2017,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Just-noticeable difference; Artificial intelligence; Human visual system model; Distortion (music); Computer science; Computer vision; Pixel; Pattern recognition (psychology); Salient; Laplace operator; Pyramid (geometry); Image (mathematics); Mathematics","score_opus":0.06411927875584555,"score_gpt":0.33055633263450857,"score_spread":0.266437053878663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2717930533","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":[0.03798366,0.00033008077,0.95964915,0.00013157113,0.00003464598,0.00005317597,0.00006954834,0.000585388,0.0011627849],"genre_scores_gemma":[0.564971,0.00052128057,0.4322976,0.00011312934,0.00006633087,0.000047418234,0.00028757035,0.00014464262,0.0015510326],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997433,0.00003793641,0.000013127966,0.00004805134,0.00013485356,0.000022758364],"domain_scores_gemma":[0.9995715,0.0001332033,0.000065858025,0.000048753416,0.00014761412,0.000033023276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004266593,0.00059235696,0.0005519297,0.0012148983,0.00019092763,0.00060222985,0.00068362744,0.000360816,0.0008690055],"category_scores_gemma":[0.0018783754,0.00026669772,0.00057685416,0.00063809217,0.00034007043,0.0008713694,0.0005580351,0.0004087316,0.00022836664],"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":[0.00050207204,0.00014577711,0.0020117962,0.000343849,0.00017274915,0.00025140066,0.00018620193,0.1356269,0.2345073,0.005926445,0.0035230056,0.61680263],"study_design_scores_gemma":[0.0000149755715,0.00007858097,0.0017310902,0.000007978445,0.000030383791,0.00018821169,0.00001922907,0.9778591,0.017347606,0.0019140306,0.0007868974,0.000021969188],"about_ca_topic_score_codex":0.0034437205,"about_ca_topic_score_gemma":0.0043138294,"teacher_disagreement_score":0.0034437205,"about_ca_system_score_codex":0.00046406966,"about_ca_system_score_gemma":0.00060281646,"threshold_uncertainty_score":0.006847322},"labels":[],"label_agreement":null},{"id":"W2735133106","doi":"10.1109/lsp.2017.2724848","title":"Adaptive Kalman Filtering by Covariance Sampling","year":2017,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kalman filter; Covariance; Covariance intersection; Noise (video); Computer science; White noise; Noise measurement; Gaussian noise; Covariance matrix; Algorithm; A priori and a posteriori; Estimation of covariance matrices; Artificial intelligence; Extended Kalman filter; Mathematics; Control theory (sociology); Pattern recognition (psychology); Statistics; Noise reduction","score_opus":0.04286700277901982,"score_gpt":0.27418640448470183,"score_spread":0.231319401705682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2735133106","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":[0.0015363509,0.0001345766,0.99749774,0.000026094414,0.00002577914,0.0000146905995,0.0000148704885,0.00022298959,0.00052694423],"genre_scores_gemma":[0.3064619,0.0010780575,0.68686503,0.00012416136,0.00016403013,0.0003346017,0.00034777977,0.00019468604,0.0044298023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887496,0.00029959885,0.00006163139,0.0002779173,0.00041562686,0.000070236136],"domain_scores_gemma":[0.99896884,0.00052910845,0.000089273424,0.00012675807,0.00026713422,0.000018871331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012229,0.0006879757,0.0011651553,0.00068942027,0.0005038761,0.0007796123,0.0009312255,0.0006837793,0.0014031976],"category_scores_gemma":[0.004674019,0.000515071,0.0009720094,0.0010874139,0.00062268047,0.0013899112,0.00091287465,0.001269957,0.00065379066],"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":[0.00014198646,0.000051580966,0.0012647624,0.00016798552,0.00011628762,0.000082277256,0.00012685753,0.64898497,0.009232186,0.042991195,0.0018963584,0.29494348],"study_design_scores_gemma":[0.00001190028,0.000018936043,0.00027123038,0.000008231621,0.000011276601,0.000032269956,0.0000042578613,0.99047804,0.0013650755,0.0059007714,0.0018831224,0.000014938413],"about_ca_topic_score_codex":0.012069405,"about_ca_topic_score_gemma":0.007660446,"teacher_disagreement_score":0.012069405,"about_ca_system_score_codex":0.00073730317,"about_ca_system_score_gemma":0.0013150459,"threshold_uncertainty_score":0.02399832},"labels":[],"label_agreement":null},{"id":"W2742523248","doi":"10.1109/lsp.2017.2735806","title":"Distributed Alamouti Relay Beamforming Scheme in Multiuser Relay Networks","year":2017,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Relay; Beamforming; Computer science; Relay channel; Signal-to-noise ratio (imaging); Transmitter power output; Optimization problem; Mathematical optimization; Power (physics); Algorithm; Mathematics; Telecommunications; Transmitter","score_opus":0.033820565308053864,"score_gpt":0.2824159891927094,"score_spread":0.24859542388465553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742523248","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":[0.0052346587,0.00026439558,0.9919755,0.00011664451,0.000026737025,0.00003209018,0.00002720429,0.0000899221,0.0022329197],"genre_scores_gemma":[0.52098495,0.0010111954,0.4718513,0.00019693177,0.00010711437,0.0002142464,0.00009271054,0.00003499942,0.0055065607],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991468,0.00036514658,0.00003564463,0.00015436644,0.00023013915,0.00006798451],"domain_scores_gemma":[0.9994112,0.00021447349,0.00009566798,0.000103695595,0.00014434032,0.000030573152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008290996,0.0008948364,0.0006942205,0.00031079858,0.00035972652,0.0006710693,0.0012532293,0.00086653035,0.0012593642],"category_scores_gemma":[0.0012585783,0.00026464098,0.00038671005,0.00059914915,0.00078388344,0.0011422009,0.0007091309,0.0008907231,0.0005651181],"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":[0.00014957518,0.000109578374,0.00034099104,0.00027103064,0.000062795116,0.00025617838,0.00020987773,0.7195461,0.03461403,0.13427545,0.0029683267,0.10719606],"study_design_scores_gemma":[0.000029258683,0.00016537315,0.000050868486,0.000011562873,0.000013402233,0.00013402612,0.000022874714,0.9726165,0.0064392965,0.017993752,0.0025024083,0.000020817459],"about_ca_topic_score_codex":0.0006392917,"about_ca_topic_score_gemma":0.0010305346,"teacher_disagreement_score":0.0012593642,"about_ca_system_score_codex":0.00061158853,"about_ca_system_score_gemma":0.00072100875,"threshold_uncertainty_score":0.004437387},"labels":[],"label_agreement":null},{"id":"W2745204051","doi":"10.1109/lsp.2017.2738027","title":"Flexible Multiple Base Station Association and Activation for Downlink Heterogeneous Networks","year":2017,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":14,"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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Base station; Telecommunications link; Computer science; Transmitter power output; Proximal Gradient Methods; Cellular network; Computer network; Heterogeneous network; Wireless; Association (psychology); Power (physics); Joint (building); Wireless network; Projection (relational algebra); Real-time computing; Gradient descent; Algorithm; Telecommunications; Transmitter; Engineering; Artificial neural network; Artificial intelligence","score_opus":0.015927957549117643,"score_gpt":0.24072992777952568,"score_spread":0.22480197023040804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2745204051","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":[0.02632155,0.00012304033,0.97184527,0.00007974001,0.000012635812,0.0000142036015,0.000010652138,0.00007438806,0.0015185297],"genre_scores_gemma":[0.8895228,0.00019699702,0.10760221,0.000065371096,0.000030155696,0.000052500007,0.000033421962,0.000036332967,0.0024603445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960154,0.00017990096,0.000008472587,0.00005696719,0.000093132854,0.000059922422],"domain_scores_gemma":[0.9997037,0.00014985217,0.0000337246,0.00004604018,0.000042483385,0.000024165523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062219764,0.00060990016,0.0005396216,0.00017812212,0.00029880932,0.0004885014,0.0006084449,0.00039630648,0.0006551404],"category_scores_gemma":[0.0014111727,0.00022698104,0.00023305806,0.00046934746,0.0006209308,0.0006207127,0.0007916364,0.00073612086,0.00022340764],"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":[0.00007259204,0.00004450461,0.00049009395,0.00002392736,0.000020595418,0.000083141436,0.00004537394,0.9387841,0.005386851,0.015953986,0.00055927405,0.03853552],"study_design_scores_gemma":[0.0000039041543,0.000019157214,0.00007451842,0.0000012631576,0.0000026966386,0.000013020043,0.000004570456,0.99604154,0.000778488,0.0027940094,0.0002647711,0.0000020267933],"about_ca_topic_score_codex":0.0017815992,"about_ca_topic_score_gemma":0.0022040966,"teacher_disagreement_score":0.0017815992,"about_ca_system_score_codex":0.00045077212,"about_ca_system_score_gemma":0.0005299016,"threshold_uncertainty_score":0.0035424232},"labels":[],"label_agreement":null},{"id":"W2765165617","doi":"10.1109/lsp.2017.2765895","title":"State Identification of Duffing Oscillator Based on Extreme Learning Machine","year":2017,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Machine Learning and ELM","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 Windsor","funders":"National Natural Science Foundation of China","keywords":"Duffing equation; Lyapunov exponent; Extreme learning machine; Chaotic; Convergence (economics); Control theory (sociology); Computer science; Rate of convergence; Algorithm; Computation; Feature (linguistics); Mathematics; Artificial intelligence; Nonlinear system; Key (lock); Artificial neural network; Physics","score_opus":0.021196774641656677,"score_gpt":0.25702056061501566,"score_spread":0.23582378597335898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765165617","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":[0.088573776,0.00013683237,0.9094624,0.00008983405,0.000022495016,0.000028036591,0.000023440198,0.00020210078,0.0014611573],"genre_scores_gemma":[0.929321,0.00008864327,0.069316655,0.00003107096,0.000013597257,0.00006086543,0.000055123113,0.000014850666,0.0010981878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998124,0.000046012003,0.000014999822,0.000055587883,0.000052412524,0.00001848176],"domain_scores_gemma":[0.99971515,0.0001315269,0.00005428259,0.000027151016,0.000059836886,0.000012125142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038607852,0.00030407216,0.0004274354,0.000448322,0.0002431081,0.00041374995,0.00048429088,0.0004149988,0.00060530443],"category_scores_gemma":[0.0011981934,0.0001358393,0.00032745002,0.00032587888,0.0003511824,0.00060265156,0.00042096808,0.0004132306,0.00010474335],"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":[0.00023251535,0.000099691184,0.010610154,0.00019820288,0.00009685363,0.00043931397,0.00037417526,0.6435529,0.0540667,0.02799415,0.001137413,0.2611979],"study_design_scores_gemma":[0.0000020524476,0.000014340555,0.00038228522,0.000002284467,0.0000023138698,0.000025156412,0.0000047557924,0.99675155,0.0013735046,0.0013090316,0.00012640774,0.0000063429766],"about_ca_topic_score_codex":0.0009186057,"about_ca_topic_score_gemma":0.0006279674,"teacher_disagreement_score":0.0009186057,"about_ca_system_score_codex":0.00024910402,"about_ca_system_score_gemma":0.00021252026,"threshold_uncertainty_score":0.002041757},"labels":[],"label_agreement":null},{"id":"W2770340319","doi":"10.1109/lsp.2018.2801836","title":"Feedback Acquisition and Reconstruction of Spectrum-Sparse Signals by Predictive Level Comparisons","year":2018,"lang":"en","type":"preprint","venue":"IEEE Signal Processing Letters","topic":"Sparse and Compressive Sensing Techniques","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":false,"ca_institutions":"Queen's University","funders":"Iran National Science Foundation; Sharif University of Technology","keywords":"Algorithm; Sliding window protocol; Compressed sensing; Computer science; Sign (mathematics); Signal reconstruction; Encoding (memory); SIGNAL (programming language); Binary number; Scheme (mathematics); Sequence (biology); Window (computing); Mathematics; Artificial intelligence; Signal processing; Telecommunications","score_opus":0.02852711670992096,"score_gpt":0.240023447183673,"score_spread":0.21149633047375205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770340319","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":[0.02368247,0.00009878353,0.9747342,0.00008237874,0.000026965923,0.00003410883,0.00003217171,0.00033223483,0.0009767446],"genre_scores_gemma":[0.5540747,0.00012300808,0.44323206,0.00012687249,0.000054596636,0.00007277257,0.00013296097,0.000072099116,0.0021109933],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994849,0.00009847889,0.000027059157,0.00009822415,0.00024731326,0.0000440235],"domain_scores_gemma":[0.99882823,0.0004908314,0.0001578367,0.00025983367,0.00022423065,0.00003897087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071682513,0.00043899828,0.0005108217,0.0003781819,0.00035848038,0.0006768465,0.0011134932,0.00064396206,0.0018549442],"category_scores_gemma":[0.0035924006,0.0002882781,0.00026211035,0.00047632333,0.00063419616,0.0015454884,0.0010244075,0.00084623264,0.00035547538],"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":[0.00083025196,0.00020429016,0.0018190863,0.00017747124,0.000046059213,0.00023199276,0.00030998947,0.35496047,0.09525359,0.038787838,0.002781245,0.5045978],"study_design_scores_gemma":[0.000013946823,0.000090286725,0.00016489929,0.0000067692704,0.000007586749,0.00005368123,0.00001226643,0.9776748,0.017702581,0.0035354095,0.00072899123,0.000008682786],"about_ca_topic_score_codex":0.0010595472,"about_ca_topic_score_gemma":0.0018261637,"teacher_disagreement_score":0.0018549442,"about_ca_system_score_codex":0.0003568893,"about_ca_system_score_gemma":0.0006838477,"threshold_uncertainty_score":0.0062054396},"labels":[],"label_agreement":null},{"id":"W2787836236","doi":"10.1109/lsp.2018.2802780","title":"Is Self-Interference in Full-Duplex Communications a Foe or a Friend?","year":2018,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Full-Duplex Wireless Communications","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Telecommunications link; Base station; Maximization; Transmission (telecommunications); Iterative method; Convex optimization","score_opus":0.03871680295729976,"score_gpt":0.2793275456708534,"score_spread":0.24061074271355362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787836236","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":[0.13408251,0.016693454,0.8141638,0.00595729,0.00063599687,0.000026793912,0.000026498654,0.00011744167,0.028296242],"genre_scores_gemma":[0.944434,0.008790642,0.03853099,0.0009757148,0.0005029466,0.000023082097,0.000013407405,0.00004655718,0.0066826935],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997286,0.00008958799,0.000006978127,0.00003503177,0.00010226587,0.00003748417],"domain_scores_gemma":[0.9988852,0.00085309095,0.00006854984,0.00007584246,0.000089132314,0.000028039534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066143484,0.00020569455,0.00037315048,0.00011966611,0.00034809485,0.0009274649,0.00056135864,0.0010420921,0.00090729597],"category_scores_gemma":[0.0021784345,0.00020755472,0.00024281163,0.00027129427,0.0014657995,0.0023042602,0.00057695614,0.00086450734,0.00033957168],"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":[0.00020784803,0.00014141176,0.0028443807,0.00069832965,0.00013095123,0.0011672268,0.0008675421,0.25732058,0.018104585,0.49849036,0.006630972,0.21339577],"study_design_scores_gemma":[0.000031319156,0.00036110924,0.0011436123,0.000112951886,0.00005107734,0.0013473157,0.00061363226,0.62223315,0.012366205,0.32753924,0.03413435,0.000065951266],"about_ca_topic_score_codex":0.00038024777,"about_ca_topic_score_gemma":0.00047534745,"teacher_disagreement_score":0.0010420921,"about_ca_system_score_codex":0.0002909287,"about_ca_system_score_gemma":0.00023006891,"threshold_uncertainty_score":0.0034980178},"labels":[],"label_agreement":null},{"id":"W2789151399","doi":"10.1109/lsp.2018.2799699","title":"AUV-Aided Joint Localization and Time Synchronization for Underwater Acoustic Sensor Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":126,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Research and Development Corporation of Newfoundland and Labrador","keywords":"Synchronization (alternating current); Computer science; Underwater; Scalability; Wireless sensor network; Real-time computing; Underwater acoustic communication; Nonlinear system; Network packet; Measure (data warehouse); Range (aeronautics); Underwater acoustics; Control theory (sociology); Telecommunications; Artificial intelligence; Engineering; Computer network; Geology","score_opus":0.010317258986930072,"score_gpt":0.2051855735355893,"score_spread":0.19486831454865924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789151399","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":[0.021908129,0.00031800114,0.97553617,0.00010492577,0.00005714113,0.000010090106,0.00001406884,0.00021074772,0.0018406619],"genre_scores_gemma":[0.8815398,0.00037119375,0.11245335,0.000050335642,0.000052869156,0.000052795745,0.00006715514,0.00003120265,0.0053812694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996729,0.00012283116,0.00001231486,0.000051363622,0.00011255981,0.000028003336],"domain_scores_gemma":[0.9997763,0.000096681746,0.00002899476,0.00003143536,0.00005843896,0.000008268283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036887967,0.00030464554,0.00030638764,0.00023596207,0.00027323148,0.00029312848,0.00034344403,0.00033934225,0.00081788143],"category_scores_gemma":[0.0012395958,0.00014299715,0.0001396012,0.00039214027,0.00034394654,0.00063378044,0.0007771249,0.0003693996,0.00020804927],"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":[0.00011469225,0.000028665734,0.00080441165,0.000080444275,0.0000286223,0.00011891977,0.00020458763,0.74024135,0.022563178,0.04406327,0.0022497782,0.18950215],"study_design_scores_gemma":[0.000004006223,0.000024500283,0.00007597876,0.0000019137779,0.0000027847643,0.000015564196,0.0000132958485,0.9944501,0.002086311,0.0020546396,0.0012670648,0.000003840392],"about_ca_topic_score_codex":0.0030399254,"about_ca_topic_score_gemma":0.0037159577,"teacher_disagreement_score":0.0030399254,"about_ca_system_score_codex":0.00033644275,"about_ca_system_score_gemma":0.00071494794,"threshold_uncertainty_score":0.0060444474},"labels":[],"label_agreement":null},{"id":"W2794968288","doi":"10.1109/lsp.2018.2820642","title":"Design of Optimal Fixed-Rate Unrestricted Polar Quantizer for Bivariate Circularly Symmetric Sources","year":2018,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bivariate analysis; Mathematics; Mathematical optimization; Statistics","score_opus":0.034463296486246735,"score_gpt":0.27913818301015164,"score_spread":0.2446748865239049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794968288","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":[0.0031548645,0.00011063031,0.9956839,0.00004131438,0.000011309759,0.000023929793,0.000016892232,0.00008981726,0.0008674314],"genre_scores_gemma":[0.27238557,0.00042107067,0.7246211,0.0001061454,0.000037413083,0.00016779729,0.00011623644,0.00005762677,0.0020870545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99961746,0.00008790778,0.000026099588,0.0000926069,0.00013245532,0.00004334442],"domain_scores_gemma":[0.99963224,0.00016246052,0.000051705258,0.000030268731,0.000107205284,0.000016105341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070315076,0.0008784516,0.00071063515,0.0003572619,0.00029117233,0.0009029175,0.0006724579,0.00057632575,0.0024464312],"category_scores_gemma":[0.0016372552,0.00037550318,0.00036663658,0.00037791414,0.00047834468,0.0008998686,0.00093555026,0.0008747294,0.00057007844],"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":[0.00035770674,0.000058644768,0.0006004445,0.00033058965,0.00006297224,0.00017282001,0.00021764834,0.45400485,0.060462456,0.074668184,0.0032209298,0.40584263],"study_design_scores_gemma":[0.00005346327,0.00010758152,0.000086813394,0.000020445868,0.000018848878,0.00010623581,0.000030086028,0.9746183,0.012884551,0.009532268,0.0025221673,0.0000193047],"about_ca_topic_score_codex":0.00097550056,"about_ca_topic_score_gemma":0.0012569777,"teacher_disagreement_score":0.0024464312,"about_ca_system_score_codex":0.00047252147,"about_ca_system_score_gemma":0.0010241801,"threshold_uncertainty_score":0.008184195},"labels":[],"label_agreement":null},{"id":"W2797506564","doi":"10.1109/lsp.2018.2825951","title":"Performance Analysis of Distributed Wireless Sensor Networks for Gaussian Source Estimation in the Presence of Impulsive Noise","year":2018,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","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":"Hydro-Québec; École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Minimum mean square error; Estimator; Gaussian noise; Fading; Computer science; Rayleigh fading; Mean squared error; Algorithm; Additive white Gaussian noise; Fusion center; Gaussian; Wireless sensor network; Noise power; Noise (video); Channel (broadcasting); Wireless; Mathematics; Statistics; Telecommunications; Power (physics); Cognitive radio; Computer network; Artificial intelligence; Physics","score_opus":0.010275055437021027,"score_gpt":0.23939041556771382,"score_spread":0.22911536013069278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797506564","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":[0.13283773,0.0019594454,0.8598389,0.0006191521,0.000048084876,0.000045683595,0.00006344916,0.00018053553,0.004407037],"genre_scores_gemma":[0.98129797,0.00069780636,0.017084084,0.00005523955,0.000046741643,0.000042203843,0.000054271848,0.000022105061,0.0006995775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974679,0.0011805812,0.00009515343,0.00035612256,0.0006743809,0.00022598066],"domain_scores_gemma":[0.9883058,0.009023581,0.0007728203,0.00038075712,0.0013968659,0.00012023028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045723077,0.00091182673,0.0009885889,0.0007547573,0.00045485585,0.0011412759,0.0010071723,0.0010681811,0.0007973752],"category_scores_gemma":[0.024198735,0.00036165625,0.00034126625,0.0007283513,0.0013785082,0.0015885676,0.0014841676,0.0006566785,0.00015609394],"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":[0.00017218174,0.000025435398,0.0013678396,0.0000958887,0.00005114747,0.00009117369,0.00008594801,0.9700145,0.0024859165,0.012526459,0.00022986166,0.012853698],"study_design_scores_gemma":[0.000003246399,0.000043141823,0.00023698808,0.000006583718,0.000010511695,0.000027325053,0.00001787784,0.99717844,0.0005503788,0.0018580966,0.00006284052,0.000004637095],"about_ca_topic_score_codex":0.002785234,"about_ca_topic_score_gemma":0.001879139,"teacher_disagreement_score":0.0045723077,"about_ca_system_score_codex":0.0014978154,"about_ca_system_score_gemma":0.001052586,"threshold_uncertainty_score":0.024180949},"labels":[],"label_agreement":null},{"id":"W2801303905","doi":"10.1109/lsp.2018.2835763","title":"Distributed Nonlinear System Identification in <inline-formula> <tex-math notation=\"LaTeX\">$\\alpha$ </tex-math> </inline-formula>-Stable Noise","year":2018,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":26,"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":"Logarithm; Noise (video); Nonlinear system; Algorithm; Mathematics; Identification (biology); Alpha (finance); Computer science; Mathematical analysis; Statistics; Artificial intelligence","score_opus":0.013335097991907725,"score_gpt":0.24143877596425697,"score_spread":0.22810367797234926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801303905","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":[0.00575949,0.00012996049,0.9908585,0.00011029067,0.000058524678,0.000016876435,0.00003379402,0.0002558465,0.0027766859],"genre_scores_gemma":[0.63967335,0.0006624386,0.3311488,0.00023776446,0.00010521374,0.00016078653,0.0003519666,0.00022034667,0.0274394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982774,0.00004327109,0.000009770806,0.000045426317,0.000058771337,0.000015050678],"domain_scores_gemma":[0.9997719,0.00010609114,0.000024572484,0.000039296534,0.000052827956,0.0000053365184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029791245,0.0004672975,0.00042876377,0.00014860448,0.0002178697,0.0006957183,0.00045037313,0.00048360793,0.0065725013],"category_scores_gemma":[0.0009819985,0.0001637494,0.00029236486,0.0002706293,0.00037811897,0.0006214985,0.0004844776,0.000763707,0.0014443686],"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":[0.00020416554,0.000055066794,0.0011347616,0.00027705648,0.00007900722,0.00039069378,0.00020926227,0.5784087,0.04153391,0.0723661,0.005371021,0.29997027],"study_design_scores_gemma":[0.000007390035,0.000013798083,0.00013571909,0.0000084655285,0.0000054840825,0.000065179054,0.000010639823,0.9868824,0.0044806185,0.0044863992,0.0038992565,0.00000474778],"about_ca_topic_score_codex":0.0018355587,"about_ca_topic_score_gemma":0.002326019,"teacher_disagreement_score":0.0065725013,"about_ca_system_score_codex":0.00030459152,"about_ca_system_score_gemma":0.00053462916,"threshold_uncertainty_score":0.02198726},"labels":[],"label_agreement":null},{"id":"W2810926963","doi":"10.1109/lsp.2018.2849683","title":"Accurate Analytical BER Performance for ZF Receivers Under Imperfect Channel in Low-SNR Region for Large Receiving Antennas","year":2018,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"MIMO; Channel (broadcasting); Bit error rate; Computer science; Signal-to-noise ratio (imaging); Maximal-ratio combining; Algorithm; Imperfect; Electronic engineering; Mathematics; Telecommunications; Control theory (sociology); Fading; Engineering","score_opus":0.021452274387306586,"score_gpt":0.2578889145882516,"score_spread":0.236436640200945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810926963","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":[0.07105157,0.004025071,0.8976837,0.00068695936,0.00013317773,0.00005307725,0.00056476926,0.0012916648,0.024509974],"genre_scores_gemma":[0.9139269,0.0037734318,0.076185554,0.00022559753,0.00008641536,0.0000635763,0.0003511246,0.00019230889,0.005195121],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991371,0.00019056254,0.000039076567,0.000093665825,0.00041371072,0.00012594597],"domain_scores_gemma":[0.99850297,0.00076812215,0.00017070472,0.00020309673,0.00033427688,0.000020877644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001005975,0.0008966155,0.0010757037,0.00056570873,0.00056454446,0.0011934077,0.000745398,0.001224946,0.0022791482],"category_scores_gemma":[0.005501186,0.0003588813,0.0004476509,0.0009052654,0.0009558116,0.0015895859,0.0006212726,0.00089031196,0.0013729498],"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":[0.000101161146,0.00003430971,0.0007862589,0.00030570725,0.0000328978,0.00022069617,0.00019725316,0.9232437,0.015831769,0.03491853,0.0020680178,0.022259768],"study_design_scores_gemma":[0.000010607608,0.00003605381,0.0004765307,0.00006644123,0.000014536899,0.00019716096,0.00005590043,0.98305756,0.0070468523,0.0077570775,0.0012535887,0.000027694525],"about_ca_topic_score_codex":0.0026771636,"about_ca_topic_score_gemma":0.002973049,"teacher_disagreement_score":0.0026771636,"about_ca_system_score_codex":0.0012165166,"about_ca_system_score_gemma":0.00096486055,"threshold_uncertainty_score":0.008826494},"labels":[],"label_agreement":null},{"id":"W2888468122","doi":"10.1109/lsp.2018.2865829","title":"Wasserstein-Distance-Based Gaussian Mixture Reduction","year":2018,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaussian; Reduction (mathematics); Divergence (linguistics); Mixture model; Computer science; Algorithm; Kullback–Leibler divergence; Exponential function; Moment (physics); Matching (statistics); Artificial intelligence; Mathematics; Pattern recognition (psychology); Statistics; Physics; Geometry","score_opus":0.018433903355717676,"score_gpt":0.2669182976932998,"score_spread":0.2484843943375821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888468122","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":[0.0030802193,0.00020902965,0.99589574,0.000052527776,0.00002011744,0.000012328545,0.000020986847,0.00028704104,0.00042198802],"genre_scores_gemma":[0.21331054,0.0006273703,0.780116,0.00020375755,0.00008185717,0.000095156145,0.0005477806,0.0003469156,0.0046706335],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908304,0.00021860155,0.000047169575,0.00021309682,0.00037146016,0.000066635446],"domain_scores_gemma":[0.9993056,0.000314908,0.00006114471,0.00009193819,0.00019086608,0.00003553838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001323594,0.0011406613,0.0015104386,0.0010577162,0.0003821068,0.0008367961,0.0018010563,0.0009861074,0.0013840556],"category_scores_gemma":[0.0034747832,0.00047024424,0.0015560337,0.0011994587,0.0007274807,0.0016328387,0.0016856076,0.00158083,0.00076882506],"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":[0.0001556895,0.000076137,0.0006672634,0.00017116278,0.00014755547,0.000112011425,0.00016877305,0.58858097,0.012379045,0.037288316,0.0036812257,0.3565719],"study_design_scores_gemma":[0.000003509596,0.000013419634,0.00008521172,0.0000036134475,0.000008556069,0.000035705307,0.000005267427,0.9933175,0.0017312736,0.00404349,0.00074287236,0.000009591558],"about_ca_topic_score_codex":0.0050918357,"about_ca_topic_score_gemma":0.0053274375,"teacher_disagreement_score":0.0050918357,"about_ca_system_score_codex":0.0008083489,"about_ca_system_score_gemma":0.001047976,"threshold_uncertainty_score":0.010124445},"labels":[],"label_agreement":null},{"id":"W2896389278","doi":"10.1109/lsp.2018.2875348","title":"Image Reconstruction Combined With Interference Removal Using a Mixed-Domain Proximal Operator","year":2018,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interference (communication); Iterative reconstruction; Distortion (music); Computer science; Smoothness; Algorithm; Imaging phantom; Domain (mathematical analysis); Computer vision; Artificial intelligence; Channel (broadcasting); Mathematics; Optics; Physics; Telecommunications","score_opus":0.009598046691337611,"score_gpt":0.21124909750160098,"score_spread":0.20165105081026335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896389278","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":[0.0060520526,0.00003348804,0.9933581,0.000031676165,0.000009619065,0.00001629907,0.000009347833,0.00015858273,0.00033088474],"genre_scores_gemma":[0.06336554,0.0000837732,0.9350562,0.000034874407,0.000014683297,0.000046493114,0.000047471367,0.000066729655,0.0012842849],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947816,0.0001641599,0.000020711432,0.00007287942,0.00022766991,0.0000364242],"domain_scores_gemma":[0.9994436,0.00023992234,0.00006131013,0.000097068754,0.00012132622,0.00003677778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010629279,0.00073326146,0.00061659096,0.00047198037,0.00024407847,0.00065288594,0.0007206835,0.00078843755,0.0015253338],"category_scores_gemma":[0.0016167347,0.0003333026,0.00068437867,0.00070189545,0.0006130513,0.00084976293,0.001091451,0.00087115815,0.00047379083],"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":[0.0008922635,0.00019064211,0.0009872464,0.00048608312,0.00017102735,0.00073353085,0.0005502775,0.15825292,0.4833759,0.050874405,0.0018385716,0.30164707],"study_design_scores_gemma":[0.0000369822,0.00019857382,0.0003468934,0.00001154884,0.000024742894,0.00059363665,0.000023195878,0.87925225,0.10969141,0.005237342,0.00454199,0.000041387342],"about_ca_topic_score_codex":0.0006743235,"about_ca_topic_score_gemma":0.0008167089,"teacher_disagreement_score":0.0015253338,"about_ca_system_score_codex":0.00022483314,"about_ca_system_score_gemma":0.000838491,"threshold_uncertainty_score":0.0056213737},"labels":[],"label_agreement":null},{"id":"W2912581987","doi":"10.1109/lsp.2019.2895749","title":"Medical Image Fusion via Convolutional Sparsity Based Morphological Component Analysis","year":2019,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":305,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Component (thermodynamics); Sparse approximation; Image fusion; Convolutional neural network; Image (mathematics); Computer vision; Component analysis; Representation (politics); Pixel","score_opus":0.007747057653277735,"score_gpt":0.22586037780437043,"score_spread":0.2181133201510927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912581987","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":[0.008905387,0.00021989844,0.98977345,0.00016013857,0.000025069243,0.000021245049,0.00004388533,0.00025980937,0.000591093],"genre_scores_gemma":[0.3504122,0.00086604076,0.6454977,0.0002950807,0.000102287195,0.00009040373,0.0003679934,0.00013758052,0.0022307779],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997048,0.00006015353,0.000017799484,0.00005027631,0.00013878108,0.00002813956],"domain_scores_gemma":[0.9996617,0.00010426565,0.000057376117,0.00005583206,0.00010123648,0.000019602898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006467298,0.0005637194,0.00060240895,0.0009064236,0.00017985883,0.0006589467,0.0006825781,0.0006154751,0.0010050711],"category_scores_gemma":[0.0015606334,0.00028703024,0.0008817381,0.0008192226,0.00039790437,0.001190588,0.0010017735,0.00082106935,0.00035507252],"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":[0.00042566835,0.00010491112,0.0014653947,0.00032988572,0.0002593744,0.00036109023,0.00015787943,0.2844144,0.11967936,0.025456501,0.0040474352,0.563298],"study_design_scores_gemma":[0.000006504675,0.000046705783,0.00033232867,0.000008747982,0.000030886953,0.00017302985,0.000011310089,0.97916025,0.014342367,0.004364647,0.0015101181,0.000013124539],"about_ca_topic_score_codex":0.0011673594,"about_ca_topic_score_gemma":0.0014020002,"teacher_disagreement_score":0.0011673594,"about_ca_system_score_codex":0.00038513233,"about_ca_system_score_gemma":0.00057537126,"threshold_uncertainty_score":0.0034202337},"labels":[],"label_agreement":null},{"id":"W2922019075","doi":"10.1109/lsp.2019.2921446","title":"Convolutional Analysis Operator Learning: Dependence on Training Data","year":2019,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Institute of Biomedical Imaging and Bioengineering; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; W. M. Keck Foundation","keywords":"Operator (biology); Filter (signal processing); Training (meteorology); Training set; Pattern recognition (psychology); Convolutional neural network; Series (stratigraphy); Upper and lower bounds","score_opus":0.04774172891518412,"score_gpt":0.2748918543182899,"score_spread":0.22715012540310575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922019075","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":[0.11208011,0.00085845724,0.880941,0.001957245,0.00007577458,0.000082005965,0.00022451996,0.0006838796,0.0030970653],"genre_scores_gemma":[0.79328567,0.0009020168,0.20126234,0.00071075384,0.00011251937,0.00027104502,0.00067266834,0.0004082123,0.0023748009],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99635774,0.0015415067,0.00020344698,0.0005765392,0.0010618888,0.00025885433],"domain_scores_gemma":[0.9169861,0.06922019,0.0030603928,0.0074909055,0.0025673837,0.0006750702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008548804,0.0010046933,0.000925941,0.0004120388,0.00060283084,0.0012212377,0.0015265297,0.0016474231,0.00133223],"category_scores_gemma":[0.08505521,0.0009143005,0.0005921673,0.0005557993,0.0026347833,0.0039932528,0.0033859466,0.0034514107,0.0003209561],"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":[0.0006389929,0.00017297207,0.009260948,0.00054137426,0.00016192016,0.00040315784,0.0003497272,0.7641533,0.024876524,0.06880988,0.0026081798,0.12802307],"study_design_scores_gemma":[0.000013193532,0.00006554594,0.0007663107,0.000028111166,0.000011067429,0.000062447696,0.00001269778,0.9809396,0.0059189624,0.011746098,0.0004230713,0.00001282278],"about_ca_topic_score_codex":0.0039217803,"about_ca_topic_score_gemma":0.005316987,"teacher_disagreement_score":0.008548804,"about_ca_system_score_codex":0.0013365378,"about_ca_system_score_gemma":0.0013679243,"threshold_uncertainty_score":0.045210958},"labels":[],"label_agreement":null},{"id":"W2941547246","doi":"10.1109/lsp.2019.2910403","title":"Single Image Dehazing with a Generic Model-Agnostic Convolutional Neural Network","year":2019,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":131,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Convolutional neural network; Image (mathematics); Artificial intelligence; Atmosphere (unit); Simple (philosophy); Artificial neural network; Plug and play; Computer vision; Pattern recognition (psychology)","score_opus":0.014948161483332039,"score_gpt":0.2192740983239218,"score_spread":0.20432593684058978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2941547246","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":[0.07951582,0.0003791012,0.91458076,0.00018730982,0.000113368886,0.000055985405,0.00011340361,0.0018966731,0.0031575502],"genre_scores_gemma":[0.6236267,0.0004509087,0.36748788,0.0002041095,0.000041466174,0.00003920356,0.00031470903,0.00013650776,0.007698463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999853,0.000013769817,0.000005798606,0.000045246175,0.000057132846,0.000025030862],"domain_scores_gemma":[0.99972326,0.000039638107,0.00003497337,0.00012583673,0.000056105593,0.00002014392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031830408,0.0005836063,0.00039065228,0.00023726444,0.00016312682,0.00046140177,0.0009556572,0.00073593884,0.00095863745],"category_scores_gemma":[0.0006273178,0.0002467052,0.00047169137,0.00018368244,0.00036019404,0.00093683647,0.00076320104,0.0010235772,0.00037725075],"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":[0.0003916335,0.00020442205,0.0017221706,0.00023074921,0.0002057538,0.0002466946,0.000086075685,0.38159853,0.27382615,0.008747013,0.0043037743,0.3284369],"study_design_scores_gemma":[0.0000054208963,0.000082219354,0.0005064986,0.000007759641,0.000022297687,0.00013257281,0.000006470347,0.9485194,0.047756616,0.0010269135,0.0019203998,0.000013372865],"about_ca_topic_score_codex":0.00217074,"about_ca_topic_score_gemma":0.004783185,"teacher_disagreement_score":0.00217074,"about_ca_system_score_codex":0.00041245198,"about_ca_system_score_gemma":0.00043448864,"threshold_uncertainty_score":0.0043162107},"labels":[],"label_agreement":null},{"id":"W2965510792","doi":"10.1109/lsp.2019.2931150","title":"An Improved Stationarity Test Based on Surrogates","year":2019,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Blind Source Separation Techniques","field":"Computer Science","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":"Institut National de la Recherche Scientifique","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Test (biology); Algorithm; Machine learning; Data mining; Artificial intelligence","score_opus":0.01111671907144038,"score_gpt":0.2569364101170627,"score_spread":0.24581969104562232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965510792","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":[0.021045057,0.00019343969,0.9767959,0.00018113392,0.000096444855,0.000038416052,0.00010922944,0.00024433743,0.0012959973],"genre_scores_gemma":[0.5050896,0.00044251393,0.48935363,0.00038310254,0.00032809033,0.00024301054,0.001415657,0.00023019363,0.002514267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9928976,0.0036416468,0.00044844588,0.000800982,0.0018637141,0.00034752116],"domain_scores_gemma":[0.97974855,0.012366249,0.0015653229,0.0023423943,0.0034104425,0.00056709297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076883137,0.0008698384,0.0016192393,0.003016198,0.00063965423,0.0016881435,0.0016794445,0.0022430313,0.0029175638],"category_scores_gemma":[0.041891243,0.0004889528,0.0013518943,0.0023557295,0.0017090109,0.0033412424,0.002353017,0.0023788163,0.0012104765],"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":[0.0015080344,0.00030351154,0.010852284,0.0004244698,0.0004109287,0.00075220334,0.00026655212,0.4096088,0.05609639,0.1783009,0.005024948,0.33645102],"study_design_scores_gemma":[0.000052606476,0.00040712435,0.0018249815,0.000038849448,0.000032555447,0.0003684362,0.000028700286,0.95039177,0.010442639,0.033685897,0.0026455845,0.00008085507],"about_ca_topic_score_codex":0.00041540782,"about_ca_topic_score_gemma":0.0002807814,"teacher_disagreement_score":0.0076883137,"about_ca_system_score_codex":0.00048793427,"about_ca_system_score_gemma":0.0011178725,"threshold_uncertainty_score":0.040660203},"labels":[],"label_agreement":null},{"id":"W2980241036","doi":"10.1109/lsp.2019.2945683","title":"Joint Design of Measurement Matrix and Sparse Support Recovery Method via Deep Auto-Encoder","year":2019,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Exploit; Encoder; Thresholding; Joint (building); Artificial intelligence; Sparse matrix; Computational complexity theory; Matrix (chemical analysis); Compressed sensing; Algorithm; Computation; Signal processing; Pattern recognition (psychology); Computer engineering; Computer hardware; Digital signal processing; Image (mathematics)","score_opus":0.03331377140293759,"score_gpt":0.24344556806698547,"score_spread":0.21013179666404788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980241036","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":[0.004468416,0.000064226486,0.9946666,0.000079608755,0.000012812019,0.000017727136,0.000030303689,0.00021965678,0.0004406231],"genre_scores_gemma":[0.3720317,0.00022975123,0.6244438,0.00022971617,0.000050169052,0.0001681579,0.00030238126,0.0000823524,0.002461918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995159,0.00012118733,0.000027656728,0.00009508148,0.00019256637,0.00004762889],"domain_scores_gemma":[0.999236,0.00030594374,0.00007730529,0.000106397514,0.00022272089,0.0000516553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078031764,0.0007573485,0.00063441566,0.00034621268,0.00025710103,0.0005527402,0.0009417053,0.00077299366,0.0018548099],"category_scores_gemma":[0.0026096175,0.00043723875,0.0004221711,0.0004538188,0.0004959019,0.0011772144,0.0012379964,0.0014101451,0.00059466664],"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":[0.0003503671,0.00016632225,0.0017141731,0.00027675074,0.000096025666,0.00022551241,0.00018815316,0.45016336,0.05039511,0.041399613,0.0057453816,0.44927928],"study_design_scores_gemma":[0.00001008355,0.000028554654,0.000064764594,0.000004605864,0.0000053043163,0.00003718384,0.000005203463,0.993306,0.004040427,0.0019517427,0.00053915736,0.0000069774937],"about_ca_topic_score_codex":0.0020524128,"about_ca_topic_score_gemma":0.003144465,"teacher_disagreement_score":0.0020524128,"about_ca_system_score_codex":0.00032020363,"about_ca_system_score_gemma":0.0011485454,"threshold_uncertainty_score":0.0062049627},"labels":[],"label_agreement":null},{"id":"W3028989134","doi":"10.1109/lsp.2020.3043990","title":"Multi-Volumetric Refocusing of Light Fields","year":2020,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Victoria","funders":"","keywords":"Finite impulse response; Filter (signal processing); Computer science; Reduction (mathematics); Filter design; Algorithm; Planar; Light field; Computer vision; Artificial intelligence; Mathematics; Geometry","score_opus":0.03168523418857924,"score_gpt":0.27214027951357944,"score_spread":0.2404550453250002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3028989134","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":[0.18140914,0.00047892903,0.8131451,0.00020256217,0.00003724173,0.00005316673,0.00012187349,0.00080114044,0.0037508956],"genre_scores_gemma":[0.65246284,0.00049138925,0.3443714,0.00013879796,0.00003088433,0.000039812116,0.00023044474,0.000072146075,0.0021622397],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978095,0.000027705724,0.00000896163,0.000038581242,0.00010369163,0.000040151826],"domain_scores_gemma":[0.9995018,0.00015055956,0.00010439762,0.00009971785,0.000107340245,0.000036166904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002945825,0.0004928192,0.00033411608,0.00062616373,0.00021924003,0.00046510398,0.0004751588,0.00047329566,0.0012856513],"category_scores_gemma":[0.000829405,0.00024755436,0.0003731405,0.0003764399,0.00037752851,0.00071685476,0.0009301543,0.00056909316,0.00035756276],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026448167,0.00006157986,0.0010905127,0.00017826885,0.00002920741,0.00022074922,0.0002169607,0.03906226,0.7616671,0.005743689,0.0009362782,0.19052888],"study_design_scores_gemma":[0.00002799676,0.00032433262,0.00277762,0.000046202455,0.000021006554,0.0009414048,0.0001391186,0.29890043,0.6855268,0.0035026253,0.0077254884,0.000066959794],"about_ca_topic_score_codex":0.0010793682,"about_ca_topic_score_gemma":0.0017279973,"teacher_disagreement_score":0.0012856513,"about_ca_system_score_codex":0.00038987157,"about_ca_system_score_gemma":0.00044241623,"threshold_uncertainty_score":0.0043008924},"labels":[],"label_agreement":null},{"id":"W3091528227","doi":"10.1109/lsp.2020.3028006","title":"A Complete Discriminative Tensor Representation Learning for Two-Dimensional Correlation Analysis","year":2020,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Tensor decomposition and applications","field":"Mathematics","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":"Toronto Metropolitan University","funders":"","keywords":"Discriminative model; Representation (politics); Tensor (intrinsic definition); Pattern recognition (psychology); Canonical correlation; Correlation; Linear discriminant analysis","score_opus":0.0880582341947177,"score_gpt":0.34963411800444544,"score_spread":0.26157588380972774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091528227","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":[0.0038500722,0.0002901129,0.99491984,0.00015123792,0.000037781836,0.000021577102,0.00006582266,0.00031441762,0.00034913758],"genre_scores_gemma":[0.1940432,0.0010984807,0.79930776,0.0004009701,0.00025437237,0.0002387694,0.0012067327,0.00024970755,0.0032000705],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987783,0.00045285883,0.000055763045,0.000311498,0.00030442845,0.00009716089],"domain_scores_gemma":[0.9985587,0.00032019275,0.00017316712,0.0003847149,0.00041628437,0.00014692606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015099965,0.0012118829,0.0011205235,0.0012709257,0.00069621444,0.0010399296,0.0013377786,0.0009466832,0.0019809166],"category_scores_gemma":[0.0043044738,0.000464456,0.0011476125,0.0020461134,0.0011818492,0.0020082453,0.0019980825,0.0020443655,0.0013264973],"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":[0.00021861042,0.00024366903,0.0018252204,0.00038128736,0.00018536355,0.0002526063,0.00024364301,0.14024375,0.052777044,0.058974702,0.020172035,0.724482],"study_design_scores_gemma":[0.000009292312,0.00008211694,0.00025524898,0.000013019926,0.000019250465,0.00012176138,0.000017383103,0.9822566,0.0038228992,0.010604495,0.0027651375,0.00003280622],"about_ca_topic_score_codex":0.0028984712,"about_ca_topic_score_gemma":0.0026450786,"teacher_disagreement_score":0.0028984712,"about_ca_system_score_codex":0.0006400155,"about_ca_system_score_gemma":0.0016088822,"threshold_uncertainty_score":0.007985711},"labels":[],"label_agreement":null},{"id":"W3114119488","doi":"10.1109/lsp.2020.3044775","title":"Time Difference of Arrival Estimation Based on a Kronecker Product Decomposition","year":2020,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"National Key Research and Development Program of China Stem Cell and Translational Research","keywords":"Multilateration; Impulse response; Reverberation; Kronecker delta; Computer science; Impulse (physics); Kronecker product; Algorithm; Beamforming; Finite impulse response; Mathematics; Acoustics; Telecommunications","score_opus":0.015761752964955664,"score_gpt":0.24258083818544635,"score_spread":0.22681908522049068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114119488","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":[0.0038540233,0.00008365984,0.99553776,0.000019787596,0.00002817302,0.000009052271,0.00002132566,0.000116384246,0.00032979986],"genre_scores_gemma":[0.11575621,0.0005175603,0.88072526,0.00004998103,0.000057565467,0.00007212179,0.0002576291,0.00007067962,0.0024928937],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99954826,0.00009578527,0.000038345486,0.00010109351,0.00018085673,0.00003562479],"domain_scores_gemma":[0.99931264,0.00023564462,0.000055845736,0.00010438443,0.000259353,0.000032070882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005662488,0.0006275307,0.0005376457,0.00063176436,0.00023166853,0.00077448087,0.00046679957,0.00047105984,0.0017667557],"category_scores_gemma":[0.0017899248,0.00034958287,0.00059005374,0.00078733877,0.00041435682,0.0010599671,0.0005995081,0.00093149126,0.0010590829],"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":[0.0002968457,0.00012268567,0.0018139698,0.00023584816,0.00011537393,0.00026457352,0.00020201538,0.26069087,0.12160223,0.03590565,0.0030372713,0.5757126],"study_design_scores_gemma":[0.0000119260885,0.00007995737,0.0006000293,0.000016125736,0.000018014693,0.00023299133,0.000023786699,0.97791004,0.013945705,0.0039587733,0.0031720486,0.000030641513],"about_ca_topic_score_codex":0.0014304182,"about_ca_topic_score_gemma":0.0012863385,"teacher_disagreement_score":0.0017667557,"about_ca_system_score_codex":0.00019325278,"about_ca_system_score_gemma":0.000988594,"threshold_uncertainty_score":0.005910337},"labels":[],"label_agreement":null},{"id":"W3118245404","doi":"10.1109/lsp.2021.3050052","title":"The Property of Frequency Shift in 2D-FRFT Domain With Application to Image Encryption","year":2021,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Chaos-based Image/Signal Encryption","field":"Computer Science","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":"Toronto Metropolitan University","funders":"","keywords":"Robustness (evolution); Frequency domain; Property (philosophy); Encryption; Image (mathematics); Time–frequency analysis; Fourier transform; Fractional Fourier transform","score_opus":0.007729995923466527,"score_gpt":0.22571469002082523,"score_spread":0.2179846940973587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118245404","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":[0.081411555,0.0061163395,0.8970872,0.00079169276,0.00028005766,0.000044442964,0.00005835965,0.00019036666,0.014019935],"genre_scores_gemma":[0.7789446,0.006570687,0.20871417,0.00022301376,0.00039806907,0.000043472864,0.00008491403,0.0000525783,0.004968552],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984384,0.000028231485,0.000008360218,0.000030323396,0.000075583506,0.000013631481],"domain_scores_gemma":[0.9997143,0.00013335198,0.00004764675,0.000048394402,0.00004797614,0.000008177358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003221658,0.00033368252,0.00025366448,0.00033110782,0.00017178468,0.00031986373,0.00018928773,0.0006620132,0.00081433635],"category_scores_gemma":[0.0008372854,0.00010378868,0.00028420103,0.00045019216,0.0007115802,0.0007288883,0.0002977256,0.00055734965,0.00028026817],"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":[0.00023730192,0.0000815713,0.0022215678,0.00042095696,0.00004622834,0.0018178362,0.00040098565,0.038795378,0.49870202,0.18607959,0.0020761192,0.2691204],"study_design_scores_gemma":[0.000036981826,0.0009509601,0.0050421683,0.00006821012,0.000052165364,0.010565901,0.00020660668,0.5845539,0.28978,0.06555722,0.043058503,0.00012748445],"about_ca_topic_score_codex":0.0001907302,"about_ca_topic_score_gemma":0.00013135969,"teacher_disagreement_score":0.00081433635,"about_ca_system_score_codex":0.0001380437,"about_ca_system_score_gemma":0.000114969436,"threshold_uncertainty_score":0.00272429},"labels":[],"label_agreement":null},{"id":"W3127483027","doi":"10.1109/lsp.2021.3055748","title":"Robust Minimum Error Entropy Based Cubature Information Filter With Non-Gaussian Measurement Noise","year":2021,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"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":"Outlier; Entropy (arrow of time); Gaussian; Linearization; Entropy estimation; Algorithm; Mathematics; Gaussian noise; Computer science; Filter (signal processing); Noise measurement; Mathematical optimization; Statistics; Artificial intelligence; Noise reduction; Nonlinear system; Estimator","score_opus":0.02402433614496674,"score_gpt":0.2108463214194965,"score_spread":0.18682198527452976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127483027","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":[0.0027249039,0.00015473994,0.99647105,0.00006282578,0.000025329015,0.000009799536,0.000017575878,0.00011590514,0.00041791343],"genre_scores_gemma":[0.4719214,0.0007363655,0.5191765,0.00031593826,0.00016093222,0.00028720452,0.00043311328,0.00016888263,0.006799531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986505,0.00031504058,0.00008586511,0.00035326724,0.00049315736,0.000102274505],"domain_scores_gemma":[0.99876404,0.0005989269,0.00015332505,0.00011536446,0.00033413633,0.000034295368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00171163,0.0009689692,0.0018711427,0.00068078656,0.0006067817,0.001521886,0.0012514384,0.0015125703,0.0015383448],"category_scores_gemma":[0.0036885398,0.000535574,0.0009097045,0.0008794265,0.00080131396,0.0024186093,0.0010265944,0.0012461388,0.00053480576],"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":[0.00047674903,0.00009477694,0.0011306528,0.0003372002,0.00018795689,0.00013342321,0.00023870596,0.6631115,0.022565007,0.07389557,0.0030631814,0.23476523],"study_design_scores_gemma":[0.000008392846,0.000041078776,0.00010657828,0.000007809538,0.000010080929,0.000031670133,0.0000048592556,0.9930184,0.0031030045,0.002771969,0.00088099245,0.00001514941],"about_ca_topic_score_codex":0.0033030752,"about_ca_topic_score_gemma":0.0019390566,"teacher_disagreement_score":0.0033030752,"about_ca_system_score_codex":0.001157079,"about_ca_system_score_gemma":0.0013588009,"threshold_uncertainty_score":0.009052098},"labels":[],"label_agreement":null},{"id":"W3150116240","doi":"10.1109/lsp.2021.3106239","title":"Cyclic Defense GAN Against Speech Adversarial Attacks","year":2021,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Spectrogram; Generative grammar; SIGNAL (programming language); Key (lock); Signal processing","score_opus":0.015164131035845817,"score_gpt":0.25572130532070914,"score_spread":0.2405571742848633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3150116240","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":[0.01540875,0.00021946599,0.978678,0.00024292323,0.00008056528,0.00003840761,0.000048476715,0.00085710076,0.0044262484],"genre_scores_gemma":[0.871858,0.00024298967,0.12070958,0.0004959788,0.00009902176,0.000105938605,0.0001999419,0.00017612349,0.006112382],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992181,0.00025253213,0.000023455972,0.00015264556,0.00026314714,0.00009014234],"domain_scores_gemma":[0.99877614,0.0006332458,0.00010321742,0.00029261602,0.00014094243,0.000053786684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012190458,0.0009773872,0.0005582032,0.00039453915,0.00029075623,0.00052267674,0.001128639,0.0010295425,0.0020288553],"category_scores_gemma":[0.0030546219,0.00024342495,0.000469839,0.00020457576,0.0009845922,0.00092844985,0.0017364176,0.0019127249,0.0006715114],"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":[0.00020928559,0.000067769644,0.00089679594,0.00008306344,0.00009167151,0.00022626773,0.00011971451,0.7743879,0.04502685,0.06682069,0.0044967546,0.107573256],"study_design_scores_gemma":[0.0000049143578,0.000044438297,0.00009230204,0.000005552004,0.0000059444797,0.00007789887,0.0000065635686,0.9863809,0.0046104724,0.0073035597,0.0014594614,0.000008079004],"about_ca_topic_score_codex":0.0005220857,"about_ca_topic_score_gemma":0.00064257556,"teacher_disagreement_score":0.0020288553,"about_ca_system_score_codex":0.0004594099,"about_ca_system_score_gemma":0.00047495164,"threshold_uncertainty_score":0.006787181},"labels":[],"label_agreement":null},{"id":"W3162075735","doi":"10.1109/lsp.2021.3078696","title":"Elimination of Undetectable Attacks on Natural Gas Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":6,"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":"Nova Scotia Department of Energy","keywords":"SCADA; Countermeasure; Computer science; Computer security; Natural gas; Operator (biology); Network topology; Electric power system; Cyber-attack; Pipeline (software); Smart grid; Vulnerability (computing); Power (physics); Computer network; Engineering; Electrical engineering","score_opus":0.007100903156426877,"score_gpt":0.20588332959926917,"score_spread":0.19878242644284227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162075735","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":[0.1578929,0.0002059339,0.83366704,0.00049541803,0.000060489852,0.00004324619,0.00005190958,0.00069704483,0.0068860776],"genre_scores_gemma":[0.9744143,0.0001821138,0.023700567,0.00006854637,0.00002148027,0.000029539455,0.000036332523,0.000031796106,0.0015153055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999406,0.00012689193,0.000021419692,0.00010786662,0.00023580264,0.000101986305],"domain_scores_gemma":[0.99846953,0.0007038931,0.00028640407,0.0003043233,0.00018200731,0.00005386872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043799027,0.00058950006,0.00055373897,0.00038925195,0.00026135746,0.0005902047,0.0006596143,0.0006838288,0.0008365693],"category_scores_gemma":[0.0024739336,0.0002220682,0.00046998434,0.00021338361,0.0008180486,0.0013684058,0.0011166496,0.000877574,0.00017822253],"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":[0.00027128647,0.00013805748,0.0018525387,0.00026529445,0.00007292519,0.0009419809,0.00020625291,0.62378377,0.14465961,0.15233141,0.0020249833,0.073451966],"study_design_scores_gemma":[0.000012013371,0.00013613913,0.00045950423,0.000014911779,0.000012920069,0.00015443233,0.000041036994,0.9623186,0.012956933,0.022678267,0.0012046908,0.000010559203],"about_ca_topic_score_codex":0.00074959215,"about_ca_topic_score_gemma":0.00055852707,"teacher_disagreement_score":0.0008365693,"about_ca_system_score_codex":0.0004120405,"about_ca_system_score_gemma":0.00060344156,"threshold_uncertainty_score":0.0029896498},"labels":[],"label_agreement":null},{"id":"W3193636205","doi":"10.1109/lsp.2021.3104503","title":"Sparse Bayesian Learning Using Generalized Double Pareto Prior for DOA Estimation","year":2021,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Guangzhou Municipal Science and Technology Bureau; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Hyperparameter; Prior probability; Convergence (economics); Computer science; Bayesian probability; Algorithm; Pareto principle; Bayesian inference; Direction of arrival; Hyperparameter optimization; Mathematical optimization; Artificial intelligence; Pattern recognition (psychology); Mathematics; Support vector machine","score_opus":0.037807666731298525,"score_gpt":0.30254015479877117,"score_spread":0.2647324880674726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193636205","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":[0.0014722105,0.00006890524,0.997919,0.00005868549,0.000009647918,0.000010546734,0.000016372125,0.00007376727,0.0003708102],"genre_scores_gemma":[0.21689765,0.00080902403,0.7771281,0.00042271885,0.00015102404,0.00023432857,0.00051855115,0.0001463644,0.0036923205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909353,0.00027828498,0.000044703058,0.00015800605,0.0003412254,0.000084293686],"domain_scores_gemma":[0.9982072,0.0009971609,0.00013656134,0.00018811258,0.00038813916,0.00008270882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016883735,0.0009947622,0.0011334972,0.0010532953,0.0005334106,0.0011521522,0.001662552,0.0011421911,0.0020609687],"category_scores_gemma":[0.0055611474,0.0007310361,0.00082555995,0.0013689923,0.0010363216,0.002085102,0.0019104542,0.0019930634,0.0010886452],"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":[0.00013611525,0.00008879897,0.001131726,0.00017194671,0.000080974925,0.00008844679,0.00014086929,0.6356748,0.0071965875,0.05227187,0.0030840186,0.29993382],"study_design_scores_gemma":[0.000012364307,0.000016189164,0.00008743387,0.000007449576,0.0000057563066,0.000026364612,0.0000072241305,0.99134177,0.0007402682,0.0069238455,0.00082112325,0.000010176538],"about_ca_topic_score_codex":0.0049100653,"about_ca_topic_score_gemma":0.0050152764,"teacher_disagreement_score":0.0049100653,"about_ca_system_score_codex":0.0008072032,"about_ca_system_score_gemma":0.0018761167,"threshold_uncertainty_score":0.009762943},"labels":[],"label_agreement":null},{"id":"W3217472497","doi":"10.1109/lsp.2021.3130504","title":"Uncertainty Estimation via Monte Carlo Dropout in CNN-Based mmWave MIMO Localization","year":2021,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Monte Carlo method; MIMO; Convolutional neural network; Expectation propagation; Algorithm; Bayesian inference; Inference; Bayesian probability; Artificial intelligence; Beamforming; Mathematics; Telecommunications; Statistics","score_opus":0.00826032944837046,"score_gpt":0.21246265854265614,"score_spread":0.20420232909428568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217472497","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":[0.040927283,0.00026549268,0.95692486,0.00020207463,0.000025202218,0.000021836842,0.00005423496,0.0003792182,0.001199769],"genre_scores_gemma":[0.945173,0.00019470167,0.052867897,0.00014349323,0.00002777359,0.000060280112,0.00012465031,0.00005463614,0.0013536103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962294,0.000112661786,0.000021721233,0.000072108836,0.00011224846,0.000058377304],"domain_scores_gemma":[0.9982457,0.0011369524,0.00022961036,0.000099292665,0.00022582865,0.000062655774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014200514,0.0005913219,0.0007141566,0.00043515782,0.00038472863,0.0006092652,0.0012278132,0.00081246876,0.00092212483],"category_scores_gemma":[0.004493424,0.00052262895,0.00040190303,0.00041629738,0.0009238216,0.0011483314,0.0010965122,0.00095576106,0.00013648863],"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":[0.00007851029,0.000010902907,0.0009279627,0.000025789524,0.0000146488155,0.000048075424,0.000023877948,0.9829496,0.0008336732,0.003250746,0.00016467429,0.0116715],"study_design_scores_gemma":[0.0000010858379,0.000003996371,0.00006873505,0.000001761689,0.0000015070882,0.0000050885183,0.0000012557582,0.9990426,0.0003307968,0.00050489727,0.000036317364,0.0000019027501],"about_ca_topic_score_codex":0.012912363,"about_ca_topic_score_gemma":0.011737866,"teacher_disagreement_score":0.012912363,"about_ca_system_score_codex":0.0016561986,"about_ca_system_score_gemma":0.0009763095,"threshold_uncertainty_score":0.025674403},"labels":[],"label_agreement":null},{"id":"W4213363428","doi":"10.1109/lsp.2022.3152108","title":"Joint Parameter and Time-Delay Estimation for a Class of Nonlinear Time-Series Models","year":2022,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":142,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Nonlinear system; Autoregressive model; Computer science; Series (stratigraphy); Estimation theory; Time series; Nonlinear autoregressive exogenous model; Algorithm; Identification (biology); Mathematical optimization; Control theory (sociology); Mathematics; Artificial intelligence; Machine learning; Statistics; Control (management)","score_opus":0.012151931040424058,"score_gpt":0.20838485608667626,"score_spread":0.1962329250462522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213363428","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":[0.01598782,0.00013877092,0.983478,0.000043312597,0.000016645707,0.000006196978,0.000015433203,0.0000639996,0.00024971334],"genre_scores_gemma":[0.77183867,0.0007407548,0.22479506,0.00004493875,0.00008759907,0.000059057467,0.000192982,0.00005345247,0.002187538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996517,0.0000767356,0.00002660877,0.00010910864,0.00010547393,0.000030467245],"domain_scores_gemma":[0.999038,0.0005592016,0.00015404889,0.00011493986,0.000110779736,0.000023092342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010297917,0.00070595404,0.0006527263,0.00045435445,0.00022838841,0.00055667514,0.00054932025,0.00059848995,0.00046662227],"category_scores_gemma":[0.0035716593,0.00026768947,0.0006271564,0.0005254213,0.0004468068,0.0012799638,0.00068455684,0.0012537725,0.00015382838],"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":[0.0001088129,0.000059737595,0.002140265,0.00012463129,0.00010017615,0.000111542045,0.00008828349,0.8179752,0.011641791,0.026604857,0.0006204918,0.14042424],"study_design_scores_gemma":[0.000001584236,0.000009480013,0.00015016262,0.0000016388236,0.00000394115,0.000015906602,0.0000026253767,0.9972063,0.0007493378,0.0016563802,0.00019921319,0.0000034999998],"about_ca_topic_score_codex":0.0021057243,"about_ca_topic_score_gemma":0.0017749657,"teacher_disagreement_score":0.0021057243,"about_ca_system_score_codex":0.0002949048,"about_ca_system_score_gemma":0.0005957019,"threshold_uncertainty_score":0.005446136},"labels":[],"label_agreement":null},{"id":"W4213366994","doi":"10.1109/lsp.2022.3150258","title":"CNN-RNN and Data Augmentation Using Deep Convolutional Generative Adversarial Network for Environmental Sound Classification","year":2022,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Music and Audio Processing","field":"Computer Science","cited_by":118,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Kids Brain Health Network","keywords":"Computer science; Artificial intelligence; Deep learning; Convolutional neural network; Recurrent neural network; Normalization (sociology); Transfer of learning; Feature extraction; Pattern recognition (psychology); Generative adversarial network; Feature (linguistics); Feature learning; Machine learning; Spectrogram; Generative grammar; Artificial neural network","score_opus":0.07475645323796545,"score_gpt":0.28630029939790425,"score_spread":0.2115438461599388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213366994","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":[0.07777866,0.00097564195,0.9110161,0.00038561877,0.00023118676,0.00009807911,0.000502526,0.004516687,0.0044954945],"genre_scores_gemma":[0.8033028,0.00043076806,0.18652752,0.00029486977,0.000072504416,0.00017219575,0.0022572805,0.00016383076,0.006778199],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960583,0.000074186115,0.000021429962,0.00012921248,0.0001094203,0.00005995126],"domain_scores_gemma":[0.9996866,0.000109776476,0.000031264342,0.000078996825,0.000078167905,0.000015265472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064609584,0.0009808864,0.00060588017,0.00053467246,0.00021453235,0.00038771244,0.0009269548,0.00063474855,0.001618777],"category_scores_gemma":[0.0011952104,0.00029020512,0.00080996694,0.0006656364,0.00045649536,0.0007578639,0.00091937935,0.001190559,0.0005814052],"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":[0.00025464554,0.00016751714,0.0024394668,0.000098117314,0.00010737836,0.00022880986,0.000053486467,0.6437798,0.021753712,0.004168205,0.005421068,0.32152778],"study_design_scores_gemma":[0.0000025721326,0.000019349476,0.00023231788,0.000003207996,0.000006565653,0.000016919457,0.0000039080624,0.99504095,0.0035338658,0.00063438836,0.00050059013,0.0000053808735],"about_ca_topic_score_codex":0.006514094,"about_ca_topic_score_gemma":0.007120866,"teacher_disagreement_score":0.006514094,"about_ca_system_score_codex":0.0005239834,"about_ca_system_score_gemma":0.00055332447,"threshold_uncertainty_score":0.012952387},"labels":[],"label_agreement":null},{"id":"W4226453938","doi":"10.1109/lsp.2022.3164328","title":"A Low-Complexity Modified ThiNet Algorithm for Pruning Convolutional Neural Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Neural Network Applications","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":"Concordia University","funders":"","keywords":"Pruning; Convolutional neural network; Computer science; Algorithm; Reduction (mathematics); Layer (electronics); Computational complexity theory; Norm (philosophy); Time complexity; Artificial intelligence; Pattern recognition (psychology); Mathematics","score_opus":0.036402694225251206,"score_gpt":0.26769570597848535,"score_spread":0.23129301175323413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226453938","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":[0.0060347836,0.0002532352,0.99167776,0.00005881,0.000065796085,0.000057146426,0.00006752858,0.0008433682,0.0009416019],"genre_scores_gemma":[0.054192413,0.00026034095,0.9405002,0.00012756823,0.000049006732,0.00015278625,0.00047900106,0.00033699555,0.0039017904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993193,0.00009863306,0.00006726312,0.00013262458,0.00030378893,0.000078325924],"domain_scores_gemma":[0.99857724,0.0006104802,0.0001253439,0.00021275342,0.00042022302,0.00005399891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001003425,0.0010515477,0.0013351917,0.0015680328,0.00070432946,0.0013297372,0.0022937593,0.001537505,0.004714324],"category_scores_gemma":[0.0037672028,0.0007251826,0.0010462018,0.0012381391,0.00072220125,0.0018472212,0.001210559,0.0017616517,0.0012636195],"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":[0.0004422422,0.000099122,0.0006899531,0.0002626999,0.00017980557,0.00035442156,0.00011156826,0.3020158,0.029630857,0.019794118,0.008884159,0.6375353],"study_design_scores_gemma":[0.000030138028,0.000056659843,0.00014235823,0.000024190247,0.00002487636,0.00016414875,0.000014550799,0.97882366,0.010983923,0.005928915,0.0037899017,0.000016697344],"about_ca_topic_score_codex":0.0032728221,"about_ca_topic_score_gemma":0.006232567,"teacher_disagreement_score":0.004714324,"about_ca_system_score_codex":0.0007068228,"about_ca_system_score_gemma":0.0013634877,"threshold_uncertainty_score":0.015770912},"labels":[],"label_agreement":null},{"id":"W4241047385","doi":"10.1109/lsp.2017.2755418","title":"IEEE Signal Processing Society Editorial Board","year":2017,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Sensor Technology and Measurement Systems","field":"Computer Science","cited_by":0,"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":"Universidad Rey Juan Carlos; Universidade Estadual de Campinas; Yonsei University; Institut national de recherche en informatique et en automatique (INRIA); McGill University; RWTH Aachen University; Johns Hopkins University; École Polytechnique Fédérale de Lausanne; University of Maryland, Baltimore County","keywords":"Signal processing; Computer science; Multidimensional signal processing; Telecommunications; Digital signal processing; Computer hardware","score_opus":0.031050193691480252,"score_gpt":0.26163352691553576,"score_spread":0.2305833332240555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241047385","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004203249,0.005086402,0.0035037417,0.05316104,0.88435316,0.00012008024,0.00035882913,0.0007258547,0.052270517],"genre_scores_gemma":[0.0047012186,0.0094768265,0.0022872898,0.015723461,0.47866264,0.00014685153,0.00074792816,0.00067351037,0.48758027],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99777895,0.00024266716,0.00020209384,0.0002856407,0.0012995518,0.00019108276],"domain_scores_gemma":[0.9836473,0.0012698657,0.0007148341,0.00072974013,0.011750578,0.0018876354],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0037984375,0.0024378677,0.0014422532,0.003933671,0.0015850608,0.0056861043,0.0013848059,0.0053541264,0.12319335],"category_scores_gemma":[0.009697855,0.0007265948,0.0008639247,0.0012368896,0.0009502636,0.0026235587,0.0016466242,0.0056240084,0.116929665],"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":[0.000039380113,0.00001950969,0.000040534618,0.00009632228,0.0000063409707,0.000050673298,0.000004916558,0.000034888115,0.000306782,0.00038090447,0.9572131,0.04180663],"study_design_scores_gemma":[0.000018941764,0.00003098929,0.00020682346,0.0000783921,0.000012513417,0.00008249236,0.000015308513,0.0002770712,0.00036795432,0.0007145185,0.9981845,0.000010380578],"about_ca_topic_score_codex":0.00077951874,"about_ca_topic_score_gemma":0.0017696543,"teacher_disagreement_score":0.8768066,"about_ca_system_score_codex":0.0012155343,"about_ca_system_score_gemma":0.0026203403,"threshold_uncertainty_score":0.41212285},"labels":[],"label_agreement":null},{"id":"W4285304111","doi":"10.1109/lsp.2022.3179168","title":"Nonlinear Orthogonal NMF on the Stiefel Manifold With Graph-Based Total Variation Regularization","year":2022,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Regularization (linguistics); Stiefel manifold; Mathematics; Algorithm; Nonlinear system; Orthogonality; Uniqueness; Computer science; Mathematical optimization; Artificial intelligence; Applied mathematics; Pattern recognition (psychology)","score_opus":0.011461955004172316,"score_gpt":0.1860767770625264,"score_spread":0.17461482205835407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285304111","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":[0.0043646363,0.00016145819,0.99462897,0.00010945926,0.000024524352,0.000014424013,0.000027872862,0.00009003881,0.00057859544],"genre_scores_gemma":[0.2806868,0.0007641978,0.7112075,0.0003297889,0.0001978757,0.00027944773,0.0005628668,0.00022141548,0.005750066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993249,0.00025961825,0.00002586864,0.0001476271,0.00019477693,0.00004725151],"domain_scores_gemma":[0.99943775,0.00025825202,0.0000731638,0.0000689728,0.00013417772,0.00002768486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011571044,0.0012213099,0.001094996,0.0007615707,0.00043934427,0.0007148887,0.0014387257,0.0015242485,0.000913203],"category_scores_gemma":[0.0022540833,0.00044451974,0.0011397054,0.00095158495,0.001025568,0.0014956426,0.0009363269,0.0015968987,0.00042941983],"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":[0.000064090425,0.00007179025,0.00046622523,0.00015017124,0.00009507344,0.00009456543,0.00009521182,0.81725705,0.008148393,0.038387388,0.003674785,0.13149527],"study_design_scores_gemma":[0.0000019385177,0.000007916536,0.000037367674,0.0000023560224,0.0000020836208,0.000009857345,0.0000022661645,0.9966017,0.00028395434,0.0026762877,0.00037064502,0.0000036837823],"about_ca_topic_score_codex":0.004218201,"about_ca_topic_score_gemma":0.004040063,"teacher_disagreement_score":0.004218201,"about_ca_system_score_codex":0.0006618718,"about_ca_system_score_gemma":0.00094548165,"threshold_uncertainty_score":0.008387268},"labels":[],"label_agreement":null},{"id":"W4320005477","doi":"10.1109/lsp.2023.3242808","title":"Off-Grid DOA Estimation for Noncircular Signals via Block Sparse Representation Using Extended Transformed Nested Array","year":2023,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Direction-of-Arrival Estimation Techniques","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":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Block (permutation group theory); Computer science; Grid; Representation (politics); Algorithm; Interpolation (computer graphics); Direction of arrival; SIGNAL (programming language); Displacement (psychology); Computer vision; Mathematics; Antenna (radio); Telecommunications; Motion (physics)","score_opus":0.04545425219593095,"score_gpt":0.318474727682301,"score_spread":0.27302047548637004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320005477","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":[0.00622763,0.000032598073,0.9931518,0.000025233669,0.000011861912,0.000008951706,0.000018878643,0.000115949624,0.00040721873],"genre_scores_gemma":[0.2476726,0.00020821983,0.74938786,0.00006517254,0.00003141156,0.00008838463,0.00023184759,0.00006606478,0.0022485096],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971026,0.000057576897,0.000015206462,0.00006319462,0.00013018797,0.000023520784],"domain_scores_gemma":[0.99955696,0.00016096336,0.00006867998,0.00007135993,0.00011926754,0.000022770686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031536986,0.0004939328,0.00043439295,0.00037383678,0.00020067459,0.00041597866,0.00064469094,0.00032346146,0.0011718576],"category_scores_gemma":[0.0015496394,0.0002291066,0.0004333042,0.00059353194,0.00030664628,0.0009352432,0.00074782595,0.0005800679,0.00048385505],"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":[0.0003195835,0.00010364834,0.0024526778,0.00017743498,0.00006896235,0.00019649818,0.00032990333,0.3477562,0.1421809,0.025373667,0.0027440565,0.47829646],"study_design_scores_gemma":[0.000011482788,0.000048872993,0.0002463969,0.0000050865424,0.000008784735,0.00009009692,0.000018784303,0.98863363,0.007339155,0.0020597868,0.0015268791,0.000010986502],"about_ca_topic_score_codex":0.0017888611,"about_ca_topic_score_gemma":0.002787175,"teacher_disagreement_score":0.0017888611,"about_ca_system_score_codex":0.0001830874,"about_ca_system_score_gemma":0.00063393847,"threshold_uncertainty_score":0.003920257},"labels":[],"label_agreement":null},{"id":"W4382119870","doi":"10.1109/lsp.2023.3289438","title":"Global Optimization of Long-Term Average Proportional Fair Throughput via Convex Reformulation","year":2023,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Wireless Network 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":"Ericsson (Canada); University of Toronto","funders":"","keywords":"Maximization; Term (time); Mathematics; Regular polygon; Mathematical optimization; Convex function; Convex optimization; Proportionally fair; Combinatorics; Throughput; Optimization problem; Discrete mathematics; Algorithm; Applied mathematics; Computer science","score_opus":0.010719856417022302,"score_gpt":0.23783938757357084,"score_spread":0.22711953115654854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382119870","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":[0.008814912,0.0004861756,0.9804315,0.00052121637,0.00006872934,0.00005747937,0.00017860525,0.00027888952,0.009162562],"genre_scores_gemma":[0.7858256,0.0013805977,0.1961888,0.00045428242,0.00023242219,0.00051557936,0.00051033084,0.0005641994,0.014328221],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989065,0.00042310372,0.000031441756,0.00019468852,0.00024561622,0.00019862935],"domain_scores_gemma":[0.998109,0.0012065715,0.00012591746,0.00012112043,0.00036794457,0.000069521644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030637705,0.0018321195,0.0015625629,0.0005588796,0.0005895635,0.0022856514,0.0015307261,0.0011640382,0.0044951458],"category_scores_gemma":[0.005366133,0.0005567603,0.00084368075,0.0008870461,0.0014376868,0.002075734,0.0014970261,0.0022271972,0.00079639745],"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":[0.000039700917,0.000031371765,0.00014447342,0.0000844861,0.000021071026,0.00006259796,0.00004014626,0.94675475,0.00075516774,0.040489297,0.0027654653,0.008811554],"study_design_scores_gemma":[0.0000037952523,0.000007833146,0.000023379698,0.000004987842,0.0000034906534,0.0000056176546,0.0000067375795,0.9900029,0.00017911712,0.00943962,0.0003197237,0.0000028302672],"about_ca_topic_score_codex":0.0057806224,"about_ca_topic_score_gemma":0.004336148,"teacher_disagreement_score":0.0057806224,"about_ca_system_score_codex":0.0025838704,"about_ca_system_score_gemma":0.002417128,"threshold_uncertainty_score":0.018747449},"labels":[],"label_agreement":null},{"id":"W4384916830","doi":"10.1109/lsp.2023.3296913","title":"Effective Online Portfolio Selection for the Long-Short Market Using Mirror Gradient Descent","year":2023,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","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":"Toronto Metropolitan University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Regret; Portfolio; Mathematical optimization; Computer science; Stochastic gradient descent; Gradient descent; Online algorithm; Selection (genetic algorithm); Algorithm; Mathematics; Artificial intelligence; Machine learning","score_opus":0.14173142515177545,"score_gpt":0.43425186841988433,"score_spread":0.2925204432681089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384916830","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":[0.016721116,0.0003895241,0.9804677,0.00030156373,0.000036752244,0.00006403827,0.00003598802,0.00036343661,0.001619837],"genre_scores_gemma":[0.62068635,0.00064042036,0.36861908,0.00043133702,0.00019492808,0.00033669666,0.0003447821,0.00021584744,0.008530526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988325,0.00049290515,0.000054674994,0.0001902236,0.0002943839,0.00013538335],"domain_scores_gemma":[0.99781185,0.0014112564,0.00020373166,0.00017058935,0.00025098835,0.00015158992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029337911,0.0012180755,0.0022211096,0.00073272997,0.0006327861,0.0013682618,0.0015470402,0.0014944737,0.0034730316],"category_scores_gemma":[0.0054234182,0.0006057887,0.00063636433,0.0010961588,0.0009147575,0.0022593762,0.0013054475,0.001601315,0.00079748157],"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":[0.00034184862,0.0002693846,0.0013178791,0.00012789441,0.00011302412,0.00021326219,0.00006948806,0.73320013,0.0027331945,0.03881072,0.006276795,0.21652637],"study_design_scores_gemma":[0.000019048077,0.000036415917,0.00008475979,0.000003992397,0.0000052708333,0.000023977786,0.0000038363833,0.9914038,0.0004047128,0.0076326244,0.00037687243,0.0000046834143],"about_ca_topic_score_codex":0.002154651,"about_ca_topic_score_gemma":0.0023183313,"teacher_disagreement_score":0.0034730316,"about_ca_system_score_codex":0.0010865823,"about_ca_system_score_gemma":0.0019311659,"threshold_uncertainty_score":0.015515506},"labels":[],"label_agreement":null},{"id":"W4385223333","doi":"10.1109/lsp.2023.3298282","title":"Bounded Projection Matrix Approximation With Applications to Community Detection","year":2023,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":1,"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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bounded function; Relaxation (psychology); Convergence (economics); Dykstra's projection algorithm; Mathematical optimization; Constraint (computer-aided design); Approximation algorithm; Projection (relational algebra); Symmetric matrix; Mathematics; Matrix (chemical analysis); Matrix decomposition; Differentiable function; Algorithm; Computer science; Eigenvalues and eigenvectors; Mathematical analysis","score_opus":0.025277830251501224,"score_gpt":0.2632764646525136,"score_spread":0.23799863440101238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385223333","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":[0.0016661226,0.00014223029,0.99748814,0.00013397192,0.00002494688,0.000013937565,0.0000122494175,0.00010449816,0.00041389404],"genre_scores_gemma":[0.20099182,0.0008150335,0.79432756,0.00019815082,0.00017370102,0.00017105631,0.00022313991,0.00013286139,0.0029666636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987675,0.00055185816,0.000035213532,0.00020000269,0.00037451362,0.00007083854],"domain_scores_gemma":[0.9963553,0.0024172142,0.0002565548,0.00032169974,0.00050946116,0.00013979271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001908472,0.0011146007,0.0012189889,0.0010768381,0.0006683857,0.00096338073,0.0014449836,0.0014056272,0.0016816958],"category_scores_gemma":[0.009137684,0.00047788615,0.0007167045,0.0017530568,0.0012886083,0.0018772724,0.0020801171,0.0029126657,0.0006111754],"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":[0.00012425217,0.000103393984,0.0007290681,0.0002175816,0.00008774297,0.00017652048,0.00014729187,0.68892926,0.006714037,0.11714466,0.0055158027,0.18011041],"study_design_scores_gemma":[0.000004503972,0.000011956046,0.00002981534,0.0000040372306,0.0000023743683,0.000024083725,0.00000590335,0.9828538,0.00045285918,0.015980119,0.0006261599,0.000004398002],"about_ca_topic_score_codex":0.0023419166,"about_ca_topic_score_gemma":0.00193027,"teacher_disagreement_score":0.0023419166,"about_ca_system_score_codex":0.00064894167,"about_ca_system_score_gemma":0.0010787232,"threshold_uncertainty_score":0.010093033},"labels":[],"label_agreement":null},{"id":"W4385322990","doi":"10.1109/lsp.2023.3299531","title":"Deadline-Aware Coded Computation Across Homogeneous Workers","year":2023,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Distributed systems and fault tolerance","field":"Computer Science","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Computer science; Scheduling (production processes); Distributed computing; Matrix multiplication; Computation; Task (project management); Homogeneous; Task analysis; Greedy algorithm; Parallel computing; Theoretical computer science; Algorithm; Mathematical optimization","score_opus":0.024702789652454128,"score_gpt":0.2870562913998942,"score_spread":0.26235350174744004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385322990","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":[0.18488815,0.00033282282,0.8071231,0.00035358328,0.00012131288,0.000084393985,0.000086547894,0.00088141393,0.0061286027],"genre_scores_gemma":[0.8887626,0.00009357473,0.10677061,0.00008379486,0.000028675147,0.00006498841,0.000063146625,0.000052236286,0.004080324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946815,0.000108815446,0.00002280702,0.00012291742,0.00011974882,0.00015768348],"domain_scores_gemma":[0.99840456,0.00061492075,0.00017678743,0.00037234157,0.00026161427,0.00016986475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076142343,0.00039321798,0.0006377207,0.0003287657,0.00062215875,0.00088521384,0.0012335947,0.00047864844,0.0018070448],"category_scores_gemma":[0.0026720567,0.00019527005,0.0002425321,0.00069940183,0.000700993,0.0008149861,0.0010254194,0.00059756625,0.00034695628],"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":[0.0010382939,0.0001415768,0.0011694322,0.00009028156,0.000029433997,0.00019750548,0.00019113791,0.8909066,0.017297717,0.020613048,0.003120065,0.06520501],"study_design_scores_gemma":[0.00002865174,0.000054855773,0.00012357024,0.0000028555407,0.0000044769563,0.000020633866,0.000024866087,0.9911033,0.0034275656,0.0044930023,0.0007102711,0.0000059750473],"about_ca_topic_score_codex":0.00473179,"about_ca_topic_score_gemma":0.005401679,"teacher_disagreement_score":0.00473179,"about_ca_system_score_codex":0.0010221987,"about_ca_system_score_gemma":0.0023508854,"threshold_uncertainty_score":0.009408534},"labels":[],"label_agreement":null},{"id":"W4385973788","doi":"10.1109/lsp.2023.3306619","title":"Dimensionality Reduction of Room Acoustic Impulse Responses and Applications to System Identification","year":2023,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Dimensionality reduction; Impulse response; Computer science; Microphone; Impulse (physics); Signal processing; Curse of dimensionality; Reduction (mathematics); Finite impulse response; Acoustics; Dynamic mode decomposition; Speech recognition; Loudspeaker; Digital signal processing; Algorithm; Physics; Artificial intelligence; Mathematics","score_opus":0.02152141471839079,"score_gpt":0.27598023164084284,"score_spread":0.25445881692245204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385973788","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":[0.006175792,0.0008548885,0.99118,0.0002782039,0.00006669791,0.000040625135,0.000084057385,0.00022853263,0.0010911773],"genre_scores_gemma":[0.21156944,0.0034636157,0.7784973,0.00026149067,0.00036819626,0.00035756588,0.00074637856,0.00016380312,0.00457216],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998865,0.00043744233,0.00008383943,0.00018669332,0.00036143468,0.00006571583],"domain_scores_gemma":[0.99815255,0.000948152,0.0001562309,0.00035296,0.00034502347,0.00004497935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001085588,0.00092675735,0.0009644607,0.0009461148,0.0005146379,0.0009027171,0.00054946926,0.0007313336,0.0018798053],"category_scores_gemma":[0.005433345,0.0003679349,0.0011137845,0.0010467422,0.0007242213,0.0010664744,0.0011431838,0.0015141389,0.00080879],"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":[0.0001382622,0.00016064246,0.0012298222,0.00040817275,0.00017830149,0.00020021392,0.00040538836,0.29752156,0.031677198,0.07507808,0.0075308736,0.5854715],"study_design_scores_gemma":[0.000009843976,0.0000746368,0.0011333666,0.00002924174,0.000021890863,0.00024045151,0.000070197355,0.9432657,0.007561119,0.041101318,0.006440455,0.000051714887],"about_ca_topic_score_codex":0.0012923306,"about_ca_topic_score_gemma":0.0010497681,"teacher_disagreement_score":0.0018798053,"about_ca_system_score_codex":0.00040385424,"about_ca_system_score_gemma":0.00062135846,"threshold_uncertainty_score":0.006288588},"labels":[],"label_agreement":null},{"id":"W4386609311","doi":"10.1109/lsp.2023.3313515","title":"Rhythm Modeling for Voice Conversion","year":2023,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech Recognition and Synthesis","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":"Ubisoft (Canada)","funders":"","keywords":"Speech recognition; Rhythm; Computer science; Prosody; Speech processing; Artificial intelligence; Pattern recognition (psychology); Acoustics","score_opus":0.04954250797524673,"score_gpt":0.2673340634600034,"score_spread":0.21779155548475665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386609311","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":[0.011038643,0.0005185469,0.98445636,0.00010982151,0.00008790238,0.000031539825,0.00022428404,0.0008502522,0.0026826141],"genre_scores_gemma":[0.7127435,0.0018880678,0.2694748,0.000280962,0.00040224227,0.00025932328,0.0023156807,0.00077327737,0.011862139],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997807,0.000058582013,0.00001073168,0.00006692898,0.00006192086,0.000021035128],"domain_scores_gemma":[0.99975556,0.0000994436,0.000028172648,0.000040794213,0.0000624683,0.000013628431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003307324,0.0005695153,0.0004210273,0.0003921457,0.00023619346,0.0006582456,0.0007331228,0.00045552538,0.0020329703],"category_scores_gemma":[0.0013603204,0.00023016347,0.00079237786,0.00038167232,0.0002632353,0.00056689646,0.000488943,0.0009871926,0.0011852466],"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":[0.00014102347,0.00011923586,0.0016716005,0.00014491648,0.00012998891,0.00013792072,0.00011736859,0.69258446,0.03632232,0.01775081,0.0056658504,0.24521446],"study_design_scores_gemma":[0.000004986108,0.000018747804,0.00045577175,0.00000840382,0.000008883606,0.000042483283,0.000010004197,0.9894595,0.0020455008,0.005143219,0.0027934585,0.000009006256],"about_ca_topic_score_codex":0.003264722,"about_ca_topic_score_gemma":0.0037457584,"teacher_disagreement_score":0.003264722,"about_ca_system_score_codex":0.00033633565,"about_ca_system_score_gemma":0.00043033288,"threshold_uncertainty_score":0.0068009496},"labels":[],"label_agreement":null},{"id":"W4387805473","doi":"10.1109/lsp.2023.3326387","title":"DPAN: A Deep Light-Weight Attention-Based Image Super Resolution Network Using Multi-Dimensional Filter Design Technique","year":2023,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Residual; Block (permutation group theory); Filter (signal processing); Computer vision; Feature extraction; Image resolution; Pattern recognition (psychology); Filter bank; Algorithm; Mathematics","score_opus":0.03529100183999435,"score_gpt":0.2791157664602518,"score_spread":0.24382476462025748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387805473","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":[0.011031856,0.0003298234,0.98593533,0.00011793427,0.00004447485,0.000030032064,0.00006176571,0.0008932502,0.0015555081],"genre_scores_gemma":[0.38581982,0.0006152872,0.6035492,0.0004755401,0.00007613548,0.00012591033,0.0004394417,0.00016582687,0.008732869],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987984,0.000017225739,0.0000047056365,0.000036334804,0.000040275307,0.000021599079],"domain_scores_gemma":[0.9998535,0.000043300854,0.00001847117,0.000022120003,0.0000424599,0.000020215542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031602147,0.00057949545,0.0005065915,0.00034700704,0.00024114033,0.00037113574,0.0012822046,0.00063390186,0.0018687112],"category_scores_gemma":[0.00052311586,0.0002862563,0.00043048913,0.00034145388,0.0003584553,0.00090366544,0.0006854401,0.0010254855,0.00046844326],"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":[0.0003030806,0.00024065055,0.0009664292,0.00021865434,0.00018593906,0.00026997298,0.000088299195,0.2928299,0.11623695,0.0218514,0.010811546,0.5559972],"study_design_scores_gemma":[0.000009146201,0.000048292306,0.000120667406,0.0000033964354,0.000013104076,0.00004987333,0.000003004656,0.98816013,0.007785281,0.0021740443,0.001627011,0.000006038775],"about_ca_topic_score_codex":0.0034689577,"about_ca_topic_score_gemma":0.0059516192,"teacher_disagreement_score":0.0034689577,"about_ca_system_score_codex":0.00058397686,"about_ca_system_score_gemma":0.00064123183,"threshold_uncertainty_score":0.006897509},"labels":[],"label_agreement":null},{"id":"W4389473849","doi":"10.1109/lsp.2023.3341001","title":"Population Monte Carlo With Normalizing Flow","year":2023,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Bayesian Methods and Mixture Models","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; Huawei Technologies (Canada)","funders":"","keywords":"Markov chain Monte Carlo; Computer science; Monte Carlo method; Algorithm; Mathematical optimization; Sampling (signal processing); Importance sampling; Inference; Rejection sampling; Population; Markov chain; Hybrid Monte Carlo; Mathematics; Machine learning; Artificial intelligence; Statistics; Bayesian probability","score_opus":0.01798211009639149,"score_gpt":0.2484016818867318,"score_spread":0.2304195717903403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389473849","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":[0.0029032966,0.00008001907,0.99569786,0.000054931355,0.00001916524,0.000042584015,0.000015413867,0.00017854471,0.0010081385],"genre_scores_gemma":[0.22432609,0.0004157318,0.76957405,0.00022538415,0.00012769875,0.0004529933,0.0002569945,0.00018172772,0.004439385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984421,0.0007595438,0.000049595685,0.00025032458,0.0003974064,0.00010103808],"domain_scores_gemma":[0.9955064,0.0031419026,0.00025329835,0.00037043385,0.0005962406,0.00013174914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035432153,0.00084985804,0.0011291396,0.0012438151,0.00069899904,0.0013464546,0.0017573126,0.0013860771,0.0028269163],"category_scores_gemma":[0.012710687,0.00069637265,0.00084226707,0.0014176375,0.0016395242,0.002006816,0.0014788884,0.0021552767,0.00080361444],"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":[0.00006585799,0.00003985353,0.0007033822,0.00004976022,0.00002834511,0.000068421024,0.000087010594,0.82461584,0.0007797196,0.117901124,0.0012755982,0.05438514],"study_design_scores_gemma":[0.000007875307,0.0000065061918,0.00003664058,0.0000033047204,0.000002886191,0.000010902359,0.0000024477317,0.98694104,0.00023398291,0.012126378,0.000623491,0.000004550517],"about_ca_topic_score_codex":0.006363938,"about_ca_topic_score_gemma":0.004054598,"teacher_disagreement_score":0.006363938,"about_ca_system_score_codex":0.0013401174,"about_ca_system_score_gemma":0.0020682043,"threshold_uncertainty_score":0.018738508},"labels":[],"label_agreement":null},{"id":"W4391547749","doi":"10.1109/lsp.2024.3362189","title":"Distributed Multi-Sensor Control for Multi-Target Tracking With a Sparsity-Promoting Objective Function","year":2024,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Guidance and Control Systems","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":"Aeronautical Science Foundation of China; China Scholarship Council","keywords":"Computer science; Function (biology); Tracking (education); Wireless sensor network; Control (management); Artificial intelligence; Computer network","score_opus":0.019667890643759463,"score_gpt":0.22993317530850768,"score_spread":0.21026528466474823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391547749","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":[0.0027927542,0.000115391274,0.99617153,0.00005629952,0.00002281428,0.000013026288,0.000007765151,0.00004867524,0.00077185937],"genre_scores_gemma":[0.8126227,0.00035265973,0.18356551,0.00013147618,0.00009722035,0.0001929326,0.00006593256,0.000043636817,0.0029277354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944586,0.00014957682,0.00002289632,0.00013908414,0.0001911747,0.0000513474],"domain_scores_gemma":[0.99926454,0.0003640171,0.00009894364,0.00006258009,0.00016036583,0.00004948708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010394453,0.000799906,0.00085325824,0.00034596917,0.00037773224,0.0006883459,0.0012345103,0.0007527909,0.0012826199],"category_scores_gemma":[0.0015925779,0.00028335553,0.0005647479,0.0005207919,0.0006404207,0.0009771287,0.0012896482,0.0012614279,0.00019009967],"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":[0.000077027806,0.000065092274,0.0002625228,0.00011107766,0.000035347588,0.00007820427,0.000058689933,0.91511893,0.007899662,0.01780694,0.001019112,0.057467457],"study_design_scores_gemma":[0.000005274158,0.000027860915,0.000024437515,0.0000019457798,0.0000024310027,0.000011345322,0.0000026012344,0.99790925,0.0003999907,0.0014059895,0.00020613306,0.0000026438438],"about_ca_topic_score_codex":0.0013762855,"about_ca_topic_score_gemma":0.0012860261,"teacher_disagreement_score":0.0013762855,"about_ca_system_score_codex":0.00059546606,"about_ca_system_score_gemma":0.00087183696,"threshold_uncertainty_score":0.0054971576},"labels":[],"label_agreement":null},{"id":"W4396941596","doi":"10.1109/lsp.2024.3401612","title":"Palette-Based Color Harmonization via Color Naming","year":2024,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"European Regional Development Fund; Agencia Estatal de Investigación; Canada First Research Excellence Fund; Generalitat de Catalunya; Natural Sciences and Engineering Research Council of Canada; European Commission; National Natural Science Foundation of China; Natural Science Basic Research Program of Shaanxi Province; Centres de Recerca de Catalunya","keywords":"Palette (painting); Computer science; Artificial intelligence; Computer vision; Harmonization; Computer graphics (images); Dither; Art; Noise shaping; Aesthetics","score_opus":0.016106683512057275,"score_gpt":0.2493979203937832,"score_spread":0.23329123688172593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396941596","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":[0.01516468,0.00014893466,0.97791755,0.00006790095,0.00007463118,0.00007549455,0.00004209979,0.002081865,0.00442681],"genre_scores_gemma":[0.25640735,0.0003242881,0.735058,0.00020859444,0.000070258546,0.00020565017,0.00018572781,0.00079361984,0.0067464095],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994816,0.00009827442,0.000023905613,0.00016071227,0.0001816926,0.000053787942],"domain_scores_gemma":[0.99918276,0.0001820281,0.00006656245,0.00038315606,0.00013755399,0.00004793728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005841639,0.00072771433,0.0006085218,0.0010423462,0.00056756125,0.0012273344,0.0014684637,0.0004558125,0.0055482937],"category_scores_gemma":[0.0017006107,0.00039826168,0.00084207486,0.0011231209,0.0012149689,0.001980596,0.0017720888,0.0009406238,0.0013883058],"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":[0.0003365736,0.00014945395,0.0007912231,0.00023813022,0.00008057885,0.00024634154,0.00041085595,0.02881255,0.32806537,0.06881676,0.0074151247,0.564637],"study_design_scores_gemma":[0.00008899494,0.00031555913,0.0012617101,0.00003510005,0.00007378235,0.0009446861,0.00020733464,0.5044945,0.37708223,0.060548756,0.054788716,0.0001585576],"about_ca_topic_score_codex":0.00055806147,"about_ca_topic_score_gemma":0.00070458354,"teacher_disagreement_score":0.0055482937,"about_ca_system_score_codex":0.0003892304,"about_ca_system_score_gemma":0.00032371352,"threshold_uncertainty_score":0.018560886},"labels":[],"label_agreement":null},{"id":"W4399311019","doi":"10.1109/lsp.2024.3408676","title":"Multi-Level Information Aggregation Based Graph Attention Networks Towards Fake Speech Detection","year":2024,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Digital Media Forensic Detection","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":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Computer science; Graph; Attention network; Voice activity detection; Graph theory; Speech recognition; Artificial intelligence; Theoretical computer science; Speech processing; Mathematics","score_opus":0.020140919160847304,"score_gpt":0.2324139981366507,"score_spread":0.2122730789758034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399311019","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":[0.112636864,0.0014987679,0.8794576,0.00056243315,0.00010091148,0.00009269179,0.00029094578,0.00199801,0.003361816],"genre_scores_gemma":[0.89398843,0.00055511034,0.10104357,0.00032924817,0.0001143207,0.00007223179,0.00058024295,0.00009641073,0.0032203733],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995309,0.00012540966,0.00001735361,0.00014648178,0.000106390056,0.0000733986],"domain_scores_gemma":[0.9990558,0.00046639887,0.00010560678,0.000111556496,0.00020546107,0.000055172775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006372948,0.0011633553,0.0006984976,0.0016200483,0.0004737974,0.00075449876,0.0011265208,0.00071315555,0.0013701603],"category_scores_gemma":[0.002485014,0.00032340566,0.00066298456,0.0010667056,0.0006453106,0.0015607554,0.0013534139,0.0009431487,0.00031424587],"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":[0.00052225095,0.00022073988,0.0037126616,0.00021706928,0.00019399323,0.00030232107,0.00029899506,0.41189072,0.026726402,0.016891338,0.0061414926,0.532882],"study_design_scores_gemma":[0.0000054765756,0.000047413287,0.000802477,0.0000062848076,0.000036564423,0.00003895274,0.000022182776,0.98810065,0.003664879,0.006379693,0.00088687113,0.000008498124],"about_ca_topic_score_codex":0.008113058,"about_ca_topic_score_gemma":0.009551064,"teacher_disagreement_score":0.008113058,"about_ca_system_score_codex":0.0012057154,"about_ca_system_score_gemma":0.0006743515,"threshold_uncertainty_score":0.01613164},"labels":[],"label_agreement":null},{"id":"W4399346132","doi":"10.1109/lsp.2024.3409188","title":"Using Maximum Weighted Likelihood to Derive Lehmer and Hölder Families of Means","year":2024,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Statistical Methods and Models","field":"Mathematics","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 de Sherbrooke","funders":"","keywords":"Maximum likelihood; Mathematics; Computer science; Maximum likelihood sequence estimation; Algorithm; Pattern recognition (psychology); Statistics; Artificial intelligence","score_opus":0.0894265735484612,"score_gpt":0.38724993797452417,"score_spread":0.29782336442606294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399346132","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":[0.0030338394,0.00020917505,0.9953727,0.00012589674,0.000019816189,0.000011282636,0.000026921782,0.000040297728,0.00115997],"genre_scores_gemma":[0.31620997,0.0024797001,0.6702865,0.0007338909,0.0005564275,0.00038728325,0.00044887915,0.00037651433,0.008520804],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969068,0.0013962615,0.00013786693,0.00045047334,0.000898269,0.00021028971],"domain_scores_gemma":[0.9876145,0.008749449,0.0011017434,0.00080362073,0.0014270578,0.00030359885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009262468,0.0009049498,0.0012492208,0.0025064205,0.0010162867,0.0020828203,0.0017883229,0.0015121116,0.0029359148],"category_scores_gemma":[0.039068,0.00075420586,0.001876591,0.0014767915,0.0024370523,0.006862228,0.0027647244,0.0029522015,0.00083095714],"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":[0.000026309772,0.000019562618,0.0010000507,0.00008225682,0.00005337511,0.00012735922,0.00027398058,0.052197725,0.0017534279,0.9176099,0.0013022015,0.025553927],"study_design_scores_gemma":[0.0000065718195,0.000030292244,0.0004468068,0.000039260864,0.000015684325,0.00014295887,0.000058340178,0.3238355,0.0011265184,0.6710715,0.00317419,0.000052390245],"about_ca_topic_score_codex":0.00121238,"about_ca_topic_score_gemma":0.0013861166,"teacher_disagreement_score":0.009262468,"about_ca_system_score_codex":0.0014428103,"about_ca_system_score_gemma":0.0013059338,"threshold_uncertainty_score":0.048985243},"labels":[],"label_agreement":null},{"id":"W4400275345","doi":"10.1109/lsp.2024.3422789","title":"A Closed-Form DOA Estimator Using Spherical Microphone Arrays in the Presence of Interference","year":2024,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","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":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Interference (communication); Estimator; Computer science; Microphone array; Microphone; Speech recognition; Algorithm; Acoustics; Mathematics; Telecommunications; Statistics; Physics","score_opus":0.024995788870860434,"score_gpt":0.2704060608574142,"score_spread":0.24541027198655377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400275345","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":[0.0022450595,0.000119940494,0.9970919,0.000037011472,0.00001900953,0.000008127984,0.00001628956,0.00015839758,0.0003043205],"genre_scores_gemma":[0.18533903,0.00089803746,0.81102353,0.00014869393,0.00012546984,0.0001175427,0.00030214712,0.00008701907,0.001958548],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99946386,0.00012367802,0.00003746116,0.00012786748,0.00021135797,0.000035784433],"domain_scores_gemma":[0.99912566,0.00041787565,0.00010349665,0.000070398964,0.00024881976,0.00003378565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008012418,0.0007801946,0.0010390244,0.00046892083,0.00033278792,0.00093823986,0.00074004073,0.0009589868,0.00094176497],"category_scores_gemma":[0.0033637742,0.00043391867,0.00058855186,0.00074101816,0.0004792616,0.0012202128,0.0009112765,0.00084649876,0.00084875344],"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":[0.00025294384,0.00009449901,0.0025406429,0.00036185156,0.0001469715,0.00030572017,0.00020318484,0.39697313,0.070267946,0.022817183,0.0037493715,0.5022866],"study_design_scores_gemma":[0.000022708455,0.000078550096,0.0005844759,0.0000225819,0.000026085478,0.000323504,0.000032191027,0.98422676,0.009088053,0.0031094125,0.0024545456,0.00003119404],"about_ca_topic_score_codex":0.0015653096,"about_ca_topic_score_gemma":0.0021780033,"teacher_disagreement_score":0.0015653096,"about_ca_system_score_codex":0.0002335528,"about_ca_system_score_gemma":0.001043893,"threshold_uncertainty_score":0.0042374134},"labels":[],"label_agreement":null},{"id":"W4401325927","doi":"10.1109/lsp.2024.3438089","title":"Cross-Terms and Spectral Leakage Minimization in Time-Frequency Distribution","year":2024,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","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":"Queen's University","funders":"","keywords":"Leakage (economics); Minification; Computer science; Time–frequency analysis; Spectral leakage; Algorithm; Mathematical optimization; Mathematics; Telecommunications","score_opus":0.00886271731325808,"score_gpt":0.2457083375972374,"score_spread":0.23684562028397932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401325927","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":[0.008641001,0.0002069539,0.9903307,0.00009248342,0.000013301752,0.0000048964375,0.000022099051,0.00009492957,0.0005936981],"genre_scores_gemma":[0.56036925,0.0010284767,0.43093017,0.0002916447,0.00015268408,0.00009611315,0.0004213122,0.00031744773,0.006392943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994667,0.00016376803,0.000027066462,0.00009412734,0.00020479792,0.000043574855],"domain_scores_gemma":[0.99908125,0.0005516666,0.00008326795,0.00010133946,0.00015363815,0.00002879368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010787047,0.0006821264,0.00040349885,0.00039784974,0.00018576093,0.0006210032,0.00063585845,0.00057617057,0.0013583726],"category_scores_gemma":[0.0029732112,0.00024406893,0.0004835973,0.0007535316,0.00056168,0.0009852588,0.0008523453,0.00061730656,0.0005341099],"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":[0.0003423975,0.00011937514,0.0016636996,0.00027233665,0.00014924041,0.00016590217,0.000108765096,0.69048864,0.039611246,0.045936454,0.0029263275,0.21821551],"study_design_scores_gemma":[0.000007430601,0.00003658164,0.00034242362,0.000006108452,0.000013199039,0.00006829301,0.000007952371,0.98586476,0.007589328,0.0050238427,0.001033418,0.00000653383],"about_ca_topic_score_codex":0.00080881314,"about_ca_topic_score_gemma":0.00083132816,"teacher_disagreement_score":0.0013583726,"about_ca_system_score_codex":0.00048581668,"about_ca_system_score_gemma":0.00051687495,"threshold_uncertainty_score":0.00570482},"labels":[],"label_agreement":null},{"id":"W4402673192","doi":"10.1109/lsp.2024.3465894","title":"On the Design of Robust Differential Beamformers From the Beampattern Error Perspective","year":2024,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Control Systems and Identification","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":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Perspective (graphical); Differential (mechanical device); Computer science; Speech recognition; Robustness (evolution); Algorithm; Mathematics; Control theory (sociology); Artificial intelligence; Engineering","score_opus":0.02543186294234063,"score_gpt":0.22056232214651714,"score_spread":0.19513045920417652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402673192","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":[0.0005974375,0.000083619416,0.99860364,0.000035207788,0.000007830877,0.000007858603,0.0000052124606,0.000030482797,0.0006286915],"genre_scores_gemma":[0.2081579,0.0019377905,0.7845195,0.0002539129,0.00014436593,0.00029163584,0.0001158432,0.0001085985,0.00447044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991642,0.00031076424,0.000052333253,0.00014633757,0.00027622422,0.0000501429],"domain_scores_gemma":[0.9984629,0.0009727041,0.0001584583,0.000112424954,0.0002606779,0.000032868327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014021752,0.0013845178,0.00076866604,0.0004262658,0.00020719822,0.0009208807,0.00087251177,0.0011789724,0.0022694059],"category_scores_gemma":[0.0045481925,0.00060262834,0.0006154634,0.0007421103,0.0010271443,0.0010054733,0.0011877917,0.0010452201,0.0009797282],"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":[0.00010037099,0.000047205438,0.00043022487,0.00039163133,0.000076521734,0.000163454,0.00017579422,0.73085696,0.042689376,0.0834528,0.0020713692,0.13954422],"study_design_scores_gemma":[0.000017627652,0.0001285311,0.00013398708,0.00003474981,0.000015752757,0.00013588581,0.000019788218,0.978455,0.006328861,0.011759595,0.0029516527,0.000018602646],"about_ca_topic_score_codex":0.0005727712,"about_ca_topic_score_gemma":0.00077314215,"teacher_disagreement_score":0.0022694059,"about_ca_system_score_codex":0.00036128185,"about_ca_system_score_gemma":0.00068090594,"threshold_uncertainty_score":0.007591963},"labels":[],"label_agreement":null},{"id":"W4404627646","doi":"10.1109/lsp.2024.3504374","title":"Quaternion Vector Quantized Variational Autoencoder","year":2024,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","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":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Quaternion; Autoencoder; Vector quantization; Mathematics; Artificial intelligence; Computer science; Algorithm; Algebra over a field; Computer vision; Pure mathematics; Artificial neural network","score_opus":0.02606726353628276,"score_gpt":0.2893311814787559,"score_spread":0.26326391794247317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404627646","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":[0.0068043373,0.0003976878,0.9908736,0.00016580243,0.00006255209,0.00002174921,0.00007646529,0.00036374512,0.0012341295],"genre_scores_gemma":[0.54624045,0.00087069796,0.44159436,0.0005172008,0.000111077374,0.00014872388,0.0007642391,0.00023818227,0.009514992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968624,0.00010398392,0.000017272769,0.000067699584,0.00009026968,0.00003459669],"domain_scores_gemma":[0.99936265,0.00030207273,0.00004309397,0.00010177949,0.00016168602,0.000028761662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010922238,0.0006724784,0.0009568649,0.00033166446,0.00024342217,0.00079172914,0.0013180759,0.0010398701,0.0025842967],"category_scores_gemma":[0.0029811887,0.00043274576,0.0005278159,0.00045397718,0.0007367388,0.0011755532,0.0009037933,0.0014608171,0.00072467834],"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":[0.000084041865,0.000034754597,0.0006270112,0.00008374057,0.00005997009,0.00006198939,0.00006108225,0.83040905,0.0053715077,0.034905735,0.0032372905,0.12506379],"study_design_scores_gemma":[0.0000027413028,0.000006042821,0.000033219676,0.000003033825,0.0000018655841,0.000007712172,0.0000024005985,0.99553937,0.00042895426,0.0035943054,0.00037787092,0.0000025102145],"about_ca_topic_score_codex":0.0045310874,"about_ca_topic_score_gemma":0.0064098616,"teacher_disagreement_score":0.0045310874,"about_ca_system_score_codex":0.0005642067,"about_ca_system_score_gemma":0.0008501521,"threshold_uncertainty_score":0.009009421},"labels":[],"label_agreement":null},{"id":"W4405178880","doi":"10.1109/lsp.2024.3512377","title":"Noise-Robust Hearing Aid Voice Control","year":2024,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Ministerio de Ciencia e Innovación","keywords":"Speech recognition; Hearing aid; Computer science; Noise (video); Noise measurement; Background noise; Audiology; Noise reduction; Artificial intelligence; Telecommunications; Medicine","score_opus":0.020783622579834536,"score_gpt":0.2405038727380894,"score_spread":0.21972025015825486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405178880","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":[0.44084603,0.0075502275,0.48516276,0.0011033192,0.0013611234,0.00057889,0.019626739,0.024244292,0.019526565],"genre_scores_gemma":[0.820324,0.0015502477,0.120584026,0.00076208665,0.00032140204,0.0005701213,0.04059315,0.00080773345,0.014487132],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99835795,0.00034964117,0.000112590606,0.00041257855,0.0006198175,0.00014742029],"domain_scores_gemma":[0.99889886,0.00035969887,0.000058790924,0.0002159272,0.00041762606,0.000049112798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013025985,0.0014158997,0.0011192141,0.0006645958,0.00039196818,0.0013462187,0.0011551711,0.0011789247,0.0029181214],"category_scores_gemma":[0.0041193003,0.0002438703,0.0008089912,0.00033733883,0.0005228935,0.0009586922,0.0015144742,0.0006967673,0.0033223997],"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":[0.0032620996,0.0004351978,0.0051692235,0.001459968,0.00033569947,0.00057342555,0.00020831097,0.08878103,0.1554219,0.0015695739,0.036371097,0.70641243],"study_design_scores_gemma":[0.0002773917,0.0013799698,0.015056651,0.00016339037,0.0003078201,0.0017189838,0.0005104944,0.6979572,0.23332228,0.0041115,0.04497109,0.00022324958],"about_ca_topic_score_codex":0.003957271,"about_ca_topic_score_gemma":0.005686654,"teacher_disagreement_score":0.003957271,"about_ca_system_score_codex":0.00045941645,"about_ca_system_score_gemma":0.0008552631,"threshold_uncertainty_score":0.009762108},"labels":[],"label_agreement":null},{"id":"W4407268455","doi":"10.1109/lsp.2025.3539908","title":"Deep Unrolled Graph Laplacian Regularization for Robust Time-of-Flight Depth Denoising","year":2025,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","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":"York University","funders":"Shanghai Rising-Star Program; National Natural Science Foundation of China","keywords":"Regularization (linguistics); Noise reduction; Laplacian matrix; Laplace operator; Artificial intelligence; Computer science; Image denoising; Graph; Algorithm; Mathematics; Theoretical computer science; Mathematical analysis","score_opus":0.009894748165495307,"score_gpt":0.24054630816262212,"score_spread":0.23065155999712683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407268455","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":[0.015468673,0.00009608639,0.9831851,0.000134211,0.00001564605,0.000011825293,0.00004068588,0.00041829734,0.00062940794],"genre_scores_gemma":[0.58183414,0.00035680237,0.41153145,0.0003131567,0.000054950255,0.000086068256,0.00041779538,0.0002734255,0.005132166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978787,0.00004069404,0.000007824702,0.000060569095,0.00007463637,0.000028455765],"domain_scores_gemma":[0.99972457,0.00011406664,0.000039085156,0.000037358895,0.00006268363,0.000022171003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049181795,0.00074310636,0.0004518944,0.00036490962,0.00021959902,0.00040571435,0.0009275984,0.00091147795,0.00088889775],"category_scores_gemma":[0.0016115798,0.00033499257,0.00047443033,0.00037213092,0.0005938431,0.0009538997,0.00085345184,0.0012159743,0.0002576652],"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":[0.00012075852,0.000091711816,0.0007869836,0.00009142015,0.000060574122,0.00010830403,0.00010476246,0.7744469,0.051775645,0.014069233,0.0031401503,0.15520354],"study_design_scores_gemma":[0.0000025080456,0.000009260502,0.000060799972,0.0000015960516,0.000002444768,0.000010630764,0.0000033912797,0.9953341,0.0024011566,0.0019213669,0.00024936715,0.0000033789347],"about_ca_topic_score_codex":0.0055424087,"about_ca_topic_score_gemma":0.008024499,"teacher_disagreement_score":0.0055424087,"about_ca_system_score_codex":0.00066332804,"about_ca_system_score_gemma":0.0007693656,"threshold_uncertainty_score":0.011020303},"labels":[],"label_agreement":null},{"id":"W4408280461","doi":"10.1109/lsp.2025.3549699","title":"Power Transformed Density Ridge Estimation","year":2025,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Fault Detection and Control Systems","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":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Ridge; Estimation; Computer science; Geology; Engineering; Paleontology","score_opus":0.004983895998184281,"score_gpt":0.21283465996534404,"score_spread":0.20785076396715976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408280461","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":[0.005598206,0.00018474345,0.9920826,0.00017139815,0.000059448328,0.00003650457,0.000080182064,0.00038081978,0.0014061633],"genre_scores_gemma":[0.40449506,0.0013527083,0.57518274,0.000669958,0.00053517724,0.00038486614,0.000973748,0.0009217564,0.0154840695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778605,0.0007713995,0.00009433252,0.0005887737,0.00057167653,0.00018774276],"domain_scores_gemma":[0.9952773,0.0018820468,0.00048455579,0.001261413,0.0009316017,0.00016314177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040384955,0.0012163155,0.0014178602,0.0013483481,0.00059142587,0.0018447987,0.0020908893,0.0016546162,0.004743475],"category_scores_gemma":[0.01789751,0.000750072,0.001440654,0.0015275853,0.0019840514,0.002533589,0.0029140066,0.0030285893,0.0020910138],"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":[0.0003648697,0.00020746699,0.0062866956,0.00037206573,0.00026664502,0.00036482356,0.00025432237,0.42683375,0.015811116,0.19149843,0.00988363,0.3478562],"study_design_scores_gemma":[0.000019340981,0.000047998255,0.0006419022,0.000019168867,0.000024986855,0.000071332935,0.000019364108,0.9681281,0.0028148522,0.024702458,0.0034931533,0.000017466406],"about_ca_topic_score_codex":0.0034491448,"about_ca_topic_score_gemma":0.0026204463,"teacher_disagreement_score":0.004743475,"about_ca_system_score_codex":0.0008730499,"about_ca_system_score_gemma":0.0017082202,"threshold_uncertainty_score":0.021357894},"labels":[],"label_agreement":null},{"id":"W4409426286","doi":"10.1109/lsp.2025.3560596","title":"MPPCAD: Minimum Power Pattern Constrained Adaptive Differential Beamforming","year":2025,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Wireless Communication Techniques","field":"Engineering","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":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Adaptive beamformer; Computer science; Beamforming; Differential (mechanical device); Power (physics); Control theory (sociology); Mathematical optimization; Mathematics; Artificial intelligence; Telecommunications; Engineering; Physics","score_opus":0.009608227606586922,"score_gpt":0.23720435940746276,"score_spread":0.22759613180087584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409426286","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":[0.0013005211,0.00008832437,0.9976417,0.000058972608,0.00001648244,0.0000105485105,0.00001485432,0.00006813466,0.0008005006],"genre_scores_gemma":[0.18158635,0.00088656356,0.8120857,0.00034495816,0.00009209386,0.00020826992,0.00016267884,0.00008980416,0.004543651],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949825,0.00015323116,0.000019885401,0.00009099822,0.00020817267,0.0000295009],"domain_scores_gemma":[0.9995852,0.00021957226,0.000042612086,0.000041132454,0.000091706956,0.000019717942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004074335,0.00071964,0.00038895852,0.00024921636,0.00018239702,0.00050656183,0.00083299173,0.0005448592,0.0018531228],"category_scores_gemma":[0.0015996593,0.00037602457,0.00032379868,0.00042243095,0.00055865163,0.00055780244,0.00087253464,0.00070346583,0.0007992827],"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":[0.00022647221,0.00008900893,0.0010419778,0.00041027358,0.0000956474,0.00030532415,0.00015543343,0.29372355,0.16936575,0.050182443,0.0069032125,0.47750086],"study_design_scores_gemma":[0.00002695686,0.00013331289,0.00027698095,0.000021604188,0.000014667809,0.00033158532,0.000016796052,0.95852345,0.024600212,0.006459952,0.009575087,0.000019461439],"about_ca_topic_score_codex":0.00049841235,"about_ca_topic_score_gemma":0.00088131643,"teacher_disagreement_score":0.0018531228,"about_ca_system_score_codex":0.00033177517,"about_ca_system_score_gemma":0.000462202,"threshold_uncertainty_score":0.0061992407},"labels":[],"label_agreement":null},{"id":"W4411799602","doi":"10.1109/lsp.2025.3584670","title":"A Novel Evaluation Criterion for Density Clustering via Circular Information Granules","year":2025,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Text and Document Classification Technologies","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 Alberta","funders":"","keywords":"Cluster analysis; Mathematics; Computer science; Pattern recognition (psychology); Probability density function; Data mining; Artificial intelligence; Statistics","score_opus":0.02650558789289352,"score_gpt":0.2806127953078521,"score_spread":0.25410720741495857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411799602","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":[0.018365547,0.0007569348,0.97761995,0.00018493299,0.00006076457,0.00012380129,0.00013779876,0.00030090014,0.0024494035],"genre_scores_gemma":[0.56078607,0.0009217198,0.43443665,0.00019260433,0.00022746665,0.0004908634,0.00070208876,0.00022804223,0.0020143744],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9960729,0.00096212846,0.0003346363,0.00055061345,0.0018390908,0.00024073613],"domain_scores_gemma":[0.9943011,0.0020419569,0.00052085373,0.00037307892,0.002546153,0.00021688634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037062385,0.0011157937,0.0013635183,0.004522385,0.0010113828,0.0030052215,0.0012969323,0.0012258785,0.0012240893],"category_scores_gemma":[0.013317383,0.00035273205,0.0009036229,0.003063692,0.0015099266,0.0035452682,0.0017392981,0.0008390896,0.00038217258],"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":[0.0009020556,0.00019268064,0.010170492,0.0009572698,0.00034378664,0.00043845526,0.00089845574,0.32498237,0.032539062,0.1920671,0.010953657,0.42555454],"study_design_scores_gemma":[0.000036584344,0.00014914408,0.0026167503,0.00006786518,0.0000736983,0.00021641373,0.00017061923,0.95396715,0.009014372,0.029063761,0.004532389,0.000091259375],"about_ca_topic_score_codex":0.002956334,"about_ca_topic_score_gemma":0.0014685764,"teacher_disagreement_score":0.004522385,"about_ca_system_score_codex":0.0021057562,"about_ca_system_score_gemma":0.0012333491,"threshold_uncertainty_score":0.01960069},"labels":[],"label_agreement":null},{"id":"W7104970165","doi":"10.1109/lsp.2025.3632232","title":"A Nonparametric Variable Forgetting Factor Recursive Least-Squares Algorithm","year":2025,"lang":"","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","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":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Variable (mathematics); Forgetting; Factor (programming language); Context (archaeology); Set (abstract data type); Nonparametric statistics; Control (management); Tracking (education)","score_opus":0.012670208707881658,"score_gpt":0.2554817576752817,"score_spread":0.24281154896740004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104970165","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":[0.0011018406,0.00010707005,0.9980933,0.000027788457,0.00002213183,0.000011897626,0.000018434152,0.00024858426,0.00036891783],"genre_scores_gemma":[0.13989355,0.0004080405,0.8542871,0.00011655256,0.00012273889,0.00013375656,0.0003101365,0.0001350308,0.0045931074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993474,0.00018851794,0.000030150242,0.00013850539,0.00024240071,0.000052950556],"domain_scores_gemma":[0.9993506,0.00025699058,0.00005173261,0.0001092612,0.00021224796,0.000019154635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009917576,0.00064696645,0.0009776074,0.00038094877,0.00025694567,0.00072237325,0.0012350266,0.0010298152,0.0021625422],"category_scores_gemma":[0.0027032774,0.00031080333,0.0006613154,0.00054924405,0.00045452145,0.0007493443,0.000759647,0.0012338221,0.0013388348],"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":[0.00017261904,0.00006554942,0.00076610304,0.00019177343,0.00010375526,0.000100352176,0.0000805729,0.37730563,0.01960629,0.022526547,0.004188347,0.5748926],"study_design_scores_gemma":[0.000015766815,0.000050755916,0.00022875794,0.000009741856,0.000011051081,0.00006944772,0.000005485932,0.990515,0.0021461127,0.0026311264,0.0043023517,0.000014377139],"about_ca_topic_score_codex":0.0022046156,"about_ca_topic_score_gemma":0.0025497053,"teacher_disagreement_score":0.0022046156,"about_ca_system_score_codex":0.0002960666,"about_ca_system_score_gemma":0.001019129,"threshold_uncertainty_score":0.007234454},"labels":[],"label_agreement":null}]}