{"meta":{"query_hash":"ce3ab122ca18","filters":{"venue":"Sampling Theory Signal Processing and Data Analysis"},"cohort_total":12,"direct_labels_cover":0,"predictions_cover":12,"exported":12,"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/ce3ab122ca18","api":"https://metacan.xera.ac/api/v1/cohort?venue=Sampling+Theory+Signal+Processing+and+Data+Analysis"},"results":[{"id":"W2796099568","doi":"10.1007/s43670-021-00012-4","title":"Memoryless scalar quantization for random frames","year":2021,"lang":"en","type":"preprint","venue":"Sampling Theory Signal Processing and Data Analysis","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Applied mathematics; Quantization (signal processing); Invertible matrix; Gaussian; Algorithm; Physics; Pure mathematics; Quantum mechanics","score_opus":0.06026650454885625,"score_gpt":0.3222777198996381,"score_spread":0.26201121535078187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796099568","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":[0.0063395784,0.0013871033,0.9872557,0.0005012727,0.00015361566,0.000017360275,0.000135258,0.000098995944,0.0041111135],"genre_scores_gemma":[0.50523746,0.0065871878,0.4607329,0.00073582376,0.0010265886,0.00022314854,0.00087463483,0.00020622245,0.024375986],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991936,0.00031153605,0.000047193604,0.00012716101,0.0002715926,0.000048959337],"domain_scores_gemma":[0.9985921,0.00077954686,0.00012933934,0.00024505655,0.00020488037,0.000048985235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011346731,0.00065239856,0.0006509825,0.000768558,0.00027724824,0.0013576794,0.0007055091,0.0010496401,0.0034104702],"category_scores_gemma":[0.006292424,0.00031445114,0.00025527278,0.0011292262,0.0011288996,0.0021907508,0.0009881058,0.0015023799,0.0007103708],"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":[0.000082585546,0.000019568943,0.00008574853,0.00012410134,0.0000124664,0.00004380371,0.00006170301,0.06115612,0.0034582985,0.87161297,0.0037973234,0.05954526],"study_design_scores_gemma":[0.000018328143,0.000036654626,0.00009530809,0.000037329486,0.0000065143804,0.00005955929,0.000021763424,0.49748334,0.001674488,0.4956492,0.004899784,0.000017789236],"about_ca_topic_score_codex":0.001076772,"about_ca_topic_score_gemma":0.0009831523,"teacher_disagreement_score":0.0034104702,"about_ca_system_score_codex":0.0006561427,"about_ca_system_score_gemma":0.00050218066,"threshold_uncertainty_score":0.011409104},"labels":[],"label_agreement":null},{"id":"W3194315893","doi":"10.1007/s43670-023-00067-5","title":"Adaptive group Lasso neural network models for functions of few variables and time-dependent data","year":2023,"lang":"en","type":"article","venue":"Sampling Theory Signal Processing and Data Analysis","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of British Columbia; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Lasso (programming language); Property (philosophy); Computer science; Constraint (computer-aided design); Matrix (chemical analysis); Algorithm; Nonlinear system; Penalty method; Function (biology); Set (abstract data type); Artificial intelligence; Mathematical optimization; Mathematics; Pattern recognition (psychology)","score_opus":0.08933312037186174,"score_gpt":0.312323117612736,"score_spread":0.22298999724087426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194315893","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":[0.0037533094,0.00030628504,0.9951923,0.00020601106,0.00004140246,0.000018070981,0.000078863915,0.00011680935,0.0002869086],"genre_scores_gemma":[0.39264458,0.0018376051,0.58999556,0.0005976128,0.0007115635,0.0008130631,0.0016058309,0.00042940176,0.011364758],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982805,0.0009731035,0.00006279884,0.00031335794,0.00027369565,0.00009648059],"domain_scores_gemma":[0.9958657,0.0029132112,0.00036493904,0.00038973184,0.00037950478,0.00008689954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033091388,0.0013141705,0.0017531225,0.0005264191,0.0004631112,0.0012594176,0.0021006023,0.0017493748,0.0016248967],"category_scores_gemma":[0.010488626,0.00064397743,0.0010517752,0.0011744642,0.0013080381,0.0016205367,0.0013741362,0.0030276494,0.00063408224],"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":[0.00014658073,0.00008696008,0.0005140364,0.00019205152,0.00014764417,0.00005221664,0.00008834761,0.88959324,0.0019035444,0.0350635,0.0038978064,0.06831409],"study_design_scores_gemma":[0.0000041496796,0.00000685206,0.000055245597,0.0000046759087,0.0000041566464,0.0000051362745,0.000002918072,0.9924314,0.00011250739,0.0071014864,0.00026776816,0.0000037296734],"about_ca_topic_score_codex":0.0032130326,"about_ca_topic_score_gemma":0.0037567022,"teacher_disagreement_score":0.0033091388,"about_ca_system_score_codex":0.00067583926,"about_ca_system_score_gemma":0.0011565721,"threshold_uncertainty_score":0.017500639},"labels":[],"label_agreement":null},{"id":"W4306411814","doi":"10.1007/s43670-022-00040-8","title":"CAS4DL: Christoffel adaptive sampling for function approximation via deep learning","year":2022,"lang":"en","type":"article","venue":"Sampling Theory Signal Processing and Data Analysis","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Sampling (signal processing); Deep learning; Function approximation; Adaptive sampling; Function (biology); Parameterized complexity; Artificial intelligence; Artificial neural network; Algorithm; Multivariate statistics; Sample (material); Monte Carlo method; Mathematical optimization; Mathematics; Machine learning; Statistics","score_opus":0.14608657932618052,"score_gpt":0.3546864623493639,"score_spread":0.20859988302318339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306411814","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":[0.001982931,0.00016169717,0.9942362,0.00009219256,0.000061216306,0.00003098371,0.000102666934,0.0022105842,0.001121565],"genre_scores_gemma":[0.15236808,0.00033137994,0.8387521,0.00039306996,0.00011804047,0.00034189498,0.00090794865,0.0014242284,0.005363246],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990746,0.00023885067,0.000038854807,0.000108070104,0.00046218676,0.00007747272],"domain_scores_gemma":[0.99870765,0.0006109208,0.00006075222,0.0002602844,0.00026545857,0.0000949535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00149095,0.001025188,0.0011143204,0.00091438973,0.00046465575,0.0013107118,0.002512031,0.0016960503,0.007320353],"category_scores_gemma":[0.005924946,0.00049565005,0.0008072047,0.00094894355,0.0009601937,0.0010324921,0.0022866498,0.0031005882,0.002142398],"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":[0.00042897018,0.00014591774,0.00093303807,0.00031977604,0.00017270393,0.00014271589,0.00008596275,0.37141836,0.0083287535,0.14826292,0.029421661,0.44033924],"study_design_scores_gemma":[0.0000138443365,0.000011579296,0.000031086784,0.000006707727,0.0000042804068,0.0000115112625,0.000002582121,0.98437274,0.0014264242,0.012436659,0.001678456,0.0000042228276],"about_ca_topic_score_codex":0.006228613,"about_ca_topic_score_gemma":0.01056008,"teacher_disagreement_score":0.007320353,"about_ca_system_score_codex":0.001266311,"about_ca_system_score_gemma":0.0017758557,"threshold_uncertainty_score":0.024488986},"labels":[],"label_agreement":null},{"id":"W4313437003","doi":"10.1007/s43670-022-00043-5","title":"NESTANets: stable, accurate and efficient neural networks for analysis-sparse inverse problems","year":2022,"lang":"en","type":"article","venue":"Sampling Theory Signal Processing and Data Analysis","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Stability (learning theory); Inverse problem; Artificial neural network; Generalization; Deep learning; Convergence (economics); Key (lock); Artificial intelligence; Inverse; Construct (python library); Mathematical optimization; Algorithm; Theoretical computer science; Machine learning; Mathematics","score_opus":0.055959147669460536,"score_gpt":0.28750844983509677,"score_spread":0.23154930216563624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313437003","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":[0.0071697454,0.00024307803,0.9883283,0.00017726076,0.00011353671,0.000042168984,0.00026063,0.0017201679,0.0019452039],"genre_scores_gemma":[0.15905291,0.00039125312,0.8282684,0.00019902937,0.00008484973,0.0002376594,0.0008884471,0.00052699674,0.010350442],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967515,0.00007181338,0.000013219282,0.000044951536,0.000171584,0.000023234881],"domain_scores_gemma":[0.9994099,0.00027300217,0.000050296054,0.000099072,0.0001310706,0.000036632915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063703186,0.00069427176,0.00061663945,0.00046610014,0.00032517686,0.0007586227,0.0012982967,0.00088403677,0.0033227482],"category_scores_gemma":[0.0034828847,0.00038232727,0.00035165023,0.00047351047,0.00053531525,0.0013752131,0.0016170663,0.0016207943,0.0008700503],"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":[0.00057049195,0.0001197714,0.0007857213,0.00029415108,0.00012052628,0.00021655961,0.000099186276,0.45801452,0.019376263,0.055353884,0.02462726,0.4404217],"study_design_scores_gemma":[0.000013986862,0.000025924526,0.00007826364,0.0000064795313,0.0000050132335,0.000027515049,0.0000051562956,0.9862715,0.0030052909,0.007982233,0.0025729125,0.0000057201405],"about_ca_topic_score_codex":0.0019213542,"about_ca_topic_score_gemma":0.0062966803,"teacher_disagreement_score":0.0033227482,"about_ca_system_score_codex":0.0004143076,"about_ca_system_score_gemma":0.00059784454,"threshold_uncertainty_score":0.01111573},"labels":[],"label_agreement":null},{"id":"W4319943411","doi":"10.1007/s43670-023-00047-9","title":"Publisher Correction: NESTANets: stable, accurate and efficient neural networks for analysis-sparse inverse problems","year":2023,"lang":"en","type":"article","venue":"Sampling Theory Signal Processing and Data Analysis","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Inverse; Artificial neural network; Computer science; Inverse problem; Artificial intelligence; Algorithm; Mathematics; Mathematical analysis; Geometry","score_opus":0.05166243762495248,"score_gpt":0.2995868959844977,"score_spread":0.24792445835954524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319943411","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":[0.00036920627,0.0016012037,0.009319986,0.022150766,0.95315784,0.00003065237,0.0020860834,0.0020704207,0.009213869],"genre_scores_gemma":[0.031266958,0.0083191395,0.031671878,0.020901026,0.2421615,0.00023623733,0.008636536,0.00857414,0.6482325],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967803,0.0004670106,0.00038375956,0.0004975499,0.001687366,0.00018402714],"domain_scores_gemma":[0.9730866,0.003763379,0.0008549872,0.0027554194,0.01856359,0.00097594195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025571939,0.0020333352,0.0019967689,0.00411573,0.002640628,0.0043483027,0.0035652928,0.004293356,0.10925808],"category_scores_gemma":[0.04863051,0.0009831851,0.0012291302,0.00331327,0.0024695352,0.0040195603,0.0025645115,0.007745122,0.06184374],"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":[0.000033396685,0.000005059983,0.000051340347,0.000118393174,0.000013631604,0.00009413497,0.000019758972,0.00027904785,0.00010381624,0.0029459405,0.983705,0.01263061],"study_design_scores_gemma":[0.000035706173,0.000025075993,0.00046125444,0.00019675834,0.000040959745,0.00046396168,0.000048188696,0.0023330094,0.0012755176,0.006106623,0.98896235,0.00005059103],"about_ca_topic_score_codex":0.00870303,"about_ca_topic_score_gemma":0.012616505,"teacher_disagreement_score":0.10925808,"about_ca_system_score_codex":0.0026411442,"about_ca_system_score_gemma":0.003519366,"threshold_uncertainty_score":0.36550468},"labels":[],"label_agreement":null},{"id":"W4385634287","doi":"10.1007/s43670-023-00065-7","title":"Embracing off-the-grid samples","year":2023,"lang":"en","type":"article","venue":"Sampling Theory Signal Processing and Data Analysis","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 British Columbia","funders":"Petrobras; Natural Sciences and Engineering Research Council of Canada; Hess Corporation; BG Group; ConocoPhillips","keywords":"Algorithm; Undersampling; Computer science; Artificial intelligence; Compressed sensing","score_opus":0.06496448420519808,"score_gpt":0.3055116528807253,"score_spread":0.24054716867552722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385634287","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":[0.09538897,0.0001890789,0.9009486,0.00034700005,0.000055579454,0.000033081324,0.00011034145,0.0001960672,0.0027313042],"genre_scores_gemma":[0.8769648,0.00019644921,0.12042424,0.00024175884,0.00006164106,0.00004804041,0.0002469563,0.00005728732,0.0017588597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983828,0.0006734621,0.00008216224,0.0003582399,0.00038629,0.00011704992],"domain_scores_gemma":[0.99520975,0.0026804945,0.00048167226,0.0011312679,0.00036048106,0.00013644401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023136244,0.00050352607,0.0007323044,0.00027430817,0.00028784116,0.0008864048,0.00093327294,0.0009400553,0.0013885061],"category_scores_gemma":[0.0127352085,0.00032319204,0.00035463038,0.0005474725,0.0014682966,0.0019594026,0.0017328984,0.0011380623,0.0003651461],"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":[0.0008935529,0.00012666381,0.008461938,0.00020171929,0.00010754361,0.0006623197,0.00033724713,0.7248224,0.019675551,0.13549036,0.0024154235,0.106805235],"study_design_scores_gemma":[0.000024951989,0.000089852474,0.00072672486,0.0000151135055,0.000008852008,0.00014409595,0.000074379226,0.96007466,0.0047121467,0.032692667,0.0014218924,0.000014610731],"about_ca_topic_score_codex":0.0017336467,"about_ca_topic_score_gemma":0.0018805383,"teacher_disagreement_score":0.0023136244,"about_ca_system_score_codex":0.00044503994,"about_ca_system_score_gemma":0.0006849745,"threshold_uncertainty_score":0.012235761},"labels":[],"label_agreement":null},{"id":"W4385705583","doi":"10.1007/s43670-023-00063-9","title":"HARFE: hard-ridge random feature expansion","year":2023,"lang":"en","type":"article","venue":"Sampling Theory Signal Processing and Data Analysis","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; Directorate for Mathematical and Physical Sciences","keywords":"Ridge; Feature (linguistics); Smoothing; Algorithm; Outlier; Mathematics; Sparse approximation; Random matrix; Pattern recognition (psychology); Computer science; Thresholding; Matrix (chemical analysis); Artificial intelligence; Statistics","score_opus":0.05151404994003881,"score_gpt":0.29819900236181424,"score_spread":0.24668495242177543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385705583","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":[0.0018433419,0.00027334483,0.9918229,0.00025757687,0.00014198806,0.000036975707,0.0002467405,0.003464888,0.0019123235],"genre_scores_gemma":[0.115085445,0.0004937614,0.8595657,0.00070008554,0.0002615592,0.0002569774,0.0018767933,0.001757296,0.020002425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900997,0.00026075804,0.000034713965,0.00013848835,0.00048636127,0.000069759284],"domain_scores_gemma":[0.998722,0.00058433873,0.000064433945,0.00035613362,0.00020532934,0.000067692614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012348437,0.00086669635,0.0008726594,0.00074000756,0.00040624652,0.0010511095,0.001103581,0.0013675094,0.011314437],"category_scores_gemma":[0.005272556,0.00034770265,0.0005191789,0.0008756696,0.00082408,0.0012194691,0.0017328251,0.0023126516,0.0061422475],"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":[0.00043630583,0.00018363059,0.0004927807,0.0001691692,0.00007173774,0.00026659257,0.000062381536,0.081097335,0.013211621,0.08800384,0.07304961,0.742955],"study_design_scores_gemma":[0.000043324733,0.000062682644,0.00027252312,0.00002294981,0.000009404509,0.00019865278,0.00001094744,0.920887,0.010108587,0.04868014,0.019677425,0.000026424515],"about_ca_topic_score_codex":0.0011374599,"about_ca_topic_score_gemma":0.0017822694,"teacher_disagreement_score":0.011314437,"about_ca_system_score_codex":0.00031687948,"about_ca_system_score_gemma":0.00056729,"threshold_uncertainty_score":0.0378505},"labels":[],"label_agreement":null},{"id":"W4387164540","doi":"10.1007/s43670-023-00069-3","title":"Nonlinear expansions in reproducing kernel Hilbert spaces","year":2023,"lang":"en","type":"article","venue":"Sampling Theory Signal Processing and Data Analysis","topic":"Holomorphic and Operator Theory","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Reproducing kernel Hilbert space; Hilbert space; Banach space; Bounded function; Hardy space; Cover (algebra); Pure mathematics; Nonlinear system; Norm (philosophy); Kernel (algebra); Series expansion; Analytic function; Mathematical analysis; Space (punctuation)","score_opus":0.11203177547797584,"score_gpt":0.373069305053445,"score_spread":0.26103752957546916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387164540","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":[0.046974827,0.0014279506,0.9443221,0.00064028724,0.00012760308,0.000019696708,0.00007305147,0.00010941609,0.0063049817],"genre_scores_gemma":[0.7825905,0.003589085,0.18774687,0.00030993816,0.00084728823,0.00012243612,0.00032200266,0.00024005641,0.02423187],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989261,0.00061895256,0.000048417292,0.00009542605,0.00026036272,0.000050723425],"domain_scores_gemma":[0.9946919,0.0037045674,0.00042521223,0.0003415624,0.0006197915,0.00021694842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023814996,0.0007239508,0.00072298414,0.0012497511,0.00029833766,0.0014734496,0.00072496734,0.0010183064,0.0021027024],"category_scores_gemma":[0.0088865375,0.00040936997,0.0005112691,0.0009336855,0.0019385832,0.0027864235,0.0012994474,0.0018873743,0.00047793105],"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":[0.00003522916,0.000028832777,0.00026156518,0.000102782666,0.000018476678,0.00009521875,0.0001947361,0.022555603,0.00423716,0.9571003,0.0008541986,0.014515824],"study_design_scores_gemma":[0.000010560751,0.000026241565,0.00035279017,0.00002041599,0.000007571038,0.00010208171,0.000050839804,0.43017334,0.0007514621,0.56660444,0.0018830195,0.000017183513],"about_ca_topic_score_codex":0.000985549,"about_ca_topic_score_gemma":0.00062046136,"teacher_disagreement_score":0.0023814996,"about_ca_system_score_codex":0.0007573561,"about_ca_system_score_gemma":0.00041634406,"threshold_uncertainty_score":0.012594759},"labels":[],"label_agreement":null},{"id":"W4392385397","doi":"10.1007/s43670-024-00082-0","title":"Nonlinear expansions in Banach spaces","year":2024,"lang":"en","type":"article","venue":"Sampling Theory Signal Processing and Data Analysis","topic":"Advanced Banach Space Theory","field":"Mathematics","cited_by":0,"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 Winnipeg","funders":"","keywords":"Banach space; Nonlinear system; Mathematics; Interpolation space; Eberlein–Šmulian theorem; Lp space; Pure mathematics; Physics; Functional analysis; Chemistry","score_opus":0.07906280035724488,"score_gpt":0.38746012532087765,"score_spread":0.30839732496363276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392385397","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":[0.045456823,0.006905177,0.88042486,0.0019600168,0.00071520737,0.000035519388,0.00013701254,0.0001668361,0.0641986],"genre_scores_gemma":[0.68750864,0.011545686,0.17673531,0.00073315494,0.002444118,0.00021271397,0.00034975764,0.00027325144,0.12019736],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993734,0.0003101045,0.000029073739,0.00007161275,0.00018370368,0.000032125427],"domain_scores_gemma":[0.99858105,0.0008551442,0.00011284292,0.00009602544,0.00027532026,0.000079602265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014105459,0.0009875995,0.00065333094,0.0013655196,0.0003435896,0.0014678442,0.00045626328,0.00084952085,0.003667181],"category_scores_gemma":[0.0034515564,0.0003850073,0.0004720791,0.0010184399,0.0017456049,0.002346434,0.0014070334,0.0022043304,0.0008170459],"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":[0.00001965954,0.000010998677,0.00008641722,0.00007348914,0.000011153125,0.00005232609,0.00012910506,0.008442225,0.0025393341,0.9766568,0.0012777948,0.010700759],"study_design_scores_gemma":[0.0000102390995,0.000025352192,0.00028147057,0.00003811383,0.000011645987,0.0001374823,0.00006092734,0.1402056,0.0009499247,0.84876925,0.009494569,0.000015302814],"about_ca_topic_score_codex":0.00070635276,"about_ca_topic_score_gemma":0.0005212923,"teacher_disagreement_score":0.003667181,"about_ca_system_score_codex":0.00087007263,"about_ca_system_score_gemma":0.00034868356,"threshold_uncertainty_score":0.012267947},"labels":[],"label_agreement":null},{"id":"W4396762500","doi":"10.1007/s43670-024-00088-8","title":"On the monotonicity of left and right Riemann sums","year":2024,"lang":"en","type":"article","venue":"Sampling Theory Signal Processing and Data Analysis","topic":"Mathematical Inequalities and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Monotonic function; Mathematics; Riemann hypothesis; Pure mathematics; Mathematical analysis; Mathematical economics","score_opus":0.11895219046856605,"score_gpt":0.3868058791166078,"score_spread":0.26785368864804177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396762500","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":[0.122791745,0.009609267,0.7744524,0.007313573,0.0007928232,0.00005428019,0.00040256968,0.0002717737,0.084311664],"genre_scores_gemma":[0.843378,0.0071804286,0.121366024,0.0023333281,0.0026399102,0.00014428321,0.00054963527,0.0007678226,0.021640504],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99453497,0.0027082786,0.00034982542,0.0006860974,0.0012095761,0.0005112145],"domain_scores_gemma":[0.9409669,0.046183974,0.0022563667,0.0038217993,0.0050372276,0.0017337961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0104064625,0.0010938508,0.0014602225,0.0029559433,0.0013644671,0.0051215785,0.0020475802,0.001856853,0.0075255167],"category_scores_gemma":[0.05754092,0.00083623256,0.0013070885,0.0015858819,0.008140248,0.013066387,0.0047655217,0.0063825725,0.001239953],"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":[0.00018635915,0.00003612816,0.0008837475,0.00013469842,0.000023832752,0.00021832644,0.00034052724,0.0034568054,0.003568045,0.9646185,0.001556884,0.02497617],"study_design_scores_gemma":[0.00001947292,0.000051348077,0.0010741361,0.00008330403,0.000024417996,0.00046437033,0.00013056102,0.05521526,0.0025574334,0.93551284,0.0048288996,0.000038055365],"about_ca_topic_score_codex":0.001259903,"about_ca_topic_score_gemma":0.0007489399,"teacher_disagreement_score":0.0104064625,"about_ca_system_score_codex":0.0013091997,"about_ca_system_score_gemma":0.0011161136,"threshold_uncertainty_score":0.055035293},"labels":[],"label_agreement":null},{"id":"W4407287089","doi":"10.1007/s43670-025-00098-0","title":"The greedy side of the LASSO: new algorithms for weighted sparse recovery via loss function-based orthogonal matching pursuit","year":2025,"lang":"en","type":"article","venue":"Sampling Theory Signal Processing and Data Analysis","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Matching pursuit; Lasso (programming language); Greedy algorithm; Algorithm; Computer science; Mathematics; Function (biology); Matching (statistics); Mathematical optimization; Compressed sensing; Statistics; Biology","score_opus":0.029611984638783583,"score_gpt":0.28073824218313737,"score_spread":0.2511262575443538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407287089","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":[0.0009833912,0.00024287563,0.99786025,0.00018527557,0.00005986027,0.000011196726,0.000025957776,0.000115400195,0.0005157781],"genre_scores_gemma":[0.06421984,0.001403354,0.9272134,0.00054853346,0.0005120482,0.00020528205,0.00025230146,0.0003774766,0.005267742],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99781114,0.0009141408,0.00008438209,0.0002613558,0.0008199871,0.00010902383],"domain_scores_gemma":[0.9981748,0.0010063266,0.00018258441,0.00028085875,0.00025606572,0.000099476674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031099808,0.0018053736,0.001539106,0.0009437784,0.0004897815,0.0014676745,0.001974381,0.0019879437,0.0020083915],"category_scores_gemma":[0.0077099563,0.0008814928,0.0009051511,0.001803071,0.0019296261,0.0032475127,0.0037404383,0.0040403986,0.0014514669],"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":[0.00053221773,0.00017953246,0.00054524693,0.00041765388,0.00021107002,0.00014677655,0.00020619138,0.21537094,0.021821827,0.24218109,0.019863188,0.49852428],"study_design_scores_gemma":[0.00002947406,0.00008146249,0.00009460021,0.000023842498,0.000014069503,0.00008680929,0.00001584034,0.9337825,0.002778447,0.058685128,0.0043798694,0.000027939193],"about_ca_topic_score_codex":0.0006645493,"about_ca_topic_score_gemma":0.0007335225,"teacher_disagreement_score":0.0031099808,"about_ca_system_score_codex":0.00041463575,"about_ca_system_score_gemma":0.0009771334,"threshold_uncertainty_score":0.016447306},"labels":[],"label_agreement":null},{"id":"W4411577251","doi":"10.1007/s43670-025-00103-6","title":"Bounds and limiting minimizers for a family of interaction energies","year":2025,"lang":"en","type":"article","venue":"Sampling Theory Signal Processing and Data Analysis","topic":"Advanced Mathematical Modeling in Engineering","field":"Computer Science","cited_by":0,"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":"Limiting; Mathematics; Statistical physics; Physics; Engineering; Mechanical engineering","score_opus":0.05160219670206482,"score_gpt":0.3377534679285526,"score_spread":0.28615127122648776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411577251","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":[0.062240433,0.0018866078,0.9093304,0.0024184315,0.00009899855,0.0000912662,0.00022009203,0.0003254453,0.02338832],"genre_scores_gemma":[0.745998,0.0041654254,0.21068586,0.0013662602,0.00066915096,0.0010682687,0.0010196617,0.0013317094,0.03369567],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968708,0.0014458903,0.0001374741,0.00042874707,0.0007283905,0.00038868227],"domain_scores_gemma":[0.9752714,0.020278836,0.0012945918,0.00085119973,0.0011482087,0.001155721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007417422,0.00323388,0.002167787,0.0043843803,0.001890863,0.0056396914,0.0038335924,0.0036575347,0.00754788],"category_scores_gemma":[0.034266084,0.0013631047,0.0020722747,0.0024447388,0.004276547,0.005695529,0.005620002,0.0059148525,0.0009510081],"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":[0.00015735932,0.00012934672,0.0006912097,0.00028890496,0.00013159317,0.00019175437,0.00033890537,0.077091746,0.0023630671,0.9022042,0.002670965,0.013740888],"study_design_scores_gemma":[0.00002274076,0.00006467388,0.00048979407,0.00011033917,0.00005334524,0.00027404274,0.0001265052,0.36381647,0.0012249706,0.63129586,0.0024723748,0.000048924157],"about_ca_topic_score_codex":0.001183256,"about_ca_topic_score_gemma":0.0008499742,"teacher_disagreement_score":0.00754788,"about_ca_system_score_codex":0.00245256,"about_ca_system_score_gemma":0.0012392517,"threshold_uncertainty_score":0.039227545},"labels":[],"label_agreement":null}]}