{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":12,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":12,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"ce3ab122ca18","filters":{"venue":"Sampling Theory Signal Processing and Data Analysis"}},"results":[{"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.05151404994003881,"gpt":0.2981990023618142,"spread":0.2466849524217754,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000936514,0.0002329979,0.000395202,0.0004648834,0.000310755,0.0002646868,0.0004176965,0.0001193514,0.00002934515],"category_scores_gemma":[0.00006672604,0.000203908,0.00009389126,0.001428522,0.00007591314,0.000335168,0.00021286,0.0002480489,0.00001783489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001305346,"about_ca_system_score_gemma":0.0000199872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001287199,"about_ca_topic_score_gemma":0.000005711052,"domain_scores_codex":[0.9986705,0.00007657349,0.0002189529,0.0005048191,0.0002150976,0.0003140254],"domain_scores_gemma":[0.9988747,0.0002309683,0.0000561896,0.0006837922,0.00006055598,0.00009382189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004004286,0.00005654961,0.001508894,0.0004403398,0.002900804,0.00008940231,0.001303564,0.1159557,0.07592299,0.0006414303,0.03052576,0.7702541],"study_design_scores_gemma":[0.0004100766,0.00001596685,0.001321454,0.000214055,0.001762417,0.000008992063,0.0002874452,0.9757068,0.003321668,0.01170274,0.004765306,0.0004830728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07588424,0.004790511,0.9152682,0.0001362517,0.0000866341,0.0001209182,0.0002437271,0.003097047,0.0003724706],"genre_scores_gemma":[0.9904668,0.0004704872,0.007343947,0.00009095861,0.0001209571,0.000007039353,0.001299088,0.00004518391,0.0001554964],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9145826,"threshold_uncertainty_score":0.8315127,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.05595914766946054,"gpt":0.2875084498350968,"spread":0.2315493021656362,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001310344,0.0002779548,0.0005232933,0.0005920987,0.0007504922,0.0003146829,0.0004214088,0.00006109195,0.00003639309],"category_scores_gemma":[0.00003240829,0.0002697798,0.0001202324,0.001969296,0.0001074051,0.0002184233,0.0004113132,0.0002821831,1.590556e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003379973,"about_ca_system_score_gemma":0.0000202903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004548669,"about_ca_topic_score_gemma":0.00004193874,"domain_scores_codex":[0.9981862,0.0001404067,0.0003819753,0.000671197,0.0002185728,0.0004015879],"domain_scores_gemma":[0.9987659,0.0003097345,0.0001465201,0.0005978054,0.00005904418,0.0001210129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006767398,0.00002438963,0.0004124638,0.00004725419,0.001350103,0.000004058494,0.000210222,0.9793264,0.0002407009,0.00009159208,0.000116595,0.01810852],"study_design_scores_gemma":[0.000186714,0.0000357397,0.0001608205,0.0000179076,0.005886746,0.000006699666,0.0004495812,0.9905753,0.00002421189,0.001727354,0.0005984524,0.0003304458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07557058,0.004826001,0.9186498,0.00002359994,0.00003740812,0.0001976804,0.0002624393,0.0003998334,0.00003263457],"genre_scores_gemma":[0.9926362,0.000103159,0.005854791,0.000112732,0.00005598164,0.00004221322,0.001138963,0.00003762274,0.00001831605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9170656,"threshold_uncertainty_score":0.9999754,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.1460865793261805,"gpt":0.3546864623493639,"spread":0.2085998830231834,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.01166703,0.0002344166,0.0004846598,0.0007011236,0.002126192,0.0004251063,0.0008677627,0.00007240895,0.0001734815],"category_scores_gemma":[0.001721272,0.0002000707,0.000151355,0.001992794,0.0001118414,0.0005654788,0.0005112684,0.0003836654,0.000004952745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007587412,"about_ca_system_score_gemma":0.00008135158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002519871,"about_ca_topic_score_gemma":0.000006134178,"domain_scores_codex":[0.9965664,0.0004449803,0.0006668573,0.001124465,0.0008483473,0.0003489453],"domain_scores_gemma":[0.9955967,0.002905519,0.0004555522,0.0006479967,0.0002768323,0.0001174014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000330205,0.00003838509,0.0002562466,0.00003153637,0.0002480227,7.423449e-7,0.0005495916,0.6691461,0.0003275422,0.002477101,0.00003490337,0.3265596],"study_design_scores_gemma":[0.0002148181,0.0001141507,0.000319777,0.00001098555,0.00110738,0.000006912742,0.002378712,0.8629069,0.000005926334,0.1310379,0.001653551,0.0002429528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002324206,0.002194061,0.9948381,0.00004608828,0.00009499623,0.0002017283,0.0001060145,0.0001368538,0.00005793434],"genre_scores_gemma":[0.9380004,0.000007551743,0.06091798,0.00007332669,0.0001345879,0.00006499629,0.0006054935,0.00002673935,0.0001688661],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9356763,"threshold_uncertainty_score":0.9991729,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.06026650454885625,"gpt":0.3222777198996381,"spread":0.2620112153507819,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001208203,0.000396113,0.0008661876,0.0004082521,0.0002716155,0.0008360103,0.0006303873,0.0003423047,0.00002332772],"category_scores_gemma":[0.0001577892,0.0003926306,0.0002229216,0.0004830397,0.0001358846,0.0002455726,0.0006108936,0.0004347439,5.013411e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002599572,"about_ca_system_score_gemma":0.00007738324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003485976,"about_ca_topic_score_gemma":0.00002331255,"domain_scores_codex":[0.9979672,0.0001319747,0.0004756993,0.0009038473,0.0002253027,0.0002959454],"domain_scores_gemma":[0.9979906,0.0004227054,0.0001833308,0.001073614,0.0002427828,0.00008694615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002650941,0.00009099146,0.0003502196,0.002336266,0.006106638,0.00001348193,0.000997572,0.6677426,0.0034333,0.0007496524,0.0005434973,0.3173707],"study_design_scores_gemma":[0.0002757829,0.000008539681,0.00004875191,0.0006561794,0.005294823,0.000002860786,0.0004182883,0.9634621,0.002399929,0.02665934,0.000248101,0.0005253212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01501544,0.01744809,0.9660943,0.00003355493,0.0001107002,0.0002217795,0.0003178228,0.0006952799,0.00006299248],"genre_scores_gemma":[0.9267313,0.0008269587,0.06655072,0.00006454042,0.0002166585,0.00003306534,0.005488511,0.00007332242,0.00001484362],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9117159,"threshold_uncertainty_score":0.9998525,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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)","retraction":null,"screen_n_in":null,"score":{"opus":0.1120317754779758,"gpt":0.373069305053445,"spread":0.2610375295754692,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005154625,0.0002243472,0.0005134267,0.0006085272,0.0003755302,0.0001977259,0.0005084572,0.000107139,0.0001045157],"category_scores_gemma":[0.0013418,0.0001882379,0.00007814776,0.002347536,0.0001566449,0.0003501248,0.0004313578,0.0002871587,0.00002724171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001960045,"about_ca_system_score_gemma":0.00008620202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008572335,"about_ca_topic_score_gemma":0.000113342,"domain_scores_codex":[0.9976146,0.0002631405,0.0004696228,0.001002988,0.0002643197,0.0003853785],"domain_scores_gemma":[0.9973894,0.001133453,0.0001505024,0.001143968,0.00007080303,0.0001119035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001825499,0.002205832,0.1352195,0.004210529,0.007206457,0.0007876194,0.0632944,0.01938948,0.02162921,0.1327664,0.004843304,0.6066218],"study_design_scores_gemma":[0.000891891,0.00006172995,0.001656489,0.0007251551,0.003463449,0.00002340571,0.02332792,0.3345717,0.0005631019,0.6323145,0.001255254,0.001145471],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8784356,0.001246539,0.1186847,0.0004442933,0.00004372282,0.000155253,0.0001911952,0.000394868,0.0004038741],"genre_scores_gemma":[0.9824486,0.0001190793,0.01620072,0.0001164595,0.0001433839,0.000009562837,0.0004590522,0.00004073215,0.0004623643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6054763,"threshold_uncertainty_score":0.7676117,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.05166243762495248,"gpt":0.2995868959844977,"spread":0.2479244583595452,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008555925,0.0002156402,0.0003673892,0.000483573,0.0004757551,0.0004682842,0.0002441176,0.0000770837,0.00001471716],"category_scores_gemma":[0.00005100216,0.0001962717,0.00008230846,0.003971234,0.00009339368,0.0004697208,0.0001278042,0.0001713822,0.00000104006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000189155,"about_ca_system_score_gemma":0.00001458514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002126641,"about_ca_topic_score_gemma":0.00007468489,"domain_scores_codex":[0.9985694,0.00003428708,0.0003246174,0.0005805098,0.0001347816,0.0003564138],"domain_scores_gemma":[0.9989231,0.000334114,0.0001003002,0.0004304542,0.0000765611,0.0001355236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001366998,0.00001143334,0.00030139,0.0000539473,0.0006779548,7.082126e-7,0.0001066607,0.9473457,0.00002082741,0.00006196045,0.0004605662,0.0509452],"study_design_scores_gemma":[0.0001528305,0.00000967951,0.0005075181,0.00001659975,0.002958668,0.000001771673,0.0004803738,0.9926919,0.000001573528,0.001968682,0.0009713377,0.0002390736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00789439,0.001187989,0.9900602,0.00004242753,0.0001115777,0.0001723938,0.00009908011,0.0003939488,0.00003800333],"genre_scores_gemma":[0.9902156,0.0002185456,0.006137773,0.00005233013,0.0001321668,0.00008156474,0.002913009,0.0000406878,0.0002083184],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9839224,"threshold_uncertainty_score":0.8003728,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.06496448420519808,"gpt":0.3055116528807253,"spread":0.2405471686755272,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001171547,0.0001990503,0.0002928192,0.0003541117,0.0004589985,0.0003021891,0.000549838,0.00006695302,0.00002290559],"category_scores_gemma":[0.00008031393,0.0001529969,0.00007709091,0.00150116,0.0001151643,0.0002703908,0.0002827037,0.0002066648,0.00001439839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001166259,"about_ca_system_score_gemma":0.00001888909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002265577,"about_ca_topic_score_gemma":0.00001612551,"domain_scores_codex":[0.9987342,0.00007676482,0.0002598207,0.0004159608,0.0001948967,0.0003184061],"domain_scores_gemma":[0.9986436,0.0004498203,0.00006005594,0.0007307217,0.00004923146,0.00006655523],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006439117,0.00002150067,0.0007280587,0.0001804285,0.00187845,0.00002074004,0.001042092,0.1490717,0.008609774,0.002411251,0.004972396,0.8309993],"study_design_scores_gemma":[0.00009867302,0.00001209714,0.001370845,0.0001393386,0.001632776,0.000009727048,0.0006382749,0.9241779,0.00133221,0.06308679,0.007092363,0.0004090013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05897013,0.008071082,0.9277853,0.0001701057,0.0001405376,0.0001236679,0.0002142012,0.003173997,0.001351009],"genre_scores_gemma":[0.9952168,0.0003256133,0.003474027,0.0001033565,0.0002649302,0.000006267801,0.000526572,0.00003758852,0.00004479825],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9362467,"threshold_uncertainty_score":0.6239032,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.02961198463878358,"gpt":0.2807382421831374,"spread":0.2511262575443538,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001109605,0.000219585,0.0003324485,0.0002021124,0.0006082873,0.0002419675,0.000708317,0.00009533815,0.000008145877],"category_scores_gemma":[0.00008843149,0.0001448652,0.0001794238,0.000913596,0.0001447736,0.0001831216,0.0001782148,0.0002160705,4.548081e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002659912,"about_ca_system_score_gemma":0.0001336899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006895239,"about_ca_topic_score_gemma":0.00008726523,"domain_scores_codex":[0.9986359,0.00009332698,0.0003992175,0.000381272,0.0002244757,0.000265832],"domain_scores_gemma":[0.9978609,0.001046424,0.00016268,0.0007500346,0.0001289921,0.00005095841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004949501,0.0000501933,0.0009520818,0.0002317072,0.00275925,0.000001971618,0.0001075072,0.1535787,0.007177078,0.002552551,0.0009806423,0.8311134],"study_design_scores_gemma":[0.0002543807,0.00002309956,0.0005787719,0.0002798372,0.002361537,0.000001673588,0.00009345078,0.7229531,0.003159852,0.2688781,0.001198019,0.0002180722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008483324,0.002465531,0.9881256,0.0001937069,0.000131306,0.0001521653,0.000105306,0.0001982677,0.0001447907],"genre_scores_gemma":[0.9824386,0.00006112202,0.01680266,0.0002131876,0.0001172863,0.00001192287,0.0001783116,0.00002997826,0.0001469542],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9739553,"threshold_uncertainty_score":0.5907431,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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)","retraction":null,"screen_n_in":null,"score":{"opus":0.08933312037186174,"gpt":0.312323117612736,"spread":0.2229899972408743,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001256299,0.0001679232,0.000351996,0.0001375554,0.0004545895,0.000137682,0.0003770595,0.00004550064,0.00006605106],"category_scores_gemma":[0.00001129027,0.0001458045,0.00005964754,0.0007077066,0.000101097,0.000575836,0.0005062398,0.0001317746,0.000001550606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003898158,"about_ca_system_score_gemma":0.00003202319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005837584,"about_ca_topic_score_gemma":0.000005156166,"domain_scores_codex":[0.99854,0.00009987342,0.0002922945,0.0006602776,0.0001412417,0.0002662803],"domain_scores_gemma":[0.9986188,0.0004489673,0.0001853822,0.0005889347,0.00006800647,0.00008994968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000242241,0.00005673369,0.0005392756,0.00005568027,0.001173355,3.36219e-7,0.0000966438,0.8193814,0.0001503432,0.009572956,0.001231071,0.1675],"study_design_scores_gemma":[0.0001948482,0.0000257667,0.00003769259,0.00003462335,0.001525946,5.673651e-7,0.000337349,0.9161226,0.000003502767,0.08141598,0.0001591029,0.0001420184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005416822,0.0006940687,0.9917331,0.00005525062,0.00003935193,0.0001256822,0.001708355,0.00006089154,0.0001664721],"genre_scores_gemma":[0.9882262,0.00003702821,0.004964765,0.00002762969,0.0003571554,0.00001298109,0.006032025,0.00002050125,0.0003217079],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9867684,"threshold_uncertainty_score":0.5945735,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Monotonic function; Mathematics; Riemann hypothesis; Pure mathematics; Mathematical analysis; Mathematical economics","retraction":null,"screen_n_in":null,"score":{"opus":0.118952190468566,"gpt":0.3868058791166078,"spread":0.2678536886480418,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002012935,0.0001289433,0.0002855137,0.0001021452,0.00025443,0.0002042006,0.0002816344,0.00004627578,0.0003870238],"category_scores_gemma":[0.0003004435,0.00007364901,0.00006322147,0.0003440331,0.0002056607,0.0001145515,0.0001692435,0.0001488108,0.000002693698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005682015,"about_ca_system_score_gemma":0.00002459495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001350375,"about_ca_topic_score_gemma":0.000007728629,"domain_scores_codex":[0.9989277,0.00009446329,0.0003190019,0.0003521561,0.0001724181,0.0001343353],"domain_scores_gemma":[0.9955251,0.003740765,0.00009335305,0.0005504271,0.00004118864,0.00004915339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001101965,0.00003968615,0.00001593908,0.0005598723,0.0003368639,5.858151e-7,0.0006392527,0.00002205988,0.0001022795,0.992952,0.0001748924,0.005145578],"study_design_scores_gemma":[0.00002828051,0.00001150323,0.00001286292,0.0001658038,0.001150958,0.000002043446,0.0004278824,0.1166875,0.0001743153,0.8808854,0.0003641621,0.00008930837],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1021732,0.001703971,0.8936296,0.000714555,0.000008126303,0.000134365,0.0003037119,0.00008438902,0.001248115],"genre_scores_gemma":[0.9935239,0.00004831506,0.006024751,0.00008056784,0.0000293906,0.000005852864,0.00005586062,0.00001312665,0.0002182218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8913507,"threshold_uncertainty_score":0.4237639,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.07906280035724488,"gpt":0.3874601253208776,"spread":0.3083973249636328,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002472261,0.0002574651,0.0004865731,0.0007616784,0.000218349,0.0003222716,0.0004945799,0.0001005848,0.0001387742],"category_scores_gemma":[0.0005211884,0.0002131944,0.0000938062,0.001895217,0.0001872705,0.0006534421,0.0003321231,0.0003756789,0.00001145289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003867032,"about_ca_system_score_gemma":0.0001026693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003862581,"about_ca_topic_score_gemma":0.0002048359,"domain_scores_codex":[0.9979284,0.000202195,0.0004015704,0.0008402092,0.0002663191,0.0003612849],"domain_scores_gemma":[0.9972506,0.00170766,0.00009535686,0.0007900934,0.00004857771,0.0001077346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005197482,0.0007488423,0.003587271,0.003954773,0.00401565,0.0003298458,0.01209593,0.007432393,0.005572848,0.3080491,0.0006820749,0.6530115],"study_design_scores_gemma":[0.0001543032,0.00002245467,0.00003948773,0.0004678729,0.002151447,0.00001008524,0.004559215,0.2504297,0.0001052375,0.7407495,0.00093525,0.0003754346],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06969282,0.009301051,0.9197116,0.0002920131,0.00003654878,0.0001106245,0.0001997835,0.0003265881,0.0003289627],"genre_scores_gemma":[0.8946567,0.0001462012,0.1043562,0.00007004582,0.0001366292,0.000008882409,0.0002729795,0.00005148038,0.0003008809],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8249639,"threshold_uncertainty_score":0.8693816,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Limiting; Mathematics; Statistical physics; Physics; Engineering; Mechanical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.05160219670206482,"gpt":0.3377534679285526,"spread":0.2861512712264878,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000781063,0.0001043946,0.0002572475,0.0002816516,0.0001589718,0.0001740991,0.000375648,0.00003500128,3.90331e-7],"category_scores_gemma":[0.0003292951,0.00009459466,0.00004032912,0.0005152631,0.00006737752,0.0005308641,0.000319077,0.00006880873,4.571765e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009777204,"about_ca_system_score_gemma":0.00003084063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003454437,"about_ca_topic_score_gemma":6.99759e-7,"domain_scores_codex":[0.9991003,0.00002431013,0.0002703282,0.0003857701,0.00008458739,0.0001346862],"domain_scores_gemma":[0.9984269,0.00100215,0.0001112351,0.0003482504,0.00008055835,0.00003091779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000863499,0.00005140645,0.0001189218,0.001232274,0.0007109031,5.97508e-7,0.0009156481,0.2426157,0.009427606,0.1676782,0.0000104303,0.577152],"study_design_scores_gemma":[0.0001013796,0.0000111684,0.000026655,0.0001727405,0.0003675462,7.050232e-7,0.0004097974,0.8862745,0.0003138561,0.1122013,0.00003655175,0.0000838425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01153278,0.001373435,0.9868298,0.00004421168,0.00002333252,0.00004213598,0.00001149981,0.00006497427,0.00007786548],"genre_scores_gemma":[0.6043828,0.0000225579,0.3955225,0.00003206418,0.000009078531,0.000004542805,0.00001255473,0.000003628004,0.0000102834],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6436588,"threshold_uncertainty_score":0.3857458,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}