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Record W4252758974 · doi:10.1109/camsap.2015.7383719

Program

2015· article· en· W4252758974 on OpenAlexfundno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMathematics
TopicStatistical and numerical algorithms
Canadian institutionsnot available
FundersAir Force Research LaboratoryUniversità degli Studi di PerugiaUniversité Nice Sophia AntipolisTechnische Universität IlmenauStony Brook UniversityUniversité de Rennes 1Agency for Defense DevelopmentCentre National de la Recherche ScientifiqueLinköpings UniversitetUniversità di PisaHarbin Institute of TechnologyTechnische Universität DarmstadtAalborg UniversitetNorthwestern Polytechnical UniversityÉcole Polytechnique Fédérale de LausanneNorthwestern UniversityUniversitat Pompeu FabraUniversidade Estadual de CampinasMississippi State UniversityRutgers, The State University of New JerseyUniversidad Carlos III de MadridInstitut Mines-TélécomOffice National d'études et de Recherches AérospatialesUniversity of MinnesotaUniversidade de BrasíliaUniversity of OklahomaWashington University in St. LouisHeriot-Watt UniversityUniversity of Ontario Institute of TechnologyHelsingin YliopistoInstitut National de la Santé et de la Recherche MédicaleTechnische Universiteit DelftCalifornia Institute of TechnologyMcGill UniversityInstitut national de recherche en informatique et en automatique (INRIA)Army Research LaboratoryUniversity of Texas at AustinOhio State UniversityIndian National Science AcademyUniversity of BristolPurdue University
KeywordsComputer scienceStatistical signal processingSignal processingScalabilityData processingCurse of dimensionalityMultidimensional signal processingRobustness (evolution)Data miningDigital signal processingComputer engineeringArtificial intelligenceComputer hardware

Abstract

fetched live from OpenAlex

The following topics are dealt with: multisensor adaptive processing; wireless sensor networks; radar signal processing; hyperspectral imaging; array signal processing; beamforming; signal resolution; graph signal processing network; data processing; tensor-based signal processing; cognitive radar; machine learning; TDOA; Big Data; and sparse signal processing.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.343
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.6570.429

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.249
GPT teacher head0.435
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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