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Record W4252758974 · doi:10.1109/camsap.2015.7383719

Program

2015· article· en· W4252758974 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.619
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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