MétaCan
Menu
Back to cohort
Record W2888400569 · doi:10.1109/icgpr.2018.8441622

Weighted-averaging operators for accurate 2.5D finite-difference frequency domain radar waves modeling

2018· article· en· W2888400569 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2018 17th International Conference on Ground Penetrating Radar (GPR) · 2018
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité LavalCenter for Northern Studies
Fundersnot available
KeywordsStencilFrequency domainAlgorithmComputer scienceComputational electromagneticsFinite difference methodGridRadarRegular gridNumerical analysisMathematical optimizationApplied mathematicsMathematicsMathematical analysisPhysicsElectromagnetic fieldGeometryTelecommunicationsComputational science

Abstract

fetched live from OpenAlex

We propose an algorithm to accurately solve the frequency domain electromagnetic wave equation by finite-differences in 2.5D. The algorithm is based on previously published methods for viscoelastic waves, and relies on a nine-point stencil to build weighted-averaging numerical operators. The weights are chosen to minimize numerical dispersion and anisotropy, which allows relaxing the requirements on grid cell size and thus decreases computational costs. This new algorithm reduces the numerical error compared to central finite-difference method without increasing the numerical bandwidth of the matrix system to solve, and can be easily transposed to 3D frequency domain modeling.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.059
GPT teacher head0.314
Teacher spread0.255 · 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