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Record W2136137310 · doi:10.2528/pier13080706

FULL-VECTORIAL PARALLEL FINITE-ELEMENT CONTRAST SOURCE INVERSION METHOD

2013· article· en· W2136137310 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueElectromagnetic waves · 2013
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaMitacsUniversity of Manitoba
KeywordsInversion (geology)Finite element methodContrast (vision)Computer scienceAlgorithmPhysicsGeologyArtificial intelligenceSeismology

Abstract

fetched live from OpenAlex

The multiplicatively regularized finite-element contrast source inversion algorithm (MR-FEM-CSI) is used to solve the fullvectorial three-dimensional (3D) inverse scattering problem. The contrast and contrast-source optimization variables are located at the centroids of tetrahedra within the problem domain; whereas the electric field is expanded in terms of edge basis functions on the same tetrahedra. A dual-mesh is created in order to apply the multiplicative regularization. To handle large-scale problems the inversion algorithm is parallelized using the MPI library, with sparse matrix and vector computations supported by PETSc. The algorithm is tested using experimental datasets obtained from the Institut Fresnel database. A synthetic example shows that the technique is able to successfully image moisture hot-spots within a partially filled grain bin.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.909
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 science0.0000.000
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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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