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Record W2069615772 · doi:10.1145/358407.358410

Superconvergent interpolants for collocation methods applied to mixed-order BVODEs

2000· article· en· W2069615772 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

VenueACM Transactions on Mathematical Software · 2000
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSuperconvergenceCollocation (remote sensing)Orthogonal collocationOrdinary differential equationApplied mathematicsCollocation methodMathematicsBoundary (topology)Boundary value problemGaussPartial differential equationComputer scienceAlgorithmDifferential equationMathematical analysisFinite element method

Abstract

fetched live from OpenAlex

Continuous approximations to boundary value problems in ordinary differential equations (BVODEs), constructed using collocation at Gauss points, are more accurate at the mesh points than at off-mesh points. From these approximations, it is possible to construct improved continuous approximations by extending the high accuracy that is available at the mesh points to off-mesh points. One possibility is the bootstrap approach, which improves the accuracy of the approximate solution at the off-mesh points in a sequence of steps until the accuracy at the mesh points and off-mesh points is consistent. A bootstrap approach for systems of mixed-order BVODEs is developed to improve approximate solutions produced by COLNEW, a Gauss-collocation-based software package. An implementation of this approach is discussed and numerical results presented which confirm that the improved approximations satisfy the predicted error bounds and are relatively inexpensive to construct.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.971
Threshold uncertainty score0.997

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)0.0040.000

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 head0.024
GPT teacher head0.322
Teacher spread0.299 · 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