Numerical comparison of some Hessian recovery techniques
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- Meta-epidemiology (narrow)
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Simulation or modelingConsensus signal: Simulation or modeling
- Genre
- Candidate signal: MethodsConsensus signal: Methods
- Teacher disagreement score
- 0.438
- Threshold uncertainty score
- 1.000
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.410 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Abstract Derivative recovery techniques are used in a posteriori error indicators to drive mesh adaptation. Their behaviour in the core of the computational domain and on boundaries constitutes an important efficiency factor for a subsequent mesh adaptation process. A methodology to compare recovery techniques for second‐order derivatives from a piecewise linear approximation is presented in this paper. A systematic approach to measuring the performance of recovery techniques using analytical functions interpolated on a series of meshes is proposed. The asymptotic behaviour of some recently published recovery techniques, as well as new ones, is numerically assessed on various type of meshes. Recommendations are done on the choice of a recovery technique. Copyright © 2007 John Wiley & Sons, Ltd.
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.
The record
- Venue
- International Journal for Numerical Methods in Engineering
- Topic
- Advanced Numerical Methods in Computational Mathematics
- Field
- Engineering
- Canadian institutions
- Polytechnique Montréal
- Funders
- Fonds Québécois de la Recherche sur la Nature et les Technologies
- Keywords
- Polygon meshHessian matrixA priori and a posterioriPiecewiseComputer scienceApplied mathematicsMathematical optimizationAlgorithmPiecewise linear functionSeries (stratigraphy)MathematicsMathematical analysis
- Has abstract in OpenAlex
- yes