Anisotropic residual based a posteriori mesh adaptation in 2D: element\n based approach
Bibliographic record
Abstract
An element based adaptation method is developed for an anisotropic a\nposteriori error estimator. The adaptation does not make use of a metric, but\ninstead equidistributes the error over elements using local mesh modifications.\nNumerical results are reported, comparing with three popular anisotropic\nadaptation methods currently in use. It was found that the new method gives\nfavourable results for controlling the energy norm of the error in terms of\ndegrees of freedom at the cost of increased CPU usage. Additionally, we\nconsidered a new $L^2$ variant of the estimator. The estimator is shown to be\nconditionally equivalent to the exact $L^2$ error. We provide examples of\nadapted meshes with the $L^2$ estimator, and show that it gives greater control\nof the $L^2$ error compared with the original estimator.\n
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".