A Language-Independent API for Unstructured Mesh Access and Manipulation
Bibliographic record
Abstract
Software for numerical solution of partial differential equations requires accessing, manipulating, and often modifying information about the geometry of the computational domain, the mesh used for the simulation, and discrete data stored on that mesh. Typically, applications programmers would prefer to avoid the difficulty and complexity of creating their own modules for tasks like interacting with multiple geometric modelers, mesh adaptation, and optimization algorithms, rightly preferring instead to focus on the problem physics and on studying the physical results that the code produces. Ideally, these modules would be provided by experts in CAD modeling, meshing, and optimization, and written so that they can use the application's data regardless of the data structures used by the application. This paper describes a language- and data-structure-independent interface supporting query and modification of mesh data conforming to a general abstract data model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.021 |
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 source (direct Gemma or distilled Codex), 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".