PolyTex: A parametric textile geometry modeling package
Bibliographic record
Abstract
PolyTex is an open-source toolkit for geometry modeling of woven textiles based on volumetric images. It provides functionality such as geometrical feature extraction, local variability analysis and textile geometry modeling. A meshing module was implemented to generate voxel meshes. Generation of tetrahedral conformal meshes will be implemented in future release. Local material properties are assigned to each cell in the generated mesh, such that the anisotropic and heterogeneity are reflected. This image-based model is commonly referred to as a “Digital Material Twin”. The toolkit is designed to provide material scientists with accurate numerical models to predict composite behaviors while not requiring extensive experience in image processing and mesh generation. Hence, Application programming interface (API) for OpenFOAM and Abaqus is provided. We release this toolbox as an open-source project aiming to facilitate the application of numerical simulations based on digital material twins to engineering problems. In this regard, the project is well documented (https://polytex.readthedocs.io/) and we would appreciate any contributions from the community (e.g. comments, suggestions, and corrections aimed at improving the software and documentation). Our issue tracker is at https://github.com/binyang424/PolyTex/issues. Please report any bugs that you find or fork the repository on GitHub and create a pull request. We welcome all changes, big or small, and we will help you make the pull request if you are new to git. PolyTex is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version. PolyTex is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE for more details.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.108 | 0.038 |
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".