A Stochastic Structure Model for Predicting Sheet Consolidation and Print Uniformity
Bibliographic record
Abstract
The microscopic response of a paper sheet to compressive forces is of great importance in predicting print quality as well as the sheet structures developed in wet pressing and calendering. In this report, we propose a new compression model that preserves the entire three-dimensional, stochastic, fibre network structure. The model includes Z-directional deformation of fibres in both compressive and shear modes. Permanent deformation of each fibre (such as caused by fibre collapse) can be achieved by adjusting the stiffness of the fibre during compression or unloading. The stiffness of the plates can also be chosen to represent, for example, a hard printing plate, a blanket, or a soft-nip calendar cover. Although we still need to collect basic fibre stiffness data in order to perform quantitative comparisons between model predictions and experimental results, simulated structures already show typical features of consolidated paper sheets. As a first application, we study the contact mechanics between a printing plate and a model paper structure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".