Simulating Effects of Fiber Crimp, Flocculation, Density, and Orientation on Structure Statistics of Stochastic Fiber Networks
Bibliographic record
Abstract
The Neyman-Scott process is adapted to the problem of simulating the statistical properties of stratified stochastic fibrous materials. The simulations suggest a relationship between the mean number of fibers per zone and mean voids, independent of the nature of the stochastic fibrous structure: the characteristic shape of the transfer function curve persists whether or not the structure contains crimped fibers or if it is random or flocculated, isotropic or anisotropic. This could be an important universal effect. The mean and standard deviation turn out to be positively related for some fiber network parameters, such as mean voids, fiber density, and mean number of fiber bounds. Also, the simulations suggest that fiber crimp has a higher impact on isotropic structures. As crimp is increased, isotropic structures tend to present smaller mean voids, higher mean number of fibers per zone, and higher total number of bonds per fiber than anisotropic structures.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".