Neural network model for paper forming process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Paper is made by a continuous high-speed filtration drainage of an aqueous suspension of fibres. This article presents a new approach to the controllable simulation of paper forming, using artificial neural network methods. The model incorporates dynamics of the forming process, like turbulence, drainage speed and preferential drainage through earlier less-dense regions, and fibre properties like propensity to clump or "flocculate", fibre flexibility and concentration of fibres in the suspension. Results for monofibre layer structures are described, showing effects of turbulence and its decay during drainage in causing clumping or "flocculation". The commercial process has as one of its main goals, the reduction to tolerable levels of the nonuniformity in mass distribution resulting from flocculation. The new model yields data corresponding to that obtainable along arbitrary scanning lines in planar stochastic fibrous structures, providing profiles, variances and histograms of local areal density, histograms of local free fibre lengths. These results closely resemble experimental data from commercial paper samples, obtained from radiographic or optical transmission images subjected to image analysis.
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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 it