Refining pulp for tensile strength
Bibliographic record
Abstract
Abstract Increased tensile strength of paper is a primary objective of low consistency refining. Although refining is typically controlled by Specific Refining Energy and Specific Edge Load, these parameters are not independent because both depend directly on power. To overcome this shortcoming, we derived simplified expressions for the number and intensity of impacts on pulp. The number of impacts reflects the capacity of the refiner to impose loading cycles on pulp. The intensity, combined with a response parameter, reflects the probability of a successful refining event at each impact. Based on these parameters, we employed an equation based on cumulative probability to predict tensile strength of pulp after refining. Non-linear fits of this equation to data from the literature for a wide range of pulps refined by various refiners gave response parameters that were remarkably similar ranging from 1.6–3.3×10−6 1.6\text{--}3.3\times {10^{-6}} kg/J. The resulting probability of successful refining events at each impact was found to be quite small, about 1–3 %. We postulated that this is likely due to most force being imposed at fibre crossings in the pulp network. Consequently, multiple cycles are required to expose other parts of fibres and new fibres to loadings. In summary, this new approach to characterizing refining reflects the stochastic nature of the process and enables a direct quantitative link between refiner operation and fibre development.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".