Merging Interstage Fractionation and Low Consistency Advantages During the TMP Refining Process: Part III – How Fibre Morphology Impacts Paper Properties
Bibliographic record
Abstract
Fibre morphology and its evolution during refining and fractionation at low consistency were studied to understand the key relationships between the mechanical properties of paper and those of fibres. A broad analysis is presented on the physical and mechanical properties characterising the intrinsic morphological properties of fibres. The experimental refining process involved a primary stage at high consistency (HC), a fractionation stage with a small aperture basket, a low consistency (LC) refining stage, and a final high consistency refining (HCR) stage. The idea was to benefit from the pulp already being at low consistency following the screening step. Using a higher proportion of low consistency refining (LCR) tended to lower the tensile strength at 100 mL CSF, but some intermediate values did exhibit better responses to refining. Fractionation permits the use of LCR to retain fibre length and to develop additional long fibre bonding. The net gain remains even with energy reduction. It is believed, among other things, that a greater number or greater intensity of fibre-to-bar contacts would help increase internal delamination of the fibre 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".