Presence of hardwood chips and its impact on pulp strength properties in the production of bleached softwood kraft pulp
Bibliographic record
Abstract
The effect of hardwood admixture (15-25% birch or aspen) in kraft cooking on the strength properties of the fully bleached pulp was investigated. Results obtained from both lab- and mill-processed ECF bleached pulps showed that adding 15-25% birch or aspen to the production of fully bleached softwood kraft pulp had a minor effect on the strength properties. No significant effect was observed for the hardwood admixture on the apparent density over a wide range of breaking length. Under the conditions studied, the results showed that pulping of mixed softwood/hardwood chips (chip blending) resulted in overall better strength properties than the pulp blending at a given freeness. It was hypothesized that the softwood fibers would be cooked to a higher kappa number in the cooking of mixed softwood/hardwood chips for the same target kappa number, thus having higher fiber strength due to higher pulp viscosity and preservation of the hemicellulose. This was supported by the results from zero-span tensile strength of the long fiber fraction of the samples from chip blending and pulp blending. The implication is that some softwood kraft pulp mills can add up to 25% of hardwood chips to the kraft cooking of softwood chips without significantly affecting the overall pulp strength properties.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".