A comparison between the effects of ozone and alkaline peroxide treatments on TMP properties and subsequent low consistency refining
Bibliographic record
Abstract
As part of a program to reduce electrical energy consumption in the refining process, the effects of the ozone and alkaline peroxide treatments on fibre and handsheet properties, prior and subsequent to low consistency (LC) refining, were assessed and compared by applying different levels of ozone and a range of peroxide and alkali charges to a primary stage hemlock thermomechanical pulp (TMP). Both highly alkaline peroxide treatments and ozone treatments decreased the specific energy required for strong mechanical pulp. The improvement in pulp strength through alkaline peroxide treatment mainly resulted from pulp surface changes caused by generation of acid groups. The highly alkaline peroxide treatments significantly increased pulp brightness but did not promote the further fibrillation during the subsequent LC refining. On the other hand, ozone treatments provided tensile strength increases, along with small brightness enhancements for the dark hemlock TMP, and increased the tensile gains obtained through LC refining. The effects of ozone treatments on tensile strength before and after LC refining were the result of pulp surface modifications, fibre swelling, and loss of fibre wall integrity due to non-selective chemical attack. High levels of ozone treatment caused tear strength to decrease during subsequent LC refining.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".