Simulating the impact of kraft pulping and bleaching parameters on Eucalyptus camaldulensis pulp properties using MATLAB
Bibliographic record
Abstract
Abstract In this work, Eucalyptus camaldulensis was evaluated as the raw material for chemical pulp under different pulping and bleaching conditions. The pulping was carried out at different H‐factors, and at different effective alkalis. The resulting pulps were then oxygen delignified and bleached using various dosages of bleaching chemicals in a D0EpD1 sequence. The effect of independent variables (pulping or bleaching variables) on dependent variables (pulp properties) was analysed based on the multivariable least square method via MATLAB software. An agreement was found between the results predicted from the models and the experimental data. To obtain a kappa number of 15, the optimum pulping conditions were a temperature of 155°C, a time of 225 min, and an EA of 23%. Also, the hexenuronic acid (HexA) content of cooked pulp was significantly decreased in the chlorine dioxide bleaching stages, and was only marginally related to the final HexA content of bleached pulps.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".