Optimization of beech wood pulping in catalyzed acetic acid media
Bibliographic record
Abstract
Abstract Fagus sylvatica wood samples were treated in HCl‐catalyzed, acetic acid solutions. The effects of selected operational variables (catalyst concentration, reaction time and liquor to wood ratio) on pulp yield, composition of pulps and composition of pulping liquors were assessed using incomplete, second‐order, centred, factorial designs. Pulp composition was measured by the contents in cellulose, xylan and lignin. The concentrations of glucose, xylose and furfural in pulping liquors were also considered as experimental variables. Under selected conditions, pulps with 5.8% to 7.5% Klason lignin (kappa numbers in the range 25 to 33), 77.2% to 85.3% cellulose and 3.3% to 6.1% xylan were obtained at 45.8% to 50.0% pulp yield. The selected pulps showed good SCAN viscosity (723 to 814 mL/g) and alkaline resistances which were affected by the acetyl group content of samples (6.6 to 7.6 weight percent).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".