Optimization of chemical use for highly filled mechanical grade papers with precipitated calcium carbonate
Bibliographic record
Abstract
Response surface methodology was used with four factors to screen for the best starch and optimize the use of chemicals in order to maximize precipitated calcium carbonate (PCC) filler retention in a peroxide-bleached TMP suspension. Three commercial starches were used in conjunction with colloidal silica and flocculant. The PCC loading level and the interactions between PCC level, starch, flocculant, and silica were investigated, and empirical models were constructed. The empirical process models were then employed to predict the retention and drainage. It was found that medium-charged cationic starch (S858) gave the highest total and filler retention, whereas high-charged cationic starch (S880) resulted in the best drainage. The ash content of the handsheet can be pushed up to 40% using the retention system with medium (S858) and high (S880) charged cationic starch. The high-charged cationic starch (S880) gave stronger paper, probably because of its higher affinity with the fiber and fines.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".