Pre-flocculation of precipitated calcium carbonate filler by cationic starch for highly filled mechanical grade paper
Bibliographic record
Abstract
Three commercial starches were evaluated in conjunction with colloidal silica and flocculant to retain precipitated calcium carbonate (PCC) filler. A unique feature of this study was the fact that the filler was pre-flocculated by a portion of starch (2kg starch/t PCC) and the rest of the starch was added after the flocculant but before the silica. The pulp used was peroxide bleached thermo-mechanical pulp (TMP). A statistical design methodology was employed and empirical process models were constructed based on the analysis of variance (ANOVA) results. The models were then employed to predict the retention and drainage. It was found that the high-charged cationic starch gave the highest retention and best drainage performance. The high-charged cationic starch S880 also resulted in stronger paper, probably because of the larger and stronger flocs produced and its higher affinity with the fiber and fines. Finally, pre-flocculation was found to provide stronger paper compared with a conventional starch/retention aid addition sequence.
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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) | 0.001 | 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".