Bibliographic record
Abstract
The wet web strength of handsheets containing PCC (precipitated calcium carbonate) fillers with different particle sizes was evaluated for various concentrations of PCC. Also the friction between wet sheets containing PCC was evaluated in the range 30-55% solids content. It was found that incorporating PCC in the sheet using cPAM (cationic polyacry-lamide) as a retention aid leads to the formation of large PCC aggregates, which have a negative impact on wet web strength. On the other hand, incorporating PCC in sheets using PEI (polyethylene imine) as a retention aid, leads to a decrease in PCC size and the wet web strength is found to be very close to that of sheets without PCC. It is suggested that PCC particles or small aggregates deposited on fibers can increase the friction between fibers, thus increasing the entanglement friction and the wet web strength, whereas large PCC aggregates, which due to their large size can easily detach from fibers, interfere with fiber entanglements during the consolidation of the sheet, resulting in lower strength. The increase in friction was confirmed by measurements of the friction force between two wet sheets. In the regime where capillary forces are absent, depositing PCC particles or aggregates on top of a wet sheet increases the friction between sheets. It was also found that the addition of microfibrils to the PCC-filled handsheets increases the wet web strength, likely because of mechanical entanglements caused by the microfibrils.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.362 | 0.230 |
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".