A filler distribution factor and its relationship with the critical properties of mineral-filled paper
Bibliographic record
Abstract
The use of mineral fillers in the paper industry has attracted much attention due to its low cost and ability to improve optical properties and printability. Besides the filler characteristics, paper properties, such as bulk, tensile, and opacity, are greatly affected by filler distribution in the z-direction. Therefore, optimization of filler distribution is an effective way to maximize the value of fillers. In this work, a filler distribution factor (Fc) was proposed to quantitatively describe the concentrated degree of filler distribution in the z-direction. The reduction in Fc resulted in an increase in paper bulk, porosity, and opacity, due to the generation of more interfaces between fibers and fillers. When filler particles were concentrated in one layer (Fc = 1), the tensile strength of the filled paper increased between 26 to 40% in comparison to the paper with various Fc values. For a given Fc, better tensile and opacity properties were achieved by increasing filler concentration on the surface layer of paper.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".