Experimental Technique for Tracking the Evolution of Local Moisture Nonuniformity in Moist Paper from Wet to Dry
Bibliographic record
Abstract
Local nonuniformity of moisture content, a basic characteristic of moist paper, affects efficiency and hence cost of paper drying, and may influence product quality. Such local nonuniformity may become even more of a problem with the current interest in combining higher intensity air convection drying and cylinder drying to produce the required higher capacity hybrid dryer sections of the future. Direct determination of local sheet moisture content under dynamic conditions during drying is unacceptable because the measuring instrument presence would change local moisture content. A novel indirect technique was developed for quantitative, precise determination of local nonuniformity of moist paper by monitoring continuously the local exit pore air temperature at many positions immediately below a moist sheet subjected to air through flow. This technique was used to investigate local non-uniformity for moist machine-formed papers of grammage 19–55 g/m2, and 20–100 g/m2 handsheets of variable formation. The effect of formation and basis weight on local nonuniformity was quantitatively documented. Formation was characterized using the new method of partitioning formation nonuniformity into its components as a function of scale of formation. The results provide some evidence that it is the components of formation nonuniformity in the range of larger scale of formation, 8 to 37 mm, which most affect moisture local nonuniformity while the formation components at 0.8 to 3 mm scale of formation appear less important. Such knowledge is relevant to the development of the improved drying processes of the future.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".