Optical Measurement of Paper Moisture Content with Application in Paper Pressing
Bibliographic record
Abstract
Abstract We present a novel non-contact method for measuring the moisture content of paper. In the method, paper is illuminated obliquely by light from an IR LED, and the light reflected from the paper is imaged by a short-wave infrared (SWIR) camera. Owing to the high absorptivity of liquid water to light in the 1400-1500 nm wavelength range, the intensity of light reflected off the paper diminishes sharply with increasing moisture content. We show that for a variety of paper samples (Whatman paper, NBSK, NBHK, tissue paper) and moisture contents of up to 200%, there is a monotonic relationship between reflectivity and moisture content. This relationship is independent of the wood species used to make the paper, but does differ between wood-based papers and cotton-based papers. As this method applies an optical camera, the spatio-temporal distribution of moisture content in paper can be measured. The method was validated by separately measuring moisture content in paper gravimetrically for both uniform and non-uniform moisture distributions. The value of this measurement technique was demonstrated by measuring the moisture distribution in paper during a simulated pressing operation.
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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 | 0.001 | 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".