A method for estimating wood chip brightness and its applications<sup>1</sup>This article is a contribution to the series The Role of Sensors in the New Forest Products Industry and Bioeconomy.
Bibliographic record
Abstract
Methods for estimating wood chip brightness are important in classifying wood chips in chip piles, stabilizing chip brightness in the pulping process, and reducing bleaching chemical consumption in pulp mills. They also allow us to understand and control factors including outdoor storage in the summer that affect chip and pulp brightness. An accurate off-line method for estimating wood chip brightness has been developed. The method involves a two-stage grinding of air-dried wood chips to powders with small particle sizes and narrow size distributions and measurement of ISO (International Standardization Organization) brightness of the resulting powders. Using this method, ISO brightness values of 20 mill or pilot-plant thermomechanical pulps (TMP) can be linearly correlated, with an r2 value of 0.885, with ISO brightness of the mill or pilot-plant wood chips. Analyses of wood chips and TMP samples taken from a TMP mill every month for 1 year show that both the chip and TMP brightness values are the lowest in July. The method can be used for laboratory analysis of chip brightness, monitoring of chip brightness monthly variation in pulp mills, and checking the accuracy of the on-line chip brightness measurement system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".