New methodology to optimize sorting in wood drying
Bibliographic record
Abstract
One important problem in lumber drying is to maintain the lumber moisture content distribution within certain minimum and maximum values. Over-drying and under-drying are common causes for lower grade recovery and dimensional stability problems. It is known that both over-drying and underdrying can be reduced by sorting green lumber into different moisture content groups and sorting also offers the opportunity to redesign drying schedules. In this study, a new methodology was designed and tested to optimize kiln drying of lumber by implementing green sorting coupled with modified drying schedules. The methodology was applied to optimize the drying of 114 by 114mm2 hem-fir lumber sorted with an NMI capacitance type meter at a local sawmill. It was found that in comparison to unsorted lumber, sorting into three groups can reduce the drying time by 7% approximately and recover around 3/4 of the under-dried lumber. The experimental data on lumber degrade and moisture gradients measured after drying were also reported in this paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".