Machine learning-based prediction of internal moisture variation in kiln-dried timber
Bibliographic record
Abstract
Monitoring the moisture content uniformity in kiln-dried wood and preventing large gradients is vital as nonuniformity renders dried timbers susceptible to warpage and degrade. This research uses a gradient-boosting machine learning model to model kiln drying by providing a predictive approach to estimate moisture levels and gradients. A population of 378 western hemlock square timbers was assigned into nine drying batches, each undergoing a different drying schedule. Inputs were four timber attributes, i.e, initial and final moisture, initial weight, and basic density, and three drying parameters, i.e. drying schedule, end-schedule conditioning, and dried timber post-storage. The results revealed that drying schedules and post-storage significantly impacted moisture gradients, while the effect of conditioning was insignificant. All the input parameters were crucial in developing the predictive machine-learning model, where wood attributes had relatively higher importance than drying parameters. Also, outputs highly depend on final moisture after drying. The best training and testing performances were achieved when predicting the shell moisture, followed by the core moisture and moisture gradient. Further research is required to enhance the predictive performance of the moisture gradient predictive model. Future studies could also develop classification models for the moisture gradient beneficial to sawmills.
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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 | 0.002 |
| 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 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".