Moisture Distribution Changes and Wetwood Behavior in Subalpine Fir Wood during Drying Using High X-Ray Energy Industrial CT Scanner
Bibliographic record
Abstract
In some species, such as subalpine fir (Abies lasiocarpa [Hook] Nutt), the water content of the confined zones in heartwood is as high as or greater than that of sapwood. Such wet zones of heartwood are referred to as “wetpocket” or “wetwood.” Wood products from subalpine fir forests are adversely affected by the wetwood-associated problems, particularly during the drying process. The objectives of the study were as follows: (1) to investigate feasibility of a high X-ray energy industrial computed tomography (ICT) scanner for imaging wetwood; and (2) to determine changes of the 2-D and 3-D moisture profiles (from core to shell) at different drying times. Although medical CT scanning has been used for attaining signal intensity profiles of typical wood at different drying times, the technology has not, to date, been used for the study of wetwood phenomenon. This study presents, for the first time, results from the ICT imaging of the wetwood phenomenon. The results indicate that the ICT imaging system provides a powerful technique for imaging wetwood at different drying times. In addition, the results show that during the initial phase of drying, almost flat moisture profiles were observed in all wood types except for the wetwood, which showed a relatively higher moisture profile. A much slower (sluggish) drying development pattern at each increment from core to shell was found within the wetwood zone than normal wood regions along the width, thickness, and length of the board.
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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 | 0.000 |
| 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) | 0.001 | 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".