On the use of X-ray computed tomography for determining wood properties: a review<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
In several processes of the forest products industry, an in-depth knowledge of log and board internal features is required and their determination needs fast scanning systems. One of the possible technologies is X-ray computed tomography (CT) technology. Our paper reviews applications of this technology in wood density measurements, in wood moisture content monitoring, and in locating internal log features that include pith, sapwood, heartwood, knots, and other defects. Annual growth ring measurements are more problematic to be detected on CT images because of the low spatial resolution of the images used. For log feature identification, our review shows that the feed-forward back-propagation artificial neural network is the most efficient CT image processing method. There are also some studies attempting to reconstruct three-dimensional log or board images from two-dimensional CT images. Several industrial prototypes have been developed because medical CT scanners were shown to be inappropriate for the wood industry. Because of the high cost of X-ray CT scanner equipment, other types of inexpensive sensors should also be investigated, such as electric resistivity tomography and microwaves. It also appears that the best approach uses various different sensors, each of them having its own strengths and weaknesses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".