Knot detection in computed tomography images of partially dried jack pine (<i>Pinus banksiana</i>) and white spruce (<i>Picea glauca</i>) logs from a Nelder type plantation
Bibliographic record
Abstract
X-ray computed tomography (CT) of logs means possibilities for optimizing breakdown in sawmills. This depends on accurate detection of knots to assess internal quality. However, as logs are stored, they dry to some extent, and this drying affects the density variation in the log and therefore the X-ray images. For this reason, it is hypothetically difficult to detect log features in partially dried logs using X-ray CT. This paper investigates the effect of improper heartwood–sapwood border detection, possibly due to partial drying, on knot detection in jack pine (Pinus banksiana Lamb.) and white spruce (Picea glauca (Moench) Voss) logs from New Brunswick, Canada. An automatic knot detection algorithm was compared to manual reference knot measurements, and the results showed that knot detection was affected by detected heartwood shape. It was also shown that logs can be sorted into two groups based on how well the heartwood–sapwood border is detected to separate logs with a high knot detection rate from those with a low detection rate. In that way, a decision can be made whether or not to trust the knot models obtained from CT scanning. This can potentially aid both sawmills and researchers working with log models based on CT.
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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 | 0.001 | 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".