Log Grading and Knot Identification by Oblique X-Ray Scanning
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The presence and location of knots within cut lumber substantially controls the physical properties and commercial value of the material. Thus, there is great practical interest in developing ways of choosing the cutting pattern for a log in a sawmill to optimize the arrangement of knots in the resulting cut lumber. X-rays can image the interior of a log to detect the arrangement of the knots; however, traditional radiography measurements are two-dimensional in character and cannot provide the needed depth information. Conversely, computed tomography (CT) can provide the required spatial details but is challenging to do in practice because of its complexity and cost. The research here aims to overcome these concerns by employing a novel “oblique” scanning technique that uses radiography to determine knot orientations with both reasonable accuracy and low cost. Image processing and detection algorithms are developed to locate and orientate the knots automatically within the scanned logs. Detection metrics of precision and recall are used to analyze the performance of the detection algorithm. Results indicate that the oblique scanning method is a viable way to detect and orientate knots within logs with both reasonable accuracy and low cost compared to existing methods. In initial tests, an average circumferential angle accuracy within 15 deg was achieved, with the detection algorithm being able to detect between 60% and 80% of the knots present within the log.
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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 | 0.001 | 0.001 |
| 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 it