Log Grading and Knot Identification by Oblique X-Ray Scanning
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".