EXPLORATION OF THE PHYSICAL PROPERTIES OF INTERNAL CHARACTERISTICS OF SUGAR MAPLE LOGS AND RELATIONSHIPS WITH CT IMAGES
Bibliographic record
Abstract
Two groups of sugar maple (Acer saccharum Marsh) logs were scanned using an X-ray scanner to identify and locate their main internal characteristics. In the design of this exploratory study, five logs produced from a freshly cut tree (Group 1) and three logs (Group 2) sampled from a sawmill yard, were scanned to identify the various wood types present: rot; knots; colored heartwood; and sapwood. Based on these computed tomography (CT) images, four or five disks (20 mm thick) were cut from each log. Blocks of 10 x 10 x 10 mm were then cut from each disk representing areas of each type of wood. The green density, basic density, and moisture content of each block were measured to assess within-tree variations between logs and within wood type. CT grey levels in the CT images were then measured for individual blocks to assess the feasibility of identifying wood types. Finally, a correlation analysis was carried out between physical properties and CT grey levels for each internal characteristic. The results generally indicated that, for both groups of logs, the type of wood was the most significant source of variation in green density, basic density, and moisture content. Statistically significant differences in these physical properties were also observed between sapwood and the other types of wood. This is a result of practical importance since sapwood in sugar maple is the main driver of product value. From the grey level variation observed in CT images, it is possible to separate sapwood from colored heartwood and knots. The contrast in grey level between the sapwood and the area of rot is not so obvious, but it can be enhanced by means of statistical methods. The correlation analysis indicated that green density was the variable that best correlated with grey level variations in CT images. A linear relationship between green density and grey level was established for each type of wood. The coefficient of determination (R2) obtained from the simple regression analysis between grey level and green density varied between 0.32 and 0.82 depending on wood type. Nevertheless, significant differences were observed in the slopes of the curves. It is hypothesized that these differences could be mainly attributed to differences in the content and orientation of crystalline structures present in each type of wood.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".