Characterization and modeling of knots in black spruce (picea mariana) logs
Bibliographic record
Abstract
A knot study on black spruce was performed on 21 trees originating from a natural stand located in Quebec. Branch (knot) frequency, distribution, and diameter along different directions in the trees were evaluated. A destructive protocol for dissection was developed to slice each knot into a series of sections for modeling the knot morphology (or internal distribution) within the log. In addition, attempts were made to establish the relationship between external branch parameters and internal knot morphology.The study showed that there were higher numbers of knots on the southern sides of the trees than on the northern sides, but the knot diameters on average were smaller on the southern sides. This heterogeneous distribution of knots around the stem may increase the chance of finding a log rotation that is optimal for lumber grade yield when the knots are considered during breakdown.The dissection data were smoothed with second degree polynomial equations using an SAS® program. The equations yielded knot angles and other knot dimensional characteristics. The study indicated that internal knots in black spruce logs showed large variations in their angles, but their diameters could be predicted from the external measurements. This information is particularly important for sawing and grading models that require precise diameter data.
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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 | 0.000 |
| 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 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".