Detection of red heartwood in paper birch (<i>Betula papyrifera</i>) using external stem characteristics
Bibliographic record
Abstract
Red heartwood, a dark nonhomogenous discolouration in paper birch trees ( Betula papyrifera Marsh.), limits the applications and uses of sawn boards to nonvisible low-value products, thus resulting in substantial value loss. The occurrence and distribution of red heartwood were investigated in 12 paper birch trees grown in the province of Quebec, Canada. The youngest tree was 62 years old at breast height and the oldest 86 years old for an average of 75 years old. In this study, 225 occurrences of external traits, relating to branch scars and forks, previously proposed as initiation points for red heartwood were identified and measured. The distribution of red heartwood was digitally mapped and the effect of these external traits on the red heartwood surface and shape inside each tree was examined. Results show that red heartwood initiates from an external trait and that multiple external traits can contribute to the development of a red heartwood column following the longitudinal axis of the stem. Red heartwood appeared to initiate mainly from external traits at the base of the tree. A modelling exercise indicated that the width of the red heartwood column inside a standing tree can be estimated from branch scar width and height from the ground. Tree vigour could not be linked to the proportion of red heartwood inside standing trees. A three-dimensional analysis of log shape could potentially be used to detect red heartwood presence in a log before processing to optimize the log sawing pattern.
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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 | 0.000 |
| 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) | 0.001 | 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".