Improving tree selection for partial cutting through joint probability modelling of tree vigor and quality
Bibliographic record
Abstract
Tree classification systems are generally designed to predict the supply of high-quality logs for the wood processing industries and to mark defective trees for removal with the aim of improving both the vigor and quality of future stands. Although these two objectives are generally inversely related, their joint consideration could enable the development of robust criteria for improved tree selection in partial cuttings of northern hardwood stands by targeting low-vigor (LV) trees of high quality (HQ). In this study, we used a copula approach to model the joint probability distribution of trees characterized by both vigor and quality, which accounts for the statistical dependence between the different classes of both classification systems. The relationships between the probability of occurrence of a tree in each joint category and tree diameter at breast height (DBH) were quite similar among the three studied species: yellow birch (Betula alleghaniensis Britt.), sugar maple (Acer saccharum Marsh.), and American beech (Fagus grandifolia Ehrh.). In particular, the probability of occurrence of LV–HQ trees was characterized by a parabolic curve with a maximum value attained at the mid-DBH range, whereas that of LV and low-quality (LQ) trees increased with increasing DBH. This suggests that instead of harvesting large and mostly LV–LQ trees, partial cutting should target smaller LV–HQ trees, so that the volume of harvested HQ trees would increase without compromising the silvicultural objective of improving the vigor of future stands.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".