Including variation in branch and tree properties improves timber grade estimates in Scots pine stands
Bibliographic record
Abstract
Deterministic modelling of roundwood quality with expected values of quality factors underestimates the variation in timber grade distribution and leads to sudden transitions of all of the trees in a size class between the quality classes. We constructed a recursive model chain that predicts the height of the lowest living and dead branches for a set of maximum branch diameters based on stem diameter, tree height, and height of the crown base. By using Monte-Carlo simulation, a timber grade distribution for a stand was integrated over the multidimensional random distribution of the recursive model chain. The results demonstrated that introducing random variation resulted in major changes in the calculated quality distributions. The timber grade distributions were wider compared with the expected value-based predictions. In particular, the proportions of the highest and lowest quality classes were under- or over-estimated in the deterministic predictions. Because of the random variation of tree properties, the timber grade distribution also became more continuous over time. The results also showed that the timber grade distributions can vary considerably between stands with similar stem diameter distributions. Understanding the effects of random quality variation will give guidance to forest managers to reach more profitable management regimes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".