Modelling the heartwood profile of Douglas fir in France based on the stem profile and other tree dendrometric characteristics—insights from experimental sites and commercial log data
Bibliographic record
Abstract
Douglas fir ( Pseudotsuga menziesii (Mirb.) Franco) is a softwood species that is becoming increasingly important in Europe. To improve the quality of products for certain specific outdoor uses, there is an interest in limiting the amount of sapwood, the non-durable part, and in enhancing the amount of heartwood. The aim of this work was to develop a model of heartwood distribution in Douglas fir stems, taking tree dendrometric characteristics and silviculture into account. Several statistical models of varying complexity were developed, using sampling data from silvicultural experiments in France. Cross-validation and validation on an independent dataset of commercial logs demonstrated the good performance of the models. Stem size at any height in the tree was the major predictor of the longitudinal heartwood profile. The other dendrometric characteristics of the trees had only minor effects, suggesting limited silvicultural control of heartwood formation. Nevertheless, plausible model behaviour and interesting insights were found for two contrasting silvicultural scenarios, using a growth simulator. A more complete simulation study, including additional wood quality criteria, should be performed in order to provide recommendations to forestry practice.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".