What do ecological regions tell us about wood quality? A case study in eastern Canadian white spruce
Bibliographic record
Abstract
There is a growing interest in improving knowledge on wood quality of forest resources to allow for an end-product oriented supply for the wood-processing industry. We investigated the differences of white spruce (Picea glauca (Moench) Voss) wood traits from 13 ecological regions of the eastern Canadian province of Quebec and identified climatic and geographic variables and tree characteristics that may help predict wood traits in novel approaches to forest inventories. One hundred and eleven wood disks were used to determine wood traits that are related to wood anatomy and to mechanical use of wood. Our results indicate significant differences between ecological regions for most of the investigated wood traits. Samples from several coastal regions showed, for example, wood anatomy that is advantageous for some pulp and paper grades, but they offer comparatively weak estimates for mechanical applications. Ordination and projection to latent structures allowed us to identify maximum temperature, degree days, geographic location, tree height, and tree diameter as important predictor variables for wood traits. The quality of models was variable and R2 values ranged from 0.13 to 0.59. Prediction errors in an independent sample were mostly below 10%, and bias was below 5%. Although the prediction of wood mechanics was less precise, the presented results are an encouraging step toward obtaining a wood trait signature for different environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 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".