Modeling regional and climatic variation of wood density and ring width in intensively managed Douglas-fir
Bibliographic record
Abstract
Characteristics of annual rings are reliable indicators of growth and wood quality in trees. The main objective of our study was to model the variation in annual ring attributes due to intensive silviculture and inherent regional differences in climate and site across a wide geographic range of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco). Ring specific gravity and ring width of Douglas-fir were examined at five long-term Levels-Of-Growing-Stock (LOGS) installations, three in the US and two in Canada, covering a latitudinal gradient between 43°N and 50°N. At each location, increment cores were collected from replicated plots with three levels of stocking: control (unthinned), lightly thinned (70% basal area retention), and heavily thinned (30% basal area retention). X-ray densitometry analysis provided ring specific gravity and width profiles for 5676 rings from 134 trees. The reduction of stand density through repeated entries resulted in decreased ring specific gravity and increased ring width. A four-parameter mixed-effects logistic model was used to predict ring specific gravity using cambial age, stand density (as number of stems per hectare), and two climatic variables: average temperature from March to May and total precipitation from April to August. A three-parameter mixed-effects logistic model was used to predict ring width using cambial age, stand density (as stand density index), and total climatic moisture deficit of June and July. Both models indicated significant site differences that were included in the models through indicator variables. Ring specific gravity increased slightly with increasing average temperature from March to May and decreasing total precipitation from April to August. Predictions of ring specific gravity of Douglas-fir appear to be more sensitive to changes in temperature compared with changes in precipitation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".