Wood Density-Climate Relationships Are Mediated by Dominance Class in Black Spruce (Picea mariana (Mill.) B.S.P.)
Bibliographic record
Abstract
The relationships between climate and wood density components, i.e., minimum ring density, maximum ring density and mean ring density have been studied mainly in dominant trees. However, the applicability of the findings to trees of other dominance classes is unclear. The aim of this study was to address whether climate differentially influences wood density components among dominance classes. X-ray densitometry data was obtained from 72 black spruce (Picea mariana (Mill.) B.S.P.) trees harvested in Northwestern Ontario, Canada. Dominant, co-dominant and intermediate trees were sampled and the data analysed using mixed-effect modelling techniques. For each density component, models were first fitted to the pooled data using ring width and cambial age as predictors, before monthly climatic variables were integrated into the models. Then, separate models were fitted to the data from each dominance class. In general, the addition of climatic factors led to a small but significant improvement in model performance. The predicted historical trends were well synchronized with the observed data. Our results indicate that trees from all dominance classes in a stand should be sampled in order to fully characterize wood density-climate relationships.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".