Influence of climate on tree rings and vessel features in red oak and white oak growing near their northern distribution limit, southwestern Quebec, Canada
Bibliographic record
Abstract
Little is known about environmental controls on vessel features in ring-porous tree species. Our objectives were to assess (i) the association between tree-ring descriptors (vessels and width) and climate in two oak species, white oak, Quercus alba L., and red oak, Quercus rubra L., and (ii) the utility of vessel series in climate reconstruction. The study was conducted in southern Quebec and 10 trees of each species were analyzed. For each species, 11 chronologies (vessel and ring width) were developed and compared. Few differences were observed between the oak species. All vessel chronologies were associated with those of ring dimension and none revealed a unique climate signal. Current growing season conditions were mainly associated with latewood features, whereas those of the year prior to ring formation were mainly associated with earlywood features. The best climate variable to reconstruct was the July Canadian Drought Code and the best reconstruction model was derived from earlywood, latewood, and ring-width chronologies. We conclude that vessel chronologies for Q. alba and Q. rubra have limited use in dendroclimatology. Vessel features are best used to identify event years recorded during the life of a tree. Vessel series could prove useful, however, in calibrating physiologically based models of tree growth.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".