Vertical gradients and tree-to-tree variation in shoot morphology and foliar nitrogen in an old-growth <i>Pinus strobus</i> stand
Bibliographic record
Abstract
Acclimation to ambient light has been assumed to be the principal cause of vertical gradients in leaf nitrogen, foliar morphology, and related traits in forest canopies. We examined the relative role of crown exposure, damage, and reproduction as correlates of vertical gradients in shoot and needle morphology, anatomy, and chemistry in a ~120-year-old stand of Pinus strobus L. (eastern white pine) in central Ontario. Internodes at the top of trees were longer, wider, and produced more fascicles, but had lower fascicle survivorship than lower-canopy shoots. Needles on upper-canopy shoots also had higher allocation to resin ducts than mid- or lower-canopy needles, contained less nitrogen on a mass basis, and showed a higher leaf mass per area, C/N ratio, and chlorophyll a/b ratio. Tree-to-tree variation among the 26 trees measured traits was high, owing, in part, to differences in crown damage and reproductive status. Crown damage was associated with reduced leaf mass per area and other traits associated with shade acclimation, while high cone production was associated with reduced leaf nitrogen and chlorophyll in the upper canopy. Our results suggest that factors other than light acclimation play an important role in determining vertical gradients in foliar morphology, nitrogen, and leaf production in forest canopies.
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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) | 0.001 | 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".