Contrasting leaf phenology in two white oaks, <i>Quercus magnoliifolia</i> and <i>Quercus resinosa</i>, along an altitudinal gradient in Mexico
Bibliographic record
Abstract
In tropical latitudes, the analysis of leaf phenology in tree species of lineages with temperate origin can help better understanding the potential effects of climate change on these forests. Over three years (2008–2010), we recorded the timing of bud burst (BB), leaf unfolding (LU), and leaf spreading (LS) and their relation to temperature, precipitation, and soil water potential in two deciduous oak species (Quercus magnoliifolia Née and Quercus resinosa Liebm.) along an altitudinal gradient at the Tequila Volcano, central Mexico. Quercus magnoliifolia was monitored at three altitudes, 1450, 1667, and 1787 m, and Q. resinosa was monitored at 1787, 2055, and 2110 m. The onset of BB, LU, and LS occurred earlier at lower elevations with higher temperature in Q. magnoliifolia, but in Q. resinosa only the onset of BB occurred later at lower elevations with higher temperature. BB, LU, and LS were not correlated with rainfall and soil water potential in the two species. The total duration time of leaf development was not significantly correlated with rainfall in Q. magnoliifolia, but a significant negative correlation with rainfall was found in Q. resinosa. Results indicated that leaf phenology of the two examined oak species exhibited contrasting responses to temperature and 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 | 0.000 | 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".