Growth and reproductive performance in bilberry (<i>Vaccinium myrtillus</i>) along an elevation gradient
Bibliographic record
Abstract
Elevation gradients provide ideal scenarios to study plant responses to environmental factors and to global warming. Physiological and morphological traits, growth, and reproduction in bilberry were investigated at 6 elevations along an elevation gradient from 350 to 2000 m asl. Chlorophyll content and growth increased with elevation, reaching a maximum at 950 m, and then decreased, with both variables being negatively influenced by high soil pH. By contrast, after removing the positive effect of tree canopy cover, the efficiency of photosynthesis did not show differences between elevations. The number of stomata per area increased with elevation, while leaf area reached maximum values at 950 m. Regarding reproductive traits, densities of flowers and fruits were not affected by elevation, and fruit set, seed set, and seed viability only varied between localities within elevation. Moreover, flower production was negatively correlated with soil pH. Reproductive success was not limited by pollen quantity at any of the elevations. However, elevation affected number of ovules, number of mature seeds per fruit, and fruit dry weight; these variables reached their highest values at around 1700 m. These results show that, while bilberry exhibited the most favourable vegetative performance at mid elevations, maximum reproductive output was observed at higher elevations.
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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".