Delayed greening of mountain birch leaves:Ecological and chemical correlates
Bibliographic record
Abstract
Flushing leaves of deciduous trees can be exposed to high herbivore pressure. Young foliage provides high quality food for herbivores, and therefore herbivore pressure is much higher in young than mature foliage. Plants may escape the effects of herbivory in time by delaying leaf greening. We measured delayed greening as an intensity of redness in leaves in mountain birch at bud burst. Furthermore, we analyzed covariation of leaf redness with leaf growth and leaf consumption by the autumnal moth, Epirrita autumnata (Bkh.), a major defoliator of mountain birch. We also analyzed concentrations of amino acids, sugars and phenolic compounds of leaves. Leaf redness was positively associated with leaf growth, and consumption by E. autumnata indicated reduced leaf resistance against herbivores. Concentrations of protein-bound amino acids and gallotannins in young leaves were closely and positively correlated with leaf redness. Concentrations of glucose exhibited close positive correlations with leaf redness at the beginning of the season, whereas the correlation was negligible in mature leaves. Concentrations of other sugars increased with increasing degree of leaf redness towards the end of leaf development. This may be one reason for high leaf biomass losses in trees with delayed greening.
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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".