Variation in 13 leaf morphological and physiological traits within a silver birch (<i>Betula pendula</i>) stand and their relation to growth
Bibliographic record
Abstract
Differences between genotypes are important for the ability of local populations to cope with environmental stress, especially in sessile, long-lived organisms such as trees. Despite the importance, differences between genotypes within local populations in traits relevant for growth are still unexplored. In this study, we examined differences between genotypes, studying 13 physiological and morphological traits and their relation to growth, using 15 silver birch (Betula pendula Roth) genotypes representing a natural population. Our data show the complex regulation of growth under field conditions. There were differences between genotypes in all measured traits, but the genotypes occupied a continuous, narrow range of values for individual traits. Although leaf morphological traits had most explanatory power for the variation in estimated biomass, the differences between genotypes in individual traits seemed random. Exceptions were specific leaf area (SLA) and the fresh mass to dry mass ratio of the leaves (FMDM), which correlated well with estimated biomass. Additionally, genotypes with high biomass tended to have more stable SLA and FMDM values across years. Thus, within a local silver birch population, different morphological and physiological configurations can lead to a similar outcome in terms of biomass, but SLA and FMDM are important for good growth under field conditions.
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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 | 0.001 | 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".