Genotype- and provenance-related variation in the leaf surface secondary metabolites of silver birch
Bibliographic record
Abstract
The cuticular wax layer on silver birch (Betula pendula Roth) leaves is rich in cyclic secondary metabolites that provide defense against various environmental factors. Micropropagated trees from the southern (60°N), central (62°N), and northern (66°N) latitudes of Finland were grown in a common garden setup and quantified for variation in leaf surface secondary metabolites and other leaf traits, and their association with genotype and provenance was studied. The 12 genotypes studied differed greatly in the quantity of surface secondary metabolites, both for individual flavonoid and triterpenoid aglycones and for the overall metabolite profile. Qualitative differences were observed for one triterpenoid that was present in a single genotype (R3). The variance explained by the provenance was low (between 1% and 36%) for most metabolites, but the profile showed clear separation by provenance. The contents of two alkyl coumarates, reported for the first time in silver birch leaf waxes, displayed differences among the provenances. Correlations between the surface secondary metabolites and damage by insect herbivores suggest an association between the surface compounds studied and herbivore resistance. Altogether, the contents of leaf surface secondary metabolites varied strongly among the silver birch genotypes, and the profile varied clearly among the provenances.
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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".