Effects of silicon application on <i>Betula pendula</i> seedlings
Bibliographic record
Abstract
Silicon (Si) is a beneficial element for many plant species, conferring resistance to drought and herbivory, but its effects on trees are less known. We studied responses of silver birch ( Betula pendula), grown in peat, to liquid Si supplementation (Si concentration 0.65 mmol/L) on (1) growth, (2) water economy, and (3) element accumulation plus (4) feeding preference of an insect, Epirrita autumnata, and a mammalian herbivore, Microtus agrestis. Plant growth was not affected but control (Si–) plants shed their old leaves earlier. Detached Si+ leaves lost water 11% units less than Si–, and the integrated water-use efficiency based on 13C analysis was higher in Si+. Foliar Se was higher and Mn and S lower in Si+. Root Mg concentrations were higher and Pb lower in Si+. Epirrita autumnata did not prefer either treatment, but M. agrestis preferred Si– stems. Silicon improved birch water relations as indicated by the leaf drying resistance and increased water-use efficiency. The changes in metal accumulation were probably beneficial, but the lower S/Se ratio requires attention. Furthermore, Si decreased palatability to a mammalian herbivore. Using Si as fertilizer in nurseries could be possible to increase birch tolerance to water stress and herbivory.
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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 | 0.001 |
| 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".