Effect of grazing history, experimental defoliation, and genotype on patterns of silicification in<i>Agrostis tenuis</i>Sibth.
Bibliographic record
Abstract
We examined the effect of grazing exclosures in reducing the concentration of silica, the effect of experimental defoliation on silica accumulation, and the existence of genotypic variability in silica content in plants of Agrostis tenuis. Silica content of plants was higher in heavily grazed areas than within exclosures during the summer, but no significant differences in content were found in winter. Laboratory experiments indicated that there were significant differences among genotypes in their silica content. Some genotypes increased their silica content after clipping at intervals of 14 days but most of them reduced their silica content when clipped weekly. The existence of a significant genotype-clipping treatment interaction term showed that silica accumulation as a response to clipping varied among genotypes, and may provide the basis for microevolutionary changes in natural populations of A. tenuis. However, the possibility of silica acting as a defence was not supported when caterpillars of the graminivorous Lepidoptera, Pararge aegeria, fed on plants. The caterpillars fed more extensively on plants from grazed areas, which had a higher silica content. We conclude that there is some genetic variation in patterns of silica accumulation in plants of A. tenuis and that different grazing histories lead to differences in silica content of plants. Although the effect of silica concentration on large herbivores remains uncertain, herbivores can exert an effect on the silica content of the leaf blades of A. tenuis.
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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".