Performance and response to defoliation of <i>Sanguisorba officinalis</i> (Rosaceae) seedlings from mown and successional habitats
Bibliographic record
Abstract
In agricultural habitats, selection may favour plants that show a pronounced ability to tolerate stress induced by specific management methods. However, genetic erosion associated with habitat fragmentation may diminish this ability. To assess the role of mowing as a selection pressure and the impact of fragmentation processes on the ability to tolerate foliage loss, we grew 215 plants of the perennial herb Sanguisorba officinalis L. originating from 16 differently sized populations, located in mown meadows and successional fallows, in a common environment, and measured their performance and response to defoliation. Plants from meadows and fallows neither differed in performance characters nor in their ability to compensate for foliage loss. However, independently from the habitat of origin, populations slightly differed in performance. This variation was not due to differences in population size, plant density, or level of genetic variation, indicating its independence from genetic erosion, which may go along with habitat fragmentation. Rather, these differences between populations appear to be the outcome of unknown selection pressures or random genetic drift. Plants from successional fallows retain their potential to cope with mowing, presumably due to the low generation turnover of the perennial species. Selection by mowing may act over time scales larger than those reflected by the developmental stage of the current habitats.
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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".