How do shoot clipping and tuber harvesting combine to affect<i>Bolboschoenus maritimus</i>recovery capacities?
Bibliographic record
Abstract
In French Mediterranean wetlands, the combined effects of predation of tubers by wildlife and grazing of aboveground tissue by livestock on the recovery capacities of Bolboschoenus maritimus (L.) Palla are not well known. A container study was conducted that applied tuber harvests at varying levels (20%–90%) and shoot clipping (with or without). Response to harvesting and clipping was recorded as changes in total biomass, number, and mean mass of tubers (calculation of variation indexes). Bolboschoenus maritimus failed to recover from even the lowest tuber harvesting level of 20% and the total number of tubers and biomass decreased. A significant decrease in mean tuber mass over time and approximately no production of new tubers accounted for this absence of compensatory response. The harvesting level had a linear effect on the variation indices of total number of tubers and mean tuber mass. By separating the relative effect of shoot clipping from that of tuber harvesting alone, the results showed that clipping had an additive effect on mean tuber mass, reducing it by about 20%, without any effect on tuber number. The absence of compensatory response under our experimental conditions suggests that clonal plant regrowth partially depends on post-disturbance environmental conditions in the growing season, in our case, dry 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 | 0.001 |
| 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".