Defoliation, waterlogging and dung influences allocation patterns of Deschampsia caespitosa
Bibliographic record
Abstract
Wet meadows are some of the most productive communities in the northern Rocky Mountains, USA but are also among the most sensitive to grazing by native ungulates and domestic livestock. These meadows typically are inundated with floodwater in spring and early summer but are relatively dry in summer. To determine the interactive effects of clipping and flooding on plant recovery after clipping, we subjected plants of tufted hairgrass (Deschampsia caespitosa (L.) Beauv) to 6-week and 10-week waterlogging treatments in combination with 1 and 2 clipping events, with and without dung amendment in a greenhouse experiment. The experiment was designed to mimic early and late growing-season patterns of herbivory by native and domestic herbivores on a dominant species of wet meadows of this region. Waterlogged plants produced a higher percentage of roots at the surface, elongated stems to the first axial leaf, increased the proportion of tillers that flowered, but increased aboveground yield and tiller height only with the addition of dung. Root biomass declined with waterlogging when dung was not added, and a second defoliation exacerbated the negative effects of waterlogging on roots. Defoliation with short-duration waterlogging increased shoot nitrogen (N) concentration and N yield/root biomass, while continuous waterlogging reduced shoot N concentration of aboveground biomass. Dung amendment did not reverse this effect. Although extended flooding in combination with moderate rates of defoliation did not reduce aboveground biomass of Deschampsia caespitosa, it aggravated total root loss, caused shifts to a shallower root distribution, and altered N concentration of aboveground biomass for herbivores.
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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".