Geese and grazing lawns: responses of the grass<i>Festuca rubra</i>to defoliation in a subarctic coastal marsh
Bibliographic record
Abstract
In coastal marshes on James Bay and southern Hudson Bay, Canada, the grass Festuca rubra L. is heavily used for forage by both Snow (Chen caerulescens caerulescens (Linnaeus)) and Canada Geese ( Branta canadensis (Linnaeus)). On Akimiski Island, James Bay, this grass occurs in a mosaic of short, heavily grazed patches and tall, lightly grazed patches. We investigated whether short plants are primarily a plastic morphological response to grazing by geese. Over two growing seasons, we measured growth of short patches protected from grazing and of tall patches subjected to mowing treatments, with unmanipulated short and tall controls. Protection resulted in rapid conversion from a short growth form to a tall form, similar in height and biomass to plants in tall control plots. Mowed tall plants also rapidly recovered to near tall control values for height and biomass. A single 3 week treatment episode often permitted nearly full recovery to tall form. In some cases, more sustained protection was required, but repeated mowing ultimately may have reduced growth rates. These results indicate that the morphology of individual plants of Festuca rubra plastically recovers from changes in defoliation pressure. Consequently, short patches likely represent grazing lawns maintained by intensive foraging by geese.
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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) | 0.000 | 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".