Responses of festuca rubra to natural and simulated foraging by geese on Akimiski Island, Nunavut Territotory
Bibliographic record
Abstract
The effects of goose foraging have been studied extensively in sub-Arctic intertidal and freshwater marshes, but impacts are less understood in supratidal marshes, where Festuca rubra is a dominant forage grass. In this study, I examined the responses of Festuca to natural and simulated goose grazing and grubbing in an area used by Snow, Canada, and Brant Geese at Akimiski Island, Nunavut Territory. Both artificial defoliation and protection from natural grazing altered patterns of growth. Protection rapidly converted short, grazed swards to a tall growth form, while tall grass resisted conversion in the opposite direction. Shoots transplanted into simulated grubbing performed well, but those transplanted into areas previously grubbed by geese usually died. This research provides evidence that both grazed and recently grubbed areas have the potential to recover if protected from further damage, but recovery following devegetation becomes increasingly difficult over time.
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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".