Foraging preferences of Canada geese among turfgrasses: Implications for reducing human–goose conflicts
Bibliographic record
Abstract
Abstract Canada geese ( Branta canadensis ) can cause serious damage to turfgrass areas and create human health and safety concerns (e.g., collisions with aircraft, disease transmission). We conducted a study during 2005–2007 to determine if Canada geese exhibit a feeding preference among various commercially available turfgrasses. Behavioral responses of captive geese to 9 turfgrasses, bare ground, and litter were observed over 6 4‐week trials during July–September following the installation of selected turfgrasses into experimental arenas. Captive geese preferred to forage on Kentucky bluegrass, creeping bentgrass, and fine fescue sods compared to centipedegrass, St. Augustinegrass, and zoysiagrass. Forage qualities and macronutrient levels varied among the turfgrasses and might explain the foraging preferences geese exhibited during this study. Canada goose feeding rate was positively correlated with crude protein, nitrogen content, and calcium, but negatively correlated with acid detergent fiber content, within various turfgrasses. Our findings suggest careful selection of turfgrasses could be an effective method for reducing Canada goose conflicts in urban and suburban areas. © 2011 The Wildlife Society.
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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".