Managing Turfgrass toReduce Wildlife Hazardsat Airports
Bibliographic record
Abstract
Multiple factors-including safety regul at ions, economic considerations, location, and attrae· tiveness to wildlife recognized as hazardous to avia· tion-influence the choice of land cover at airports. The principal land cover at airports within North America has historically been turfgrass. usually cool· season perennial grass species native to Europe. However, recent research has determined that, from a wildlife perspective, not all turfgrasses are alike. Some grasses are more palatable to herbivorous hazardous wildlife (e.g., Canada geese [Branta canadensis]) than others, and thus are more likely to increase the potential for wildlife-aircraft collisions when planted near critical airport operating areas. How turfgrasses are managed (e.g., by mowing or herbicide use) can also influence the degree of use by wildlife. In this chapter we (1) review the role of vegetation in the airport environment, (2) review traditional and current methods of vegetation management on ai rfields, (3) discuss se lection criteria for plant materials in reseeding efforts, and (4) provide recommendations for future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; both teacher heads agree on what is shown here.
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".