Are Turf-type Tall Fescue Cultivars Useful for Reducing Wildlife Hazards in Airport Environments?
Bibliographic record
Abstract
Wildlife-aircraft collisions pose a serious risk to aircraft and cost civil aviation over US$1 billion worldwide annually. Habitat management within airport environments is the most important long-term component of an integrated approach to reduce the use of airfields by hazardous wildlife. Recent research has demonstrated that Canada geese avoid foraging on endophyte-infected tall fescue; consequently, this turfgrass might be useful in airfield revegetation and seeding projects. Although some research evaluating commercially available tall fescue cultivars on airfields has been conducted, additional information is needed to determine if tall fescue cultivars might be viable for airfields in various regions of the U.S. In 2007, a study was initiated to examine the establishment of currently available high-endophyte �‘turf-type’ tall fescue grasses at 9 airfields. The objectives were to: 1) determine if selected tall fescue cultivars establish on airfields across the U.S. and 2) provide airport-specific recommendations for tall fescue cultivar selection. At each airfield, 12 tall fescue cultivars were seeded into 3 replicate experimental plots in either fall of 2007 or spring of 2008. Although tall fescue cover varied among airports, most cultivars resulted in similar amounts of tall fescue cover after one or two growing seasons. This study demonstrates and identifies tall fescue cultivars that will grow successfully in the environmental conditions found on these airfields while providing airfield vegetation that is minimally attractive to wildlife hazardous to aviation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 teacher head, 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".