MANAGEMENT OF RODENT POPULATIONS AT AIRPORTS
Bibliographic record
Abstract
Birds and other wildlife are a serious problem at U.S. airports. Certain species are more hazardous to aviation safety than others, most often due to the size and behavior of the species. Raptors, including hawks, vultures, and eagles, were the fourth most common bird group reported in bird strikes to the Federal Aviation Administration from 1991 - 1997, and hawks specifically were the fifth most common bird species group reported in bird strikes in Canada during the same time period. Red-tailed hawks were the fifth most common bird species reported in U.S. Air Force bird strikes from 1985 - 1999, resulting in over $12 million in damage costs. Since raptors are protected under the Migratory Bird Treaty Act, the ability to directly manage raptor populations is limited. Management of their habitat, however, is often more easily accomplished. Raptors are attracted to airport habitats that provide their basic necessities: food, water, and cover. Small mammals, such as mice and voles, are attractive prey for raptors. Reduction of small mammal populations at an airport may decrease raptor populations in the area and therefore, reduce the risk that raptors pose to aircraft. Reduction of small rodent populations can be achieved through a variety of methods, including habitat manipulation and the use of rodenticides. Habitat management can be accomplished through a grass height management regime or through the introduction of an endophyte-infected grass which may support fewer herbivores, both of which are currently being studied. Zinc phosphide, a rodenticide, was tested for efficacy at Kansas City International Airport. This presentation will discuss these options and the implications of the studies.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".