Bibliographic record
Abstract
Airports attract large numbers of birds and deer primarily because they offer immense tracts of foraging and nesting habitats free from the threat of predation. Border Collies can serve as an effective means of wildlife control in these environments by introducing a predator into the ecosystem. Many wildlife dispersal methods seek to imitate predators or the effect of predators and become increasingly ineffective as the birds or deer habituate to the stimuli. Border Collies however, are true predators, representing an actual, not perceived, threat to wildlife thereby eliminating the problems of habituation. Since Border Collies are under the direct control of a handler, they disperse wildlife only in prescribed areas and at the direction of the handler. Border Collies can be stopped at any point in time, by either recalling the dog to the handler or lying the dog down. Border Collies, being top predators, elicit flight reactions from almost all forms of wildlife and birds. Border Collies have been bred to run a hundred miles a day and will work for hours on end. Not only can they deter the largest of birds, particularly Canada geese, but are also highly effective against wildlife like deer and rabbits. Border Collies are also bred not to harm wildlife, including birds, so they can be employed in dispersing protected or endangered species of birds or mammals. A single Border Collie and handler can easily maintain an area of approximately 2 square miles free of larger birds and wildlife. In February 1999, Southwest Florida International Airport became the first commercial airport in the world to employ Border Collies in an airfield wildlife control program. Since then, several other airports and airbases have instituted similar programs at their facilities – including Vancouver
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 | 0.001 | 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 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".