Avanex TM Unique Endophyte Technology--Bird Deterrent Endophytic Grass for Amenity Turf and Airports
Bibliographic record
Abstract
Bird strike is recognised throughout the civil and military aviation industries as a significant cost with more than $US 2 billion/year attributed to this problem globally. To manage this risk the aviation industries have in the past adopted many practices to frighten wildlife from airports rather than reducing the attractiveness of the area by grass habitat management. There has been little proven research on ground cover species evaluation using fungal endophyte to reduce wildlife. Our research has shown that the adoption of a specialist Neotyphodium fungal endophyte (strain AR601) placed in a turf type tall fescue cv. Jackal (Festuca arundinacea Schreb.), is reducing bird numbers and strike rates on the test airfields under study. In the recreational industry, large birds such as Canada geese (Branta canadensis), which forage on tender new shoots and stems, can cause severe damage to turf with the added issue of faecal contamination and associated heath issues. The inoculation of another specialist endophyte (strain AR95) into a turf type ryegrass (Lolium perenne L.) cv. Colosseum offers another tool to reduce this problem. This paper reviews the results from trials of these selected turf type grasses containing the unique fungal endophytes and shows effects on herbivorous, insectivorous and omnivorous birds that visit airfields and surrounding parklands for foraging. Specialty endophyte grasses may also have a place in the viticulture and horticulture industries to reduce the use of insecticides. Extracts and mulches offer a new opportunity to use grass endophyte associations.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".