Grass Management Regimes Affect Grasshopper Availability and Subsequently American Crow Activity at Airports
Bibliographic record
Abstract
With large expanses of open vegetation, airports serve as major attractants for numerous bird species, such as the American crow (Corvus brachyrhynchos), which can lead to high risk of bird–aircraft collision. Previous observations of large influxes of crows at the Prince George Airport (British Columbia, Canada) in July and August suggested that crows were opportunistically foraging on grasshopper (Melanoplus sp.) population eruptions in mown grass during those months. We tested whether grasshoppers were more visible (i.e., easier for crows to detect) under different grass lengths, and whether crows were preferentially attracted to these same grass lengths. Employing line transects during July to August 2010 and 2011, we detected >6 times as many grasshoppers in short-cut grass (0 to 15 cm) than in uncut grass (>30 cm). Data from 2011 also revealed that grasshopper detections by crows was significantly higher in short-cut grass than in grass left at intermediate lengths (long-cut grass [15 to 30 cm]). Crow densities also varied with grass length, with significantly more crows foraging in short-cut than long-cut or uncut grass lengths. Our results indicate that allowing the grass to grow to >15 cm could reduce the attraction of crows to the airfield and may reduce bird–aircraft collisions.
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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 | 0.001 |
| 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) | 0.002 | 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 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".