Impacts of Resident Canada Goose Movements on Zoonotic Disease Transmission and Human Safety at Suburban Airports.
Bibliographic record
Abstract
Over the past two decades, an increase in resident (non-migratory) Canada geese (Branta canadensis) in suburban areas of the United States has heightened the awareness of human-goose interactions and the associated risks to human health and safety.Resident geese may cause goose-aircraft collisions, transmit zoonotic diseases, decrease water quality through fecal deposition, and show aggression toward humans.In response, we evaluated resident Canada goose movements at and around a suburban airport, tested geese for zoonotic diseases, and provided monitoring strategies for geese.In 2008, we neck-and leg-banded 763 resident geese at 14 sites in and around Greensboro, North Carolina.We affixed satellite transmitters to a subset of geese and collected fecal samples for analysis.To evaluate goose movements, we resighted the geese with spotting scopes 2-3 times per week for 18 months and analyzed telemetry data.We calculated survival rates, home range sizes, and core areas, and evaluated the speed, altitude, and distance of goose movements.Additionally, we monitored site recolonization of nuisance geese after conducting a lethal removal.The annual survival of marked geese was 0.9 and the frequency of satellite-tagged goose movements peaked daily within the first 2 hours after sunrise and again at sunset, and all goose movements occurred at altitudes 64 m.We determined that 2.8% of goose movements occurred during the molt (1 Jun-15 Jul), 20.7% during post-molt I 2008 (16 Jul-31 Oct), 15.2% during post-molt II (1 Nov-31 Jan), 32.3% during breeding/nesting (1 Feb-31 May), and 29.0% during post-molt I 2009 (16 Jul-31 Oct).The mean distance travelled per day by satellite-tagged geese was between 2.0 km (SE = 0.3) and 4.9 km (SE = 0.4) with Services, the Berryman Institute, NC Department of Transportation Aviation, and Piedmont
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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 | 0.001 | 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) | 0.000 | 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".