The Sustainable Management of Canada Geese. Report prepared part of the GEOG309 Research for Resilient Communities and Environments course, University of Canterbury, 2025.
Bibliographic record
Abstract
• Canada Geese were introduced to Aotearoa in the early 1900s and now inhibit much of the country.• Christchurch City Council conduct annual culls with 356 geese culled in 2025.• There are concerns about the environmental impacts of Geese.• Our research aim was to investigate what impacts Canada Geese have on the New Brighton environment.• Our other research aim was to investigate if there is a more humane and sustainable way to manage them.• Conducted in collaboration with Danette Wereta from the Animal Justice Party Aotearoa.• Fieldwork was conducted along the Avon River red zone using transect and quadrant sampling to estimate Goose dropping density.• Canada Geese population counts were conducted over three observation days using visual and photographic surveys.• Average population within the study site was 70 geese.• Data was analysed using inferential statistics to extrapolate results from the sample transects to the wider study area.• Estimated 718,000 droppings per year, producing 4,000 kg of waste annually.• Lethal control offers immediate reduction but is expensive and raises ethical issues.• Non-lethal control options such as habitat modification, hazing and reproductive control are more human but require long-term commitment.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".