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Record W7115902080 · doi:10.26021/16167

The Sustainable Management of Canada Geese. Report prepared part of the GEOG309 Research for Resilient Communities and Environments course, University of Canterbury, 2025.

2025· dissertation· en· W7115902080 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Canterbury Research Repository (University of Canterbury) · 2025
Typedissertation
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTransectPopulationGooseHabitatWaterfowlCensusEnvironmental justiceSample (material)

Abstract

fetched live from OpenAlex

• 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.004
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.270
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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