Resolving Conflicts with Canada Geese: An Animal Welfare Perspective
Bibliographic record
Abstract
The scope and significance of human conflicts with urban and suburban Canada goose populations has been growing rapidly since the mid 1980s. A lack of basic understanding about the biology and ecology of locally abundant goose populations has led, in part, to argument between opposing camps over the appropriate approaches and methodologies to resolve human-goose conflicts. Animal welfare interests have focused on the humaneness of roundup and slaughter programs, and advocated non-lethal approaches coupled with what they view as the more benign population control activity of egg addling. Some traditional wildlife managers have argued that non-lethal approaches have been tried and have failed, and that procedures such as addling do not work quickly or effectively. Differences have led to legal confrontations that absorb considerable energy and effort and may make cooperative involvement more difficult. This paper articulates some of the arguments that comprises the basis for the perspective of animal welfarists. It ends with a call for greater cooperation and involvement between all interests concerned with Canada geese.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".