Assessment of a Western Canada Goose translocation: Landscape use, movement patterns and population viability
Bibliographic record
Abstract
To provide new hunting opportunities in California and reduce nuisance and damage complaints \nin Nevada, 645 western Canada geese (Branta canadensis moffi tti) were trapped n moffifi near Reno, Nevada, and \nreleased on state wildlife areas around Humboldt Bay, California, 1987???1992. Numbers increased to about \n3,200 by 1997 and an annual September sport hunt was initiated in 1998. The fl ock numbered about 1,500 \nindividuals, 1999-2001. Farms used by the geese in recent years had more water bodies and were closer to \nroost sites than unused farms. Other landscape variables such as area/size and roads around farms were not \nsignifi cantly different between used and unused farms. Sixty-eight of 630 (11%) banded birds were encountered \noutside the study area; 70% of the 68 emigrants were goslings (<1 year old) or yearlings (<2 years old). \nTwenty-one of 23 birds not killed when they were known to be outside the area returned to Humboldt Bay. \nMovements were in the north, northeast direction and were as far as British Columbia and Alberta, Canada, \nindicating that the small, resident Humboldt Bay fl ock is a part of the Pacifi c population of western Canada \ngeese. A population viability analysis modeling the response of this small fl ock to harvest indicated stable \nnumbers can be maintained with an annual harvest of ~200 birds. The model also predicts a rapid decline \nwhen harvests exceed 300 birds and a rapid increase in numbers when harvest levels were reduced. This \nstudy presents 1 of the few post-translocation assessments of a wildlife population.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".