Translocation, survival, and recovery of Kansas‐banded Canada geese
Bibliographic record
Abstract
Abstract Temperate‐breeding, or resident, Canada geese were once extirpated in Kansas, USA, but currently provide abundant viewing and hunting opportunities. Kansas Department of Wildlife, Parks, and Tourism (KDWPT) began reintroducing geese in 1980 with a goal of re‐establishing a breeding population. Successful reintroductions led to translocating flocks to regions with no previous records of nesting geese; however, KDWPT continues to translocate individuals from nuisance flocks in urban areas to rural reservoirs to reduce human conflicts with urban geese. Our goal was to determine the effects of such translocations on survival and recovery of adult, sub‐adult, and juvenile temperate‐breeding Canada geese. We used Brownie dead‐recovery models in Program MARK to compare survival and recovery probabilities between translocated and nontranslocated (normal wild) Kansas‐banded Canada geese for 2012–2017. Model‐estimated annual survival differed between status (normal wild = 0.761, 95% CI 0.734–0.785; translocated = 0.598, 95% CI 0.528–0.665). Recovery probability differed between normal and translocated adults (normal wild = 0.074, 95% CI = 0.069–0.078; translocated = 0.138, 95% CI = 0.120–0.158) and juveniles (normal wild = 0.067, 95% CI = 0.059–0.075; translocated = 0.250, 95% CI = 0.199–0.310). Recovery probability did not differ between status in the sub‐adult age class (normal wild = 0.126, 95% CI = 0.115–0.137; translocated = 0.090, 95% CI = 0.055–0.144). Translocation is a viable management option to successfully reduce survival and increase recovery probability of urban nuisance geese in Kansas.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".