The influence of risk factors associated with captive rearing on post-release survival in translocated cirl buntings <i>Emberiza cirlus</i> in the UK
Bibliographic record
Abstract
Abstract Population decline resulting from agricultural intensification led to contraction of the range of the cirl bunting Emberiza cirlus in the UK to a small area of south Devon. As part of the UK Biodiversity Action Plan for the species, a project to re-establish a population in suitable habitat in Cornwall was undertaken during 2006–2011, in which chicks were removed from the nest in Devon, hand-reared and then delayed-released. The survival of the birds to four time points in the year after release was analysed in relation to the effect of rearing factors, using a multivariable logistic regression model. Individuals with higher body weight at capture were more likely to survive to 1 January and 1 May in the year following release, and individuals released in June and July were more likely to survive than those released in August. Individuals released in 2006 and 2011 had a higher survival rate than those released during 2007–2010. Timing of capture, time spent at each stage in captivity, medication and the detection of parasites in the brood had no significant effect. Immunosuppressive disease, weather factors and predator activity may have led to some of the observed differences in survival. This analysis provides evidence with which to plan future translocation projects for cirl buntings and other passerine birds.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".