Evaluating nest box addition as a population augmentation strategy for tree swallows, Hirondelle bicolore, in interior British Columbia, Canada
Bibliographic record
Abstract
Tree swallow populations across North America are exhibiting negative annual trends. An exception to the broader trend is interior BC, Canada where tree swallow populations are growing. Augmentation of successful populations by the addition of nest boxes to cavity poor landscapes may be an appropriate strategy to mitigate or reverse the national decline. This was evaluated through an analytical comparison of the reproductive performance (clutch size and hatchlings) of tree swallow breeding pairs nesting in natural cavities and nest boxes in the William’s Lake region of BC, Canada. For the 2001-2003 breeding seasons mean clutch size (means ± SE, nest boxes: 5.94 ± 0.199 eggs (n=83), cavities: 4.2 ± 0.204 eggs (n=74)) and number of living hatchlings (means ± SE, nest boxes: 4.51 ± 0.265 chicks(n=83), cavities: 2.51 ± 0.27 chicks(n=74)) detected were significantly higher (clutch size: p-value=0.0001, hatchlings: p-value 0.0001, alpha=0.05) for pairs using nest boxes. Nest boxes provide tree swallow breeding pairs with additional nesting territories and result in higher reproductive rates. Nest box addition has the potential to be a long term population augmentation mechanism but further studies on long term impacts at the community level are necessary before it can be implemented as a management plan.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".