Breeding settlement and dispersal in a northern population of American kestrels.
Bibliographic record
Abstract
Birds should choose breeding sites that will maximize fitness. In so doing, they must evaluate a host of environmental and ecological cues that signal the costs and benefits that may be realized at a particular breeding site. This evaluation begins at a broad spatial scale, such as when deciding whether to undergo large among-year movements, and continues to the finest scale which involves an evaluation of the specific attributes of potential nest sites. Furthermore, parents should invest in reproduction to maximize their fitness according to the conditions existing at their breeding site. I evaluated the influence of prey abundance and structural features of the nest and surrounding habitat on nest-site selection, and reproductive investment and success of a population of American kestrels (Falco sparverius) in north-central Saskatchewan. I also evaluated the influence of the perception of the risk of nest predation on these variables by experimentally manipulating auditory and visual cues of the presence of a common nest predator near nest sites. Finally, I utilized stable hydrogen isotope ratios in feathers to examine among-year settlement decisions (breeding dispersal) and conducted preliminary analyses to ascertain the mechanisms responsible for the observed variation in hydrogen-isotope values in the feathers of kestrels ...My study showed that further examination of the processes affecting individual physiologies during breeding may lead to a better understanding of deuterium enrichment of raptor feathers. My study also highlights the complexity of the ecology of nest-site selection, suggesting that while food is important for certain aspects of reproduction, interspecific and scale-dependent interactions with landscape features play an important role in nest-site selection and breeding decisions. --P. i-ii.
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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 | 0.000 | 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".