Effects of Nesting Bald Eagles (<i>Haliaeetus leucocephalus</i>) on Behaviour and Reproductive Rates in a Great Blue Heron (<i>Ardea herodias</i>) Colony in Ontario
Bibliographic record
Abstract
Bald Eagles (Haliaeetus leucocephalus) and Great Blue Herons (Ardea herodias) are known to occasionally nest in mixed colonies, even though the former is one of the primary predators of the latter. I observed the two species in four heron colonies near Lake Simcoe, Ontario during two field seasons to assess whether rates of heron chick mortality or nest abandonment were greater in a colony that supported a nesting pair of Bald Eagles than in three nearby single-species colonies. I assessed the effects of eagle presence on heron behaviour using heron movement rates, the number of heron sentries left in colonies during the nesting period, heron nest mortality rates, and the average number of successfully fledged herons per nest. There was no statistically significant difference in movement rate among the four colonies, proportion of birds remaining as sentries, nor nest mortality rates. However, nests in the mixed colony successfully fledged significantly more heron young per nest than did nests in the single-species colonies. The mixed colony was located in a wetland and open lake system that provided extensive foraging habitat and an abundance of the preferred fish prey species of both Great Blue Herons and Bald Eagles, thus reducing predation pressure on the herons.
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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".