Diving schedules of Common Loons in varying environments
Bibliographic record
Abstract
Many species of diving birds adjust their foraging behaviour in response to variation in their environment. The Common Loon (Gavia immer) is a visually oriented predator sensitive to environmental variation, yet little is known about the flexibility of its diving behaviour. We tested the hypothesis that loons adjust their diving schedules by increasing or decreasing the dive duration during foraging bouts to accommodate environmental variation during the breeding season. The dive duration and dive-pause components of the loon dive cycle did not vary among lakes with different lake chemistry, lake morphometry, mercury levels in their blood, or fish abundance. We observed some variation among loons in different stages of breeding in mean dive-pause intervals. The dive-pause component of the diving cycle of Common Loons does not seem to be related to the amount of time spent underwater. To our knowledge, this is the first report of such a nonlinear relationship in diving birds. We propose that loons vary the components of their diving behaviour independently and seem to alter their diving time budgets regardless of the external stimuli we addressed. This unresponsive diving schedule may make loons susceptible to catastrophic changes in prey densities within their foraging areas, as they are obliged to forage on one, or very few, lakes. Conversely, loons may only forage in lakes with fish abundance above a certain minimum threshold and preferentially avoid lakes with reduced prey abundance.
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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".