Inter‐annual variation in the breeding chronology of arctic shorebirds: effects of weather, snow melt and predators
Bibliographic record
Abstract
Arctic breeding shorebirds travel thousands of kilometres between their wintering and breeding grounds, yet the period over which they arrive and begin to initiate nests spans only several weeks. We investigated the role of local conditions such as weather, snow cover and predator abundance on the timing of arrival and breeding for shorebirds at four sites in the eastern Canadian arctic. Over 11 years, we monitored the arrival of 12 species and found 821 nests. Weather was highly variable over the course of this study, and the date of 50% snow cover varied by up to three weeks between years. In contrast, timing of arrival varied by one week or less at our sites, and was not well predicted by local conditions such as temperature, wind or snow melt. Timing of breeding was related to the date of 50% snow melt, with later snow melt resulting in delayed breeding. Higher predator abundance resulted in earlier nesting than would be predicted by snow cover alone. We hypothesise that when predation risk is high, the value of potential re‐nesting exceeds the energetic risks of early breeding. Synchrony of breeding was significantly higher in late breeding years suggesting a relatively fixed date for the termination of nest initiation, after which nesting is no longer profitable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".