Tufted puffin reproduction reveals ocean climate variability
Bibliographic record
Abstract
Anomalously warm sea-surface temperatures (SSTs) are associated with interannual and decadal variability as well as with long-term climate changes indicative of global warming. Such oscillations could precipitate changes in a variety of oceanic processes to affect marine species worldwide. As global temperatures continue to rise, it will be critically important to be able to predict the effects of such changes on species' abundance, distribution, and ecological relationships so as to identify vulnerable populations. Off the coast of British Columbia, warm SSTs have persisted through the last two decades. Based on 16 years of reproductive data collected between 1975 and 2002, we show that the extreme variation in reproductive performance exhibited by tufted puffins (Fratercula cirrhata) was related to changes in SST both within and among seasons. Especially warm SSTs corresponded with drastically decreased growth rates and fledging success of puffin nestlings. Puffins may partially compensate for within-season changes associated with SST by adjusting their breeding phenology, yet our data also suggest that they are highly vulnerable to the effects of climate change at this site and may serve as a valuable indicator of biological change in the North Pacific. Further and prolonged increases in ocean temperature could make Triangle Island, which contains the largest tufted puffin colony in Canada, unsuitable as a breeding site for this species.
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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 | 0.001 | 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".