Mercury biotransport by auklets: two-colony comparison
Bibliographic record
Abstract
Abstract: Long-term monitoring data of seabirds have revealed the annual trend of mercury exposure in seabirds that are known to play a role of biovectors transporting methylmercury from the ocean to the land. However, it is unclear how wintering distribution and habitat affect the efficacy of seabirds as mercury biovectors. To address this issue, we tested whether seabirds that fly through and hence feed in regions of high [Hg] have a higher biovectors capability, and whether their at-sea behavior is more heavily impacted. To time exposure, we exploited the differential turnover rate of different bird tissues in storing Hg until they reach their colony in the Spring; to assess Hg biotransport, we sampled terrestrial plants at their colony; to assess wintering behavior, we monitored stable isotopes of nitrogen (δ15N; trophic position) and equipped birds with data loggers (geolocation and activity). By contrasting two areas on both sides of the North Pacific of high (Japan; n = 10) and low (Alaska; n = 11) Hg emissions and using the Rhinoceros auklet as a model species, we show that all sampled individuals have high Hg (0.3-11.4 μg g-1) that are ultimately deposited at their colony via feces. While exposure levels of auklet tissues were associated with environmental contamination levels during wintering, at-sea behavior was transient Alaskan birds, but long-lasting in Japanese birds. These results not only indicate that seabirds can be used as a tracer of large-scale Hg emission rates, but also that seabirds may suffer long-term behavioral changes when subjected to higher Hg during winter. Authors: Akiko Shoji¹, Kyle Elliott², Yutaka Watanuki³, Stéphane Aris-Brosou¹, Shannon Wheeler², Scott Hatch⁴ ¹University of Ottawa, ²McGill University, ³Hokkaido University, ⁴Institute for Seabird Research and Conservation
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".