Regional patterns in mercury and selenium concentrations of yellowfin tuna (<i>Thunnus albacares</i>) and bigeye tuna (<i>Thunnus obesus</i>) in the Pacific Ocean
Bibliographic record
Abstract
Mercury (Hg) concentrations in high trophic level fish, such as bigeye tuna ( Thunnus obesus ) and yellowfin tuna ( Thunnus albacares ), can often exceed consumption advisories. Here we sampled 444 yellowfin and bigeye tuna to determine whether tuna Hg concentration varies regionally in the eastern and central Pacific Ocean and whether this variation corresponds to environmental characteristics that promote the bioavailability of Hg. Of the five regions sampled, we found significantly higher Hg concentrations in the eastern equatorial region (5°S–5°N; 110°W–120°W) for both species. Hg concentrations in this region were elevated by 0.22 and 0.17 µg·g–1for yellowfin and bigeye tuna, respectively, compared with Hg concentrations in the other regions. Tuna selenium concentrations, which may alter the toxicity of Hg, did not vary by region. Oceanographic data indicated that the eastern equatorial region had elevated chlorophyll a concentrations and shallow minimum oxygen depths, both of which promote Hg methylation. These findings suggest that methylation-promoting mechanisms may translate into regional variation in the Hg concentrations of highly mobile, high trophic level fish.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".