Mercury in tunas and blue marlin in the North Pacific Ocean
Bibliographic record
Abstract
Models and data from the North Pacific Ocean indicate that mercury concentrations in water and biota are increasing in response to (global or hemispheric) anthropogenic mercury releases. In the present study, we provide an updated record of mercury in yellowfin tuna (Thunnus albacares) caught near Hawaii that confirms an earlier conclusion that mercury concentrations in these fish are increasing at a rate similar to that observed in waters shallower than 1000 m. We also compiled and reanalyzed data from bigeye tuna (Thunnus obesus) and blue marlin (Makaira nigricans) caught near Hawaii in the 1970s and 2000s. Increases in mercury concentrations in bigeye tuna are consistent with the trend found in yellowfin tuna, in both timing and magnitude. The data available for blue marlin do not allow for a fair comparison among years, because mercury concentrations differ between sexes for this species, and sex was identified (or reported) in only 3 of 7 studies. Also, mercury concentrations in blue marlin may be insensitive to modest changes in mercury exposure, because this species appears to have the ability to detoxify mercury. The North Pacific Ocean is a region of both relatively high rates of atmospheric mercury deposition and capture fisheries production. Other data sets that allow temporal comparisons in mercury concentrations, such as pacific cod (Gadus macrocephalus) in Alaskan waters and albacore tuna (Thunnus alalunga) off the US Pacific coast, should be explored further, to aid in understanding human health and ecological risks and to develop additional baseline knowledge for assessing changes in a region expected to respond strongly to reductions in anthropogenic mercury emissions. Environ Toxicol Chem 2017;36:1365-1374. © 2017 SETAC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".