Balancing the risks and the benefits of local fish consumption in Bermuda
Bibliographic record
Abstract
Fish consumption today is widely recognized as highly beneficial since it constitutes a good source of several essential nutrients, such as selenium and polyunsaturated fatty acids (n-3 PUFA). However, fish can also contain contaminants such as mercury, which make the consumer, especially pregnant women, confused about the risk-benefit balance associated with fish consumption. This is particularly true for tropical fish species for which little information is available. We have previously reported that some Bermudian neonates had elevated mercury in their umbilical blood compared with international guidelines. The objective of this study was to give precise and balanced information on the content of mercury, selenium and PUFA in the most consumed fish species in Bermuda. In 2003 and 2006, a total of 307 fish were collected from 43 fish species and 351 samples were analysed (305 flesh samples, 44 liver samples, one roe and one fat sample) by inductively coupled plasma-mass spectrometry (ICP-MS) (metals) and high-resolution gas chromatography (HRGC) (fatty acids). Results show that mercury varies among species from 0.03 to 3.3 microg g(-1) and that it is possible for at-risk groups such as pregnant women to make informed choices concerning fish consumption, e.g. maximizing fish species rich in nutrients and low in mercury.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".