Processes and pathways of ciguatoxin in aquatic food webs and fish poisoning of seafood consumers
Bibliographic record
Abstract
Ciguatera food poisoning (CFP) is widespread in tropical and sub-tropical waters, and it is the most common food poisoning caused by marine biotoxins. The toxins involved, ciguatoxins, are produced by certain dinoflagellates of the genus Gambierdiscus, and undergo biotransfer and biomagnification up the food web to planktivorous and ultimately, top predator fishes. In this paper, we reviewed the factors and processes that regulate the production of ciguatoxins, the ecological distribution and the pathways of their biotransfer, and fish consumption guidelines to prevent ciguatera-related food poisoning. Warm waters are commonly suggested as the most important factor that enhances toxic algal blooms and ciguatoxin production. Ecological distribution of ciguatoxic fish shows great regional specificity. In most endemic areas, carnivores such as groupers and other large fish have higher toxicity than their herbivorous and smaller counterparts, supporting the food chain hypothesis proposed by J.E. Randall (J.E. Randall, Bull. Mar. Sci. 8(3): 236–267, 1958); while in other areas, for example, French Polynesia, the opposite situations also exist, questioning the biomagnification hypothesis. Some countries and regions have taken measurements to prevent ciguatera poisoning through consumption guidelines. In this review, we look at some of the measures that could be used to prevent poisoning, while encouraging people to consume fish. For example, choosing smaller and lower trophic level fish are likely to be safer to consume. We suggest an approach to maintain better databases on ciguatera cases to instruct people on fish consumption safety, and develop a general guideline for fish consumption to reduce CFP.
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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 | 0.001 | 0.001 |
| 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".