Mercury Transfer in the Marine Food Chain Rationalizing Safe Consumption of Seafood
Bibliographic record
Abstract
Recent years witnessed the deleterious effect of mercury (Hg) transfer from seafood in humans through seawater to secondary consumers in the marine ecosystem [1][2].Multiple inputs of industrial, domestic, and recreational pollutants caused heavy problems to the marine environment [3].The Kuwait Bay from the Arabian Gulf caters the commercial seafood catch to the residents of Kuwait Thus, quality assurance of the marine environment is pivotal for economic feasibilities [4].This study focussed on the (a) trace trophic transfer of Hg from primary producer to primary and secondary consumers in the marine environment, (b) validated Hg concentrations in biota from pollution interests' loci (c) validated BAF in selected species of interests and (d) recommended remedial measures to seafood consumers.This study selected eight Kuwait Coast sites.Marine species were exposed to Hg for 96 h and 30 d in experimental tanks containing seawater.A direct mercury analyzer (DMA-80) with the least detection limit of 0.0015 ng/g was used for this purpose.Hg concentrations were in the sequence of Lunella coronata > phytoplankton > Acanthopagrus berda > Portunus pelagicus > seawater > zooplankton at Sites III>IV>VI>V>I>VIII>VII>II, irrespective of the summer and winter seasons, respectively.Bioaccumulation factor (BAF) of Hg >1 in the analysed species except zooplankton and in seawater at 0.6 ng/L-1.2ng/L concentrations indicated the probable transfer of Hg to other species labelling positive biomagnification whereas, the Hg value <1 was considered as the occurrence of trophic dilution for a given species [5].Thus, Hg from industrial effluent evidenced Hg accumulation in the marine ecosystem.As Hg is a perilous pollutant, their transfer from the seawater and accumulation in higher trophic organisms are of importance and pose a risk to humans who consume such Hg-contaminated seafood.
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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 | 0.000 |
| 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".