Mercury Biomagnification in Marine Zooplankton Food Webs in Hudson Bay
Bibliographic record
Abstract
While much research has been carried out on mercury in large marine mammals and associated food webs in northern regions, comparatively less has been conducted on lower trophic levels including zooplankton and the subsequent transfer to predators, which marks the entry of mercury into northern marine food webs. We present here the first database for mercury uptake and transfer exclusively within zooplankton food webs in northern marine waters. We have investigated both total (THg) and monomethylmercury (MMHg) concentrations, and isotopic signatures (δ(15)N and δ(13)C) in individual zooplankton taxa collected over a period of eight years (2003-2010) from across Hudson Bay (including Hudson Strait and Foxe Basin) as part of research icebreaker cruises. δ(15)N values ranged from 3.4 to 14.0‰, implying trophic levels ranging from 1 to 4, and THg concentrations ranged from 5 to 242 ng g(-1) dw. Food web linkages were identified within the data set, and mercury biomagnification was evident both with THg and MMHg concentrations increasing from prey to predator, and with trophic magnification factors (TMFs). Total mercury and MMHg transfer in a unique prey-predator linkage (Limacina helicina-Clione limacina) are investigated and discussed with regard to known physiological and biochemical characteristics. The results suggest that exposure to mercury at higher trophic levels including humans can be affected by processes at the bottom of Arctic marine food webs.
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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) | 0.001 | 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".