Methyl mercury in zooplanktonthe role of size, habitat, and food quality
Bibliographic record
Abstract
Pathways of methyl mercury (MeHg) accumulation in zooplankton include ingestion of organic matter (OM). We analyzed fatty acid (FA) biomarkers in zooplankton to (i) investigate the effect of allochthonous and autochthonous OM ingestion on MeHg concentrations ([MeHg]) in zooplankton and (ii) examine how algal and bacterial food sources affect MeHg bioaccumulation. We partitioned bulk zooplankton samples (i.e., >500, 202, 100, and 53 μm) from Lake Lusignan (Québec) and measured [MeHg] and [FA] in each fraction. [MeHg] increased with increasing body size and was significantly higher in pelagic than in littoral macrozooplankton (>500 μm). The amount of the ingested terrestrial FA biomarker 24:0 indicated that less than 1% of the total FA in zooplankton was derived from allochthonous sources. More than 60% of the ingested FA originated from algal biomarkers and <10% from bacterial biomarkers. Relative amounts of algal-derived essential FA and bacterial FA were not associated with [MeHg] in any size fraction. In pelagic zones, the amount of MeHg in zooplankton related positively to the number of large organisms such as Calanoid copepods and Daphnia. We propose that the accumulation of MeHg in lacustrine zooplankton depends on the zooplankton habitat rather than on the quality of ingested food.
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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 | 0.000 | 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".