Effect of Algal and Bacterial Diet on Methyl Mercury Concentrations in Zooplankton
Bibliographic record
Abstract
We studied the effect of zooplankton diet on MeHg accumulation in different zooplankton size-fractions from lakes of different trophic status. Using fatty acid biomarkers, we tested the hypotheses that (a) variations of MeHg concentrations are determined bythe taxonomic composition of zooplankton and (b) concentrations of dietary algal and bacterial compounds can predict MeHg concentrations of seston (10-64 microm), micro- (100-200 microm), meso- (200-500 microm), and macrozooplankton (>500 microm) in lakes on Vancouver Island, Canada. MeHg concentrations increased from seston (4-48 ng g dry weight(-1)) to macrozooplankton (94-240 ng g dry weight(-1)), indicating that MeHg accumulated as a function of plankton size. Results from linear regression analysis showed that MeHg concentrations were not significantly related to the taxonomic composition of zooplankton. However, using dietary lipid biomarkers, we demonstrated that bacterial diet (R2 = 0.50; p < 0.01) could better predict variations of MeHg concentrations in zooplankton than essential algal diet (R2 = 0.35; p < 0.01). Because MeHg accumulation within the planktonic food web was higher (20x) than the observed accumulation of total bacterial (6.5x) and algal (4.7x) diet biomarkers, zooplankton retained dietary MeHg more efficiently than bacterial and algal diet compounds. These results indicate that MeHg of macrozooplankton, the preferred prey size of planktivorous fish, is more efficiently transferred than essential diet compounds to organisms at higher trophic levels.
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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 | 0.001 |
| 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) | 0.000 | 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".