Modeling selenium bioaccumulation through arthropod food webs in San Francisco Bay, California, USA
Bibliographic record
Abstract
Trophic transfer is the main process by which upper trophic level wildlife are exposed to selenium. Transfers through lower levels of a predator's food web thus can be instrumental in determining the threat of selenium in an ecosystem. Little is known about Se transfer through pelagic, zooplankton-based food webs in San Francisco Bay ([SFB], CA, USA), which serve as an energy source for important predators such as striped bass: A dynamic multipathway bioaccumulation model was used to model Se transfer from phytoplankton to pelagic copepods to carnivorous mysids (Neomysis mercedis). Uptake rates of dissolved Se, depuration rates, and assimilation efficiencies (AE) for the model were determined for copepods and mysids in the laboratory. Small (73-250 microm) and large (250-500 microm) herbivorous zooplankton collected from SFB (Oithona/Limnoithona and Acartia sp.) assimilated Se with similar efficiencies (41-52%) from phytoplankton. Mysids assimilated 73% of Se from small herbivorous zooplankton; Se AE was significantly lower (61%) than larger herbivorous zooplankton. Selenium depuration rates were high for both zooplankton and mysids (12-25% d(-1)), especially compared to bivalves (2-3% d(-1)). The model predicted steady state Se concentrations in mysids similar to those observed in the field. The predicted concentration range (1.5-5.4 microg g(-1)) was lower than concentrations of 4.5 to 24 microg g(-1) observed in bivalves from the bay. Differences in efflux between mysids and bivalves were the best explanation for the differences in uptake. The results suggest that the risk of selenium toxicity to predators feeding on N. mercedis would be less than the risk to predators feeding on bivalves. Management of selenium contamination should include food webs analyses to focus on the most important exposure pathways identified for a given watershed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".