Elements and omega-3 fatty acids in fishes along a large, dammed river
Bibliographic record
Abstract
Damming of a river can trap and elevate levels of sediment-bound elements and alter food web dynamics in created reservoirs. It follows that dams may alter how elements and other nutrients, like the beneficial omega-3 fatty acids (n-3 FAs) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are accumulated in fish and thus the chemical composition of species above and below this barrier to migration. This study examined the spatial and species differences in contaminants and nutrients in fish from the Wolastoq | Saint John River (New Brunswick, Canada) in association with a large hydroelectric dam (Mactaquac Generating Station; MQGS), a river which supports both recreational fisheries and subsistence fishing by Indigenous communities. In 2020 and 2021, Smallmouth Bass, Yellow Perch, American Eel, and Striped Bass were collected from locations upstream (reservoir and river) and downstream of the MQGS and analyzed for mercury (Hg) and 30 other trace elements, n-3 FAs, δ15N, and δ13C. Fish from the reservoir were highest in the beneficial elements P, S, and K, while fish from upstream of the reservoir had lower levels of toxic elements, including Hg. The dam appeared to alter food web dynamics, as fish from the reservoir and immediately downstream of the dam had higher δ15N and reservoir fish were depleted in δ13C. DHA and Hg were positively corelated with δ15N, and EPA in Smallmouth Bass was higher in sites where fish had higher δ13C. Overall, this study suggests that the dam altered food web dynamics and the uptake of contaminants and nutrients by fish, and that location and species are important factors when examining the risks and benefits of consuming wild fish from a system impacted by a large dam.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".