Enhanced Bioaccumulation and Transfer of Monomethylmercury through Periphytic Biofilms in Benthic Food Webs of a River Affected by Run-of-River Dams
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Run-of-river (ROR) power plants impound limited terrestrial areas compared to traditional hydropower plants with large reservoirs and are assumed to have reduced impacts on mercury cycling. We conducted a study on periphyton and benthic communities from different habitats of the St. Maurice River (Québec, Canada) affected by two ROR power plants and their effect on the bioaccumulation and biomagnification of monomethylmercury (MMHg). Proportion of total mercury as MMHg reached maximum values about 2.9 times higher in flooded sites compared to unflooded sites. Impoundment by ROR would therefore provide favorable environments for the growth of periphyton, which can produce and accumulate MMHg. Periphyton MMHg concentrations significantly explained concentrations in some benthic macroinvertebrates, reflecting a local transfer. Through the analysis of δ 13 C and δ 15 N signatures, we found that flooding, creating scattered lenthic habitats, led to modifications in trophic structures by the introduction of new organic matter sources. The computed trophic magnification slopes did not show significant differences in the transfer efficiency of MMHg between sectors, while intercepts of flooded sectors were higher. Increases in MMHg concentrations in flooded areas are likely due to the impoundment, combined with watershed disturbances, and the creation of small habitats favorable to periphyton should be included in future predictive models.
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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) | 0.000 | 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".