A critical review of the biogeochemistry and ecotoxicology of selenium in lotic and lentic environments
Bibliographic record
Abstract
Anthropogenic activities resulting in elevated selenium (Se) levels in aquatic ecosystems can result in teratogenic and reproductive effects in fish and waterfowl. However, relationships between observed effects and exposure concentrations or body burdens are ambiguous. Therefore, it is critical to identify factors that affect Se ecotoxicity before defining adequate protective environmental regulations. One important political debate questions if Se ecotoxicity differs between standing (lentic) and flowing (lotic) waters and, if so, how this should be incorporated into the definition of protective criteria. In the present review, we compile and discuss the scarce literature regarding Se ecotoxicity in lotic systems, and we compare it to the substantial body of evidence for lentic systems. General differences between lentic and lotic systems with respect to ecology, hydrology, and biogeochemistry are identified and related to Se ecotoxicity. The limited knowledge regarding Se speciation in the biomagnification process is reviewed and put in context. Fundamental considerations suggest that Se ecotoxicity in lotic systems should be reduced compared to lentic systems, but we conclude that this statement is not substantiated by the existing data. Additionally, we identify critical gaps of knowledge that must be resolved in future studies before the argument can be decided conclusively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".