Selenium distribution in a lake system receiving effluent from a metal mining and milling operation in Northern Saskatchewan, Canada
Bibliographic record
Abstract
The release of selenium (Se) at relatively low concentrations into aquatic ecosystems over time can result in the accumulation and, if thresholds are exceeded, subsequent adverse effects in sensitive species, including higher trophic levels (such as fish). A milling operation in Northern Saskatchewan, Canada, releases treated effluent into a small stream system, and Se has accumulated in sediments and aquatic biota over time. The present study evaluated four small lakes downstream of the effluent discharge point, and one lake upstream, in order to describe and understand the distribution of Se in abiotic environmental compartments and the transfer of Se into benthic macroinvertebrates. The concentrations of Se in sampled sediments were highly variable but exceeded proposed thresholds for the protection of fish and aquatic birds in all study lakes downstream of the effluent discharge point. Selenium concentrations in surface water, whole-sediment, and sediment pore water revealed that whole-body Se concentrations in benthic invertebrates (chironomids) are best correlated with Se in pore water. It is proposed that Se accumulates in sediments through an association with the total organic carbon content of sediment and that Se is fixed from the surface water by micro-organisms and primary producers. The relationship between Se in pore water and Se in whole sediments appears to be influenced by the organic carbon content of each medium, and Se bioavailability in sediment and transfer to higher trophic levels via benthic macroinvertebrates is likely speciation dependent.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".