Distribution of Experimentally Added Selenium in a Boreal Lake Ecosystem
Bibliographic record
Abstract
Human activities have increased the release of selenium (Se) to aquatic environments, but information about the trophic transfer dynamics of Se in Canadian boreal lake systems is limited. In the present study, Se was added as selenite to limnocorrals (2-m-diameter, 3000-L in situ enclosures) in a boreal lake in northwestern Ontario to reach nominal concentrations of 1 and 10 µg Se/L in triplicate each for 77 d, and 3 additional limnocorrals were controls with no Se added. Total Se concentrations were determined in water, sediment, periphyton, benthic macroinvertebrates, zooplankton, and reproductively mature female fathead minnows (Pimephales promelas; added on day 33) collected throughout (and at the end of) the exposure period. Mean measured water Se concentrations in the control, 1-, and 10-µg/L treatments were 0.12, 1.0, and 8.9 µg/L. At the end of exposure (day 77), enrichment functions ranged from 7772 L/kg dry mass in the 8.9-µg/L treatment to 23 495 L/kg dry mass in the 0.12-µg/L treatment, and trophic transfer factors for benthic macroinvertebrates ranged from 0.49 for Gammaridae to 2.3 for Chironomidae. Selenium accumulated in fathead minnow ovaries to concentrations near or above the current US Environmental Protection Agency criterion (15.1 µg/g dry mass for fish ovary/egg) in the 1.0- and 8.9-µg/L treatments, suggesting that, depending on aqueous Se speciation, such exposures have the potential to cause Se accumulation in fish to levels of concern in cold-water, boreal lake systems. Environ Toxicol Chem 2019;38:1954-1966. © 2019 SETAC.
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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 | 0.001 | 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) | 0.000 | 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".