A Dietary Assessment of Selenium Risk to Aquatic Birds on a Coal Mine Affected Stream in Alberta, Canada
Bibliographic record
Abstract
Selenium is a trace element of concern in the McLeod River basin downstream from two recently-closed coal mines in west-central Alberta, Canada. Aquatic birds breed on streams in the area and may be exposed to excessive amounts of selenium through their diets. In this article, we present the results of a dietary-based assessment of the risk that selenium may pose to two aquatic bird species, the American Dipper (Cinclus mexicanus) and the Harlequin Duck (Histrionicus histrionicus), on one of the coal mine–affected streams, the Gregg River. The study consisted of (1) a literature-based toxicity assessment, (2) simulation of selenium exposure in the diets and eggs of the two species, and (3) a risk assessment that coupled information on toxicity and exposure. Diet and egg selenium concentrations associated with a 20% hatch failure rate were 6.4 and 17 μ g · g−1 dry wt, respectively. Simulated dietary selenium concentrations were about 2.0–2.5 μ g · g−1 higher on the Gregg River than on reference streams for both species. Simulated predicted egg concentrations averaged 2.0 and 1.4 μ g · g−1 higher on the Gregg River than on reference streams for American Dippers and Harlequin Ducks, respectively. When simulated dietary concentrations were considered, hatch failure rates on the Gregg River were predicted to average 12% higher in American Dippers and 8% higher in Harlequin Ducks than at reference streams. Corresponding values were only 3% for both species when predicted egg concentrations were used. Elevated levels of selenium in insects in some of the reference streams were unexpected and raised a question as to whether aquatic birds have evolved a higher tolerance level for dietary selenium in these areas.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".