Bioassays with caged<i>Hyalella azteca</i>to determine in situ toxicity downstream of two Saskatchewan, Canada, uranium operations
Bibliographic record
Abstract
The main objectives of this in situ study were to evaluate the usefulness of an in situ bioassay to determine if downstream water bodies at the Key Lake and Rabbit Lake uranium operations (Saskatchewan, Canada) were toxic to Hyalella azteca and, if toxicity was observed, to differentiate between the contribution of surface water and sediment contamination to in situ toxicity. These objectives were achieved by performing 4-d in situ bioassays with laboratory-reared H. azteca confined in specially designed, paired, surface water and sediment exposure chambers. Results from the in situ bioassays revealed significant mortality, relative to the respective reference site, at the exposure sites at both Key Lake (p </= 0.001) and Rabbit Lake (p = 0.001). No statistical differences were found between survival in surface water and sediment exposure chambers at either Key Lake (p = 0.232) or Rabbit Lake (p = 0.072). This suggests that surface water (the common feature of both types of exposure chambers) was the primary cause of in situ mortality of H. azteca at both operations, although this relationship was stronger at Key Lake. At Key Lake, the primary cause of aquatic toxicity to H. azteca did not appear to be correlated with the variables measured in this study, but most likely with a pulse of organic mill-process chemicals released during the time of the in situ study-a transient event that was caused by a problem with the mill's solvent extraction process. The suspected cause of in situ toxicity to H. azteca at Rabbit Lake was high levels of uranium in surface water, sediment, and pore water.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".