Basin-wide impacts of compounds with estrogen-like activity on longnose dace (<i>Rhinichthys cataractae</i>) in two prairie rivers of Alberta, Canada
Bibliographic record
Abstract
Environmental compounds with estrogen- or antiestrogen-like activity can enter rivers from multiple sources, including municipal wastewater and agricultural runoff. We used longnose dace (Rhinichthys cataractae) to investigate exposure to compounds with estrogen-like activity, which we measured in water at multiple sites in the Oldman and Bow rivers (AB, Canada). We evaluated changes in vitellogenin mRNA with quantitative reverse transcription-polymerase chain reaction, then compared vitellogenin levels to sex ratios and fish performance indices to assess how exposure to compounds with estrogen-like activity affects longnose dace populations. Vitellogenin levels were elevated at least 59 to 110 km downstream of municipalities. In the Oldman River, increased vitellogenin expression and female-biased sex ratios suggest severe endocrine disruption, likely resulting from the combined impacts of municipal wastewater, agriculture, and large cattle operations within the basin. In the Bow River, municipal wastewater may be the major source of compounds with estrogen-like activity that affect longnose dace. The sex ratios were not heavily skewed, as in the more agriculturally influenced Oldman River. We detected organic contaminants in river samples at every site, but the highest concentrations were found downstream of municipalities and in areas with intense agriculture. Vitellogenin levels and sex ratios of longnose dace suggest basin-wide exposure to compounds with estrogen-like activity. Our results demonstrate that it is important to assess rivers at large spatial scales to detect fully the impacts of municipal wastewater and agriculture on fish populations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".