Urban-Derived Contaminants Cause Reproductive Disruption in an Aquatic Sentinel Species, Longnose Dace
Bibliographic record
Abstract
We investigated potential adverse impacts of urban-derived environmental contaminants, such as pharmaceuticals, steroids, surfactants and plasticizers, on Longnose Dace (Rhinichthys cataractae) along two rivers, the Bow and Elbow Rivers, in the City of Calgary. Fish were sampled to evaluate physiological and morphological endpoints associated with reproduction and development, including adult sex ratios, changes in body and organ weight, and gonad malformation. Significant male bias was observed downstream of three wastewater treatment plants (WWTPs) on the Bow River, and significant female bias was observed on the Elbow River, suggesting the presence of environmental contaminants with hormone-like activity, dependent on location. To investigate the mechanisms of adverse fish health effects we quantified the expression of liver vitellogenin, estrogen receptor alpha, cytochrome P450, and insulin-like growth factor-1. Decreased IGF1 and ERα expression levels were observed downstream of WWTP effluent in the Bow River, while increased vitellogenin and ERα expression levels were noted in the Elbow River within Calgary. Results support the hypothesis that waterborne environmental contaminants may be responsible for the adverse health effects, such as biased sex ratios, of Longnose Dace within the City of Calgary.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".