The Effect of Synthetic Estrogens on Fathead Minnow Populations
Bibliographic record
Abstract
The fathead minnow (Pimephales promelas) is a freshwater fish with a wide distribution in lakes, streams, and ponds across North America, including Ontario. This species has been recognized as one of the most useful model organisms for freshwater toxicology monitoring, due to its tolerance of its range for aquatic conditions. Synthetic estrogens are excreted as a metabolic product in the urinary waste of women using oral contraceptives. Estrogens are not effectively filtered out by sewage treatment plants and are subsequently released into water effluents, affecting aquatic wildlife. In response to experimentally heightened estrogen concentrations, male and female fathead minnows have shown increases in vitellogenin protein, and females have displayed delayed ovarian development and increased numbers of underdeveloped ovarian follicles. Males also experienced lowered gonadosmatic indices, and some males had primary stage oocytes in their testes. As would be expected, synthetic estrogen additions influenced fathead minnow reproductive success, leading to a collapse of the experimental fish population. By taking a multi-disciplinary approach, this study looks to identify how synthetic estrogens are threatening local fish populations, and how this may eventually disrupt the freshwater food chain. Collaboration with The Canadian Association on Water Quality (Kingston), Ravensview Wastewater Treatment Plant, and the Catarqui Region Conservation Authority will provide a greater understanding of the hormonal content in Kingston’s wastewaters. Ultimately, this study will provide more insight on the level of risk faced by freshwater fish populations on a local scale, and propose a method to reduce estrogen deposition in freshwater environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".