Nevertheless, They Persisted: Can Hyporheic Zones Increase the Persistence of Estrogens in Streams?
Bibliographic record
Abstract
Abstract The presence of estrogens has been linked to adverse ecological effects in surface waters downstream of agricultural and domestic wastewater sources. While laboratory studies suggest that these estrogens should not persist because of fast degradation rates, elevated concentrations in surface waters impacted by agricultural activities are commonly observed. Using a combination of measured data and a stream‐hyporheic zone (HZ) model applied to a 100 km reach in a tile‐drained catchment, we show that the HZ can increase the persistence of estrogens. Field data reveal high concentrations of sorbed estrogens in sediments and elevated in‐stream concentrations during low‐flow summer months, suggesting that the HZ acts as a source of estrogens when transport into the streams is minimal. Model results provide further insight into the underlying mechanisms that enable sustained estrogen concentrations in streams, with the HZ acting as a source of dissolved estrogens for 95% of the year. We show that stream water interactions with the HZ may lead to overall suppression of degradation processes and an increase in the persistence of estrogens. Results suggest that when the model considered exchange in the HZ, approximately 28%–49% of estrogen mass remained in the stream ecosystem, while all estrogen mass was degraded in a 100‐km reach in the model without the HZ. The remaining mass increased with increasing estrogen sorption coefficient, and this would potentially increase the lag time for lowering estrogen concentrations in surface water bodies even when inputs have ceased. Our findings highlight the importance of including HZ dynamics in estrogen transport models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".