Hyporheic Exchange and Nutrient Uptake in A Forested and Urban Stream in the Southern Appalachians
Bibliographic record
Abstract
Hyporheic exchange (HE) controls stream water quality by regulating biogeochemical processes, ecosystem functioning, and nutrient dynamics. The objective of this study was to better understand and quantify the extent of urban impact on HE and how that affects stream nutrient uptake. Hyporheic exchange and nutrient uptake were studied through tracer injection experiments in an urban stream, Boone Creek, and a forested stream, Winkler Creek in the Southern Appalachians, USA. In this study, two sets of metrics were evaluated including transient storage and nutrient uptake metrics. The average dimensionless transient storage metrics Fmed, the fraction of the median travel time through a 200-m reach that is due to transient storage, of Winkler Creek was found to be 3-fold greater than that of Boone Creek. With regard to nutrient uptake metrics, Boone Creek was found to have an uptake length 13-fold longer and an uptake velocity 16.7 times slower than Winkler Creek. The results show a greater extent of HE and higher nutrient uptake in the forested stream than the urban stream, which indicate that urbanization can deteriorate stream ecosystem functions by reducing HE and nutrient retention capacity. As a result, extra amounts of nutrients might export downstream and create a eutrophication problem. Thus, hyporheic restoration is crucial and has to be taken into account in restoring the ecosystems of urban streams.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".