Impacts of environmental and hydrologic factors on urban stream water quality.
Bibliographic record
Abstract
Urban streams can be impacted by a multitude of hydrologic and environmental factors, making maintaining these water sources difficult. Urbanization can exacerbate these impacts creating new challenges in preserving suitable urban stream water quality. Urbanization is the development of city landscape and suburban living within an otherwise natural region. For this environmental impact study, the effects of city development on urban stream water quality was monitored for Mill Creek in Louisville, Kentucky. To study the effects of urbanization on Mill Creek, this project was completed utilizing the BACI method for comparing impacts. The results of the water quality monitoring were acceptable for water quality standards in Kentucky in the categories of pH, water temperature, and conductivity from July 19th to August 14th of 2019. The dissolved oxygen concentration in the creek was below the standard for Kentucky regulations. The e. coli concentration of the studied creek were above regulations for state water quality standards in almost the entire stretch of Mill Creek (5 of 6 sampling locations). The e. coli was highest on the days of precipitation, while the dissolved oxygen was lowest in times of limited to no rainfall with rising temperatures. The e. coli concentration was a result of the high percentages of impervious pavements within the region leading to runoff of pollutants residing on urban surfaces. The dissolved oxygen was a result of reduced mixing of the water column in low flow with no rainfall. The conclusion was that urbanization is having an effect on these two parameters and will continue to deteriorate these water conditions if trends in city runoff and environmental destruction continue.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".