Bibliographic record
Abstract
An urban area inventory for watershed development conditions should be part of any comprehensive stormwater management plan, when the goal is to understand the sources of pollution and the magnitude of the expected runoff.The watershed inventory would, therefore, assist in the selection of the most beneficial stormwater control practices.The type of urban development in an area can have a major impact on the local hydrology and water environment.This inventory can therefore be used to support many decision making activities and to increase the success of local stormwater monitoring.Past studies (Schueler 1994; USEPA 1993;Arnold and Gibbons 1996;Booth and Jackson 1997) have demonstrated the importance of knowing the areas of the different land covers in each land use category and their storm drainage characteristics (grass swales, curb and gutters, and the roof drains).Increasing levels of impervious surfaces associated with urbanization result in higher volumes of runoff with higher peak discharges, shorter travel times, and more severe pollutant loadings.Urban imperviousness is an important indicator for urban watersheds in measuring the impact of land development on drainage systems and aquatic life (Schueler 1994).However, there are many different types of impervious surfaces, and their direct connectivity to the drainage system is an important attribute affecting stormwater runoff.The purpose of this chapter is to show the measured variability associated with land surface covers for different land uses in a large urban area in the state of Alabama.
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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 | 0.001 | 0.001 |
| 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".