Land Development Characteristics in Jefferson County, Alabama
Bibliographic record
Abstract
For a stormwater monitoring study to be successful, a careful examination of the study watershed is required.An inventory of watershed development conditions is needed both as part of a comprehensive stormwater quality plan for an area, and for many decision support activities.Past studies using the Source Loading and Management Model (WinSLAMM) (Pitt and Voorhees 1995) have demonstrated the importance of knowing the areas of the different land covers in each land use category and the storm drainage characteristics (grass swales, curb and gutters, and the roof drains).As this chapter describes, six to twelve homogeneous neighborhoods usually need to be surveyed for each land use category.Aerial photographs or satellite images of each site are also needed for measurements of each source area type.Impervious cover has been increasingly used as an indicator in measuring the impact of land development on drainage systems and aquatic life (Schueler 1994).It is also one of the variables that can be quantified for different types of land development.There are many different types of impervious surfaces; how they connect to the drainage system is important.Although much interest has been expressed concerning impervious areas in urban areas, data for their patterns of use is generally lacking.The procedures described in this chapter to obtain the field data information have been used for many years in stormwater research projects, including several Nationwide Urban Runoff Program (NURP) projects that were conducted in
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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 | 0.001 | 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) | 0.004 | 0.001 |
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".