Hydrologic Connectivity for Highway Runoff Analysis at Watershed Scale
Bibliographic record
Abstract
Roadway networks represent a unique type of drainage area, encompassing hundreds of miles (kilometers) of linear stretches of paved surfaces that frequently cross watershed boundaries, streams and sensitive water bodies.Runoff from this land use category contains a variety of pollutants such as sediments, trace metals, hydrocarbons and nutrients.As mandated by the United States Clean Water Act and other environmental regulations, state transportation agencies are required to implement control measures to comply with the allocation of allowable pollutant loads that are established by the total maximum daily loads (TMDL) requirements.Adapting a technically sound methodology for watershed modeling is the key to providing reliable estimates of pollutant loads from highway runoff.Various modeling tools are available.Regression models are based on analyzing field monitoring data to determine the relationships between causal and explanatory variables (Chui et al., 1982;Kerri et al., 1985;Schueler, 1987;Driscoll et al., 1990;Driver and Tasker, 1990;Irish et al., 1998;Wu et al., 1998;Kayhanian et al., 2007;Wu and Allan, 2010).Simulation models include the principal mechanisms of generation, transport and dispersal of stormwater runoff and the associated pollutants, as illustrated by the USEPA's Storm Water Management Model.Existing methodologies used for estimating pollutant loadings from highway runoff can be subject to the following limitations:1. Regression models derived from site specific monitoring data
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".