Calibration of BASINS HSP-F in Support of a Watershed Approach to CSO Long Term Control Planning
Bibliographic record
Abstract
UZSN and LZSN (upper and lower zone nominal soil moisture storage, respectively).Larger errors in model estimates for the years 1990, 1992, and 1995 frequently were traced to the rainfall data used to drive the runoff simulations.The rainfall record used in the calibration was for only one gauge site, the Buffalo Airport.Observation of weather radar and qualitative notation from field personnel indicated that considerable spatial variability occurred in rainfall patterns for the area.Validation runs were conducted for the year 2000 using only the Buffalo Airport rainfall data and spatially averaged rainfall data that also included two other rain gauges within the watershed.The validation run with the spatially averaged rainfall data had a higher r 2 and Nash Sutcliffe coefficient as compared to the validation run with the Buffalo Airport data alone.A lengthy time series of observed suspended solids data was not available to calibrate the sediment erosion and transport component of the model.However, turbidity data measured continuously at various sites along the Buffalo River in 2000 were available and relationships between suspended solids and turbidity were developed using least squares regression.For the purpose of model calibration the daily mean turbidity values were run through the appropriate regression equation to construct a suspended solids time series.Results of the calibration run for an example river reach that represents Cazenovia Creek, near the city line, and for the lower Buffalo River, near the Ohio St. bridge were evaluated in detail.Visually, the observed and modeled sediment time series for the two reaches corresponded, although quantitatively, the r 2 was low (in the range of 0.29).The model did a better job of representing erosion and transportation from the upper part of the watershed and had greater difficulty in representing the sediment deposition processes within the more hydraulically complex dredged channel (Ohio St. bridge site).
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".