Identifying a Representative Year of Precipitation in Support of a Wet Weather Plan
Bibliographic record
Abstract
In support of a regional wet weather plan, computer models have been developed to simulate baseline conditions to establish the frequency and duration of combined sewer overflow (CSO) and sanitary sewer overflow (SSO) discharges, the potential water quality impacts they produce, and to develop and assess alternative control strategies.Much of the uncertainty in a carefully constructed hydrologic and hydraulic model is derived from uncertainty in the rainfall record.Therefore, increasing the level of detail of the rainfall input, both spatially and temporally, increases the accuracy and precision of the model results.Careful attention to rainfall collection and analysis is critical to the modeling effort.The refinement of precipitation data becomes an important process because precipitation is the driving force that increases wastewater flow along sewers and transports pollutants via CSO and SSO discharges to receiving waters.The U.S. Environmental Protection Agency CSO Control Policy (1994) requires the characterization of the combined sewer system area and evaluation of control measure performance using the complete rainfall record for the geographic area of its existing combined sewer systems (CSS) using sound statistical procedures.However, for most US cities historical precipitation data is available for periods anywhere from fifty to hundred years.It is not possible to run complex and large models for all the years for which precipitation data is
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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) | 0.002 | 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".