Bibliographic record
Abstract
Developing simple and accurate hydrologic models for wet-weather sanitary sewer flow has long been enigmatic for the engineering community. Hydrologic models suitable for surface water hydrology often perform poorly when simulating sanitary sewer hydrology when they do not account for antecedent moisture conditions and seasonality. The result is underpredicting peak flow and volume in wet conditions and overpredicting the same in dry conditions. The Antecedent Moisture Model is an empirically-calibrated method which models a rainfall capture fraction that varies by antecedent moisture and season, and transforms the captured rainfall into a flow hydrograph. The Antecedent Moisture Model has been particularly successful in modeling sanitary sewer infiltration, for which other methods perform poorly. This paper suggests a reparameterization of the original model which provides virtually identical results while improving interpretability and ease of use. The reparametrized model is designed to be time step independent and more physically relatable, intuitive, and scaleable than the original parameterization while remaining functionally equivalent to the original parameterization. The paper also presents two computational examples which demonstrate the application of the Antecedent Moisture Model formulas and math.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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".