Stormwater capture and antecedent moisture characteristics of permeable pavements
Bibliographic record
Abstract
Abstract An approach based on individual rainfall events is introduced to mathematically describe the hydrologic responses and estimate the stormwater capture efficiencies of permeable pavement systems (PPSs). A stochastic model describing the instantaneous dynamic water balance of a PPS is established, from which the probability distribution of the antecedent moisture content of the PPS at the beginning of a rainfall event is analytically derived. Based on this probability distribution and the event‐based approach, an analytical equation that can be used for estimating the stormwater capture efficiencies of PPSs is also derived. The derived analytical equation is verified by comparing its results with those from continuous simulations for a wide range of PPSs with different sizes and underlying soils and operating under various climate conditions. It was found that the antecedent moisture contents of PPSs at the test locations are usually fairly close to zero, suggesting that PPSs at these locations are always almost empty at the start of a rainfall event. The derived analytical equation accounts for many key processes influencing the behaviour and operation of PPSs; it may serve as an easy‐to‐use tool that is essential for the planning and design of PPSs.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".