Feasibility of a Permeable Pavement Option in SWMM for Long-term Continuous Modeling
Bibliographic record
Abstract
With increasing concern for the natural environment, current practices in stormwater management planning have moved away from traditional applications such as detention as a means of quantity control, towards techniques that combine both quantity and quality control.These techniques lower stormwater runoff volumes, recharge groundwater and mitigate the transportation of contaminants.One such type of management practice is installation of permeable pavement.The purpose of this study was to examine the feasibility of code for the Storm Water Management Model (SWMM) to allow planners and designers to simulate the responses of permeable pavements in long-term modeling applications.The int!ltration capacity of the porous pavement was determined from past studies, and takes into account degradation over time and regeneration by mechanical means. IntroductionHumans tend to congregate in large cities with high population densities.To support these populations, cities require extensive infrastructures.Forests, fields, marshes, and creeks have been replaced with buildings, roads, parking lots Kipkie, C. and W. James.2000."Feasibility of a Permeable Pavement Option in SWMM for Long-term Continuous Modeling."
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.006 | 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".