Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In September 2010, the U.S. Environmental Protection Agency (USEPA) released a new version (5.0.021) of Storm Water Management Model Version 5 (SWMM5) that offers low impact development (LID) modeling capability for the first time. The same LID modeling capability was soon enabled in the PCSWMM software (Version 2010, from Computational Hydraulic Int.). Five types of LIDs can be modeled in SWMM5 and PCSWMM: bioretention cells (rain gardens), infiltration trenches, porous pavement, cisterns (rain barrels) and vegetative swales. Using these new software releases and real world examples, this chapter presents some LID modeling features of SWMM5 and PCSWMM, input data requirements, modeling procedures, and output results for quantifying the LID impacts on sewer overflows. The modeling results presented here can help quantify the LID impacts on sewer overflows and allow sustainable developers to answer questions such as How much rainfall can be captured in a typical design year using a certain type of LID? or How many rain gardens are needed in a sewershed to capture a certain volume of storm water? A predictive model is presented to calculate the optimal number of rain gardens to achieve a target level of sewer overflow control.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it