Calibration and Testing of Simple Mass Balance Model for Quantifying Stormwater Management Benefit of an Extensive Green Roof
Bibliographic record
Abstract
A simple mass balance based model for quantifying stormwater management benefits of an extensive green roof was developed and tested based on data from Lawrence Technological University. Model simulated green roof runoff peaks and volume agreed with those measured between April to September 2008 within a factor of 0.97 to 1.4 and 0.8 to 1.6 respectively. The objective of limiting roof runoff peaks from 2 to 100 year design storms to equal or be lower than the corresponding pre-developmental peaks for the tested regional conditions was not met with the green roof alone. Provision of an additional storage of 40 m3/ 1000 m2 green roof area in series with the green roof is expected to be able to achieve this objective. The developed procedure is expected to be useful for the assessment of stormwater management benefits of extensive green roofs in other geographical locations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".