PRIVATE LOT FLOOD PEAK ATTENUATION BY STORMWATER DETENTION TANKS
Bibliographic record
Abstract
Changes in the hydrological cycle due to climate change and urbanization augment and accelerate runoff and flooding, degrade the urban environment, and cause human and material losses.Thus, it is important to implement measures that ensure urban hydrological conditions are kept as close as possible to pre-urbanization conditions, preventing floods.In addition to the conventional major and minor systems, cities may establish criteria for percentage of permeable area as well as stormwater management practices such as stormwater detention tanks, a type of low impact development technology (LID).The present study evaluates the adequacy of current practices in private lot detention tank design.It analyses time to empty, total detention time and flood peak abatement provided by detention tanks designed according to Curitiba's (Brazil) Bylaw 176/2007.Based on the results obtained, modifications were suggested to existing legislation to increase the efficiency of the detention tanks and, thus, reduce urban flooding and adapt to climate change.The proposed methodology can be applied elsewhere to guide detention tank design.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".