The Impact of Leaf Litter on the Performance of Catch Basin Inlets
Bibliographic record
Abstract
A full-scale model roadway was constructed at the Ocean, Coastal and River Engineering Research Centre of the National Research Council Canada (NRC-OCRE) in Ottawa, Canada, to measure the conveyance capacity of catch basin inlets. A total of 178 tests were performed with the primary goal of measuring the impact of leaf litter on conveyance in both ponding and flow through conditions. The inlet studied was a rectangular inlet design that is surface mounted and follows the profile of a 10 cm high rolled curb which was used in the study. Incident water depths ranging from 0.002 to 0.366 m were examined and the model roadway was tested in various orientations in flow through conditions with a 2.0% cross-slope and road grades ranging from 0.5% to 5.0%. The maximum flow reduction observed in the study was for the ponding or sag tests where the initial leaf litter obstructing approximately 95% of the inlet resulted in a conveyance which was only 35% of the unobstructed value. This work aims to assist municipalities in understanding the performance of their storm water management systems and inform where limited infrastructure funds should be allocated in order to adapt to increasingly severe urban flooding.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".