A Comparative Study of 2-Dimensional Hydraulic Modeling Software, Case Study: Sorrento Valley, San Diego, California
Bibliographic record
Abstract
Sorrento West business park, a business campus for biotechnology and scientific research, is a flood prone hotspot in San Diego, California.Roselle Street and Dunhill Street experience flooding multiple times a year due to overtopping of adjacent channels, including Carroll Canyon Creek, Los Peñasquitos Creek and Flintkote Channel.A flood significantly affects the businesses and people of the area, trapping employees and customers inside businesses, forcing the shutdown of the Coaster service and access to the I-5 freeway, and causing hundreds of thousands of dollars in property damage.Previous one-dimensional hydraulic studies conducted on the flood capacity of these channels show that they only have capacity to contain the 5 y storm event.This study incorporates two different two-dimensional hydraulic modeling techniques to supplement previous one-dimensional hydraulic studies.These two models were prepared with the United States Army Corps of Engineers Hydrological Engineering Center's River Analysis System (USACE HEC-RAS) and PCSWMM.In addition, a sensitivity analysis of the two two-dimensional models was performed to compare the response of the models to the research assumptions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".