''Let It Rain'' — Gage-Adjusted Radar Rainfall (GARR) Data for Peachtree Creek Sewer Basin Modeling
Bibliographic record
Abstract
Sewer hydraulic models are developed to reveal and simulate problem areas in sewer system networks. During model development precipitation input data, typically from rain gages, are applied and system parameters are adjusted until the calibrated model output matches the collected system flow meter data. The calibrated model is later used to evaluate problem areas and identify solutions to the system's deficiencies in order to comply with regulatory requirements, which are met by eliminating surcharge and overflow locations within the sewer system. The study compared the hydraulic models that were developed using a GARR dataset, and a dataset consisting of rain gages only. Both datasets used information from 30 "tipping-bucket" rain gages from March 2001. The GARR analysis incorporated NEXRAD radar data on a 2 x 2 km grid, with a 15-minute sample rate. Correlations between the two precipitation measurement systems were strong. Rainfall timing was well matched. Incorporating the gage volumes resulted in lowering the radar rainfall estimated by 20%. The sewer hydraulic modeling results showed that the Flow (Q), Velocity (V) and Depth (d) response to the flow meter data matched more closely when GARR data is used as compared to the conventional rain gage data.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".