Modeling Surface Runoff, Groundwater Flow and their Interaction with PCSWMM and MODFLOW - for the City of Rostov the Great, Russia
Bibliographic record
Abstract
The City ofRostov the Great (Rostov) is one of the oldest cities in the Russian Federation.Many buildings need to be restored due to advanced degradation of the wooden and stone foundations.This degradation and other environmental problems are in large part caused by high groundwater levels, which are in turn directly associated with human activity over the years.Technical analyses were completed for surface runoff and groundwater flow in order to define potential interventions to lower the groundwater levels.Surface runoff for the old part was modeled with PCSWMM, while the groundwater was analyzed with MODFLOW.The chapter describes the complex conditions that were analyzed, and different approaches to use the results from the surface runoff model as input to the groundwater model.The PCSWMMmodel was developed using the availableinfonnation on the drainage system and limited data for calibration gathered during one spring and one summer.For MODFLOW, the model was based on historical data on groundwater regime at different wells installed since 1977 as well as other wells put in place specifically for the project in 2000 and 2002.Different approaches to integration of the two models are discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".