Assessing “Urban Karst” Effects from Groundwater–Storm Sewer System Interaction in a Till Aquitard
Bibliographic record
Abstract
Storm sewer systems and their associated utility trenches may strongly influence the effects of urbanization on a groundwater system. This study was undertaken to identify the causes of district-wide basement infiltration in an aquitard system. It comprised widespread continuous monitoring of utility trench wells and dye tracing from storm sewer system exfiltration tests. The results indicate that a major effect of urbanization on shallow groundwater is related to storm sewer system exfiltration, which is marked by a characteristic pattern of head variations in the aquitard unrelated to distributed surface infiltration. The aquitard constrains flow from storm sewer system exfiltration to the utility trench, creating an urban flow path for groundwater discharge. Temporary buildup of water levels in the utility trench drives relatively high-velocity flow through the permeable sewer bedding material of the utility trench to a separate foundation drainage collector system, ultimately causing a severe "urban karst" effect that produces system surcharging and widespread basement water infiltration. The main conditions causing the "urban karst" are the large hydraulic conductivity ratio between the utility trench material and the aquitard, and the shallow depth and low gradient of the storm sewer system imposed by a very flat drainage basin.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".