Detecting a Defective Casing Seal at the Top of a Bedrock Aquifer
Bibliographic record
Abstract
An improperly sealed casing can produce a direct hydraulic connection between two or more originally isolated aquifers with important consequences regarding groundwater quantity and quality. A recent study by Richard et al. (2014) investigated a monitoring well installed in a fractured rock aquifer with a defective casing seal at the soil-bedrock interface. A hydraulic short circuit was detected that produced some leakage between the rock and the overlying deposits. A falling-head permeability test performed in this well showed that the usual method of data interpretation is not valid in this particular case due to the presence of a piezometric error. This error is the direct result of the preferential flow originating from the hydraulic short circuit and the subsequent re-equilibration of the piezometric levels of both aquifers in the vicinity of the inlet and the outlet of the defective seal. Numerical simulations of groundwater circulation around the well support the observed impact of the hydraulic short circuit on the results of the falling-head permeability test. These observations demonstrate that a properly designed falling-head permeability test may be useful in the detection of defective casing seals.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".