Field performance of four vibrating-wire piezometer installation methods
Bibliographic record
Abstract
Vibrating-wire piezometers provide a number of advantages over the traditional hydraulic piezometer design. There are currently many methods and configurations for installing vibrating-wire piezometers, the most common being: single piezometers in sand packs (SP), multilevel piezometers in sand packs (MLSP), and fully grouted multilevel piezometers using either bentonite (FGB) or cement−bentonite grout (FGCB). This study assesses the performance of these four different installation methods at a field site possessing complex stratigraphy, including glacial and marine sediments. To accomplish this objective, pore pressure data recorded between December 2017 and July 2019 were analyzed. Data indicate that SP, MLSP, and FGB piezometers performed most reliably, because piezometers installed at the same depth with these methods recorded similar pressure variations that were coherent with the hydrogeological setting. Of the two fully grouted installations using cement−bentonite grout, one installation failed completely due to a hydraulic short circuit, likely caused by preferential flow occurring along the wires of the embedded instruments. The lack of a standard method for mixing cement−bentonite grout at the time of construction likely contributed to the failure of the FGCB installations, as the grout mixture used in this study was likely too viscous to provide a suitable seal.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".