Field Permeability Tests: Importance of Calibration and Synchronous Monitoring for Barometric Pressure Sensors
Bibliographic record
Abstract
Abstract Pressure transducers (PTs) and an atmospheric pressure transducer (APT) were used to register test data during two types of permeability tests, which were performed in 14 wells monitoring a confined aquifer installed in the lab, and a field rising-head test in clay. The constant-head tests were performed using a peristaltic pump and thus functioned as constant flow rate tests until stabilization of the water level in the well riser pipe. The rising-head tests were started by the sudden removal of a slug of water. This article presents, first, the method used to calibrate the transducers to assess their systematic calibration error (offset) values. Then, it quantifies the influence of synchronized monitoring for the (PT-APT) pair on short- and long-term test data, which had never been done before. The results indicate that the pair calibration cannot be neglected and that the synchronized monitoring is important for all tests, except maybe for a short-duration variable-head test. For most tests, however, the barometric fluctuation with time plays a significant role.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".