Effects of pressurization procedures on calibration results for precise pressure transducers
Bibliographic record
Abstract
The output of electromechanical pressure gauges depends on not only the currently applied pressure, but also the pressurization history. Thus, the calibration results of gauges are affected by the pressurization procedure. In this paper, among several important factors influencing the results, we report the effects of the interval between the calibration cycles and the effects of the preliminary pressurizations. In order to quantitatively evaluate these effects, we developed a fully automated system that uses a pressure balance to calibrate pressure gauges. Subsequently, gauges containing quartz Bourdon-type pressure transducers were calibrated in a stepwise manner for pressures between 10 MPa and 100 MPa. The typical standard deviation of the data over three cycles was reduced to a few parts per million (ppm). The interval between the calibration cycles, which ranges from zero to more than 12 h, exerts a strong influence on the results in the process of increasing the pressure, where at 10 MPa the maximum difference between the results was approximately 40 ppm. The preliminary pressurization immediately before the calibration cycle reduces the effects of the interval on the results in certain cases. However, in turn, the influence of the waiting time between the preliminary pressurization and the main calibration cycle becomes strong. In the present paper, we outline several possible measures for obtaining calibration results with high reproducibility.
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 | 0.018 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| 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".