Calibration of a Wall-Shear-Stress Sensor Made of a Flush-Mounted Hot-Wire Over a Shallow Rectangular Slot
Bibliographic record
Abstract
The design, construction, and calibration of a wall-shear-stress sensor (WSSS) made of a flush-mounted hot-wire over a shallow rectangular slot are described. The rectangular slot beneath the hot-wire reduces the heat loss to the substrate and enhances the frequency response of the WSSS. The WSSS was connected to a constant-temperature anemometer (CTA) and calibrated by making measurements in fully developed flows of air in a parallel-plate channel, at three different channel heights. Initially, the square of the time-averaged voltage supplied to the hot-wire by the CTA was plotted against the time-averaged wall-shear-stress. With this practice, however, for a fixed value of the overheat ratio, even relatively small fluctuations of the ambient air temperature produced a fair amount of scatter in the calibration data, and this adverse effect was compounded by minor drifts in the electrical resistance of the hot-wire (unheated value) over the course of the measurements. For overcoming this difficulty, an alternative practice for correlating the calibration data was proposed and successfully implemented. The details of this WSSS, the proposed novel calibration practice for the WSSS, and related results are presented and discussed in this paper.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".