Calibration of a calorimeter for measuring the spark energy of an electrostatic discharge
Bibliographic record
Abstract
This paper deals with the factors that influence the accuracy of the measurements performed with a calorimeter, developed to detect the optical signal emitted by the spark generated to facilitate experiments with electrostatic discharge. Experiments are described which were performed with a special calorimeter and a compound optical source consisting of a spark gap, a human-body-model electrostatic discharge (ESD) signal generator, and four LEDs. The spark gap was used as a versatile and more powerful source of optical emission than the human-body-model circuit; the LED source was used for calibration and alignment. For control, the spectrum of the spark discharge was measured with a spectrometer and a broadband photodetector. The calorimeter was used as a means for the determination of the energy contained in the optical signal, yielding the value of the spark-gap emission in the fundamental units for energy. The long-term stability of the system was measured, and the system response was studied for threshold optical signals. The calorimeter detectivity, defined as the overall instrument sensitivity, measured in volts per joule, was D=4.3/spl times/10/sup 7/ V.J/sup -1/ and its detection limit 2.3/spl times/10/sup -13/ J. The system's response to the IEC standard human-body-model circuit was consistent with the measurements of the system's detectivity. Most parts of the developed apparatus have been tested in an industrial environment.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
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".