Electrical breakdown in low pressure gases
Bibliographic record
Abstract
The paper presents the results of investigation of the electrical breakdown in low pressure gases when the secondary electrons released from the cathode play the dominant role in the initiation of electrical breakdown. The secondary electrons are created by the charged and neutral species formed during the previous breakdown and discharge as well as by γ-rays. Electrical breakdown investigations are based on the measurements of electrical breakdown voltage and electrical breakdown time delay for gas-filled tubes with spherical electrodes with diameters much larger than an interelectrode distance. Stochastic nature of both the breakdown voltage and time delay are discussed and their distributions based on experimental data are shown. The methods for the determination of static breakdown voltage are also analysed. The influence of different parameters (overvoltage, cathode material and its surface purity, gas pressure, glow current, etc) on time delay are studied. A special attention is paid to the memory effect in various gases that depends on the positive ion recombination times, catalytic recombination times in the case of nitrogen and hydrogen, as well as metastable states deexcitation times in noble gases. The analysis of this effect is done by memory curves on the basis of which the presence of long-lived neutral active states can be followed to their very low concentrations when cosmic and environment radiation play the dominant role in electrical breakdown initiation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".