Influence of electrode geometry on breakdown in mercury vapor in crossed electric and magnetic fields
Bibliographic record
Abstract
In an electrical discharge in crossed electric and magnetic fields, electrons describe cycloidal paths between collisions causing many more collisions with gas molecules. As a result, the crossed magnetic field exerts a considerable influence on the Townsend first ionization coefficients alpha and gamma . These coefficients are a function of E/N, where E is the electric field and N is the gas number density, and determine the sparking potential of a uniform electric field geometry of the electrodes. In a nonuniform electric field geometry such as that which exists in a coaxial cylindrical geometry, the Townsend criterion will yield the corona inception potential. In the present work, the sparking voltages in the presence of electric and magnetic fields are examined with the ratios of the electrode diameters as parameters and varying in the range of 2 to 50. The lower value represents a quasi-uniform electric field and the higher values highly nonuniform fields. To facilitate these calculations the electron energy distribution in electric fields alone is examined first. Using the energy distribution function, the mean energy and drift velocities are calculated and compared with available results. The theory is then extended to a crossed magnetic field to calculate the current multiplication. It is shown that a magnetic field increases the breakdown voltage according to the effective reduced electric field concept.>
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".