Bibliographic record
Abstract
To investigate properties of gas discharges and to evaluate the design and implementation of a Langmuir probe diagnostic, an array of several probes was designed and inserted into an intermediate pressure direct-current (DC) gas discharge. In particular, this work was motivated by: studying electron kinetics in gas discharge plasmas and measurement of the electron energy distribution function (EEDF). First, measurements of the discharge were performed including obtaining current-voltage (IV) characteristics and determining breakdown voltages for several gases. Images of discharges were recorded using a digital camera, and emission spectra were obtained using a visible light spectrometer. Then, Langmuir probe measurements were then taken of the potential distribution in the cathode region and IV characteristics were obtained to determine plasma properties. Electron density and temperature were determined both by a graphical method and by calculation of the EEDF from the second derivative of the IV characteristic. Probe measurements were taken at a vacuum pressure of 1Torr and an electrode separation of 5cm. The results from a single experiment are reported for which the discharge voltage was set to 276V and discharge current to 0.238mA. The plasma properties obtained graphically and by the EEDF calculation were compared and their interpretation discussed. The results of the graphical analysis method indicated a plasma density of 9e14m-3 and an electron temperature of 5.4eV. The results of the EEDF method indicated a plasma density of 1.17e14m-3 and an electron temperature of 11.0eV. The results were compared to simulations performed using the open-source Boltzmann equation solver called BOLSIG. The main input parameters were: average reduced field of 170Td, and degree of ionization of 1e-8. The output from the program indicated a mean energy of 7.65eV, which falls between the values obtained graphically and by the EEDF calculation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".