A compact and portable PC-based Gundestrup–Langmuir probe diagnostic system
Bibliographic record
Abstract
A compact Gundestrup–Langmuir probe diagnostics system capable of data acquisition as well as data analysis was conceived at INRS-EMT, Canada, and used at IPP-FZJ, Germany. Data acquisition and analysis can be done with this system using several types of probes (Langmuir, double-Langmuir, Mach, Gundestrup,…). The versatility as to the different types of probe that one can use and the relative small size of the whole system makes it advantageous. Using a laptop computer makes the system small size and highly portable. The system acquires data at 1000scans∕s at 12bit resolution on two probe systems simultaneously, using a total of 12 input channels. Bias is done by a DAC-ADC card and is amplified to give a ±100V sweeping range. Measured temperatures ranged from 0.75eVto6.0eV with densities observed as low as 1×1010cm−3 up to 5×1011cm−3, which are the range of conditions to be found in our experimental device. This system is also easy to reproduce since the hardware is commercially available and the scripts can be duplicated and modified according to the specifics of the hardware.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".