SINGLE ANODE TRIPLE GEM TISSUE EQUIVALENT PROPORTIONAL COUNTER AS THE BASIS FOR A PERSONAL NEUTRON DOSIMETER
Bibliographic record
Abstract
This paper reports on a tissue-equivalent proportional counter (TEPC) based on a triple gas electron multiplier structure, with a single pad readout, as a basis for a personal neutron dosimeter. Its dosimetric response was studied using the 252Cf neutron source at the Health Physics Generator Facility of the Canadian Nuclear Laboratories. Measured lineal energy spectra were found to be in agreement with numerical simulations performed with Monte Carlo N-Particle eXtended (MCNPX). Both simulations and measurements showed that the mean pathlength of secondary charged particles in the TEPC gas was best represented by the thickness of the drift region of the device. It was determined that the Cauchy Theorem, used to calculate the mean chord length in spherical and cylindrical TEPCs, overestimated the simulated mean chord length by nearly a factor of two. Important operational characteristics of the device were investigated, including gas gain, sensitivity and dosimetric response, as functions of tissue-equivalent gas pressure. This work demonstrates that the proposed design can serve as the basis for a personal neutron dosimeter device, which would satisfy the angular dosimetric response criteria of the personal dosimeter standard IEC61526.
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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".