Measuring Workload with Paired Detectors
Bibliographic record
Abstract
Linear accelerator workloads for each available photon energy are important quantities to know for radiation safety considerations, and presented is a technique to measure the workload using paired detectors. The signals from the two detectors can give sufficient information to separate the signal contributions from 6 and 18 MV photon fields and, combined with a signal-per-monitor-unit calibration factor, yields the number of monitor units delivered for each energy. CR-39 NTD is a neutron detector chosen for its ability to discriminate between 6 MV and 18 MV radiation fields. TLD-100 is a detector responsive to both 6 MV and 18 MV fields. These appeared to be a good choice for a detector pair. This experiment had both failures and successes to report. The CR-39 NTD and TLD-100 were not a successful pairing. The CR-39 NTD signals saturated under this experiment's exposure conditions. The TLD-100 had a combination of detector noise and detector sensitivity that made extracting the 6 MV signal from the total signal impractical, unless the total exposure was overwhelmingly 6 MV. Nevertheless, the TLD-100 proved to be excellent for determining workloads when it was exposed to a single energy with 1% accuracy and 3% precision. The theory and data analysis showed the importance of understanding the noise contributions for the more general problem of pairing any two detector types. This experiment indicated the TLD-100 could be an excellent detector choice if paired with a suitable second detector.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".