PROBLEMS ENCOUNTERED DURING THE CALIBRATION OF THE NEW CAMECO MOBILE LUNG COUNTER: DETECTOR SIZE OR PHANTOM LIMITATION?
Bibliographic record
Abstract
This paper describes the calibration of the new Cameco mobile lung counter and, more importantly, the problems encountered with recommendations for their long-term solution. The new Cameco lung counting system, which is based on an array of four 80-mm-diameter Canberra BeGe detectors, has used the JAERI phantom for its primary calibration as it more closely resembles ICRP reference data for lung dimensions compared with the LLNL phantom. Although the JAERI phantom's lung dimensions offer advantages over the LLNL phantom's lungs, this phantom is still not ideal. The work described in this paper leads to the conclusion that the LLNL be modified to more closely resemble the ICRP reference data if large area germanium detectors comprise the lung counter. Overlay plate stacking was necessary to achieve the range of chest wall thicknesses found within the Cameco work force (2-8 cm) when using the JAERI phantom. This technique has proved to be robust and is useful for extending the calibration range. Cameco is using group monitoring, which adds spectra to simulate very long counting times (10-20 h), and it is essential that all materials be low background. This was not initially the case here as found from overnight background counts.
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 | 0.040 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".