THE HUMAN MONITORING LABORATORY???S NEW LUNG COUNTER: CALIBRATION AND COMPARISON WITH THE PREVIOUS SYSTEM AND THE CAMECO CORPORATION???S LUNG COUNTER
Bibliographic record
Abstract
The Human Monitoring Laboratory has replaced its lung counting system with four large area (85 mm x 30 mm) HPGe detectors, electronics, and software. The system has been calibrated with the same lung set and phantom that was used to calibrate the Human Monitoring Laboratory's previous lung counting system and the Cameco Corporation's mobile lung counter. The performance characteristics (efficiency and sensitivity) of all three systems are compared, with the Human Monitoring Laboratory's new system being more sensitive than the other systems by factor of 1.3. The large area detectors highlight the design deficiency of the Lawrence Livermore National Laboratory's torso phantom, namely short lungs, as the lower two detectors are over inactive tissue (approximately 40%). As a result, both a two-detector and a three-detector array are actually more sensitive than a four-detector array in certain circumstances. This is, however, an unrealistic finding as human lungs are much longer (approximately 10 cm) than the Lawrence Livermore National Laboratory's phantom's lungs. The dosimetric implications of the new system's minimum detectable activities are put into perspective using (57)Co, (235)U, (238)U, (239)Pu, (241)Am, and natural uranium as example radionuclides.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".