THE UNCERTAINTY IN THE ACTIVITY ESTIMATE FROM A LUNG COUNT DUE TO THE VARIABILITY IN CHEST WALL THICKNESS PROFILE
Bibliographic record
Abstract
Calibration of a lung counter requires the use of a realistic torso phantom. The depth profile of both torso phantoms' (LLNL and JAERI) chest plate covers is fixed and assumed to be equivalent to a person's chest wall; however, ultrasound measurements of humans have shown this to be an approximation. When the depth profile of a calibration phantom is different from that of a subject, then a systematic uncertainty will be introduced into the activity estimate. Monte Carlo simulation has shown that changes in the depth profile of the chest wall thickness affect the counting efficiency. Ultrasound measurements have suggested that the coefficient of variation in the depth profile of the chest wall thickness lies between 13% and 26% for male workers; therefore, the added uncertainty to an activity estimate will be an over or underestimate of about a factor of 1.07 resulting from the different depth profile. The factor will be somewhat higher for females, probably about 1.2 at the extreme. These additional uncertainties resulting from depth profile differences are small compared with other uncertainties commonly encountered in lung counting: detector positioning, deposition patterns of the activity, measurement of the chest wall thickness, etc.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".