Large area Germanium detector arrays for lung counting: what is the optimum number of detectors?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using the Lawrence Livermore National Laboratory (LLNL) torso phantom to calibrate a lung counting system can lead to the conclusion that three large area (i.e. >70 mm diameter) Ge detectors will outperform a four-detector array and provide a lower MDA as a four-detector array of large area Ge detectors covers a significant portion of inactive tissue (i.e. non-lung tissue). The lungs of the LLNL phantom, which are approximately 10 cm too short compared with real lungs, also suggests that a two-detector array could be used under limited circumstances. When tested with modified lungs that are more human-like, it was found that the four-detector array showed the best counting efficiency and the lowest MDA. Fortunately, these findings indicate that, although the LLNL phantom's lungs are too short, there is no adverse impact on the calibration of a lung counter.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it