Sci‐Sat AM (1) General‐09: Dose dependence of MOSFET sensitivity for clinical photon energy spectra between 30kV and 60Co
Bibliographic record
Abstract
Since MOSFETs medical applications in radiotherapy and radiology are gaining popularity, evaluating them under radiation of different energies is of major interest. This study is a characterization of MOSFET sensitivity with regards to total integrated dose. Their sensitivity is expressed by the calibration factor (CFw) and allows the user to associate the reading displayed by the device (mV) to a dose value (cGy). The CFw of seven p‐type, duals bias MOSFETs were measured at several points in time. The radiation sources used were a unit (〈E〉γ: 1.25 MeV), an HDR unit (〈E〉γ: 0.38 MeV) and an orthovoltage unit providing two x‐rays energies respectively of 30 kV (〈E〉γ: 14.8 keV) and 150 kV (〈E〉γ: 70.1 keV). The CFw value diminishes with increasing threshold voltage, especially for low energy radiation. It was stable for irradiations. Decreases of 6%, 5% and 15% were observed respectively for radiation energies of , 150 kV and 30 kV. This behavior is explained by an alteration of the effective field applied on the MOSFET (bias), caused by the accumulation of holes at the interface. It is strongly dependent on the nature of the radiation (LET) and particularly affects low x‐ray energies. Those results are of major interest since, following the company recommendations, the device should be calibrated every 7 000 mV; this recommendation could lead to significantly underestimated doses. A calibration of the device before every use, and multiple measurements to get a mean dose value should compensate for the observed phenomenon.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.369 | 0.082 |
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".