A more physical approach to model the surface treatment of scintillation counters and its implementation into DETECT
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
DETECT is a Monte Carlo simulation capable of realistically modeling the optics of scintillation detectors. A limitation of this widely used program is its lack of realism and flexibility in dealing with the surface finish and reflector coating of photon counters. To address these limitations, we initiated the implementation into DETECT of a more physical model to treat the interactions of scintillation photons with dielectric surfaces. Inspired from the initial work of Nayar et al. (1991), this approach has the particular advantage of unifying, into a single parametrization, models that usually apply over a very limited range of surface roughness values. This flexibility is ensured by using the standard deviation of the surface slope as a model parameter that can be extracted from simple measurements.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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