A new surface parameterization for modeling thin layers of reflector material in the DETECT2000 optical modeling program
Bibliographic record
Abstract
A new surface parameterization has been implemented in DETECT2000 that allows for the efficient representation of a thin film of reflector material with non-negligible transmission. Material is usually modeled in DETECT by specifying the optical attenuation and scatter lengths. For a material such as Teflon, commonly used as a reflector material for nuclear medicine scintillators, the scatter length can be very short. Modeling the propagation of photons in a material with a very short scattering length can be computationally intensive as there can be many scatter interactions before the photon either exits the material or is absorbed. In a detector design that makes generous use of Teflon, most of the computations can be spent simply transporting photons in the Teflon. To address this issue, a parameterized surface has been implemented which is specified not by the bulk optical scatter and attenuation lengths, but rather by a reflection and transmission coefficient. This allows computationally efficient modeling of thin film reflectors where surface properties and transmission are relevant.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".