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Record W1678065162 · doi:10.1109/nssmic.1996.591410

A more physical approach to model the surface treatment of scintillation counters and its implementation into DETECT

2002· article· en· W1678065162 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue1996 IEEE Nuclear Science Symposium. Conference Record · 2002
Typearticle
Languageen
FieldEngineering
TopicCalibration and Measurement Techniques
Canadian institutionsTRIUMF
Fundersnot available
KeywordsScintillationParametrization (atmospheric modeling)Monte Carlo methodPhotonSurface roughnessFlexibility (engineering)OpticsDetectorReflector (photography)PhysicsPhoton countingRange (aeronautics)Computer scienceSurface (topology)Electronic engineeringAerospace engineeringEngineeringRadiative transferMathematicsGeometry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.274
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it