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Record W2765317087 · doi:10.1109/nssmic.2016.8069728

SCoTSS modular survey spectrometer and compton imager

2016· article· en· W2765317087 on OpenAlexaff
P.R.B. Saull, A.M.L. MacLeod, L.E. Sinclair, Pierre-Luc Drouin, Lorne Erhardt, Jens Hovgaard, Bohdan Krupskyy, R. Ueno, D. Waller, A. McCann

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsGeological Survey of CanadaNatural Resources CanadaDefence Research and Development CanadaNational Research Council Canada
Fundersnot available
KeywordsDetectorSilicon photomultiplierSpectrometerPhysicsScintillatorModular designOpticsPhotomultiplierNoise (video)Computer science

Abstract

fetched live from OpenAlex

We present the development of a mobile survey spectrometer and fieldable Compton gamma-ray imager. The detector employs CsI(Tl) scintillator coupled to SensL silicon photomultipliers (SiPMs) and incorporates a unique modular design, where individual units can be employed separately in applications requiring a compact detector, e.g. military operations, or combined together for those requiring a more sensitive detector, e.g. aerial surveying. Each module is a fully functional imager, providing both mapping and imaging capabilities along with isotope detection and identification. We describe the design of an imager module, its custom electronics readout, its integration into the Radiation Solutions Inc (RSI) RadAssist software, and its performance in the lab and field. The energy reconstruction is shown to perform well across the full spectrum of interest up to 3 MeV, with resolutions and noise levels suitable for low-energy measurement in both the scatter and absorber parts of the detector. Successful truck-borne field tests of the imager demonstrate that it is capable of localizing a shielded 10 mCi Cs-137 source at distances up to 40 m to within a few degrees in tens of seconds.

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.

How this classification was reachedexpand

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.999

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)0.0020.000

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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2016
Admission routes1
Has abstractyes

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