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Record W2024705300 · doi:10.1063/1.3665340

Development and Testing of an Air Fluorescence Imaging System for the Detection of Radiological Contamination

2011· article· en· W2024705300 on OpenAlexaff
Elizabeth Inrig, Vern Koslowsky, Bob Andrews, Michael Dick, Patrick Forget, H. Ing, Roger Hugron, Marianne E. Hamm, Robert W. Hamm

Bibliographic record

VenueAIP conference proceedings · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsCanadian Nuclear Safety Commission
Fundersnot available
KeywordsContaminationDetectorEnvironmental scienceRadiological weaponRadiationShutterParticle detectorRadiation monitoringRemote sensingOpticsComputer scienceMaterials scienceNuclear medicinePhysicsRadiochemistryChemistry

Abstract

fetched live from OpenAlex

Detection of radionuclides emitting short‐range radiation, such as α and low‐energy β particles, has always presented a challenge, particularly when such radionuclides are dispersed over a wide area. In this situation, conventional detection methods require the area of interest to be surveyed using a fragile probe at very close range—a slow, error‐prone, and potentially dangerous process that may take many hours for a single room. The instrument under development uses a novel approach by imaging radiation‐induced fluorescence in the air surrounding a contaminated area, rather than detecting the radiation directly. A robust and portable system has been designed and built that will allow contaminated areas to be rapidly detected and delineated. The detector incorporates position‐sensitive photo‐multiplier tubes, UV filters, a fast electronic shutter and an aspherical phase mask that significantly increases the depth‐of‐field. Preliminary tests have been conducted using sealed 241Am sources of varying activities and surface areas. The details of the instrument design will be described and the results of recent testing will be presented.

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 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 score0.599
Threshold uncertainty score0.193

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 head0.038
GPT teacher head0.234
Teacher spread0.196 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations14
Published2011
Admission routes1
Has abstractyes

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