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Record W3048394765 · doi:10.3390/s20164510

Atmospheric Neutron Monitoring through Optical Fiber-Based Sensing

2020· letter· en· W3048394765 on OpenAlexaffabout
Sylvain Girard, Adriana Morana, Cornelia Hoehr, M. Trinczek, Jeoffray Vidalot, Philippe Paillet, C. Bélanger-Champagne, Julien Mekki, N. Balcon, Gaetano Li Vecchi, Cosimo Campanella, Damien Lambert, Emmanuel Marin, A. Boukenter, Y. Ouerdane, E. W. Blackmore

Bibliographic record

VenueSensors · 2020
Typeletter
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsTRIUMF
Fundersnot available
KeywordsOptical fiberRemote sensingFiberMaterials scienceEnvironmental scienceOptoelectronicsOpticsComputer sciencePhysicsGeologyComposite material

Abstract

fetched live from OpenAlex

The potential of fiber-based sensors to monitor the fluence of atmospheric neutrons is evaluated through accelerated tests at the TRIUMF Neutron Facility (TNF) (BC, Canada), offering a flux approximatively 109 higher than the reference spectrum observed under standard conditions in New York City, USA. The radiation-induced attenuation (RIA) at 1625 nm of a phosphorus-doped radiation sensitive optical fiber is shown to linearly increase with neutron fluence, allowing an in situ and easy monitoring of the neutron flux and fluence at this facility. Furthermore, our experiments show that the fiber response remains sensitive to the ionization processes, at least up to a fluence of 7.1 × 1011 n cm−², as its radiation sensitivity coefficient (~3.36 dB km−1 Gy−1) under neutron exposure remains very similar to the one measured under X-rays (~3.8 dB km−1 Gy−1) at the same wavelength. The presented results open the way to the development of a point-like or even a distributed dosimeter for natural or man-made neutron-rich environments. The feasibility to measure the dose caused by the neutron exposure during stratospheric balloon experiments, or during outer space missions, is presented as a case study of a potential future application.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.656
Threshold uncertainty score1.000

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 integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.020
GPT teacher head0.245
Teacher spread0.224 · 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 designNot applicable
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

Citations22
Published2020
Admission routes2
Has abstractyes

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