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Record W2981154696 · doi:10.1088/1361-6668/ab4e5c

Energy-resolved neutron imaging with high spatial resolution using a superconducting delay-line kinetic inductance detector

2019· article· en· W2981154696 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

VenueSuperconductor Science and Technology · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsUniversity of British ColumbiaTRIUMF
Fundersnot available
KeywordsNeutron imagingNeutronNeutron detectionDetectorImage resolutionOpticsPhysicsKinetic inductanceMaterials scienceNuclear physicsInductanceVoltage

Abstract

fetched live from OpenAlex

Abstract Neutron imaging is one of the key technologies for non-destructive transmission testing. Recent progress in the development of intensive neutron sources allows us to perform energy-resolved neutron imaging with high spatial resolution. Substantial efforts have been devoted to developing a high spatial and temporal resolution neutron imager. We have been developing a neutron imager aiming at conducting high spatial and temporal resolution imaging based on a delay-line neutron detector, called the current-biased kinetic-inductance detector, with a conversion layer 10 B. The detector allowed us to obtain a neutron transmission image with four signal readout lines. Herein, we expanded the sensor active area, and improved the spatial resolution of the detector. We examined the capability of high spatial resolution neutron imaging over the sensor active area of 15 × 15 mm 2 for various samples, including biological and metal ones. We also demonstrated an energy-resolved neutron image in which stainless-steel specimens were discriminating of other specimens with the aid of the Bragg edge transmission.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.229
Teacher spread0.219 · 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