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Record W2328482192 · doi:10.1093/rpd/ncr358

Review of bubble detector response characteristics and results from space

2011· review· en· W2328482192 on OpenAlexafffundabout
B.J. Lewis, M. B. Smith, H. Ing, H. R. Andrews, R. Machrafi, Leena Tomi, Tristan Matthews, L. M. Veloce, Vyacheslav Shurshakov, I.V. Tchernykh, N. Khoshooniy

Bibliographic record

VenueRadiation Protection Dosimetry · 2011
Typereview
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsCanadian Space AgencyOntario Tech UniversityRoyal Military College of Canada
FundersCanadian Space AgencyChina Institute of Atomic Energy
KeywordsDetectorInternational Space StationBubbleNeutronSatelliteAerospace engineeringPhysicsSpace environmentNeutron detectionNuclear physicsSpace (punctuation)SpectrometerSpace researchNuclear engineeringEnvironmental scienceOpticsComputer scienceEngineeringAstronomyMechanics

Abstract

fetched live from OpenAlex

A passive neutron-bubble dosemeter (BD), developed by Bubble Technology Industries, has been used for space applications. Both the bubble detector-personal neutron dosemeter and bubble detector spectrometer have been studied at ground-based facilities in order to characterise their response due to neutrons, heavy ion particles and protons. This technology was first used during the Canadian-Russian collaboration aboard the Russian satellite BION-9, and subsequently on other space missions, including later BION satellites, the space transportation system, Russian MIR space station and International Space Station. This paper provides an overview of the experiments that have been performed for both ground-based and space studies in an effort to characterise the response of these detectors to various particle types in low earth orbit and presents results from the various space investigations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.029
GPT teacher head0.287
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations32
Published2011
Admission routes3
Has abstractyes

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