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Record W1503387605

Bubble detector for neutron and gamma discrimination

2010· article· en· W1503387605 on OpenAlexaboutno aff
S.G. Vaijapurkar

Bibliographic record

VenueRadiation Protection and Environment · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsDosimeterNeutronNeutron detectionNuclear physicsPhysicsNuclear engineeringDetectorGamma rayBubbleRadiochemistryRadiationOpticsChemistryEngineering
DOInot available

Abstract

fetched live from OpenAlex

Depending on neutron energy, a number of personnel neutron dosimeters such as NTA film, Albedo TLD dosimeter and Nuclear Track Detectors (CR39) have been developed and reported so far. At present, CR-39 is in use as personnel neutron dosimeters for neutron monitoring of occupational workers in most of the nuclear installations due to non-availability high sensitive real time gamma insensitive personnel neutron dosimeters as per ICRP recommendations. The Superheated Emulsion Detector or bubble detector assess the magnitude of detrimental effects on health of person exposed to neutron radiation in terms of absorbed dose or equivalent dose even in presence of high gamma radiations flux in real time unlike nuclear track detectors. Bubble Technology Industries (BTI), Canada and Aphel Enterprises, USA have commercialized these type of bubble detectors for neutron and gamma measurements. The paper will elaborate the basic principle, special features, mechanism of bubble formation, national and international status including it's applications in accelerator Physics, Medical sciences, nuclear submarine and neutron /gamma dose measurements in nuclear reactors. These detectors may be most popular in due course among health physicists due to it's reusability and precise measurements for neutron measurements in reactor environment, pulse neutron measurements and personal neutron dosimetery.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.251

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 head0.007
GPT teacher head0.204
Teacher spread0.197 · 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 designOther design
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

Citations0
Published2010
Admission routes1
Has abstractyes

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