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Record W2904161558 · doi:10.24218/jeert.2018.03

Software to Compensate Coincidence Losses in the Environmental Sample Analysis of a Digital Anti-Compton Spectrometer

2018· article· en· W2904161558 on OpenAlexaff
Weihua Zhang

Bibliographic record

VenueJournal of Energy and Environmental Research and Technology · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadioactive Decay and Measurement Techniques
Canadian institutionsHealth Canada
Fundersnot available
KeywordsSpectrometerCoincidenceSoftwareSample (material)PhysicsComputer scienceEnvironmental scienceOpticsOperating system

Abstract

fetched live from OpenAlex

Anti-Compton spectroscopy is a useful approach to analyze low-level contaminants in environmental samples that would otherwise be obscured by the Compton signal of the natural radionuclides present in the sample matrix. An example of this is the sensitive identification of 137 Cs in soil samples. A challenge of the anti-Compton technique is that the anti-coincidence veto to remove Compton scattered photons also suppresses the identification of contaminant radionuclides that have cascade gamma emissions such as 60 Co. This is a major limitation of the technique in environmental sample analysis. The time stamped pulse analysis method presented in this paper recovers the undesired full-energy-peak (FEP) coincidence losses for cascade radionuclides that trigger the anti-Compton spectrometer veto while retains suppression of true Compton events to keep the improved detection limit, thus extending the usefulness of the anti-Compton spectroscopy. In this study, the software has been used to analyse the radionuclides with a relative simple decay scheme, such as 22 Na, 60 Co and 88 Y, and a complex decay scheme, such as 125 Sb, 134 Cs, 152 Eu and 154 Eu. The results have indicated that in the reconstruct anticoincidence spectra the FEP restoration efficiency, both for minor and major peaks, is near 100%.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.025
GPT teacher head0.291
Teacher spread0.266 · 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 designObservational
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
Published2018
Admission routes1
Has abstractyes

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