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Record W7132074769

[no title]

2015· article· en· W7132074769 on OpenAlexaboutno aff
Chunsheng Li, Barry M. Hauck, Kevin Capello, Pedro Nogueira, María Antonia López, Gary H. Kramer

Bibliographic record

VenueOpenAgrar · 2015
Typearticle
Languageen
FieldHealth Professions
TopicRadioactivity and Radon Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsCounting efficiencyImaging phantomDetectorPosition (finance)Photon countingSkull
DOInot available

Abstract

fetched live from OpenAlex

Skull counting can be used to assess the activity of radionuclides internally deposited in the bone. The Human Monitoring Laboratory (HML) at Health Canada conducted the measurement of 241Am in the BfS (Bundesamt für Strahlenschuts) skull phantom on contact with the skull for various positions. By placing the detector in contact, the HML can improve the counting efficiency by over 20% compared to placing the detector 1 cm above the surface of the skull. Among all the positions tested, the forehead position is the preferred counting geometry due to the design of HML’s counting facility and the comfort it would provide to the individual being counted, although this counting position did not offer the highest counting efficiency for the gamma rays (either the 59.5 keV or the 26.3 keV) emitted by 241Am.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.512
GPT teacher head0.531
Teacher spread0.019 · 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 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
Published2015
Admission routes1
Has abstractyes

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