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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 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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0170.008

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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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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