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RECALIBRATION OF THE CAMECO MOBILE LUNG COUNTER

2009· article· en· W2060338573 on OpenAlexaff
Gary H. Kramer, Barry M. Hauck, Steve A. Allen

Bibliographic record

VenueHealth Physics · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsHealth Canada
Fundersnot available
KeywordsTorsoImaging phantomCalibrationLungNuclear medicineLung volumesBiomedical engineeringMedicineMathematicsStatisticsAnatomyInternal medicine

Abstract

fetched live from OpenAlex

The Cameco lung counter has been recalibrated using the Lawrence Livermore National Laboratory torso phantom using an extended lung set (i.e., longer than the original set). The previous calibration had used the Japanese Atomic Energy Research Institute torso phantom. The new calibration had lower counting efficiencies and, as a result, higher minimum detectable activities. The recalibrated Cameco lung counter was used to measure an extended lung set that had the volume reduced by using modified planar inserts for the sliced lungs as the extended lung set has a lung volume that is larger than Reference Man. This work has shown that sliced lungs can be used to alter the characteristics of the phantom's lung inserts substantially and provide flexibility in measurements previously not recognized.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
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 head0.014
GPT teacher head0.331
Teacher spread0.317 · 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 designBench or experimental
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
Published2009
Admission routes1
Has abstractyes

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