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Record W2090023542 · doi:10.1063/1.2964120

Development of a combined K-edge subtraction and fluorescence subtraction imaging system for small animals

2008· article· en· W2090023542 on OpenAlexafffundabout
Brian Bewer, Honglin Zhang, Ying Zhu, Limei Zhang, Graham N. George, Ingrid J. Pickering, Dean Chapman

Bibliographic record

VenueReview of Scientific Instruments · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsSaskatchewan Health Research Foundation
KeywordsSubtractionOpticsPencil (optics)BeamlineRaster scanMaterials scienceImage subtractionDetectorPhysicsBeam (structure)Image processingComputer scienceComputer visionImage (mathematics)Mathematics

Abstract

fetched live from OpenAlex

A novel combined imaging system for small animals using dilute concentrations of iodine as a contrast agent was developed for wide and pencil photon beam image acquisitions. This combined imaging system used K-edge subtraction (KES) and fluorescence subtraction imaging (FSI) and was tested at the Hard x-ray Microanalysis beamline at the Canadian Light Source. The initial wide beam KES image acquired with a charge-coupled device camera was used to identify regions of interest for further investigation and determine the location and area of the raster scan for pencil beam imaging. The pencil photon beam scanning mode acquired simultaneously KES and FSI measurements with an ionization chamber measuring the KES data and a multielement germanium detector measuring the FSI data. A description of the system is given as well as preliminary results using an iodine test object.

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)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.024
GPT teacher head0.245
Teacher spread0.221 · 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

Citations7
Published2008
Admission routes3
Has abstractyes

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