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Record W1519920454 · doi:10.1107/s160057751501036x

Design of a mouse restraint for synchrotron-based computed tomography imaging

2015· article· en· W1519920454 on OpenAlexafffundabout
Kenneth B. Gagnon, Sally Caine, Nazanin Samadi, Mercedes Martinson, Melanie van der Loop, Jane Alcorn, Dean Chapman, George Belev, Helen Nichol

Bibliographic record

VenueJournal of Synchrotron Radiation · 2015
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersNational Research Council CanadaWestern Economic Diversification CanadaSaskatchewan Health Research FoundationCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaUniversity of SaskatchewanCanadian Light Source
KeywordsSynchrotronComputed tomographyTomographySynchrotron radiationPhysicsNuclear medicineNuclear magnetic resonanceMaterials scienceMedicineOpticsRadiology

Abstract

fetched live from OpenAlex

High-resolution computed tomography (CT) imaging of a live animal within a lead-lined synchrotron light hutch presents several unique challenges. In order to confirm that the animal is under a stable plane of anaesthesia, several physiological parameters (e.g. heart rate, arterial oxygen saturation, core body temperature and respiratory rate) must be remotely monitored from outside the imaging hutch. In addition, to properly scan the thoracic region using CT, the animal needs to be held in a vertical position perpendicular to the fixed angle of the X-ray beam and free to rotate 180°-360°. A new X-ray transparent mouse restraint designed and fabricated using computer-aided design software and three-dimensional rapid prototype printing has been successfully tested at the Biomedical Imaging and Therapy bending-magnet (BMIT-BM) beamline at the Canadian Light Source.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.318
Teacher spread0.281 · 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
GenreMethods

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

Citations1
Published2015
Admission routes3
Has abstractyes

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