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Record W2167577015 · doi:10.1002/mrm.20215

Multiple mouse biological loading and monitoring system for MRI

2004· article· en· W2167577015 on OpenAlexafffund
Jun Dazai, Nicholas A. Bock, Brian J. Nieman, Lorinda Davidson, R. Mark Henkelman, Xianhao Chen

Bibliographic record

VenueMagnetic Resonance in Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsHospital for Sick Children
FundersUniversity of Toronto
KeywordsComputer scienceNuclear magnetic resonancePhysics

Abstract

fetched live from OpenAlex

The use of mice to study models of human disease has resulted in a surge of interest in developing mouse MRI. The ability to take 3D, high-resolution images of live mice allows significant insight into anatomy and function. However, with imaging times on the order of hours, high throughput of specimens has been problematic. To facilitate high throughput, concurrent imaging of multiple mice has been developed; however, this poses further complexities regarding the ease and rapidity of loading several animals. In this study, custom-built equipment was developed to streamline the preparation process and to safely maintain seven mice during a multiple-mouse imaging session. Total preparation time for seven mice was approximately 24 min. ECG and temperature were monitored throughout the scan and maintained by regulating anesthetic and heating. Proof of principle was demonstrated in a 3-h imaging session of seven mice.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.329
Teacher spread0.289 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations63
Published2004
Admission routes2
Has abstractyes

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