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Record W2026915032 · doi:10.1002/nbm.1142

MR technology for biological studies in mice

2007· review· en· W2026915032 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNMR in Biomedicine · 2007
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersNational Institutes of HealthOntario Innovation Trust
KeywordsPhenotypeComputational biologyHuman diseaseThroughputComputer sciencePopularityBiologyDiseaseArtificial intelligenceBioinformaticsPathologyGeneticsMedicineGenePsychology

Abstract

fetched live from OpenAlex

Mouse models are crucial for the study of genetic factors and processes that influence human disease. In addition to tools for measuring genetic expression and establishing genotype, tools to accurately and comparatively assess mouse phenotype are essential in order to characterize pathology and make comparisons with human disease. MRI provides a powerful means of evaluating various anatomical and functional changes and hence is growing in popularity as a phenotypic readout for biomedical research studies. To accommodate the large numbers of mice needed in most biological studies, mouse MRI must offer high-throughput image acquisition and efficient image analysis. This article reviews the technology of multiple-mouse MRI, a method that images multiple mice or specimens simultaneously as a means of enabling high-throughput studies. Aspects of image acquisition and computational analysis in multiple-mouse studies are also described.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.155
GPT teacher head0.452
Teacher spread0.297 · 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