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Record W2415803807 · doi:10.1007/978-1-61737-992-5_17

MRI Phenotyping of Genetically Altered Mice

2010· article· en· W2415803807 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

VenueMethods in molecular biology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene expression and cancer classification
Canadian institutionsUniversity of TorontoToronto Centre for PhenogenomicsHospital for Sick Children
FundersCanadian Institutes of Health Research
KeywordsPhenotypeComputational biologyBiologyGenetic similarityGenetically modified organismSimilarity (geometry)Set (abstract data type)Genetically engineeredGeneticsComputer scienceImage (mathematics)Artificial intelligenceGeneMedicine

Abstract

fetched live from OpenAlex

The laboratory mouse, with its genetic similarity to humans and rich set of tools for manipulating its genome, has emerged as one of the key models for experimental investigation of the genotype/phenotype relationships in mammals. Recent innovations have made MRI an increasingly popular tool for examining the phenotype of genetically altered mice. Advances in field strengths, mouse handling, image analysis and statistics have contributed greatly in this regard. In this chapter, we illustrate the methods necessary to achieve high-throughput phenotyping of genetically altered mice using multiple-mouse MRI combined with advanced image analysis techniques and statistics.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.128
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.386
Teacher spread0.369 · 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