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Record W2013279598 · doi:10.4161/bbug.3.1.17696

Genetic analysis of Down syndrome facilitated by mouse chromosome engineering

2012· article· en· W2013279598 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.

Bibliographic record

VenueBioengineered · 2012
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsCanadian Sleep SocietyBrock University
FundersNational Institute of Neurological Disorders and StrokeNational Heart, Lung, and Blood Institute
KeywordsChromosome 21Down syndromeBiologyPhenotypeTrisomyGeneticsAneuploidyChromosomeDYRK1AGene

Abstract

fetched live from OpenAlex

Human trisomy 21 is the most frequent live-born human aneuploidy and causes a constellation of disease phenotypes classified as Down syndrome, which include heart defects, myeloproliferative disorder, cognitive disabilities and Alzheimer-type neurodegeneration. Because these phenotypes are associated with an extra copy of a human chromosome, the genetic analysis of Down syndrome has been a major challenge. To complement human genetic approaches, mouse models have been generated and analyzed based on evolutionary conservation between the human and mouse genomes. These efforts have been greatly facilitated by Cre/loxP-mediated mouse chromosome engineering, which may result in the establishment of minimal critical genomic regions and eventually new dosage-sensitive genes associated with Down syndrome phenotypes. The success in genetic analysis of Down syndrome will further enhance our understanding of this disorder and lead to better strategies in developing effective therapeutic interventions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.269
Teacher spread0.248 · 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