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Record W2154619004 · doi:10.1152/physiol.00009.2004

Deciphering the Renal Code: Advances in Conditional Gene Targeting

2004· review· en· W2154619004 on OpenAlexaff
Alexander Gawlik, Susan E. Quaggin

Bibliographic record

VenuePhysiology · 2004
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsSt. Michael's HospitalLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
Fundersnot available
KeywordsGene silencingConditional gene knockoutGeneBiologyRecombinaseRNA interferenceGene targetingGene expressionGene knockoutComputational biologyTransgeneRegulation of gene expressionFunction (biology)Cre recombinaseGeneticsBioinformaticsRNAPhenotypeGenetically modified mouse

Abstract

fetched live from OpenAlex

Several powerful new techniques can examine gene function in mammals. Recombinase systems and kidney-specific promoters enable gene knockout and overexpression. Genetic systems induced on administration or removal of antibiotics or hormones permit control of gene expression. Gene silencing using short interfering RNA expression systems should accelerate loss-of-function studies. Thorough characterization of animals that have undergone conditional gene targeting has already provided insights into renal development and diseases. Here we discuss the advantages and pitfalls of currently available gene-targeting systems.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.762

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 head0.013
GPT teacher head0.349
Teacher spread0.336 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations19
Published2004
Admission routes1
Has abstractyes

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