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Recent advances in understanding the cellular roles of GSK-3

2017· preprint· en· W2589299515 on OpenAlexafffund
Kevin W. Cormier, James R. Woodgett

Bibliographic record

VenueF1000Research · 2017
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicWnt/β-catenin signaling in development and cancer
Canadian institutionsSinai Health SystemLunenfeld-Tanenbaum Research Institute
FundersCanadian Institutes of Health Research
KeywordsGSK-3NeuroscienceKinaseCrosstalkSignal transductionBiologyProtein kinase AOpen peer reviewCell biologyPlant biologyBioinformatics

Abstract

fetched live from OpenAlex

Glycogen synthase kinase-3 (GSK-3) is a ubiquitously expressed protein kinase that sits at the nexus of multiple signaling pathways. Its deep integration into cellular control circuits is consummate to its implication in diseases ranging from mood disorders to diabetes to neurodegenerative diseases and cancers. The selectivity and insulation of such a promiscuous kinase from unwanted crosstalk between pathways, while orchestrating a multifaceted response to cellular stimuli, offer key insights into more general mechanisms of cell regulation. Here, we review recent advances that have contributed to the understanding of GSK-3 and its role in driving appreciation of intracellular signal coordination.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.077
GPT teacher head0.355
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), 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

Citations124
Published2017
Admission routes2
Has abstractyes

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