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The Organizing Principles of Eukaryotic Ribosome Recruitment

2019· review· en· W2951890467 on OpenAlexafffund
Jerry Pelletier, Nahum Sonenberg

Bibliographic record

VenueAnnual Review of Biochemistry · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchMcGill UniversityUniversity of California, DavisUniversité de StrasbourgNational Institutes of HealthCase Western Reserve University
KeywordsRibosomeMessenger RNAProtein biosynthesisInternal ribosome entry siteTranslation (biology)Ribosomal binding siteStart codonComputational biologyEukaryotic RibosomeBiologyCell biologyInitiation factorChemistryGeneticsRNAGene

Abstract

fetched live from OpenAlex

The stage at which ribosomes are recruited to messenger RNAs (mRNAs) is an elaborate and highly regulated phase of protein synthesis. Upon completion of this step, a ribosome is positioned at an appropriate initiation codon and primed to synthesize the encoded polypeptide product. In most circumstances, this step commits the ribosome to translate the mRNA. We summarize the knowledge regarding the initiation factors implicated in this activity as well as review different mechanisms by which this process is conducted.

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)0.002
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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.004

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 head0.065
GPT teacher head0.343
Teacher spread0.277 · 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

Citations282
Published2019
Admission routes2
Has abstractyes

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