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Record W4283809270 · doi:10.1002/cbic.202200357

Cover Feature: Identification of Genes Essential for Sulfamate and Fluorine Incorporation During Nucleocidin Biosynthesis (ChemBioChem 15/2022)

2022· article· en· W4283809270 on OpenAlexaff
A. R. Ola Pasternak, Andreas Bechthold, David L. Zechel

Bibliographic record

VenueChemBioChem · 2022
Typearticle
Languageen
FieldEnergy
TopicMetalloenzymes and iron-sulfur proteins
Canadian institutionsQueen's University
Fundersnot available
KeywordsBiosynthesisGeneChemistryEnzymeBiochemistryNucleosideGene clusterStreptomycesCombinatorial chemistryStereochemistryComputational biologyBiologyGeneticsBacteria

Abstract

fetched live from OpenAlex

Nucleocidin is a rare example of naturally occurring nucleoside containing fluorine and sulfamate substituents. Identification of Streptomyces strains that are better producers of nucleocidin has facilitated the trek towards elucidating the biosynthesis of this novel molecule. For the first time gene inactivation experiments have identified three genes, nucGIJ, encoding two predicted sulfatases and a radical SAM enzyme, that are essential for sulfamate biosynthesis. This opens the door to further functional analysis of the biosynthetic gene cluster encoding nucleocidin. More information can be found in the Research Article by D. L. Zechel et al.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.414
Threshold uncertainty score0.836

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.4140.184

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 head0.008
GPT teacher head0.212
Teacher spread0.203 · 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.

Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2022
Admission routes1
Has abstractyes

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