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Record W4394421497 · doi:10.6084/m9.figshare.20479737

Additional file 2 of In silico-guided metabolic engineering of Bacillus subtilis for efficient biosynthesis of purine nucleosides by blocking the key backflow nodes

2022· dataset· en· W4394421497 on OpenAlexaff
Aihua Deng, Qidi Qiu, Qinyun Sun, Zhenxiang Chen, Junyue Wang, Yu Zhang, Shuwen Liu, Tingyi Wen

Bibliographic record

VenueOpen MIND · 2022
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiochemical and Molecular Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBacillus subtilisBlocking (statistics)In silicoBackflowKey (lock)ChemistryComputational biologyBiochemistryComputer scienceBiologyBacteriaGeneGeneticsEngineeringComputer networkComputer security

Abstract

fetched live from OpenAlex

Additional file 2: Table S1. The production yield, productivity, and conversion rate of inosine engineered strains. Table S2. Genes and reactions used in the iBsu1103V2 model. Table S3. The lower bounds and upper bounds of reactions based on the 13C flux data. Table S4. The targets possible targets for overproduction of IMP and inosine predicted by OptForceMUST.

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)0.008
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.341
Threshold uncertainty score0.941

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.3410.090

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 head0.019
GPT teacher head0.269
Teacher spread0.250 · 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 designNot applicable
Domainnot available
GenreDataset

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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