MétaCan
Menu
Back to cohort
Record W4403274909 · doi:10.1002/slct.202402861

Characterization of Simulated Cytoplasmic Fluids

2024· article· en· W4403274909 on OpenAlexafffund
Mariam El‐Morched, Charles Barraco, Razvan Simionescu, Thad A. Harroun, Hongbin Yan

Bibliographic record

VenueChemistrySelect · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsBrock University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCharacterization (materials science)CytoplasmChemistryMaterials scienceNanotechnologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Molecularly crowded solutions have been used to simulate cytoplasmic fluids, but little justification is provided in the literature. This work compared three key physical properties of solutions containing polyethylene glycol and sucrose against those in the cytoplasmic fluid from bacteria Escherichia coli : fluid viscosity, relaxation time (T1 and T2) of water, and the diffusion coefficient of water. It was concluded that appropriate mixtures of sucrose and polyethylene glycol 10,000, such as 10% sucrose and 15% PEG10,000 and 7.5% sucrose and 19% PEG10,000, closely mimic these properties of cytoplasmic fluid from E. coli containing around 100 mg/mL of proteins.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.414

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 head0.004
GPT teacher head0.222
Teacher spread0.218 · 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 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

Citations2
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueChemistrySelectSame topicProtein Structure and DynamicsFrench-language works237,207