MétaCan
Menu
← Back to cohort
Record W4393844522 · doi:10.5281/zenodo.3464533

Expression test and purification of SETDB1 catalytic domain constructs' for structural studies by X-ray crystallography.

2019· dataset· en· W4393844522 on OpenAlexaff
Serap Beldar, Dong Cheng, Ming Lei, Yanjun Li, P. Loppnau, Jinrong Min

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCrystallographyX-ray crystallographyDomain (mathematical analysis)X-rayChemistryMaterials scienceDiffractionPhysicsMathematicsOptics

Abstract

fetched live from OpenAlex

Experiment: Expression test and purification of SETDB1 catalytic domain constructs’ for structural studies by X-ray crystallography. Aim: In the present section of this study, we focused on the development of efficient bacterial expression systems to produce large amounts of soluble SETDB1 catalytic domain for structural studies. This report involves a summary of expression test results of different fragments of SETDB1 and purification of various fusion 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 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)0.006
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.027
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0270.026

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 head0.262
Teacher spread0.243 · 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
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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicCancer, Hypoxia, and Metabolism→French-language works237,207→