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Record W6911923153 · doi:10.5281/zenodo.13891494

A guide to selecting high-performing antibodies for Stearoyl-CoA desaturase (SCD1) (UniProt ID: O00767)

2024· article· en· W6911923153 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsAntibodyImmunoprecipitationProtocol (science)Characterization (materials science)Foundation (evidence)NatCell culture

Abstract

fetched live from OpenAlex

A peer-reviewed antibody characterization article corresponding to this Zenodo preprint is openly available at F1000Research: https://doi.org/10.12688/f1000research.160217.1 This report provides a guide for selecting high-quality commercial antibodies against Stearoyl-CoA desaturase (SCD1). Nine SCD1 antibodies were evaluated in western blot, immunoprecipitation and immunofluorescence. The characterization data was generated using a standardized protocol that compares read-outs in knockout cell lines and their isogenic parental controls. Thank you to the Work Michael J. Fox Foundation for Parkinson's Research for nominating this target and supporting the study.

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)0.005
metaresearch head score (Gemma)0.009
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: Methods · Consensus signal: Methods
Teacher disagreement score0.171
Threshold uncertainty score0.572

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0070.004
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0040.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.1710.205

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 head0.034
GPT teacher head0.313
Teacher spread0.279 · 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
GenreMethods

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 routes1
Has abstractyes

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