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Record W3208085307 · doi:10.5281/zenodo.5061103

Antibody Characterization Report for Superoxide dismutase [Cu-Zn] (SOD1)

2021· article· en· W3208085307 on OpenAlexaffabout
Riham Ayoubi, Walaa Alshafie, Zhipeng You, Thomas M. Durcan, Peter S. McPherson, Carl Laflamme

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typearticle
Languageen
FieldMedicine
TopicMetal complexes synthesis and properties
Canadian institutionsMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsSuperoxide dismutaseSOD1AntibodyDismutaseChemistryCharacterization (materials science)BiochemistryImmunologyMedicineEnzymeMaterials scienceNanotechnology

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.132952.2 This report presents a guide to selecting high-quality commercial antibodies against Superoxide dismutase [Cu-Zn] (SOD1) by immunoblot (Western blot), immunoprecipitation and immunofluorescence using a standardized experimental protocol based on comparing read-outs iin knockout cell lines and isogenic parental controls. This work is part of the ALS-Reproducible Antibody Platform (ALS-RAP). ALS-RAP was created as a public-private partnership by three leading ALS charities - the ALS Association (USA), the Motor Neurone Disease Association (UK), and the ALS Society of Canada.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.948
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.066
GPT teacher head0.292
Teacher spread0.226 · 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; both teacher heads agree on what is shown here.

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
Published2021
Admission routes2
Has abstractyes

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