MétaCan
Menu
Back to cohort
Record W4393468452 · doi:10.5281/zenodo.10119577

Dataset for PPP2R5D antibody screening study

2023· dataset· en· W4393468452 on OpenAlexaff
Kathleen Southern

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Biosensing Techniques and Applications
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsAntibodyComputational biologyMedicineBiologyImmunology

Abstract

fetched live from OpenAlex

This antibody characterization dataset is related to the F1000 research article openly available at F1000Research. This project contains the underlying data included in a study which characterized six commercially-available antibodies for Serine/threonine-protein phosphatase 2A 56 kDa regulatory subunit delta isoform (PPP2R5D). The original study is also available on the Zenodo YCharOS community (https://doi.org/10.5281/zenodo.10108248). The Dataset is in the format of a zip file. Once downloaded, please expand the zip file to access the folders containing the underlying data for Western blot (Wb), immunoprecipitation (IP) and immunofluorescence (IF).

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)0.009
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.077
Threshold uncertainty score0.256

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0770.058

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 head0.050
GPT teacher head0.346
Teacher spread0.296 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicAdvanced Biosensing Techniques and ApplicationsFrench-language works237,207