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Record W2150708590 · doi:10.1074/mcp.o111.009969

Managing the Quality of Life for Proteins: Focusing on the Exit Path

2011· article· en· W2150708590 on OpenAlexaffabout
Peipei Ping, Thibault Mayor, Lan Huang

Bibliographic record

VenueMolecular & Cellular Proteomics · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProteomicsProtein degradationComputational biologyPosttranslational modificationComputer scienceBioinformaticsBiologyCell biologyGeneticsBiochemistry

Abstract

fetched live from OpenAlex

This special issue of Molecular Cellular Proteomics is dedicated to highlight outstanding scientific advances reported at the 2010 Proteomics of Protein Degradation and Ubiquitin Pathways (PPDUP) meeting in Vancouver, British Columbia, Canada. This conference was jointly sponsored by the International Forum of Proteomics and Proteomics of Protein Degradation and Ubiquitin Pathways, and it is the first of a series of conferences on the related topic. The major goal of this series of conferences is to promote the elucidation of protein degradation pathways and the understanding of downstream physiologic consequences using cutting-edge proteomic tools.

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)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0030.006
Scholarly communication0.0140.017
Open science0.0020.005
Research integrity0.0050.014
Insufficient payload (model declined to judge)0.0100.005

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 head0.052
GPT teacher head0.256
Teacher spread0.204 · 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 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

Citations0
Published2011
Admission routes2
Has abstractyes

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