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Record W1978397479 · doi:10.4161/auto.7.11.17661

A comprehensive glossary of autophagy-related molecules and processes (2<sup>nd</sup>edition)

2011· article· en· W1978397479 on OpenAlexaff
Daniel J. Klionsky, Eric H. Baehrecke, John H. Brumell, Charleen T. Chu, Patrice Codogno, Ana María Cuervo, Jayanta Debnath, Vojo Deretić, Zvulun Elazar, Eeva‐Liisa Eskelinen, Steven Finkbeiner, Juàn Fueyo, David A. Gewirtz, Marja Jäättelä, Guido Kroemer, Beth Levine, Thomas J. Melia, Noboru Mizushima, David C. Rubinsztein, Anne Simonsen, Andrew Thorburn, Michael Thumm, Sharon A. Tooze

Bibliographic record

VenueAutophagy · 2011
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsHospital for Sick Children
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNational Cancer Institute
KeywordsAutophagyGlossaryBiologyTerminologyCell biologyVocabularyFunction (biology)Computational biologyMechanism (biology)BiochemistryLinguisticsPhysics

Abstract

fetched live from OpenAlex

The study of autophagy is rapidly expanding, and our knowledge of the molecular mechanism and its connections to a wide range of physiological processes has increased substantially in the past decade. The vocabulary associated with autophagy has grown concomitantly. In fact, it is difficult for readers--even those who work in the field--to keep up with the ever-expanding terminology associated with the various autophagy-related processes. Accordingly, we have developed a comprehensive glossary of autophagy-related terms that is meant to provide a quick reference for researchers who need a brief reminder of the regulatory effects of transcription factors and chemical agents that induce or inhibit autophagy, the function of the autophagy-related proteins, and the roles of accessory components and structures that are associated with autophagy.

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)0.004
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: Other · Consensus signal: Other
Teacher disagreement score0.226
Threshold uncertainty score0.757

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.009
Science and technology studies0.0010.001
Scholarly communication0.0040.006
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.2260.179

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 head0.023
GPT teacher head0.256
Teacher spread0.232 · 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
GenreOther

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

Citations291
Published2011
Admission routes1
Has abstractyes

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