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Record W2924515523 · doi:10.1038/s41467-019-09253-3

Coming together to define membrane contact sites

2019· review· en· W2924515523 on OpenAlexafffund
Luca Scorrano, Maria Antonietta De Matteis, Scott D. Emr, Francesca Giordano, György Hajnóczky, Benoı̂t Kornmann, Laura L. Lackner, Tim P. Levine, Luca Pellegrini, Karin M. Reinisch, Rosario Rizzuto, Thomas Simmen, Harald Stenmark, Christian Ungermann, Maya Schuldiner

Bibliographic record

VenueNature Communications · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiotin and Related Studies
Canadian institutionsUniversity of AlbertaUniversité Laval
FundersNational Institute of General Medical SciencesVolkswagen FoundationBiotechnology and Biological Sciences Research CouncilNatural Sciences and Engineering Research Council of CanadaAssociazione Italiana per la Ricerca sul CancroCanadian Institutes of Health ResearchNational Institutes of HealthFondation Schlumberger pour l’Education et la RechercheÉcole Polytechnique Fédérale de LausanneAgence Nationale de la RechercheInstitut National de la Santé et de la Recherche MédicaleDeutsche Forschungsgemeinschaft
KeywordsField (mathematics)VocabularyDiversity (politics)Computer scienceData scienceControlled vocabularyWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

Close proximities between organelles have been described for decades. However, only recently a specific field dealing with organelle communication at membrane contact sites has gained wide acceptance, attracting scientists from multiple areas of cell biology. The diversity of approaches warrants a unified vocabulary for the field. Such definitions would facilitate laying the foundations of this field, streamlining communication and resolving semantic controversies. This opinion, written by a panel of experts in the field, aims to provide this burgeoning area with guidelines for the experimental definition and analysis of contact sites. It also includes suggestions on how to operationally and tractably measure and analyze them with the hope of ultimately facilitating knowledge production and dissemination within and outside the field of contact-site research.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.003
Science and technology studies0.0010.003
Scholarly communication0.0030.009
Open science0.0020.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.005

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 head0.369
Teacher spread0.317 · 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
GenreReview

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

Citations749
Published2019
Admission routes2
Has abstractyes

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