MétaCan
Menu
Back to cohort
Record W3170939697 · doi:10.1096/fba.2021-00040

Plant extracellular vesicles: Trojan horses of cross‐kingdom warfare

2021· review· en· W3170939697 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFASEB BioAdvances · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesCanadian Institute for Advanced ResearchNational Institutes of HealthNational Science Foundation
KeywordsTrojanExtracellular vesiclesKingdomVesicleBiologyCell biologyComputer securityComputer scienceBotanyBiochemistryMembrane

Abstract

fetched live from OpenAlex

Plants communicate with their interacting microorganisms through the exchange of functional molecules. This communication is critical for plant immunity, for pathogen virulence, and for establishing and maintaining symbioses. Extracellular vesicles (EVs) are lipid bilayer-enclosed spheres that are released by both the host and the microbe into the extracellular environment. Emerging evidence has shown that EVs play a prominent role in plant-microbe interactions by safely transporting functional molecules, such as proteins and RNAs to interacting organisms. Recent studies revealed that plant EVs deliver fungal gene-targeting small RNAs into fungal pathogens to suppress infection via cross-kingdom RNA interference (RNAi). In this review, we focus on the recent advances in our understanding of plant EVs and their role in plant-microbe interactions.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.927
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.342
Teacher spread0.302 · 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