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Record W2475196617 · doi:10.1371/journal.ppat.1005631

Escaping Host Immunity: New Tricks for Plant Pathogens

2016· review· en· W2475196617 on OpenAlexafffund
Ren Na, Mark Gijzen

Bibliographic record

VenuePLoS Pathogens · 2016
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsAgriculture and Agri-Food CanadaWestern University
FundersAgriculture and Agri-Food Canada
KeywordsPlant ImmunityImmunityHost (biology)BiologyVirologyImmune systemImmunologyEcologyGeneticsArabidopsis

Abstract

fetched live from OpenAlex

Fungal and oomycete plant pathogens cause destructive diseases in crops and pose real economic and food security threats [1]. These filamentous, eukaryotic organisms can also upset natural ecosystems when they spread invasively [2]. The capability of plant immune systems to detect and respond to pathogen effector proteins is a major determinant of disease susceptibility. Plant pathogen effector proteins that trigger host immunity are often encoded by conditionally detrimental genes that are under strong and contrasting selective pressures [3,4]. Pathogen effectors evolved to play a positive role in virulence by enabling growth and reproduction on host plants [5,6]. Nonetheless, effectors can meet their match with host immune receptors that recognize their presence, a result that ends badly for the pathogen. Such immunity-triggering proteins are known as avirulence (Avr) effectors, encoded by Avr genes.

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)0.001
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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.003

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 head0.090
GPT teacher head0.280
Teacher spread0.190 · 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

Citations34
Published2016
Admission routes2
Has abstractyes

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