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Record W3006182604 · doi:10.1126/science.aax4079

The pan-genome effector-triggered immunity landscape of a host-pathogen interaction

2020· article· en· W3006182604 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScience · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPseudomonas syringaeEffectorBiologyGenomeArabidopsis thalianaArabidopsisPlant ImmunityGeneGeneticsImmunityPathogenComputational biologyImmune systemCell biologyMutant

Abstract

fetched live from OpenAlex

A plant pan-genome immunity landscape Plant pathogens elicit an immune response through effector proteins. In turn, plant genomes encode genes that determine species-specific recognition of these effectors by a process known collectively as effector-triggered immunity (ETI). By examining a range of strains of the pathogen Pseudomonas syringae that infect the model plant Arabidopsis thaliana , Laflamme et al. generated a P. syringae Type III Effector Compendium (PsyTEC) and in turn identified the genes responsible for ETI in Arabidopsis. This pan-genome analysis revealed that relatively few A. thaliana genes are responsible for recognizing the majority of P. syringae effectors. These results provide insight into why most pathogenic microbes only infect specific plant species. Science , this issue p. 763

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.235
Teacher spread0.214 · 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