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Record W2995730770 · doi:10.1016/j.xplc.2019.100016

Plant NLRs: The Whistleblowers of Plant Immunity

2019· review· en· W2995730770 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

VenuePlant Communications · 2019
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsPlant ImmunityPlant biologyBiologyEffectorComputational biologyImmunityImmune systemGeneticsCell biologyArabidopsisGeneBotany

Abstract

fetched live from OpenAlex

The study of plant diseases is almost as old as agriculture itself. Advancements in molecular biology have given us much more insight into the plant immune system and how it detects the many pathogens plants may encounter. Members of the primary family of plant resistance (R) proteins, NLRs, contain three distinct domains, and appear to use several different mechanisms to recognize pathogen effectors and trigger immunity. Understanding the molecular process of NLR recognition and activation has been greatly aided by advancements in structural studies, with ZAR1 recently becoming the first full-length NLR to be visualized. Genetic and biochemical analysis identified many critical components for NLR activation and homeostasis control. The increased study of helper NLRs has also provided insights into the downstream signaling pathways of NLRs. This review summarizes the progress in the last decades on plant NLR research, focusing on the mechanistic understanding that has been achieved.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.946
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0040.001
Research integrity0.0000.001
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 head0.139
GPT teacher head0.308
Teacher spread0.169 · 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