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Record W2066465924 · doi:10.4161/gmcr.1.4.13225

Genetic engineering for increasing fungal and bacterial disease resistance in crop plants

2010· review· en· W2066465924 on OpenAlex
Owen Wally, Zamir K. Punja

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.

Bibliographic record

VenueGM Crops · 2010
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBiologyGenetically modified cropsBiotechnologyResistance (ecology)Fungal diseaseFungal pathogenPlant disease resistanceGenetically engineeredCropTransgenePathogenGeneMicrobiologyEcologyGenetics

Abstract

fetched live from OpenAlex

We review the current and future potential of genetic engineering strategies used to make fungal and bacterial pathogen-resistant GM crops, illustrating different examples of the technologies and the potential benefits and short-falls of the strategies. There are well- established procedures for the production of transgenic plants with resistance towards these pathogens and considerable progress has been made using a range of new methodologies. There are no current commercially available transgenic plant species with increased resistance towards fungal and bacterial pathogens; only plants with increased resistance towards viruses are available. With an improved understanding of plant signaling pathways in response to a range of other pathogens, such as fungi, additional candidate genes for achieving resistance are being investigated. The potential for engineering plants for resistance against individual devastating diseases or for plants with resistance towards multiple pathogens is discussed in detail.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.024
GPT teacher head0.247
Teacher spread0.223 · 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