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Record W3044027650 · doi:10.3389/fpls.2020.01126

CRISPR/Cas9 Gene Editing: An Unexplored Frontier for Forest Pathology

2020· review· en· W3044027650 on OpenAlexafffund
Erika Dort, Philippe Tanguay, Richard C. Hamelin

Bibliographic record

VenueFrontiers in Plant Science · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversité LavalNatural Resources CanadaCanadian Forest ServiceUniversity of British Columbia
FundersNatural Resources CanadaFPInnovationsCanadian Food Inspection AgencyGenome Canada
KeywordsCRISPRGenome editingCas9BiologyComputational biologyGenePlasmidTrans-activating crRNAGenetics

Abstract

fetched live from OpenAlex

CRISPR/Cas9 gene editing technology has taken the scientific community by storm since its development in 2012. First discovered in 1987, CRISPR/Cas systems act as an adaptive immune response in archaea and bacteria that defends against invading bacteriophages and plasmids. CRISPR/Cas9 gene editing technology modifies this immune response to function in eukaryotic cells as a highly specific, RNA-guided complex that can edit almost any genetic target. This technology has applications in all biological fields, including plant pathology. However, examples of its use in forest pathology are essentially nonexistent. The aim of this review is to give researchers a deeper understanding of the native CRISPR/Cas systems and how they were adapted into the CRISPR/Cas9 technology used today in plant pathology-this information is crucial for researchers aiming to use this technology in the pathosystems they study. We review the current applications of CRISPR/Cas9 in plant pathology and propose future directions for research in forest pathosystems where this technology is currently underutilized.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.024
GPT teacher head0.330
Teacher spread0.306 · 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 teacher head, not a consensus.

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

Citations55
Published2020
Admission routes2
Has abstractyes

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