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Record W4387389354 · doi:10.1038/s41597-023-02588-z

Transcriptome profiling of Fraxinus excelsior genotypes infested by emerald ash borer

2023· article· en· W4387389354 on OpenAlexaff
James Doonan, Chatchai Kosawang, Michael Eisenring, Tim R. Ladd, Amanda D. Roe, Katharina B. Budde, Hans Jørgen Lyngs Jørgensen, Valentin Queloz, Martin M. Goßner, Lene Rostgaard Nielsen

Bibliographic record

VenueScientific Data · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsNatural Resources CanadaCanadian Forest Service
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsFraxinusBiologyEmerald ash borerTranscriptomeBotanyGeneGene expression

Abstract

fetched live from OpenAlex

European ash, Fraxinus excelsior is facing the double threat of ongoing devastation by the invasive fungal pathogen, Hymenoscyphus fraxineus and the imminent arrival of the non-native emerald ash borer (EAB), Agrilus planipennis. The spread of EAB which is currently moving westwards from European Russia and Ukraine into central Europe, poses an additional substantial threat to European ash, F. excelsior. While the molecular basis for resistance or variation in resistance among European ash genotypes is heavily investigated, comparatively little is known about the molecular ash traits involved in resistance against EAB. In this study we have gathered transcriptomic data from EAB inoculated genotypes of F. excelsior that have previously shown different levels of susceptibility to EAB. Resultant datasets show differential gene expression in susceptible and resistant genotypes in response to EAB infestation. This data will provide important information on the molecular basis of resistance to the EAB and allow the development of management plans to combat a pending threat of a culturally and ecologically important European tree species.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.030
GPT teacher head0.264
Teacher spread0.234 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations4
Published2023
Admission routes1
Has abstractyes

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