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Record W3210392161 · doi:10.3390/f12111509

Fungi from Galleries of the Emerald Ash Borer Produce Cankers in Ash Trees

2021· article· en· W3210392161 on OpenAlexaboutno aff
Nickolas N. Rajtar, Benjamin W. Held, Robert A. Blanchette

Bibliographic record

VenueForests · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
FundersU.S. Department of Agriculture
KeywordsEmerald ash borerFraxinusAgrilusBiologyBuprestidaeCankerBotanyHorticulture

Abstract

fetched live from OpenAlex

The emerald ash borer (EAB, Agrilus planipennis) is a devastating invasive pest that has killed millions of ash trees in the United States and Canada. EAB was discovered in the US in 2002 and first reported in Minnesota in 2009. It attacks ash trees that are native to the United States, including Fraxinus americana (white ash), F. nigra (black ash) and F. pennsylvanica (green ash). It also attacks Chionanthus virginicus (white fringe tree). Seven species of fungi isolated and identified only from EAB-infested trees in a previous study as having the potential to cause cankers were used to test their pathogenicity in F. americana (white ash). The fungi used were Cytospora pruinosa, Diplodia mutila, Diplodia seriata, Paraconiothyrium brasiliense, Phaeoacremonium minimum, Phaeoacremonium scolyti, and Thyronectria aurigera. Two field experiments that used F. americana used two inoculation methods: woodchip and agar plug inoculations. Results indicated that all of the fungi tested caused cankers in varying amounts, as compared to the controls. The largest cankers were caused by D. mutila (270 mm2), C. pruinosa (169 mm2), and D. seriata (69 mm2). All fungi except for T. aurigera were re-isolated and sequenced to confirm Kochs’ postulates. Canker-causing fungi found in association with EAB galleries have the potential to contribute to tree dieback and mortality.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.998

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)0.0030.000

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 head0.006
GPT teacher head0.200
Teacher spread0.194 · 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 designObservational
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

Citations10
Published2021
Admission routes1
Has abstractyes

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