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Record W2186504032

Developing a Sustainable Approach to Emerald Ash Borer Management

2015· article· en· W2186504032 on OpenAlexaboutno aff
William Davidson

Bibliographic record

VenueUKnowledge (University of Kentucky) · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsEmerald ash borerEmeraldBusinessEnvironmental scienceBiologyGeologyEcology
DOInot available

Abstract

fetched live from OpenAlex

Emerald ash borer, Agrilus planipennis Fairmaire, is an invasive wood boring beetle native to eastern Asia which was first detected in North America in 2002. All North American Fraxinus (ash) species are suitable hosts and susceptible to attack. Emerald ash borer larvae feed on phloem beneath the bark of infested trees resulting in girdling and mortality in as little as five years following initial infestation. Since its introduction near Detroit, Michigan, emerald ash borer has rapidly spread throughout much of the United States and portions of Canada, threatening the persistence of ash in invaded regions. I tested a management strategy for emerald ash borer which combines pesticide applications with releases of three species of classical biological control agents: Tetrastichus planipennisi, Spathius agrili, and Oobius agrili. My data suggest that the two approaches are compatible and pesticides did not negatively impact establishment success of T. planipennisi and O. agrili. Additionally, I characterized the assemblage of natural enemies native to the central United States that might be capable of helping regulate emerald ash borer populations, and found twelve morpho-species of natural enemies being recruited to emerald ash borer in this region. Finally, I evaluated the impact of ash decline on native hymenopteran parasitoids and found a positive correlation between ash decline and parasitoid abundance.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.620
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.200
Teacher spread0.182 · 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
GenreOther

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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueUKnowledge (University of Kentucky)Same topicForest Insect Ecology and ManagementFrench-language works237,207