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Record W2105051314 · doi:10.48044/jauf.2012.014

Estimates of the Potential Cost of Emerald Ash Borer (Agrilus planipennis Fairmaire) in Canadian Municipalities

2012· article· en· W2105051314 on OpenAlexaboutno aff
Daniel W. McKenney, John Pedlar, Denys Yemshanov, D. Barry Lyons, K Campbell, Kevin Lawrence

Bibliographic record

VenueArboriculture & Urban Forestry · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsEmerald ash borerAgrilusFraxinusGeographyBuprestidaeForestryEcologyFisheryBiology

Abstract

fetched live from OpenAlex

Emerald ash borer (EAB) is an invasive phloem-feeding insect causing extensive mortality to ash (Fraxinus sp.) in North America. Economic costs associated with EAB-related mortality of street and backyard trees in Canadian urban areas were estimated over a 30-year time horizon. The approach employed a simple spread model to approximate EAB arrival times at each community based on three maximum spread rates: slow (~10 km/year), medium (~30 km/year), and fast (~50 km/year). Costs are estimated for four discount rates (0%, 2%, 4%, and 10%) and three treatment rates (0%, 10%, and 50% of trees treated with an insecticide). Ash density along urban roads was estimated from a variety of sources, including a recently developed survey that allows for rapid assessment of street tree compositions. Based on the 30 km/year spread rate, a 4% discount rate, and a 10% treatment rate, the present value of the costs is estimated to be approximately CAD $524 million (2010 currency rate); this value increases to roughly $890 million when costs associated with backyard trees are included. These estimates are conservative because they focus only on damage to street (and backyard) trees; nonetheless, their magnitude suggests considerable justification for investments to slow the spread of EAB in Canada.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.714

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)0.0010.000

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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations62
Published2012
Admission routes1
Has abstractyes

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