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Record W3099316602 · doi:10.1038/s41598-020-75549-w

Assessing the climate suitability and potential economic impacts of Oak wilt in Canada

2020· article· en· W3099316602 on OpenAlexaffabout
John Pedlar, Daniel W. McKenney, Emily S. Hope, Sharon E. Reed, Jon Sweeney

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsNatural Resources CanadaOntario Forest Research InstituteCanadian Forest Service
Fundersnot available
KeywordsBiological dispersalGeographyClimate changeForest productRange (aeronautics)Resource (disambiguation)Distribution (mathematics)ForestryEnvironmental scienceEcologyForest managementBiologyDemographyPopulation

Abstract

fetched live from OpenAlex

We assess risks posed by oak wilt-a disease caused by the fungal pathogen Bretziella fagacearum. Though not currently found in Canada, our distribution models indicate that suitable climate conditions currently occur in southern Ontario for B. fagacearum and two of its main insect dispersal vectors, Colopterus truncatus and Carpophilus sayi. Climate habitat for these species is projected to expand northward under climate change, with much of the oak range in eastern Canada becoming climatically suitable within the next two decades. Potential costs for the removal and replacement of oak street trees ranged from CDN$266 to $420 million, with variation related to uncertainty in costs, rate of tree replacement, and city-level estimates of oak street tree density. The value of standing oak timber in eastern Canada was estimated at CDN$126 million using provincial stumpage fees and as a CDN$24 million annual contribution to national Gross Domestic Product (GDP) when calculated using a combination of economic and forestry product statistics. These values can help inform the scale of eradication and/or management efforts in the event of future oak wilt introductions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.222
Teacher spread0.214 · 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

Citations34
Published2020
Admission routes2
Has abstractyes

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