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Record W2329662707 · doi:10.5558/tfc2013-122

Incidence of beech bark disease resistance in the eastern Acadian forest of North America

2013· article· en· W2329662707 on OpenAlexafffundvenueabout
Anthony R. Taylor, Donnie McPhee, Judy Loo

Bibliographic record

VenueThe Forestry Chronicle · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsNatural Resources CanadaCanadian Forest Service
FundersCanadian Forest ServiceNatural Resources CanadaU.S. Forest Service
KeywordsBeechResistance (ecology)GeographyBark (sound)ForestryIncidence (geometry)BiologyEcologyDemography

Abstract

fetched live from OpenAlex

Beech bark disease (BBD) is a fatal affliction of American beech (Fagus grandifolia Ehrh.) in North America. Although natural resistance to BBD has been observed, reports vary with respect to incidence of resistance, with 1% being most commonly acknowledged. In this paper, we provide the first formal, empirical estimate of BBD resistance over a wide geographical area where BBD has been prevalent for longest in North America. We conducted our study in the Acadian Forest region of eastern Canada. Thirty-five beech-dominated stands (>5 ha each) were surveyed across the provinces of New Brunswick, Nova Scotia, and Prince Edward Island, spanning a time since infection (TSI) period between 1890 and 1975. Stands were surveyed for incidence of disease-free beech trees, which was used as a proxy for BBD resistance. Across our study area, the average percentage of disease-free trees observed was 3.3%; however, the occurrence of disease-free trees varied significantly geographically, with the oldest, most southerly TSI zone indicating 2.2% and the youngest, most northerly TSI zone showing 5.7%. Although geographic variation of disease-free beech trees may reflect disease exposure time, we speculate that lower minimum winter temperatures, combined with less intensive land-use history are the underlying mechanisms that explain the higher observed percentage of disease-free trees in the most northerly TSI zones.

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 score0.043
Threshold uncertainty score0.237

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)0.0000.000

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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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

Citations22
Published2013
Admission routes4
Has abstractyes

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