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Record W2973834490 · doi:10.1094/pdis-06-19-1177-re

Ecological Status of <i>Juglans cinerea</i> in New Brunswick

2019· article· en· W2973834490 on OpenAlexafffundabout
Martin Williams, Eric R.D. Moise, Kathleen Forbes, Charlene Williams, Ian DeMerchant, Tannis Beardmore

Bibliographic record

VenuePlant Disease · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant and Fungal Interactions Research
Canadian institutionsVineland Research and Innovation CentreNatural Resources CanadaCanadian Forest Service
FundersCanadian Forest ServiceU.S. Forest ServiceNatural Resources CanadaEnvironment and Climate Change CanadaU.S. Department of Energy
KeywordsCankerBiologyJuglansTree healthDutch elm diseasePathogenEcologyBotany

Abstract

fetched live from OpenAlex

. Since it was first reported in Canada in 1990, this fungal pathogen has spread rapidly and established in New Brunswick in 1997. Apart from the first report in 1997 and another in 2004, no surveys have been conducted to assess the spread of the pathogen in the province. The purpose of this research was to survey butternut throughout its range in New Brunswick, evaluate disease occurrence as well as tree health, and investigate the impact of different topographic and tree health factors on canker incidence. Results showed that the disease has spread throughout the range of butternut in New Brunswick. The disease likely only recently (2007) spread to the northeastern-most populations, given that lower rates of canker occurrence and higher health ratings are found further away from the point of initial occurrence of the disease. Although canker incidence is high throughout the province, tree dieback is minimal, and trees still producing nuts could support opportunities for ex situ conservation. Because of the rate of pathogen spread in the province, implementing a tree improvement strategy might be the only means for maintaining the butternut genome on the landscape.

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 score0.239
Threshold uncertainty score0.247

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 head0.014
GPT teacher head0.272
Teacher spread0.258 · 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

Citations2
Published2019
Admission routes3
Has abstractyes

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