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Record W1981900817 · doi:10.5558/tfc84558-4

Temperature and plant hardiness zone influence distribution of balsam woolly adelgid damage in Atlantic Canada

2008· article· en· W1981900817 on OpenAlexafffundvenueabout
Dan T. Quiring, Don P. Ostaff, L. K. Hartling, Dan Lavigne, Keith M. Moore, Ian DeMerchant

Bibliographic record

VenueThe Forestry Chronicle · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsNova Scotia Department of AgricultureNatural Resources CanadaCanadian Forest ServiceUniversity of New Brunswick
FundersCanadian Forest ServiceU.S. Forest ServiceNatural Resources CanadaDepartment of Natural Resources, Government of Newfoundland and Labrador
KeywordsBalsamAbiotic componentHardiness (plants)ForestryPEST analysisGeographyBiologyEcologyAbies balsameaBotany

Abstract

fetched live from OpenAlex

Management of balsam woolly adelgid (Adelges piceae Ratz.) and of trees damaged by this pest may pose one of the biggest challenges to forest management in Atlantic Canada during the next decade. Feeding by the balsam woolly adelgid is restricted to Abies species in which it causes gouting, branch and upper crown death. Approximately 100 years after its introduction into eastern Canada, symptoms of feeding by the balsam woolly adelgid on balsam fir are found throughout all of Nova Scotia, most of Newfoundland and in southern and eastern New Brunswick. The distribution of symptomatic balsam fir trees coincides with areas where mean January temperatures are below -11°C and where plant hardiness zones are higher than 4a. The presence of balsam fir trees with obvious symptoms of BWA damage throughout much of Atlantic Canada emphasizes the ubiquitous presence of this pest in these provinces and highlights the need to develop hazard rating systems to establish pest management programs to diminish its impact. Key words: global warming, Adelges piceae Ratz., forest insect pest management, abiotic factors

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 score0.361
Threshold uncertainty score0.723

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 head0.004
GPT teacher head0.178
Teacher spread0.174 · 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

Citations12
Published2008
Admission routes4
Has abstractyes

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