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Record W4386226567 · doi:10.5558/tfc2023-024

Applying three decades of research to mitigate the impacts of hemlock woolly adelgid on Ontario’s forests

2023· article· en· W4386226567 on OpenAlexafffundvenueabout
William C. Parker, Victoria Derry, Ken A. Elliott, Chris J.K. MacQuarrie, Sharon E. Reed

Bibliographic record

VenueThe Forestry Chronicle · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsNatural Resources CanadaCanadian Forest ServiceOntario Forest Research InstituteMinistry of Natural Resources and Forestry
FundersNatural Resources CanadaU.S. Forest Service
KeywordsTsugaBiologyInvasive speciesInfestationClimate changeEcologyAgroforestryGeographyAgronomy

Abstract

fetched live from OpenAlex

Over the past 70 years, the introduced, invasive hemlock woolly adelgid (Adelges tsugae Annand) has become established and caused considerable decline and mortality of eastern hemlock (Tsuga canadensis (L.) Carr.) across much of the tree’s natural range. Hemlock is a foundation tree species with little inherent resistance to this exotic species and infestation by this sap-feeding insect results in progressive crown decline and tree mortality within 4 to 15 years. Continued climate warming favours the spread of this insect to Ontario and other areas at the northern edge of hemlock’s range. More than 30 years of basic and applied research directed towards control and mitigation of damage by this insect indicates that the rate of development of hemlock decline and mortality depends on climate, site, and stand factors that affect both insect performance and hemlock vigour. Here we synthesize these research findings to provide science-based management recommendations to (1) increase the resilience of Ontario’s hemlock forest resource to this insect before it spreads, (2) mitigate hemlock woolly adelgid damage once it gets established, and (3) facilitate degraded hemlock forest restoration.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.037
GPT teacher head0.309
Teacher spread0.272 · 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; both teacher heads agree on what is shown here.

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

Citations10
Published2023
Admission routes4
Has abstractyes

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