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Record W4415616791 · doi:10.3390/biology14111506

Infestation Patterns and Climate-Based Projections for European Spongy Moth (Lymantria dispar) in Whirlpool Forest, Ontario, Canada

2025· article· en· W4415616791 on OpenAlexafffundabout
Xiaolong Guo, Qianqian Wang

Bibliographic record

VenueBiology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsSt. Francis Xavier University
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of Veterans Affairs
KeywordsInfestationPEST analysisOutbreakIntegrated pest managementSampling (signal processing)Tree (set theory)

Abstract

fetched live from OpenAlex

This study investigates spongy moth (Lymantria dispar) infestation patterns in Whirlpool Forest, Ontario, offering a region-specific perspective while largely corroborating existing findings. We analyzed egg mass distribution across 43 sampling plots, relating it to tree characteristics. Results revealed a preference for red oak species, with significant egg-laying above one meter. Positive correlations were found between tree diameter and egg mass quantity (ρ = 0.458, p < 0.001 above 1 m; ρ = 0.218, p = 0.006 below 1 m). Tree health was significantly associated with egg mass presence (χ2 = 6.08, p = 0.014). A climate-based regression model (R2 = 0.714, p < 0.05) projected substantial increases in outbreak area by 2100, with the most severe scenario predicting 9,927,378.49 hectares at risk. Sensitivity analysis showed a 1 °C temperature increase could expand the outbreak area by 814,100 hectares. These findings underscore complex infestation dynamics, challenging simplified models and emphasizing the need for tailored, adaptive forest management strategies in response to changing environmental conditions and pest behaviors.

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 score0.176
Threshold uncertainty score0.281

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 head0.006
GPT teacher head0.218
Teacher spread0.212 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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