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Record W4406707502 · doi:10.3390/insects16020108

Cost-Effective Monitoring of Spruce Budworm Larvae

2025· article· en· W4406707502 on OpenAlexaffabout
Marc Rhainds, Pierre Therrien

Bibliographic record

VenueInsects · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsMinistère des Ressources naturelles et des ForêtsNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsSpruce budwormChoristoneura fumiferanaBiologyTortricidaeAbies balsameaBalsamLepidoptera genitaliaForestryTachinidaeOverwinteringRange (aeronautics)EcologyAgroforestryLarvaBotanyGeography

Abstract

fetched live from OpenAlex

The spruce budworm, Choristoneura fumiferana Clem. (Lepidoptera: Tortricidae), is the most severe defoliator of balsam fir (Abies balsamea) in eastern Canada and northeast U.S.A. A large budworm outbreak is currently underway in the province of Québec, with 10.5 million hectares defoliated in 2023, up 14.1% from the year before. Populations of budworms are controlled using management guidelines of Forest Protection Strategy (FPS); the approach aims at killing defoliating larvae with aerial application of microbial insecticide, with the objective to limit defoliation and prevent tree mortality. The decision to treat/not treat a given forest block is based, in part, on local density of overwintering second instars (L2) collected at ca. 600 sampling points each year across the entire province at a cost of CAD 350 for three branches per site; the threshold for FPS (TFPS) corresponds to 20 L2/branch. Aerial defoliation maps also guide management decisions because FPS generally target areas within or in the vicinity of defoliated forest stands. Budworm abundance rapidly declines with distance to aerial defoliation (in km), to the extent that larval density rarely attains TFPS outside the core range of FPS (>15 km from defoliation). A cost-effective monitoring approach is proposed whereby forest blocks outside the core range of FPS are sampled every second year (as opposed to every year), representing a potential economy of CAD 40,000 annually.

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 score0.134
Threshold uncertainty score0.437

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 head0.012
GPT teacher head0.257
Teacher spread0.245 · 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 routes2
Has abstractyes

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