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Record W2911695323 · doi:10.1111/afe.12327

Impact of climate change on <i>Ostrinia nubilalis</i> (Lepidoptera: Crambidae) phenology and its implications on pest management

2019· article· en· W2911695323 on OpenAlexafffundabout
Annie‐Ève Gagnon, Gaétan Bourgeois, L. Bourdages, Patrick Grenier, Anne Blondlot

Bibliographic record

VenueAgricultural and Forest Entomology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsOuranosCégep Saint-Jean-sur-RichelieuAgriculture and Agri-Food Canada
FundersNatural Resources CanadaAgriculture and Agri-Food CanadaGreen Fund
KeywordsCrambidaeOstriniaVoltinismEuropean corn borerClimate changePEST analysisPhenologyBiologyClimate change scenarioOverwinteringIntegrated pest managementAgricultureDiapauseRepresentative Concentration PathwaysAgronomyEcologyClimate modelLarvaHorticulture

Abstract

fetched live from OpenAlex

Abstract Climate change can cause negative impacts to the agricultural sector by increasing pest damage to crops. The European corn borer (ECB) is a major insect pest of corn in North America. Its speed of development could potentially accelerate under a warmer climate, leading to an earlier development of the first generation and an increase in the number of generations per year. The main objective of this study was to assess the potential impacts of climate change on ECB management for the future period 2041–2070 in Quebec, Canada, using bioclimatic modelling and climate analogues. First flight of ECB moths could occur about 15 days earlier in the season in 2041–2070 compared to the reference period 1970–1999. The window for insecticide interventions may be reduced under climate change by 15.6% to 27.8% for univoltine ECB and by 13.8% to 52.7% for bivoltine ECB. Climate change could promote the development of an additional generation in the southern region for both races, considering temperature increases and factors inducing the overwintering diapause. ECB management could become more costly both economically and environmentally under the future climate, and it should be revised according to the results of this study.

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 score0.478
Threshold uncertainty score0.249

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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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

Citations20
Published2019
Admission routes3
Has abstractyes

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