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Record W2564412623 · doi:10.4039/tce.2015.71

Chemical control in forest pest management

2016· article· en· W2564412623 on OpenAlexaffabout
Stephen B. Holmes, Chris J.K. MacQuarrie

Bibliographic record

VenueThe Canadian Entomologist · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsSpruce budwormTortricidaeChoristoneura fumiferanaIntegrated pest managementLepidoptera genitaliaBuprestidaeBiologyPest controlForest managementAgroforestryChemical controlEcologyAgronomy

Abstract

fetched live from OpenAlex

Abstract Chemical insecticides have been an important tool in the management of forest insect pests in Canadian forests. Aerial application of insecticides began in the 1920s and expanded greatly after World War II with the widespread adoption of DDT primarily for the suppression of spruce budworm, Choristoneura fumiferana Clemens (Lepidoptera: Tortricidae), and other defoliating insects. Significant progress was made in the development of new chemical insecticides and formulations including fenitrothion and tebufenozide, as well as technology for the application of insecticides against various insect pests. However, widespread opposition to the use of chemical insecticides in forest management has led to significant reductions in the number of insecticides registered for use in Canadian forests. Developments in the past 20 years have focussed on new insecticides, formulations, and technologies that seek to limit the impacts on non-target organisms and subsequent ecosystem effects. These developments have resulted in significant improvements in the management of traditional management targets, such as the spruce budworm ( Choristoneura fumiferana (Clemens); Lepidoptera: Tortricidae) but also the management of invasive species, especially wood-boring beetles (Coleoptera: Buprestidae, Cerambycidae).

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 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 score0.787
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.007
GPT teacher head0.197
Teacher spread0.190 · 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

Citations46
Published2016
Admission routes2
Has abstractyes

Explore more

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