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Record W1533780971 · doi:10.22230/jem.2009v10n2a417

An integrated management system for the Douglas-fir tussock moth in southern British Columbia

2009· article· en· W1533780971 on OpenAlexaffabout
Lorraine Maclauchlan, P. M. Hall, I. S. Otvos, Julie E. Brooks

Bibliographic record

VenueJournal of Ecosystems and Management · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsCanadian Forest ServiceUniversity of VictoriaGovernment of British Columbia
Fundersnot available
KeywordsTussockOutbreakBiologyBiological pest controlNuclear Polyhedrosis VirusDouglas firEcologyBotanyVirologyLarva

Abstract

fetched live from OpenAlex

An integrated, long-term system to detect and treat infestations of Douglas-fir tussock moth in the Southern Interior of British Columbia was successfully implemented between 1984 and 1999. All aspects of recent research were implemented during an integrated control program conducted between 1991 and 1993. Many localized, incipient outbreak populations of tussock moth were detected prior to significant defoliation and treatments of a nuclear polyhedrosis virus (NPV) were applied. The application of NPV to sites with increasing tussock moth populations effectively terminated the localized infestations. The combination of early detection and application of NPV greatly reduced damage when compared to previous tussock moth outbreaks. Other program components were evaluated during the outbreak including: 6-trap cluster pheromone monitoring sites and singlet pheromone monitoring sites that correctly predicted outbreak level tussock moth populations; comparison between stored and new virus; comparison between virus formulations (Virtuss® versus TM Biocontrol-1®); evaluation of alternate swath versus entire coverage application of virus; and reduced dosages of virus. All virus trials were effective in reducing tussock moth populations to pre-outbreak levels. Future research and applications of the methodology are discussed.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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 designOther design
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

Citations5
Published2009
Admission routes2
Has abstractyes

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