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Record W4386138623 · doi:10.22543/0090-0222.2294

Improvement of Pheromone Trapping in Low Density Populations of <i>Choristoneura Pinus Pinus</i> (Lepidoptera: Tortricidae)

2018· article· en· W4386138623 on OpenAlexafffundabout
Jeffrey G. Fidgen, Peter J. Silk, Krista Ryall, Taylor Scarr

Bibliographic record

VenueThe Great Lakes Entomologist · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsOntario Forest Research InstituteMinistry of Natural Resources and ForestryNatural Resources Canada
FundersCanadian Forest ServiceU.S. Forest ServiceMinistry of Natural Resources
KeywordsTortricidaeLepidoptera genitaliaSpruce budwormBiologyPheromone trapPinus <genus>TrappingPEST analysisEcologyBotanyForestryHorticultureGeography

Abstract

fetched live from OpenAlex

Pheromone baited bucket traps (e.g., Multipher) are popular as a monitoring tool for the jack pine budworm, Choristoneura pinus pinus Freeman (Lepidoptera: Tortricidae), in Canada. However, there is no evidence to support their use when budworm populations are low. We therefore evaluated the capture rate of bucket traps at two placement heights (2 vs 6 m) in two jack pine forests in 2011, having low (≤5 fifth instars per mfoliated branch length) budworm populations. Compared to wing traps (e.g., Pherocon 1C), the trap design used initially to evaluate efficacy of the C. pinus pheromone, bucket traps caught fewer C. pinus and capture rates of both trap designs did not differ significantly between the two heights tested. Loss of bucket traps at 2 m, due to black bears, suggested that higher placement of traps was warranted to maintain the integrity of the array. However, wing traps are recommended due to their ability to consistently catch more moths when C. pinus populations are low.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.243
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2018
Admission routes3
Has abstractyes

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