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Record W2137434931 · doi:10.1111/afe.12050

Relationships among male <i>Coleophora deauratella</i> ( <scp>L</scp> epidoptera: <scp>C</scp> oleophoridae) pheromone‐baited trap capture, larval abundance, damage and flight phenology

2014· article· en· W2137434931 on OpenAlexaff
Boyd A. Mori, Calvin Yoder, Jennifer Otani, Maya L. Evenden

Bibliographic record

VenueAgricultural and Forest Entomology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsAgriculture and Agri-Food CanadaAgriculture Food and Rural DevelopmentUniversity of Alberta
Fundersnot available
KeywordsPhenologyBiologyPheromone trapPheromonePEST analysisPopulationLarvaAbundance (ecology)HorticultureSex pheromoneEcologyBotanyDemography

Abstract

fetched live from OpenAlex

Abstract The red clover casebearer Coleophora deauratella L einig and Z eller is an invasive pest of red clover ( Trifolium pratense L .) grown for seed production in C anada. Drastic yield losses (80–99.5%) have been reported in several growing regions over the last 30 years as a result of larval feeding damage. Field trials conducted in red clover seed production fields in A lberta, C anada, during the summers of 2010–2012 tested the efficacy of pheromone‐baited traps to predict population density and assess male flight phenology of C. deauratella . Male moth pheromone‐trap capture was positively related to larval abundance and proportion seed damage at both moderate and high population densities. Phenological models based on degree days ( DD 11.7 ) were better at describing median (50%) male flight compared with ordinal date models. Median C. deauratella male flight occurs at 258.39 DD 11.7 , starting from 1 J anuary each year in the P eace R iver region of A lberta. The results obtained in the present study demonstrate that pheromone‐baited traps can be used to detect the spread of this invasive species. Future work could incorporate pheromone‐based monitoring into assessment for the need and timing of control measures to target this invasive pest.

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)0.001
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 score0.058
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.018
GPT teacher head0.215
Teacher spread0.196 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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