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Record W2606634398 · doi:10.1093/jee/tox081

Evaluation of Attractants for Monitoring Drosophila suzukii (Diptera: Drosophilidae)

2017· article· en· W2606634398 on OpenAlexafffundabout
Andrew J. Frewin, Justin M. Renkema, Hannah Fraser, Rebecca H. Hallett

Bibliographic record

VenueJournal of Economic Entomology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect behavior and control techniques
Canadian institutionsMinistry of Agriculture, Food and Rural AffairsUniversity of Guelph
FundersOntario Ministry of Agriculture, Food and Rural AffairsUniversity of Guelph
KeywordsDrosophila suzukiiBiologyDrosophila (subgenus)PEST analysisIntegrated pest managementBlowing a raspberryDrosophilidaeHorticultureEcologyDrosophila melanogaster

Abstract

fetched live from OpenAlex

Drosophila suzukii Matsumura is an economically important pest of soft and small fruit crops. Unlike other drosophilid flies, D. suzukii is capable of infesting ripe and partially ripe fruit, which poses a significant pest management challenge, as there is no tolerance for infested or damaged fruit in the marketplace. As a result, producers in many regions rely on calendar-scheduled insecticide applications for D. suzukii management. In order to develop an integrated pest management approach, better tools for monitoring adult fly populations are needed. Over two growing seasons in southern Ontario, we evaluated three commercial (Pherocon SWD dual-lure, Suzukii Trap, and Scentry Lure) and two homemade baits (apple cider vinegar and yeast + sugar water) for their ability to capture D. suzukii in raspberry and highbush blueberry fields. In general, commercial attractants captured D. suzukii earlier in the growing season and in greater numbers, with fewer nontarget captures, compared with homemade baits. Scentry Lure tended to perform better than other attractants, capturing D. suzukii earlier and in larger numbers during peak harvest. Pherocon SWD dual-lure was highly specific to D. suzukii. The combination of Scentry Lure with Suzukii Trap had possible synergistic effects, as numbers of D. suzukii captured to the combined treatment were more than additive. Using commercial attractants improves D. suzukii monitoring and will allow growers to improve the timing of insecticide applications with D. suzukii activity in the field.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.093
GPT teacher head0.345
Teacher spread0.252 · 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

Citations35
Published2017
Admission routes3
Has abstractyes

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