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Record W2161259392 · doi:10.1111/jen.12254

Real‐time <scp>PCR</scp> identification of the ambrosia beetles, <i>Trypodendron domesticum</i> (L.) and <i>Trypodendron lineatum</i> (Olivier) (Coleoptera: Scolytidae)

2015· article· en· W2161259392 on OpenAlexafffund
G.P. Robideau, Robert G. Foottit, Leland M. Humble, Meghan K. Noseworthy, Tao Wu, Guillaume J. Bilodeau

Bibliographic record

VenueJournal of Applied Entomology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsNatural Resources CanadaCanadian Forest ServiceAgriculture and Agri-Food CanadaCanadian Food Inspection Agency
FundersGovernment of CanadaCanadian Food Inspection Agency
KeywordsAmbrosia beetleBiologyAmbrosiaAmbrosia artemisiifoliaPEST analysisIdentification (biology)BotanyEcologyRagweedCurculionidae

Abstract

fetched live from OpenAlex

Abstract The European hardwood ambrosia beetle (Trypodendron domesticum) and the striped ambrosia beetle (Trypodendron lineatum) are wood‐boring pests that can cause serious damage to lumber, resulting in a need for management of these pests in logging and lumber industries. Natural populations of ambrosia beetles exist throughout the world, but movement of ambrosia beetles into new habitats, particularly via international trade, can result in the establishment of invasive species that have the potential to spread into new territory. Efforts to monitor ambrosia beetle populations are time‐consuming and could be greatly enhanced by the use of molecular methods, which would provide accurate and rapid identification of potentially problematic species. Here, we present new real‐time PCR assays for the detection and identification of T. domesticum and T. lineatum. The methods described herein can be used with a variety of sampling strategies to enable timely and well‐informed decision‐making in efforts to control these ambrosia beetles.

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)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.214
Teacher spread0.207 · 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
GenreMethods

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
Published2015
Admission routes2
Has abstractyes

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