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Record W4292707072 · doi:10.1093/jipm/pmac016

The Ambrosia Beetle<i>Megaplatypus mutatus</i>: A Threat to Global Broad-Leaved Forest Resources

2022· article· en· W4292707072 on OpenAlexafffund
Esteban Ceriani-Nakamurakare, Carolina A. Robles, Paola González‐Audino, Andrés E. Dolinko, Patricia Mc Cargo, Juan C. Corley, Jeremy D. Allison, Cecilia C. Carmarán

Bibliographic record

VenueJournal of Integrated Pest Management · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsCanadian Forest Service
FundersAgencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la InnovaciónConsejo Nacional de Investigaciones Científicas y TécnicasCanadian Food Inspection Agency
KeywordsCurculionidaeAmbrosia beetleEcologyAgroforestryInvasive speciesPEST analysisBiologyRange (aeronautics)Host (biology)GeographyBotany

Abstract

fetched live from OpenAlex

Abstract We provide scientists and decision-makers with up-to-date information on Megaplatypus mutatus (Coleoptera: Curculionidae: Platypodinae), a forest insect native to South America that has invaded other regions. Emphasis is given to information that may prove relevant for the development of early detection, containment and management programs and improved risk analyses. The increase in global movement of people and goods, coupled with expected climatic scenarios, suggests that M. mutatus may arrive and establish populations in new areas. The major impact of this forest pest has always been reported in exotic tree species, even within its native range. The absence of a coevolutionary history with ‘naïve’ host trees is a relevant factor when analyzing and understanding the magnitude of the problem posed by this beetle and fungi associated with it. Notably, among preferred hosts are the widely planted Eucalyptus and Populus spp., facilitating the invasion of this insect into new regions and posing a threat to commercial forestry.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.212
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 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

Citations9
Published2022
Admission routes2
Has abstractyes

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