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Record W2128569925 · doi:10.1093/forestry/cpm029

The threat of the ambrosia beetle Megaplatypus mutatus (Chapuis) (=Platypus mutatus Chapuis) to world poplar resources

2007· article· fr· W2128569925 on OpenAlexaffabout
René I. Alfaro, Leland M. Humble, Paula González, Raul Villaverde, Gianni Allegro

Bibliographic record

VenueForestry An International Journal of Forest Research · 2007
Typearticle
Languagefr
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsAmbrosia beetleBiologySubtropicsJuglansTemperate climateWoody plantBotanyPEST analysisEcologyCurculionidae

Abstract

fetched live from OpenAlex

We describe the life cycle of Megaplatypus mutatus (Chapuis) (=Platypus mutatus) and the damage it causes to poplar resources in Argentina. This insect, native to the subtropical and tropical areas of South America, has extended its range into temperate regions, reaching as far south as Neuquén in Argentinean Patagonia. The damage is caused by the adult insects, which bore large gallery systems into living poplars (Populus spp.), willows (Salix spp.) and many other broadleaf species, including important fruit trees species such as apples (Malus spp.), walnuts (Juglans spp.) and avocados (Persea spp.). The galleries degrade the lumber and weaken the tree stems, which often then break during windstorms. A recent introduction of M. mutatus to Italy demonstrates that this insect can be transported long distances between countries, and therefore presents a threat worldwide—particularly to poplar cultivation. We review the taxonomic nomenclature for this pest, provide a summary of the life cycle, hosts and damage and summarize actions taken to reduce the risk of introduction of M. mutatus to Canada.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

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 communication0.0000.000
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 head0.027
GPT teacher head0.342
Teacher spread0.315 · 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

Citations61
Published2007
Admission routes2
Has abstractyes

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Same venueForestry An International Journal of Forest ResearchSame topicForest Insect Ecology and ManagementFrench-language works237,207