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Record W2063505353 · doi:10.1139/x01-171

Allozyme analysis of host selection by bark beetles in central Mexico

2002· article· en· W2063505353 on OpenAlexvenueno aff
Carlos Fabián Vargas-Mendoza, A. López, Hermilo Sánchez Sánchez, Blanca Rodríguez

Bibliographic record

VenueCanadian Journal of Forest Research · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
FundersNational Science and Technology CouncilConsejo Nacional de Ciencia y TecnologíaNational Science Council
KeywordsBiologyBark (sound)Bark beetleBotanyHost (biology)CurculionidaeInfestationEcology

Abstract

fetched live from OpenAlex

A study was done to determine if the bark beetles Dendroctonus adjunctus Blandford and Dendroctonus valens LeConte exhibit a nonrandom selection pattern of susceptible Pinus lawsonii Rozel and Pinus montezumae Lamb. trees that can be related to genetic differences in the tree species. The study was done in an unmanaged, mature forest in which bark beetle infestations have been reported for 15 years. Samples from attacked and nonattacked trees of both pines species were characterized using allozymes. Collected leaf material was run in starch gels with a lithium hydroxide buffer and 15 markers. Attacked trees were more heterozygous, with the allele frequencies for the enzymes aspartate transaminase-1, carboxylesterase-3, L-leucine aminopeptidase-1, lactate dehydrogenase-2, and peroxidase-2 being significantly higher. Both a hierarchical analysis of genetic variability and measurement of genetic distance found differences between attacked and nonattacked trees in both species. It is suggested that the susceptibility of the trees chosen for infestation by the bark beetles is related to the genetic composition of the trees.

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 score0.960
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.022
GPT teacher head0.263
Teacher spread0.241 · 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

Citations4
Published2002
Admission routes1
Has abstractyes

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