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Record W4220719431 · doi:10.1111/afe.12498

<scp>3‐Methylcyclohex</scp> ‐2‐en‐1‐one reduces the aggregation of <i>Dendroctonus pseudotsugae barragani</i> and corresponding mortality of <i>Pseudotsuga menziesii</i> in northern Mexico

2022· article· en· W4220719431 on OpenAlexaboutno aff
Guillermo Sánchez‐Martínez, Constance J. Mehmel, Ernesto González‐Gaona, Sylvia R. Mori, Juan Antonio López‐Hernández, José Carlos Monárrez‐González, José Leonardo García‐Rodríguez, Jorge Manuel Mejía‐Bojórquez, Nancy E. Gillette

Bibliographic record

VenueAgricultural and Forest Entomology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPEST analysisBiologySubspeciesBotanyDouglas firEcology

Abstract

fetched live from OpenAlex

Abstract Dendroctonus pseudotsugae is the most important forest insect pest of Douglas‐fir Pseudotsuga menziesii in North America. Two subspecies of Douglas‐fir beetle are recognized: D . pseudotsugae pseudotsugae , which inhabits southwestern Canada and western United States, and Dendroctonus pseudotsugae barragani , which occurs in northern Mexico. This study aimed to determine the effectiveness of 3‐methylcylohex‐2‐en‐1‐one (MCH) in reducing aggregation of D . pseudotsugae barragani , and corresponding Douglas‐fir mortality by this insect. Two field experiments were conducted: the first consisted of three doses of MCH in bubble caps (plus a control treatment) applied within 16 0.5 ha plots. The second consisted of three doses of MCH disrupt micro‐flakes (plus a control treatment) applied within 16 1.0 ha plots. MCH bubble caps at 28.8 and 44.0 g AI/ha and MCH disrupt micro‐flakes at 185.3 and 741 g AI/ha significantly reduced the aggregation of D . pseudotsugae barragani . When applying MCH as bubble caps, only the highest dose (44.0 g AI/ha) significantly reduced the number of successfully attacked trees. However, MCH micro‐flake doses of 185.3 and 741 g AI/ha reduced the number of successfully attacked trees to zero.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.048
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.208
Teacher spread0.200 · 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 teacher head, 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

Citations2
Published2022
Admission routes1
Has abstractyes

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