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Use of pityol to reduce damage by the white pine cone beetle, <i>Conophthorus coniperda</i> (Col., Scolytidae) in seed orchards

2004· article· en· W2122857036 on OpenAlexaff
Richard Trudel, Claude Guertin, Peter de Groot

Bibliographic record

VenueJournal of Applied Entomology · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsCanadian Forest ServiceNatural Resources CanadaInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsBiologyPEST analysisMating disruptionSeed orchardOrchardHorticultureIntegrated pest managementWhite (mutation)Conifer coneBlock (permutation group theory)BotanyPest controlAgronomyMathematics

Abstract

fetched live from OpenAlex

Abstract: The white pine cone beetle, Conophthorus coniperda (Schwarz), is a common and destructive pest of eastern white pine cones. The potential of mating disruption as a pest management tool against C. coniperda was tested during this experiment. The 5.5 ha white pine seed orchard used in this study was separated in three equal sections and different blocks were treated with pityol over 2‐year experiments. Ten and 20 pityol dispensers (bubble caps) were hung per treated block in 2001 (east block) and 2002 (centre block), respectively, to evaluate their impact on cone protection. During both seasons, the percentage of damaged cones at permanent stations was evaluated at monthly intervals in the middle of the treated block and at different places in the control area. For both years, the mean reduction in cone damage was 63.7% in the pheromone‐treated blocks. With this study, we have been able to demonstrate that mating disruption could be used as a biological pest management tool for the control of C. coniperda in white pine seed orchards.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

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 head0.035
GPT teacher head0.275
Teacher spread0.240 · 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
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

Citations15
Published2004
Admission routes1
Has abstractyes

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