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Record W2177375879 · doi:10.1603/0046-225x-30.5.919

Forest Thinning Affects Reproduction in Pine Engravers (Coleoptera: Scolytidae) Breeding in Felled Lodgepole Pine Trees: Fig. 1.

2001· article· en· W2177375879 on OpenAlexaff
Trevor D. Hindmarch, Mary L. Reid

Bibliographic record

VenueEnvironmental Entomology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPinus contortaBiologyThinningReproductionBark (sound)Biological dispersalAbiotic componentEcologyBark beetleReproductive successPopulationForestryGeography

Abstract

fetched live from OpenAlex

Reproduction in bark beetles (Coleoptera: Scolytidae) is known to be affected by abiotic factors, especially temperature, and by the quality of individual beetles. Both of these factors are affected by forest structure, yet the effects of forest structure on reproduction in bark beetles have not been widely shown in field studies. Here we investigate how changes in forest structure due to thinning of mature lodgepole pine, Pinus contorta variety latifolia Engelmann, stands affect reproduction in pine engravers, Ips pini (Say), breeding in felled trees. To do this, we excavated pine engraver gallery systems in thinned and unthinned stands at the end of the breeding season. Males in thinned stands attracted more females than in unthinned stands. Also, females in thinned stands extended their egg galleries farther, laid more eggs, and had higher egg densities than in unthinned stands. These results are consistent with increased temperatures in thinned stands, but may also be attributable to differences in individual quality resulting from easier dispersal in thinned stands. Regardless, the observed increases in reproduction likely reflect higher reproductive success in thinned stands than in unthinned stands, and the effects of thinning on population dynamics of bark beetles should be further investigated.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score1.000

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.214
Teacher spread0.206 · 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; both teacher heads agree on what is shown here.

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

Citations19
Published2001
Admission routes1
Has abstractyes

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