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Influence of boreal forest succession and dead wood qualities on saproxylic beetles

2006· article· en· W2169714394 on OpenAlexaff
Joshua M. Jacobs, John R. Spence, David W. Langor

Bibliographic record

VenueAgricultural and Forest Entomology · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsUniversity of AlbertaCanadian Forest Service
Fundersnot available
KeywordsCoarse woody debrisSnagBiologyTaigaEcologyEcological successionLonghorn beetleDeciduousBorealBiodiversityHabitat

Abstract

fetched live from OpenAlex

Abstract 1 Saproxylic insects, a functional group dominated by beetles, are dependent on dead or moribund trees as habitat elements. 2 Although there are few studies of saproxylic insects from the North American boreal zone, European studies demonstrate that forest harvest can lead to a biologically significant decrease in saproxylic beetle diversity. 3 We studied saproxylic beetles in the North American boreal mixedwood forest using flight intercept traps established on naturally dead and girdled trembling aspen and spruce trees along a successional gradient of undisturbed stands from deciduous to coniferous overstory trees. 4 Composition and diversity of beetle assemblages differed among forest successional types. 5 Snag age class was an important determinant of composition for saproxylic beetle assemblages. 6 Multivariate regression analysis of these data indicated that saproxylic beetles are responding to changes in coarse woody debris, and not to the relative densities of canopy tree species, although these variables are strongly correlated. 7 Coarse woody debris management should be a primary concern in forest management plans seeking to conserve saproxylic organisms and the critical ecosystem functions (i.e. nutrient cycling) in which they participate.

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)0.000
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 score0.019
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.011
GPT teacher head0.205
Teacher spread0.194 · 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

Citations86
Published2006
Admission routes1
Has abstractyes

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