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Record W2141536199 · doi:10.3897/zookeys.147.2098

Landscape patterns of species-level association between ground-beetles and overstory trees in boreal forests of western Canada (Coleoptera, Carabidae)

2011· article· en· W2141536199 on OpenAlexafffundabout
J. David Spence, Jan Volney

Bibliographic record

VenueZooKeys · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsNatural Resources CanadaUniversity of Alberta
FundersCanadian Forest ServiceNatural Sciences and Engineering Research Council of CanadaU.S. Forest Service
KeywordsEcologyBiodiversityGround beetleGeographyOrdinationTaigaBorealBasal areaHabitatBiology

Abstract

fetched live from OpenAlex

Spatial associations between species of trees and ground-beetles (Coleoptera: Carabidae) involve many indirect ecological processes, likely reflecting the function of numerous forest ecosystem components. Describing and quantifying these associations at the landscape scale is basic to the development of a surrogate-based framework for biodiversity monitoring and conservation. In this study, we used a systematic sampling grid covering 84 km(2) of boreal mixedwood forest to characterize the ground-beetle assemblage associated with each tree species occurring on this landscape. Projecting the distribution of relative basal area of each tree species on the beetle ordination diagram suggests that the carabid community is structured by the same environmental factors that affects the distribution of trees, or perhaps even by trees per se. Interestingly beetle species are associated with tree species of the same rank order of abundance on this landscape, suggesting that conservation of less abundant trees will concomitantly foster conservation of less abundant beetle species. Landscape patterns of association described here are based on characteristics that can be directly linked to provincial forest inventories, providing a basis that is already available for use of tree species as biodiversity surrogates in boreal forest land management.

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 score0.328
Threshold uncertainty score0.340

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 head0.035
GPT teacher head0.187
Teacher spread0.152 · 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

Citations14
Published2011
Admission routes3
Has abstractyes

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