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Ground beetle responses to patch retention harvesting in high elevation forests of British Columbia

2004· article· en· W2107205138 on OpenAlexfundaboutno aff
Jeffrey P. Lemieux, B. Staffan Lindgren

Bibliographic record

VenueEcography · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
FundersMinistry of Forests, Lands and Natural Resource OperationsEntomological Society of Canada
KeywordsClearcuttingAbundance (ecology)Generalist and specialist speciesEcologyHabitatDisturbance (geology)CanopyGround beetleGeographyLoggingOld-growth forestBiology

Abstract

fetched live from OpenAlex

The effect of a forest harvesting system whereby small (typically 0.1–2.0 ha) patches of standing timber are retained inside of harvests, was compared to conventional clearcutting for its effect on ground beetle assemblages. Two seasons of pitfall trapping entailed 46 451 trap days, and yielded 15 799 individuals of 28 species; abundance was dominated by four species comprising 92.4% of the catch. Most species were known to have wide geographic distributions in Canada and Alaska but many species seemed to respond to disturbance on a site‐specific basis. Contrary to findings of similar studies, no species could be characterized as “mature‐forest specialists”, or “forest generalists”. Forest patches and edge habitats immediately inside the forest canopy contained assemblages more closely related to mature forest than to cleared areas. Harvested areas with patches yielded catches distinct from typical clearcuts, based primarily on changes in abundance of one common species. Climatic regimes and landscape disturbance levels were the two important factors distinguishing our study from others, and we have suggested that these may influence the degree to which patches are an effective conservation tool.

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 score0.677
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.196
Teacher spread0.178 · 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

Citations38
Published2004
Admission routes2
Has abstractyes

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