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Record W2068324062 · doi:10.1139/x06-310

Biodiversity and multiple disturbances: boreal forest ground beetle (Coleoptera: Carabidae) responses to wildfire, harvesting, and herbicide

2007· article· en· W2068324062 on OpenAlexaffvenue
Tyler Cobb, David W. Langor, John R. Spence

Bibliographic record

VenueCanadian Journal of Forest Research · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsUniversity of AlbertaNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsGround beetleDisturbance (geology)BiodiversityEcologyHabitatTaigaSpecies richnessBorealSalvage loggingCoarse woody debrisForest managementEnvironmental scienceSustainable forest managementAgroforestryGeographyBiologySnag

Abstract

fetched live from OpenAlex

Rising societal demands for forest resources along with existing natural disturbance regimes suggest that sustainable forest management will increasingly depend on better understanding the cumulative effects of natural and anthropogenic disturbances. In North America, for example, there is increasing economic pressure to salvage log burned forests, although the ecological consequences of combining fire and harvesting on the same sites are unclear. We examined the short-term (2 year) responses of boreal forest ground beetles (Coleoptera: Carabidae) to the individual and combined effects of wildfire, harvesting, and herbicide. Ground beetle responses to wildfire and forestry-related disturbances differed strongly and suggested that, although some species may appear to benefit from disturbance combinations (e.g., Sericoda quadripunctata (DeGeer)), these effects are detrimental to others (e.g., Sericoda bembidioides Kirby). Species compositional variability was significantly reduced by disturbance combinations suggesting that multiple disturbances may lead to a simplification of this entire assemblage. In addition, ground beetle responses were correlated with changes in several key habitat parameters such as amount of woody debris, exposed ground, and plant species richness suggesting avenues for future study. Overall, however, our results suggest that efforts to avoid compounding disturbances on any site should be considered when developing current and future forest management guidelines.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.776
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.054
GPT teacher head0.268
Teacher spread0.214 · 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

Citations107
Published2007
Admission routes2
Has abstractyes

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