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Record W2141649977 · doi:10.4141/s01-017

Effects of two silvicultural practices on soil fauna abundance in a northern hardwood forest, Québec, Canada

2002· article· en· W2141649977 on OpenAlexvenueaboutno aff
Jean‐David Moore, Rock Ouimet, Claude Camiré, Daniel Houle

Bibliographic record

VenueCanadian Journal of Soil Science · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsClearcuttingAbundance (ecology)EcologyFaunaSoil biologyForest managementForest ecologyForest floorForestryBiologyAgroforestryEcosystemEnvironmental scienceGeographySoil water

Abstract

fetched live from OpenAlex

Soil fauna play a key role in soil fertility and productivity of forest ecosystems and represent an important base of terrestrial food chains. The impact of forest management on soil fauna should be considered when sustainable forest management and conservation of biodiversity are desired. We evaluated the impact of selective cutting and strip clearcutting on soil fauna abundance in a northern hardwood forest of the Lower Laurentians of Québec. Twelve years after strip clearcutting, the abundance of carabid beetles (Coleoptera: Carabidae), collembolans (Hexapoda: Collembola) and snails (Stylommatophora: Sigmurethra) was greater in the strip clearcuts than the adjacent undisturbed strips. Snails and millipedes (Polydesmida: Polydesmidae) were more abundant in the selective cuts 6 to 8 yr after treatment. Spiders (Arachnida) were the only organism whose abundance was lower in the selective cuts than in the adjacent undisturbed forest . No significant negative effect of the silvicultural treatments was noted for the abundance of other caught organisms. This one-season sampling suggests there are few negative impacts associated with low intensity selective cutting and strip clearcutting on the abundance of soil fauna in this northern hardwood forest stands 6 to 12 yr after harvest. Key Words: Salamander, arthropod, shrew, northern hardwood, selective cutting, strip clearcutting

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)0.001
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 score0.037
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.192
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

Citations40
Published2002
Admission routes2
Has abstractyes

Explore more

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