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Record W2111007885 · doi:10.1603/en11298

Effects of Reforestation Practices on Staphylinid Beetles (Coleoptera: Staphylinidae) in Southwestern China Forests

2013· article· en· W2111007885 on OpenAlexfundno aff
Tian‐Hong Luo, Yu Xiaodong, Hong‐Zhang Zhou

Bibliographic record

VenueEnvironmental Entomology · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
FundersChinese Academy of SciencesInstitute of Zoology, Chinese Academy of SciencesAgriculture and Agri-Food CanadaNational Natural Science Foundation of China
KeywordsReforestationThreatened speciesEcologyLarchBiologySpecies richnessHabitatAbundance (ecology)Secondary forestOld-growth forestForestryAgroforestryGeography

Abstract

fetched live from OpenAlex

In 2004, Staphylinid beetle (Coleoptera) assemblages were studied via pitfall trapping to examine the effects of reforestation in southwestern China forests. Sites included two 100-yr-old mature forest types (hemlock-spruce forest and birch forest), and three 40-yr-old forest types established after harvesting (spruce plantation, larch plantation, and natural broad-leaved forest). Staphylinid species richness was greater in natural broad-leaved forests than those in hemlock-spruce forests and spruce plantations, but no significant difference was found in abundance among the five forest types. Beetle assemblages from young forest stands were significantly different from those in older forest stands, and some environmental characteristics, i.e., elevation, proportion of broad-leaved trees, and coarse woody debris, significantly affected species abundances. Moreover, some staphylinid species predominantly found only in older forest stands indicate that mature forest specialists might be threatened by loss of habitat. So it is necessary to retain adequate patches of older successional stages for conserving these beetle assemblages.

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 score0.013
Threshold uncertainty score0.459

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 head0.009
GPT teacher head0.209
Teacher spread0.200 · 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

Citations9
Published2013
Admission routes1
Has abstractyes

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