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Record W2025764383 · doi:10.1139/z00-073

Differential responses of <i>Clethrionomys</i> species to forest disturbance in Europe and North America

2000· article· en· W2025764383 on OpenAlexvenueno aff
Joanna Gliwicz, Beata Glowacka

Bibliographic record

VenueCanadian Journal of Zoology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPeromyscusBiologyHolarcticGeneralist and specialist speciesEcologyClethrionomys glareolusRodentHabitatNicheDisturbance (geology)GenusPopulation

Abstract

fetched live from OpenAlex

In Holarctic forests, newly disturbed areas constantly appear as the result of clear-cutting and burning. Rodent communities in mature forests of Europe and North America are comparable, being dominated by a Clethrionomys species: Clethrionomys glareolus in Europe and Clethrionomys gapperi in America. In disturbed areas, however, they differ considerably, as C. glareolus persists on clearcuts in the Palearctic region, while in the Nearctic region, C. gapperi is replaced by Peromyscus species. Meta-analysis was applied to published data to summarize inter continenal differences. The aim of this paper is to explain why C. gapperi is not as successful as C. glareolus in colonizing disturbed areas, and to support one (or more) of the following hypotheses: (i) Peromyscus species are more suited to colonizing disturbed areas than other rodents; thus, the absence of Peromyscus species in Europe leaves the disturbed habitats free for C. glareolus; (ii) C. glareolus is more of a generalist and therefore better adapted for resettling such areas than C. gapperi; (iii) the long historical period of human management of forests has affected the niches of rodents, and thus their ability to colonize disturbed areas. The data analyzed indicate that the second hypothesis best explains the ultimate cause, while the third presents a probable proximate cause of the stated difference.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.203
Teacher spread0.192 · 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.

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

Citations23
Published2000
Admission routes1
Has abstractyes

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