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Record W2021467789 · doi:10.3390/f6020344

Development of Northern White-Cedar Regeneration Following Partial Cutting, with and without Deer Browsing

2015· article· en· W2021467789 on OpenAlexaffabout
Catherine Larouche, Jean‐Claude Ruel

Bibliographic record

VenueForests · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsUniversité LavalGovernment of British Columbia
Fundersnot available
KeywordsOdocoileusCanopyWildlifeHabitatAbundance (ecology)White (mutation)Regeneration (biology)EcologyBiologyNatural regenerationRange (aeronautics)GeographyForestry

Abstract

fetched live from OpenAlex

Northern white-cedar (Thuja occidentalis L.) is an important commercial species with a high wildlife value, both as a food source and habitat for many bird and mammal species. Concerns have been expressed about its decreasing abundance across its range, and especially in mixedwood stands, where it has to compete with several other species and can suffer from heavy browsing. In this study, we quantified the development of natural northern white-cedar seedlings and saplings under various partial cutting regimes, with and without white-tailed deer (Odocoileus virgianus Zimmerman) browsing, in three selected sites in Quebec (Canada) and in Maine (USA). Our data show that northern white-cedar regeneration was present in all studied stands, but that only a few stems were taller than 30 cm on the two sites with high densities of deer. In the absence of heavy browsing, stems reached a height of 30 cm in 11 years, and 130 cm in 28 years. Height growth of northern white-cedar regeneration increased with canopy light transmittance, while ground-level diameter increment increased after partial cutting. This suggests that partial cutting can be used in mixedwood stands to release natural northern white-cedar regeneration, but also that the recruitment of northern white-cedar seedlings to larger size classes constitutes a major challenge in stands subject to heavy deer browsing.

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 score0.019
Threshold uncertainty score0.253

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 head0.017
GPT teacher head0.227
Teacher spread0.210 · 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

Citations12
Published2015
Admission routes2
Has abstractyes

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