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Record W2752731985 · doi:10.3390/f8090326

Development of Northern White-Cedar (Thuja occidentalis L.) Plantations within and outside Deer Yards

2017· article· en· W2752731985 on OpenAlexafffundabout
Olivier Villemaire‐Côté, Jean‐Claude Ruel, Luc Sirois

Bibliographic record

VenueForests · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversité du Québec à RimouskiUniversité LavalMinistère des Ressources naturelles et des Forêts
FundersFonds de recherche du Québec – Nature et technologies
KeywordsCompetition (biology)Natural regenerationAbundance (ecology)ThujaForestryBiologySnagEcologyGeographyAgroforestryHabitat

Abstract

fetched live from OpenAlex

Regional surveys done over the last decades show a clear decline in abundance of Northern white-cedar (Thuja occidentalis L.) throughout its range. A lack of seed trees, difficulties in the establishment of natural regeneration and high browsing pressure caused by increasing deer populations have been identified as plausible causes. Current silvicultural strategies for cedar restoration recommend partial cutting to promote and release natural regeneration, but there is also a need to restore the species in areas where it became absent. Yet, little attention has been given to cedar plantations. This study provides a first characterisation of the effects of competition, silvicultural treatments and deer, moose and hare browsing on planted cedar growth, survival, and stem form. Pure and mixed cedar plantations aged 5–27 years located in Eastern Québec were sampled. Both inside and outside deer yards, planted cedars showed high survival rates and were generally subject to low browsing pressure, but 45% were forked. Cedars showed high growth rates and strong reaction to stand opening. Results suggest that at reduced competition levels, a 9-year browser exclusion could be sufficient to establish safe-from-browsing cedar stands of >3 m in height.

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 score0.271
Threshold uncertainty score0.742

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 studies0.0010.000
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 head0.014
GPT teacher head0.255
Teacher spread0.242 · 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

Citations10
Published2017
Admission routes3
Has abstractyes

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