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Record W2093146950 · doi:10.3390/f4020364

Ecosystem Responses to Partial Harvesting in Eastern Boreal Mixedwood Stands

2013· article· en· W2093146950 on OpenAlexaffabout
Suzanne Brais, Timothy T. Work, Émilie Robert, Christopher D. O’Connor, Manuella Strukelj, Arun K. Bose, Danielle Celentano, Brian D. Harvey

Bibliographic record

VenueForests · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsDawson CollegeCegep de ThetfordCégep de l'Abitibi TémiscamingueUniversité du Québec à MontréalUniversité du Québec en Abitibi-Témiscamingue
FundersNorthwestern University
KeywordsAbies balsameaBalsamBorealTaigaEnvironmental scienceEcosystemEcologyAgroforestryBiologyBotany

Abstract

fetched live from OpenAlex

Partial harvesting has been proposed as a key aspect to implementing ecosystem management in the Canadian boreal forest. We report on a replicated experiment located in boreal mixedwoods of Northwestern Quebec. In the winter of 2000–2001, two partial harvesting treatments, one using a dispersed pattern, and a second, which created a (400 m2) gap pattern, were applied to a 90-year-old aspen-dominated mixed stand. The design also included a clear cut and a control. Over the course of the following eight years, live tree, coarse woody debris, regeneration and ground beetles were inventoried at variable intervals. Our results indicate that all harvesting treatments created conditions favorable to balsam fir (Abies balsamea) sapling growth and trembling aspen (Populus tremuloides) sapling recruitment. However, balsam fir and trembling aspen regeneration and ground beetles response to gap cuts were closer to patterns observed in clear cuts than in dispersed harvesting. The underlying reasons for these differing patterns can be linked to factors associated with the contrasting light regimes created by the two partial harvesting treatments. The study confirms that partially harvesting is an ecologically sound approach in boreal mixedwoods and could contribute to maintaining the distribution of stand ages at the landscape level.

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 score0.108
Threshold uncertainty score0.906

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)0.0000.001

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 head0.028
GPT teacher head0.225
Teacher spread0.197 · 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

Citations30
Published2013
Admission routes2
Has abstractyes

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