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Record W2901883469 · doi:10.1080/10549811.2018.1546597

Partial cutting in mixedwood stands: Effects of treatment configuration and intensity on stand structure, regeneration, and tree mortality

2018· article· en· W2901883469 on OpenAlexafffund
Benoît Lafleur, Brian D. Harvey, Marc J. Mazerolle

Bibliographic record

VenueJournal of Sustainable Forestry · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversité LavalUniversité du Québec en Abitibi-Témiscamingue
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsLoggingClearcuttingBasal areaEnvironmental scienceSilvicultureRegeneration (biology)Forest managementScarificationAgroforestryForest ecologyEcosystemBorealEcologyAgronomyBiologyGermination

Abstract

fetched live from OpenAlex

In temperate and boreal mixedwood forests of eastern North America, partial disturbances such as insect outbreaks and gap dynamics result in the development of irregular forest structures. From a forest ecosystem management perspective, management of these forests should therefore include silvicultural regimes that incorporate medium- to high-retention harvesting. We present 12-year results of a field experiment undertaken to evaluate the effects of variable retention harvesting on stand structure, recruitment, and mortality. Treatments were gap harvesting (GAP), diameter-limit harvesting (DL), careful logging (CL), and careful logging followed by scarification (CL + SCAR), and an unharvested control. Although post-harvest basal area in the GAP treatment was significantly lower than that of controls, it maintained a diameter distribution profile and densities of balsam fir regeneration similar to those of pre-harvest conditions. Lower retention treatments (DL, CL, and CL + SCAR) tended to favor regeneration of pioneer, shade-intolerant species. Except for black spruce (for which mortality was highest in DL), stem mortality was similar among harvesting treatments. From an ecosystem management perspective, this study suggests that gap harvesting can maintain, in the short term, forest stand composition and structure similar to unharvested forests, and could be used where management objectives include the maintenance of late successional forest conditions.

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 score0.067
Threshold uncertainty score0.321

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 head0.008
GPT teacher head0.244
Teacher spread0.236 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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