MétaCan
Menu
Back to cohort
Record W2169016210 · doi:10.5558/tfc85631-4

Regeneration of aspen following partial and strip understory protection harvest in boreal mixedwood forests

2009· article· en· W2169016210 on OpenAlexafffundvenue
Alison D Lennie, Simon M. Landhäusser, Victor J. Lieffers, Derek Sidders

Bibliographic record

VenueThe Forestry Chronicle · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsCanadian Forest ServiceUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaForest Resource Improvement Association of Alberta
KeywordsUnderstorySuckerBasal areaCanopyTaigaClearcuttingRegeneration (biology)SilvicultureBorealEnvironmental scienceForestryAgronomyNatural regenerationAgroforestryBiologyBotanyEcologyGeography

Abstract

fetched live from OpenAlex

Trembling aspen regeneration was studied in 2 types of partial harvest systems designed to harvest mature aspen but protect immature spruce and encourage natural aspen regeneration. Two partial harvest systems, where the residual aspen was either left in strips or was dispersed uniformly, were compared to traditional clearcuts. After the first and second year since harvest, aspen sucker density and growth was similar between the 2 partial harvests, but was much lower than in the clearcuts. However, in the partial cuts the regeneration density was very much dependent on the location relative to residual trees. The density of regeneration was inversely related to the basal area of residual aspen; however, sucker height was inversely related to the basal area of the residual spruce. Although there were adequate numbers of suckers after partial harvest, their viability and contribution to the long-term productivity of these mixedwood stands is not clear. Key words: silvicultural systems, forest management, residual canopy, white spruce, Populus tremuloides, Picea glauca, traffic

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 score0.361
Threshold uncertainty score0.378

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 head0.013
GPT teacher head0.224
Teacher spread0.211 · 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

Citations11
Published2009
Admission routes3
Has abstractyes

Explore more

Same venueThe Forestry ChronicleSame topicForest ecology and managementFrench-language works237,207