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Record W4282826724 · doi:10.3390/f13060929

Effects of Planting, Vegetation Management, and Pre-Commercial Thinning on the Growth and Yield of Lodgepole Pine Regenerated after Harvesting in Alberta, Canada

2022· article· en· W4282826724 on OpenAlexafffundabout
W. R. Dempster

Bibliographic record

VenueForests · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsWestern Forest Products
FundersForest Resource Improvement Association of AlbertafRI Research
KeywordsThinningSowingStockingVegetation (pathology)Environmental scienceAgroforestrySilvicultureStand developmentForest managementAgronomyLoggingScarificationForestryHerbaceous plantPinus contortaBiologyGeographyGermination

Abstract

fetched live from OpenAlex

A large long-term field trial was established in the Upper and Lower Foothills sub-regions of the Canadian boreal forest to monitor the effects of planting, vegetation management, and pre-commercial thinning on the development of lodgepole pine stands following harvesting. Data collected at the end of the trial’s regeneration phase, 17 to 20 years after its establishment, were tested for treatment effects and projected to rotation age. Planting generally improved stocking and increased projected growth and yield of lodgepole pine. On modal sites, planted trees were often greatly outnumbered by natural regeneration; however, on others, typically with either poorer or richer soils, satisfactory restocking was not achieved without planting. Control of competing vegetation by herbicide application facilitated regeneration of pine where it was otherwise difficult or impossible on sites with excessive herbaceous or hardwood competition. Pre-commercial thinning accelerated the growth of individual trees and was projected to shorten rotations in dense stands. Responses to the treatments varied depending on environmental factors. Particular treatments may be effective to meet management objectives under some site conditions but unnecessary or counterproductive elsewhere.

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 score0.233
Threshold uncertainty score0.348

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 head0.003
GPT teacher head0.170
Teacher spread0.167 · 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

Citations8
Published2022
Admission routes3
Has abstractyes

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