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Record W4391875012 · doi:10.1139/cjfr-2023-0199

Lower wood stiffness in old-growth than in post-cut and post-fire stands indicates forest structure is a key driver of wood properties in black spruce

2024· article· en· W4391875012 on OpenAlexafffundvenue
L. Leyva Cardona, Pierre-Luc Couillard, Alexis Achim

Bibliographic record

VenueCanadian Journal of Forest Research · 2024
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsUniversité LavalMinistère des Ressources naturelles et des ForêtsMinistère des Ressources naturelles et des Forêts (Québec)
FundersMinistère des Ressources Naturelles et de la FauneMinistère des Forêts, de la Faune et des Parcs
KeywordsBlack spruceTaigaPithForestryEnvironmental scienceBotanyGeographyBiology

Abstract

fetched live from OpenAlex

Forest fires and logging drive the structure of boreal forest landscapes. According to recent studies, stand origin is a key driver of the variation in wood properties in black spruce (Picea mariana (Mill.) BSP.), although the underlying mechanisms remain to be elucidated. By comparing post-cut, post-fire and old-growth forests, this study aimed to better distinguish the effects of stand structure from those of the seed or layer origin of the trees on wood properties. We conducted comparative analyses based on ecological characteristics of the sites and static bending tests of small, defect-free wood specimens. Black spruce stands with a regular structure established after logging or fire exhibited higher stiffness at a given cambial age than old-growth forests with irregular structures, as well as lower wood density in the first 40 rings near the pith. However, the bending strength was comparable in all three types of forests studied. Differences in wood stiffness among stand types appeared to be driven more by stand structure than by the seed or layer origin of the stems.

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)0.001
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 score0.919
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.249
Teacher spread0.229 · 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

Citations2
Published2024
Admission routes3
Has abstractyes

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