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Record W2011141582 · doi:10.1139/x10-101

Effect of growth suppression and release on strength and specific gravity of yellow-poplar

2010· article· en· W2011141582 on OpenAlexvenueno aff
Thammarat Mettanurak, Audrey Zink-Sharp, Carolyn A. Copenheaver, Shepard M. Zedaker

Bibliographic record

VenueCanadian Journal of Forest Research · 2010
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSpecific gravityGrowth rateCore (optical fiber)MathematicsStress (linguistics)Animal scienceHorticultureCompression (physics)Environmental scienceBotanyMaterials scienceChemistryBiologyComposite materialGeometry

Abstract

fetched live from OpenAlex

Compression tests and specific gravity analyses were conducted to investigate the impacts of growth suppression and growth release on wood quality in yellow-poplar ( Liriodendron tulipifera L.). Growth ring widths in 23 increment cores were determined and the years of minimum suppression and maximum release were identified based on a modified radial growth averaging technique. Three specimens (1 mm × 1 mm × 4 mm) from both minimum suppression and maximum release years were prepared from each increment core. Data analysis using paired-samples t tests revealed that the mean ultimate crushing stress of the maximum release years was significantly higher than that of the minimum suppression years, yet the mean specific gravity was not significantly different. Even though there was no statistical difference in specific gravity for the two growth conditions studied, the ultimate crushing stress was statistically higher for the release growth specimens. This finding provides support for the concept that growth rate can have an added effect on strength properties that is not entirely captured by specific gravity. Thus, to improve wood quality in yellow-poplar stands, forest managers might consider increasing the likelihood of periods of growth release by controlling the competition experienced by the trees.

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 score0.150
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.014
GPT teacher head0.267
Teacher spread0.252 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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