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Record W1925573258 · doi:10.1139/x11-070

Influence of stocking on radial and longitudinal variation in modulus of elasticity, microfibril angle, and density in a 24-year-old <i>Pinus radiata</i> thinning trial

2011· article· en· W1925573258 on OpenAlexvenueno aff
Michael S. Watt, Branislav Zoric, Mark O. Kimberley, Jonathan J. Harrington

Bibliographic record

VenueCanadian Journal of Forest Research · 2011
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersMinistry of Education, IndiaMinistry of Earth Sciences
KeywordsStockingThinningPinus radiataRadiataForest managementYoung's modulusMathematicsBiologyAnimal scienceHorticultureBotanyMaterials scienceComposite materialAgroforestryEcologyVigna

Abstract

fetched live from OpenAlex

Detailed radial measurements of wood properties at four heights (0, 1.4, 5, and 20 m) were taken from 24-year-old Pinus radiata D. Don growing at four final crop stockings (200, 350, 500, and 1100 stems·ha –1 ). Using these measurements, the objectives of the study were to examine pith-to-bark trends at several heights to (i) determine how stocking influenced modulus of elasticity (MoE), wood density, and microfibril angle (MFA), (ii) quantify the relations among these properties and age at different stocking levels, and (iii) develop a graphical model for MoE across the stocking range. The influence of stocking on all wood properties was primarily expressed through a highly significant interaction between age and stocking. Wood properties in the highest stocking treatment diverged from those in the lowest stocking treatment at tree age 5 to reach a maximum difference of 92 kg·m –3 (488 vs. 580 kg·m –3 ) at tree age 18 for density, –5.7° (29.2° vs. 23.5°) at tree age 10 for MFA, and 5.1 GPa (12.1 vs. 17.2 GPa) at tree age 20 years for MoE. Graphical predictions from the model show greatest gains in MoE at high final crop stocking to occur over the lower part of the stem.

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 score0.054
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.055
GPT teacher head0.255
Teacher spread0.200 · 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

Citations32
Published2011
Admission routes1
Has abstractyes

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