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Record W2613420373 · doi:10.1139/cjfr-2016-0290

Effect of growth rate on the physical and mechanical properties of Douglas-fir in western Europe

2017· article· en· W2613420373 on OpenAlexvenueno aff
Caroline Pollet, Jean-Marc Henin, Jean‐Louis Hébert, Benoît Jourez

Bibliographic record

VenueCanadian Journal of Forest Research · 2017
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersInstitut National de la Recherche AgronomiqueEdinburgh Napier University
KeywordsDouglas firJuvenileForestryDendrochronologyEnvironmental scienceHorticultureMathematicsBiologyBotanyEcologyGeography

Abstract

fetched live from OpenAlex

To quantify the impact of forest management practices and tree growth rate on the potential uses of Douglas-fir wood, nine physico-mechanical properties were studied on more than 1250 standardized clear specimens. These were collected from trees cut in 11 even-aged stands (six trees per stand) located in Wallonia (southern Belgium). Stands were 40 to 69 years old, and mean tree girth was ca. 150 cm. Mean ring width of the 66 trees ranged from 3 to more than 7 mm. Statistical analysis showed significant but weak effects of ring width on the studied properties. Considered jointly, mean ring width and cambial age of the test specimens only explained 28% to 40% of the variability of their properties. Also, when ring width increases, these properties display higher decreases in juvenile wood than in mature wood. From a technological standpoint, maintaining mean ring width under 4 mm in juvenile wood and 6 mm in mature wood should accommodate all potential uses of Douglas-fir wood. However, considering that density appeared to be the main driver of wood properties, our results and the literature corroborate the importance of genetic selection as a complement to silvicultural measures to improve or guarantee the technological properties of Douglas-fir wood.

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 score0.426
Threshold uncertainty score0.998

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 integrity0.0000.000
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 head0.053
GPT teacher head0.283
Teacher spread0.230 · 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

Citations21
Published2017
Admission routes1
Has abstractyes

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