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Record W2182276490 · doi:10.1139/cjfr-2013-0207

Assessing wood quality by spatial variation of elastic properties within the stem: Case study of <i>Pinus pinaster</i> in the transverse plane

2013· article· en· W2182276490 on OpenAlexvenueno aff
José Xavier, J.J.L. Morais, José Lousada

Bibliographic record

VenueCanadian Journal of Forest Research · 2013
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaPrograma Operacional Temático Factores de CompetitividadeEuropean Commission
KeywordsPinus pinasterTransverse planeOrthotropic materialPinus <genus>Materials scienceUltimate tensile strengthDigital image correlationInverseBasal areaAnisotropyMathematicsComposite materialPhysicsGeometryOpticsStructural engineeringBotanyBiologyEcologyThermodynamicsFinite element method

Abstract

fetched live from OpenAlex

The wood quality of maritime pine (Pinus pinaster Ait.) is discussed in view of spatial variability of density, annual growth ring characteristics, and transverse elastic properties within and among five basal logs. X-ray microdensitometry measurements are carried out to assess both average (D mean ) and local (D EW , D LW ) densities of annual growth rings, as well as the respective dimensions (EW W , LW W ) and fractions of earlywood (EW) and latewood (LW) layers. Mechanical tensile tests on a set of specimens with different ring orientation are proposed. Transverse elastic properties are determined by coupling an inverse identification strategy with full-field deformation measurements provided by digital image correlation. For a suitable range of off-axis angles, this method allows the simultaneous characterisation of the four orthotropic elastic properties in the RT plane: E R , E T , ν RT , and G RT . These results are found in agreement with reference values. From the sampled specimens among logs, E R and G RT are robustly identified in all cases and therefore are selected for further analysis of structure-properties relationships. Correlations between D mean and E R and G RT are positive and statistically significant. Furthermore, other parameters also show significant correlations with the elastic constants, especially D min , LW W , and D EW .

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.100
GPT teacher head0.301
Teacher spread0.201 · 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 designQualitative
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
Published2013
Admission routes1
Has abstractyes

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