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Record W2192544081

Water State Study of Wood Structure of Four Hardwoods Below Fiber Saturation Point with Nuclear Magnetic Resonance

2014· article· en· W2192544081 on OpenAlexfundno aff
Leandro Passarini, Cédric Malveau, Roger E. Hernández

Bibliographic record

VenueWood and Fiber Science (Society of Wood Science and Technology) · 2014
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRobiniaHardwoodEquilibrium moisture contentSaturation (graph theory)EucalyptusBound waterWater contentMoistureDesorptionBotanySoftwoodSorptionChemistryMaterials scienceComposite materialBiologyMathematicsGeologyAdsorption
DOInot available

Abstract

fetched live from OpenAlex

Nuclear magnetic resonance (NMR) is a useful, powerful, and noninvasive technique to study the dynamics of wood-water relations, both quantitatively and qualitatively. The main objective of this study was to use NMR to characterize the state of water below the FSP. Two tropical hardwood species, huayruro ( Robinia coccinea Aublet) and cachimbo ( Cariniana domesticata [ C. Martius ] Miers), a plantation-grown eucalyptus species ( Eucalyptus saligna Smith), and a temperate species, red oak ( Quercus rubra L.), were studied. These species were chosen for their diversity in terms of anatomical and physical properties. Desorption tests were carried out at 21°C in a single-step procedure from full saturation state for huayruro, cachimbo, and red oak and from green condition for E. saligna. Discrete T 2 times were obtained for each species and equilibrium moisture content (EMC). The results showed that even under EMC, there was a region in the hygroscopic range in which the loss of bound water takes place before all liquid water was drained. This region varies according to wood species. Furthermore, variation in the fast T 2 values among the different wood species gives an indication of how bound water is distributed and arranged in sorption sites.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.176
Teacher spread0.171 · 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 designBench or experimental
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

Citations22
Published2014
Admission routes1
Has abstractyes

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