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Record W3041609237 · doi:10.1515/hf-2019-0228

Water sorption hysteresis in wood near 100 °C

2020· article· en· W3041609237 on OpenAlexaff
Jingbo Shi, Yasuo Kawai, Stavros Avramidis, Jiabin Cai, Huijun Dong

Bibliographic record

VenueHolzforschung · 2020
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of British Columbia
FundersNanjing Forestry UniversityAkita Prefectural University
KeywordsSorptionHysteresisEquilibrium moisture contentSofteningDesorptionRelative humidityAdsorptionThermodynamicsHumidityMoistureWater contentMaterials scienceChemistryComposite materialOrganic chemistryGeotechnical engineeringGeologyPhysics

Abstract

fetched live from OpenAlex

Abstract Whether water sorption hysteresis exists above 75 °C and the representation of its relationship with wood softening remains unclear. In this study, sorption hysteresis at three temperature levels, namely, 75, 90 and 99.5 °C, was investigated using accurately controlled conditioning chambers using powder samples from seven wood species. The dry mass loss percentage of the samples and proper equilibrium time was evaluated by pretests to ensure reliable sorption isotherms afterwards. The dry mass loss percentage after the entire sorption isotherm running needs to be kept within 0.5% to avoid complications in determining the equilibrium time. The abrupt increase of equilibrium moisture content at high relative humidity regions and the crossover of adsorption isotherms were not observed. Hysteresis was conspicuous at 75 and 90 °C and was statistically confirmed at 99.5 °C. High sample extractives content or unnecessarily long equilibrium time would cause significant sample dry mass loss, which may suppress the desorption isotherm to an extent that leads to unjustified hysteresis disappearance. The hysteresis behavior observed in this study suggests that wood softening does not necessarily result in hysteresis disappearance, and the explanation of hysteresis progression as a function of wood softening temperature must be evaluated with care in the future.

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 score0.166
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.023
GPT teacher head0.188
Teacher spread0.165 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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