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Record W2604434860 · doi:10.1515/hf-2016-0178

Effect of conditioning history on the characterization of hardness of thermo-mechanical densified and heat treated poplar wood

2017· article· en· W2604434860 on OpenAlexaff
Tao Li, Jiabin Cai, Stavros Avramidis, Dali Cheng, Magnus Wålinder, Zhou Ding-guo

Bibliographic record

VenueHolzforschung · 2017
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConditioningMaterials scienceCharacterization (materials science)Relative humidityComposite materialHumidityMathematicsThermodynamicsNanotechnology

Abstract

fetched live from OpenAlex

Abstract Poplar wood was modified by a combination of thermo-mechanical densification (TMD) and heat treatment (HT) process at five temperatures ranging from 170 to 210°C. A new two-step conditioning method (CM) is suggested, in the course of which the modified wood is submitted to 50°C/99% RH→25°C/65% RH, where RH means relative humidity in the climate chamber. The traditional one-step CM (25°C/65% RH) served as reference. The effects of conditioning history on hardness were observed and analyzed along with the change of dimensional stability. The hardness of the modified wood was lower in the case of the proposed CM due to more set-recovery release, but the extent of that decreased with the HT temperature. For a good hardness, HT 200°C should be selected with the proposed CM, which is different from the optimization output of 180°C obtained from the traditional CM. In conclusion, a specific assessment method for the performance characterization of this type of modified wood would be beneficial for the combined TMD and HT processes.

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 score0.025
Threshold uncertainty score0.264

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)0.0000.000

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 head0.016
GPT teacher head0.201
Teacher spread0.185 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

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