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Record W7101159024

ISSN online 0718-221X COMPATIBILITY OF FOUR EASTERN CANADIAN WOODS WITH GYPSUM AND GYPSUM-CEMENT BINDERS BY ISOTHERMAL CALORI-

2015· article· en· W7101159024 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicEducation, Psychology, and Social Research
Canadian institutionsnot available
Fundersnot available
KeywordsGypsumCompatibility (geochemistry)BalsamPortland cementHot water extractionIsothermal processRaw material
DOInot available

Abstract

fetched live from OpenAlex

In order to determine the feasibility of manufacturing wood-gypsum and wood-gypsum-cement particleboards, the hydration reactions of four Eastern Canadian wood species with gypsum and Port-land cement were studied. The compatibility of hot water extracted and raw wood particles with both gypsum and a gypsum-Portland cement mixture were determined for jack pine, balsam fi r, aspen and white birch by isothermal calorimetry. The four species were found to be compatible and suitable for the manufacturing of inorganic-bonded wood composite boards on the basis of the compatibility factor. The hot water extractive content was low in the four species and did not have a consistent impact on the compatibility factor. Nevertheless, hot water soluble extractives did have an impact on the time required to reach the maximum heat fl ux, delaying it to undesirable levels particularly in the case of balsam fi r mixed with neat gypsum. However, the effect of balsam fi r on neat gypsum hydration was markedly reduced by the addition of 30 % Portland cement. Hot water extraction of the wood particles and the addition of Portland cement to the wood-gypsum mixture signifi cantly reduced the time requi-red to reach the maximum heat fl ux. The gypsum-cement mixture was less sensitive to the inhibitory effect of wood than neat gypsum.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.793
Threshold uncertainty score0.412

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.183
GPT teacher head0.433
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2015
Admission routes1
Has abstractyes

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