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Record W2907935765 · doi:10.15376/biores.14.1.264-288

Performances of white pine and white spruce treated with organic fungicides using an aqueous buffered amine oxide preservation system

2018· article· en· W2907935765 on OpenAlexafffund
Simon Pépin, Pierre Blanchet, Véronic Landry

Bibliographic record

VenueBioResources · 2018
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFungicideAmine gas treatingPropiconazoleHorticultureAqueous solutionMaterials scienceChemistryNuclear chemistryOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Wood is an environmentally friendly material for the construction of buildings, and it possesses great physical and mechanical properties. However, under certain circumstances, it needs to be protected from degradation. This can be achieved either by proper design or treatment. In this study, eastern white pine (Pinus strobus L.) and white spruce (Picea glauca (Moench) Voss) were impregnated with propiconazole and 3-iodo-2-propynyl butylcarbamate, which are two organic fungicides. Unlike most impregnation techniques, no pressure treatment was needed. Instead, an aqueous buffered amine oxide system was used to allow the fungicides to diffuse rapidly into the wood. Many combinations of fungicides and amine oxides, as well as different diffusion times were tested to study the effect of the treatment on the dimensional stability and resistance to decay fungi. It was found that only the amine oxide affected the dimensional stability of the treated wood, with anti-swelling and anti-shrinking efficiencies values up to 30%. Amine oxides and fungicides both had an impact on the weight loss caused by the brown rot fungi. The weight loss after 10 weeks of exposure to Rhodonia placenta was reduced by half when using amine oxides or fungicides, and it was completely inhibited when they were combined.

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 score0.163
Threshold uncertainty score0.523

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 head0.011
GPT teacher head0.188
Teacher spread0.177 · 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

Citations9
Published2018
Admission routes2
Has abstractyes

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