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Characterization of the diffusion of organic fungicides with amine oxides in white pine and white spruce

2019· article· en· W3002667718 on OpenAlexafffund
Simon Pépin, Pierre Blanchet, Véronic Landry

Bibliographic record

VenueBioResources · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFungicideLeaching (pedology)Amine gas treatingPropiconazoleChemistryWhite rotHorticultureMaterials scienceNuclear chemistryOrganic chemistryBiologySoil waterLignin

Abstract

fetched live from OpenAlex

Wood products, especially those used in outdoor conditions, can be damaged by dimensional changes and decay fungi. It is therefore advised to use impregnation treatments to mitigate these hazards. While the potency of the chemicals employed in the treatments is important, characterization of the treatments is also crucial to ensure deep and durable protection. 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 (IPBC) through diffusion. Instead of using pressure treatments, the samples were dipped in solutions containing amine oxides, which can diffuse into the wood. The treatments were characterized by the mass of fungicide impregnated, fungicide leaching, and the impregnation depths of both the fungicides and the amine oxides. It was found that the treatment impregnated slightly more than 0.040 kg/m3 of both fungicides, meeting EU standards. It was also shown that the presence of amine oxides slightly prevented the leaching of the fungicides in white pine. The penetration of the amine oxides was several millimeters deep in all directions, but the penetrations of the fungicides were much shorter and only longitudinal.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.604

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)0.0010.000

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 head0.003
GPT teacher head0.159
Teacher spread0.156 · 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 designObservational
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

Citations4
Published2019
Admission routes2
Has abstractyes

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