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Characterization of pacific silver fir impregnated with extractives from western redcedar

2019· article· es· W2941810385 on OpenAlexafffund
Rod Stirling, Stacey Kus

Bibliographic record

VenueMaderas Ciencia y tecnología · 2019
Typearticle
Languagees
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsFPInnovations
FundersCanadian Forest ServiceNatural Resources CanadaU.S. Forest Service
KeywordsSawdustLeaching (pedology)ChemistrySoftwoodShrinkagePulp and paper industryDouglas firBotanyHorticultureComposite materialEnvironmental scienceMaterials scienceSoil scienceBiologyOrganic chemistrySoil waterEngineering

Abstract

fetched live from OpenAlex

Alternative and higher value uses are needed for residual materials generated from wood products processing. Western redcedar sawdust contains an array of unique chemicals and has the potential to be used to alter the appearance, stability and durability of non-durable timbers. Here it was hypothesized that impregnation of Western redcedar heartwood extractives into Pacific silver fir would decrease shrinkage, hygroscopicity, and susceptibility to decay. Western redcedar hot water extracts were obtained from sawdust and used to pressure-treat Pacific silver fir blocks. Extractive impregnation enhanced the wood's appearance and gave it a more cedar-like brown colour. Impregnated samples had reduced shrinkage, but were not as dimensionally stable as Western redcedar heartwood. Pacific silver fir blocks impregnated with water-soluble extractives were also highly susceptible to leaching, and leached samples had similar shrinkage values to untreated controls. In contrast, leaching had little effect on the hygroscopicity of the Western redcedar heartwood. Extractive-impregnated Pacific silver fir blocks had increased resistance to decay by two brown-rot fungi, and showed resistance similar to that of Western redcedar heartwood. However, leached blocks did not have the same decay resistance.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.007
GPT teacher head0.182
Teacher spread0.175 · 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.

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

Citations1
Published2019
Admission routes2
Has abstractyes

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