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Record W4280507010 · doi:10.13073/fpj-d-22-00004

Field Performance of Refractory Softwoods Treated with CA or ACQ after 10 Years of Exposure in Korea and Canada

2022· article· en· W4280507010 on OpenAlexfundaboutno aff
Rod Stirling, Jong-Bum Ra, Jae-Yun Ryu, Jieying Wang

Bibliographic record

VenueForest Products Journal · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
FundersCanadian Forest ServiceNatural Resources CanadaU.S. Forest ServiceFPInnovations
KeywordsSoftwoodRefractory (planetary science)Environmental scienceTest sitePenetration (warfare)ToxicologyMaterials scienceMining engineeringComposite materialGeologyEngineeringBiology

Abstract

fetched live from OpenAlex

Abstract Korean wood preservation standards require deep penetration, which precludes the use of many refractory species. However, such treatments of refractory species have been shown to be effective in other parts of the world. A field test was therefore initiated to evaluate the performance of western hemlock, a moderately refractory species, and white spruce, a highly refractory species, pressure treated with either copper azole or alkaline copper quaternary under Korean field conditions that included both decay and termite hazards. After 10 years of exposure in a ground proximity and field stake test in Jinju, Korea, the treated materials remained largely sound, while untreated controls failed much earlier, largely due to termite attack. These data suggest that material that does not meet current Korean penetration requirements could still provide effective protection against biodegradation under Korean conditions. Decay was more advanced in matched treated stakes exposed at a test site in Canada than at the site in Korea.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.540

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)0.0010.000

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 head0.009
GPT teacher head0.171
Teacher spread0.162 · 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 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

Citations3
Published2022
Admission routes2
Has abstractyes

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