MétaCan
Menu
Back to cohort
Record W3083207690 · doi:10.3390/coatings10090862

Shelling of Growth Rings at Softwood Surfaces Exposed to Natural Weathering

2020· article· en· W3083207690 on OpenAlexafffund
Lukie H. Leung, Philip D. Evans

Bibliographic record

VenueCoatings · 2020
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSoftwoodLarchPithMaterials scienceComposite materialWeatheringBark (sound)VeneerBotanyGeologyBiologyEcology

Abstract

fetched live from OpenAlex

Shelling is the delamination of growth rings and the projection of woody tissue from wood surfaces. Shelling disrupts coatings and makes refinishing difficult, and a better understanding of the phenomenon is needed to help alleviate its unwanted effects. We tested whether confocal profilometry could quantify shelling in flat-faced and profiled-faced western larch deckboards exposed to natural weathering and examined the effects of growth-ring orientation and angle on shelling. Confocal profilometry was able to quantify shelling in both deckboard types. Shelling developed at the surface of flat-faced deckboards oriented pith-side-up, whereas it was absent from boards oriented bark-side-up. We found an inverse correlation between the height of shelled growth rings and the angle of growth rings to the surface of flat-faced boards. Shelling occurred in profiled-faced boards oriented pith-side-up due to the delamination of growth ring tips and projection of latewood from wood surfaces. A superficially similar although less pronounced phenomenon was seen in profiled-faced boards oriented bark-side-up. The shelling of profiled-faced boards oriented pith-side-up created lanceolate-shaped slivers of latewood that projected from the peaks of profiles. Some of these latewood tips were sharp and, for this reason, we suggest that profiled-faced western larch deckboards should always be oriented bark-side-up rather than pith-side-up.

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 score0.024
Threshold uncertainty score0.506

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 head0.018
GPT teacher head0.181
Teacher spread0.164 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueCoatingsSame topicWood Treatment and PropertiesFrench-language works237,207