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Record W7024900342

TECHNICAL NOTE: POSITIVE EFFECTS OF DOUBLE-SIDED PROFILING ON THE CUPPING AND CHECKING OF ACQ-TREATED DOUGLAS FIR, WESTERN HEMLOCK AND WHITE SPRUCE DECKBOARDS EXPOSED TO NATURAL WEATHERING

2020· article· en· W7024900342 on OpenAlexaff

Bibliographic record

VenueWood and Fiber Science (Society of Wood Science and Technology) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Influence and Diplomacy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProfiling (computer programming)SoftwoodCopperCopper alloyPrinted circuit board
DOInot available

Abstract

fetched live from OpenAlex

Machining grooves into the upper surface of wooden deckboards reduces undesirable checking that develops when deckboards are exposed to weather. But profiled boards cup more than unprofiled boards. We sought a solution to this problem and hypothesized that profiling both sides of boards would reduce the cupping of profiled boards. We tested the effects of profile type (Flat, single-, and double-sided profiles) and growth ring orientation (concave vs convex) on the cupping and checking of alkaline copper quaternary-treated deckboards made from Douglas fir, western hemlock, and white spruce. There were significant differences in the cupping of deckboards made from the three different wood species (Douglas fir<white spruce<western hemlock), and boards with concave growth ring orientations cupped significantly less than boards with convex growth ring orientations. Most importantly, our results show that double-sided profiling reduces the cupping of deckboards, irrespective of wood species, and growth ring orientations of deckboards. Double-sided profiling also significantly reduced checking of deckboards exposed to the weather. We conclude that profiling the underside or profiled deckboards to create a “balance” double-sided board is a simple solution to the problem of increased cupping that develops when profiled (single-sided) softwood deckboards are exposed to weather.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.019
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.007
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.286
Teacher spread0.268 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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