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Record W70765910

Characteristics of sugar maple wood surfaces produced by helical planing

2006· article· en· W70765910 on OpenAlexfundno aff
Luiz Fernando de Moura, Roger E. Hernández

Bibliographic record

VenueWood and Fiber Science (Society of Wood Science and Technology) · 2006
Typearticle
Languageen
FieldEngineering
TopicMaterial Properties and Processing
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWettingMaterials scienceSurface roughnessMapleSurface finishComposite materialSugarChemistryBotany
DOInot available

Abstract

fetched live from OpenAlex

In real helical planing, the knives form a continuous oblique cutting edge with an angle to the cutterhead rotation axis. Tool manufacturers affirm that the helical cutterheads produce superior quality surfaces. However, literature on the effect of this cutting geometry on the surface quality of planed wood is scarce. The surface quality of helical-planed sugar maple was evaluated as a function of two planing modes, four feed speeds, and three cutting depths. The helical planing across the grain produced surfaces with higher roughness and improved wetting properties. A slight torn grain was observed in some samples that were helical-planed obliquely to the grain. As feed speed increased, surfaces became rougher and wetting was accelerated. Increasing cutting depth reduced surface roughness, mainly when planing across the grain. Cross-grain helical planing appears to have a good potential to reduce dependence on sanding to improve surface adhesion properties and enhance performance of coatings.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 studies0.0000.000
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 head0.006
GPT teacher head0.188
Teacher spread0.182 · 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 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

Citations10
Published2006
Admission routes1
Has abstractyes

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