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Record W2523055489

Effects of knife jointing and wear on the planed surface quality of sugar maple wood

2002· article· en· W2523055489 on OpenAlexfundno aff
Roger E. Hernández, Gerson Rojas

Bibliographic record

VenueWood and Fiber Science (Society of Wood Science and Technology) · 2002
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMapleSugarSoftwoodHardwoodComposite materialEnhanced Data Rates for GSM EvolutionMaterials scienceEnvironmental sciencePulp and paper industryBotanyEngineeringChemistry
DOInot available

Abstract

fetched live from OpenAlex

Jointing is a common practice required to produce the same cutting circle for all knives mounted in a cutterhead of a peripheral knife planer.Initially the jointed land at the cutting edge has a 0 degree clearance angle, which becomes negative with the workpiece motion relative to the cutterhead and as the cutting edge wears.Jointed knives could crush a thin layer of the planed board and affect the wood quality and performance.Sugar maple wood gluing properties were evaluated in samples that had been planed using one of four jointed land widths, each tested at four states of wear.With increased jointed land and planing length, the damage to the surface and subsurface of wood increased, but the gluing strength and percent wood failure decreased.The depth of this damaged layer was positively correlated with the magnitude of the normal cutting force.In samples where moisture content had fluctuated, the effects of jointing and wear on gluing were more pronounced.The results suggest that a jointed land of 0.9 mm may be used as maximum allowable width for planing sugar maple wood.Also, the planed surface quality of this wood may be negatively affected using a knife with 45 km of rake face recession and 60 krn of clearance face recession, which results in a damaged layer 0.20 mm thick.

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)0.000
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 score0.010
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.004
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 head0.016
GPT teacher head0.205
Teacher spread0.189 · 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

Citations20
Published2002
Admission routes1
Has abstractyes

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