MétaCan
Menu
Back to cohort
Record W127523576

Equalizing and conditioning spruce-pine-subalpine fir lumber

2004· article· en· W127523576 on OpenAlexaboutno aff
Liping Cai, Luiz C. Oliveira

Bibliographic record

VenueForest Products Journal · 2004
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsKilnConditioningWater contentEnvironmental scienceMontane ecologyWood dryingGreen woodMoisturePulp and paper industryDouglas firWaste managementForestryMaterials scienceComposite materialEngineeringMathematicsGeotechnical engineeringGeographyEcologyBiology
DOInot available

Abstract

fetched live from OpenAlex

Problems associated with grade recovery in spruce-pine-subalpine fir (SPF) lumber drying have been largely attributed to the wide variation in final moisture content (MC) distribution and drying stresses. Grade recovery is largely affected during planing and re-manufacturing because ofthe development of warp that results from drying stresses. Over 10 sawmills in the interior of British Columbia and Alberta were surveyed. The results indicated that the SPF lumber drying problems related to uneven final MC distribution and warp during re-manufacturing still exist and affect grade recovery. Sixteen kiln-drying runs, using Forintek's experimental 3-foot kiln, were performed using different combinations of 3, 6, 9, and 12 hours of equalizing and conditioning with two types of humidification systems (cold water and low-pressure steam). Initial MC, final MC, and drying stresses were evaluated for drying each run. In general, 9 to 12-hour equalizing/conditioning treatment can significantly reduce the amount of over-dried lumber as well as reduce internal stresses.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.493

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 head0.016
GPT teacher head0.215
Teacher spread0.199 · 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

Citations4
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueForest Products JournalSame topicWood Treatment and PropertiesFrench-language works237,207