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Record W2017202868 · doi:10.1080/07373930601184023

Moisture Distribution Changes and Wetwood Behavior in Subalpine Fir Wood during Drying Using High X-Ray Energy Industrial CT Scanner

2007· article· en· W2017202868 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDrying Technology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsIntertek (Canada)
Fundersnot available
KeywordsWater contentAbies lasiocarpaScannerMoistureMaterials scienceCore (optical fiber)Industrial computed tomographyIntensity (physics)Environmental scienceMontane ecologyHorticultureComposite materialTomographyOpticsPhysicsGeologyBiologyEcology

Abstract

fetched live from OpenAlex

In some species, such as subalpine fir (Abies lasiocarpa [Hook] Nutt), the water content of the confined zones in heartwood is as high as or greater than that of sapwood. Such wet zones of heartwood are referred to as “wetpocket” or “wetwood.” Wood products from subalpine fir forests are adversely affected by the wetwood-associated problems, particularly during the drying process. The objectives of the study were as follows: (1) to investigate feasibility of a high X-ray energy industrial computed tomography (ICT) scanner for imaging wetwood; and (2) to determine changes of the 2-D and 3-D moisture profiles (from core to shell) at different drying times. Although medical CT scanning has been used for attaining signal intensity profiles of typical wood at different drying times, the technology has not, to date, been used for the study of wetwood phenomenon. This study presents, for the first time, results from the ICT imaging of the wetwood phenomenon. The results indicate that the ICT imaging system provides a powerful technique for imaging wetwood at different drying times. In addition, the results show that during the initial phase of drying, almost flat moisture profiles were observed in all wood types except for the wetwood, which showed a relatively higher moisture profile. A much slower (sluggish) drying development pattern at each increment from core to shell was found within the wetwood zone than normal wood regions along the width, thickness, and length of the board.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.010
GPT teacher head0.215
Teacher spread0.205 · 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