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Record W2347387842

Silviscan:State-of-the-art Instrument for Measuring Wood/fiber Properties

2008· article· en· W2347387842 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueChina Pulp & Paper · 2008
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsPulp (tooth)FiberWood industrySuiteProcess engineeringPulp and paper industryMaterials scienceEnvironmental scienceComposite materialEngineeringForestry
DOInot available

Abstract

fetched live from OpenAlex

Physical and chemical properties of wood and fibers strongly influence paper products quality and processing cost in wood-based industries.Traditional wood/fiber properties analytical technologies are time-consuming.Silviscan is a suite of instruments designed for the rapid and non-destructive assessment of wood and fiber properties.It can provide a better understanding of the role that fiber properties play in determining end-use product quality and value rapidly.This paper mainly introduced mechanism of Silviscan,and its application in the forestry and pulp and paper industry.A research about wood/fiber properties of forests and their predictions using NIR/Raman spectra combined with Silviscan data is going on in Newfoundland,Canada.The preliminary results showed that wood and fiber properties can be rapidly measured by Siliviscan. The wood density,MOE and MFA can be well predicted using NIR spectra.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.522

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 head0.027
GPT teacher head0.177
Teacher spread0.150 · 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