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Record W2093752192 · doi:10.1515/hf.2003.014

Non-Deterministic Description of Wood Radio Frequency Vacuum Drying

2003· article· en· W2093752192 on OpenAlexfundno aff
Diego Elustondo, Anastasios Koumoutsakos, Stavros Avramidis

Bibliographic record

VenueHolzforschung · 2003
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStandard deviationDispersion (optics)Range (aeronautics)Frequency distributionWater contentMathematicsStatisticsStochastic modellingStochastic processBiological systemStatistical physicsEnvironmental scienceApplied mathematicsMaterials scienceEngineeringPhysicsGeotechnical engineeringComposite materialOptics

Abstract

fetched live from OpenAlex

Summary A numerical method is proposed to predict the stochastic dispersion of data that unavoidably occurs in a real drying process. The method is based on the use of discrete frequency distribution curves to predict a random variation on some parameters of the process and is applied to the case of radio frequency vacuum (RFV) drying of wood. Experimental data of RFV drying of western hemlock timbers were used to obtain the numeric values of the stochastic parameters and their standard deviation. A mathematical model was designed to simulate the final moisture content dispersion on hypothetical examples. Results were quantified using three indices: average, range and standard deviation.

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 categoriesInsufficient payload (model declined to judge)
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 score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.037
GPT teacher head0.268
Teacher spread0.231 · 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

Citations8
Published2003
Admission routes1
Has abstractyes

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