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Record W4251732101 · doi:10.5383/ijtee.09.02.005

Turning to Service, Preliminary Study of a Wood Conventional Semi Industrial Dryer and Cost Price of the Wood Drying in the African Tropical Context

2015· article· en· W4251732101 on OpenAlexvenueno aff
Merlin Simo Tagne, Romain Rémond, Yann Rogaume, André Zoulalian, Éric Mougel

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
FundersInternational Tropical Timber OrganizationUniversité de Lorraine
KeywordsContext (archaeology)Environmental scienceCombustionDesorptionService (business)Wood industryPulp and paper industryWaste managementProcess engineeringEngineeringForestryBusinessAdsorptionGeographyChemistry

Abstract

fetched live from OpenAlex

As all apparatus used permanently or not, dryers of wood require a regular maintenance in other that the entry orders are effectively operated in the dryer and these components: interior air, interior wall and wood during the drying. This maintenance also permits to obtain the real values of the measured orders to following very well the drying operation. In this paper, we present the equipment and the calibration of wood semi industrial dryer of the National Higher College of Wood Technologies and Industries (ENSTIB) of the University of Lorraine situated at Epinal-France. These actions are much importance to use this dryer for the serious scientific studies. Then, the results that we have obtained in this dryer are analyzed and validated. Ayous wood (Triplochiton Scleroxylon) is used, because desorption isotherms are well-know and desorption energy is deducted very easily. We have also estimated the appropriated wood waste to bring the energy needed by combustion. An estimation of the cost price of the drying of wood is doing in order to motivate the workers of the domain.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.088

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 head0.023
GPT teacher head0.214
Teacher spread0.191 · 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 designObservational
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

Citations1
Published2015
Admission routes1
Has abstractyes

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