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Record W9491067 · doi:10.4088/jcp.v59n0109

Empirical and Artificial Neural Network Approach for Determination of Constant Drying Rate Phase of Medicinal and Aromatic Plants

2014· article· en· W9491067 on OpenAlexaff
Nour Eddine Benaouda, Lyes Bennamoun, Abdellah Khellaf

Bibliographic record

VenueInternational Journal of Energy Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicEnergy Load and Power Forecasting
Canadian institutionsUniversity of New Brunswick
FundersNational Institute of Mental Health
KeywordsPayback periodWind powerElectricityProfit (economics)Environmental scienceDuration (music)Renewable energyEngineeringWind speedEnvironmental engineeringEnvironmental economicsProcess engineeringProduction (economics)MeteorologyElectrical engineeringEconomicsPhysics

Abstract

fetched live from OpenAlex

The paper concerns to the economical aspects of wind energy application in Azerbaijan. Economical effectiveness of wind energy plants transforming wind energy into electricity have been calculated. Electricity cost obtained from the wind energy plants being in exploitation, payback duration of the plants, expenditures spent to the exploitation and total economical profit were determined. Finally dependence of wind energy plant payback duration on some parameters were calculated

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.265
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2014
Admission routes1
Has abstractyes

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