MétaCan
Menu
Back to cohort
Record W2526417712 · doi:10.5539/mas.v11n1p23

Assessment of Biomass Energy Sources in Electricity Generation Using Analytic Network Process Method

2016· article· en· W2526417712 on OpenAlexvenueno aff
Mojtaba Safari, Fatemeh Joghataee, Mahtab Afsari

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
Fundersnot available
KeywordsBiomass (ecology)Renewable energyElectricityEnvironmental scienceEnergy sourceElectricity generationProcess (computing)AgricultureEnvironmental economicsWaste-to-energyAgricultural engineeringRenewable resourceMunicipal solid wasteWaste managementComputer sciencePower (physics)EngineeringEconomics

Abstract

fetched live from OpenAlex

The purpose of this Paper is to assess and select the preferable and optimum biomass energy source using Analytic Network Process (ANP) for making an efficient Policy in Iran electricity generation industry. There are four major biomass energy sources in Iran comprising agriculture and forest wastes, biodegradable municipal waste, animal manure, and Municipal and industrial wastewater. In this paper, we assess and compare these resources, systematically, based on different Criteria and Sub-criteria derived from literatures and expert’s views to choose the most preferable biomass source for producing required fuel for power plants. The required data will be gathered from experienced experts in Iran Renewable Energy Organization (SUNA). Results show that the criterion “economic and legal factors” has the highest importance (weight) among other identified criteria used in biomass source assessment. In addition, “biodegradable municipal waste” recognized as the most preferable biomass energy source for generating electricity in Iran power plants.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.839
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.287
Teacher spread0.266 · 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 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

Citations4
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueModern Applied ScienceSame topicForest Biomass Utilization and ManagementFrench-language works237,207