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Record W2143833223 · doi:10.5539/ibr.v3n3p84

A Survey on the Role of System Dynamics Methodology on Fossil Fuel Resources Analysis

2010· article· en· W2143833223 on OpenAlexvenueno aff
Behdad Kiani, Saeed Mirzamohammadi, Seyed Hossein Hosseini

Bibliographic record

VenueInternational Business Research · 2010
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsFossil fuelSystem dynamicsWork (physics)Environmental economicsOrder (exchange)Energy supplyComputer scienceEnergy resourcesEnergy (signal processing)Natural resource economicsBusinessEnvironmental resource managementEnvironmental scienceEconomicsEngineeringWaste management

Abstract

fetched live from OpenAlex

In today’s world, fossil fuel resources are still the main source for energy supply. Many attempts have been done in order to develop alternative sources of energy, since fossil fuel resources are limited and will be depleted soon or late. Despite these efforts, as international energy organizations declare, fossil fuel resources have the largest share in the world’s energy supply. Therefore, the study of exploration, production, and exploitation dynamics of these resources, have been considered a significant topic for many researchers. System dynamics, which is an appropriate method for dynamic studies and policy analysis, has been used as one of the most impressive methodologies in systemic research and application in this area. Besides the illustration of the role of system dynamics in fossil fuel resources analysis, this paper reviews the pioneering system dynamics models in this field. The results of this work would be helpful for researchers who are interested in the dynamic studies of fossil fuel resources’ systems.

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)0.007
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.008
Science and technology studies0.0010.002
Scholarly communication0.0030.005
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.080
GPT teacher head0.391
Teacher spread0.311 · 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 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

Citations22
Published2010
Admission routes1
Has abstractyes

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