ANALYSIS OF SELECTED COUNTRIES ACCORDING TO THEIR ENERGY CONSUMPTION BY CLUSTER ANALYSIS K-MEANS METHOD
Bibliographic record
Abstract
In this study, countries (Australia, Austria, Azerbaijan, Belgium, Brazil, Bulgaria, Canada, China, Chile, Colombia, Denmark, Egypt, Finland, France, Germany, Greece, Hungary, India, Indonesia, Iran, Iraq, Italy, Japan, Kazakhstan, Luxembourg, Mexico, New Zealand, the Netherlands, Norway, Pakistan, Poland, Portugal, Russia, Spain, Sweden, Switzerland, Thailand, Turiye, the United Kingdom, the United States, and Uzbekistan) were clustered based on their energy consumption for the years 2000 and 2021. In line with the objectives of the study, data on nuclear energy, coal consumption, oil consumption, natural gas consumption, hydropower consumption, and renewable energy consumption were used to represent energy consumption. The clustering analysis revealed differences between countries in the clusters formed between 2000 and 2021. The transition of countries such as Iran, the Netherlands, Mexico, and Luxembourg from Cluster 1 in 2000 to Cluster 2 in 2021 illustrates the complexity of changes in energy consumption patterns. Factors underlying these changes include changes in energy policies, economic conditions, international relations, and technological advances. Similarly, the transition of countries such as Canada, Germany, Italy, Spain, and the United Kingdom from Cluster 2 in 2000 to Cluster 1 in 2021 can be attributed to various factors such as changes in energy policies, economic growth or stagnation, technological progress, shifts in international trade relations, and environmental considerations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".