Visualizing the Energy Production, Imports and Exports for Countries
Bibliographic record
Abstract
There are extensive conceptual difficulties in understanding a country’s energy story. Every country in the world uses some combination of energy production, imports, and exports energy to meet their society’s needs. Thermal inefficiencies converting primary energy into electricity further confuse the issues. A visualization using large, publicly available data can help illustrate these different energy perspectives. This data visualization helps clarify the following perspectives: Production, Imports, Exports, Total Primary Energy Supply (TPES), Total Final Consumption (TFC), and the conversion losses from turning TPES into TFC. TPES refers to the total amount of energy a country obtains directly from natural resources such as fossil fuels or wind. TFC refers to the addition of the all energy directly consumed by a user for an energy service such as electricity for lighting in a house. This paper discusses the interactive simulation that was built to allow users to explore the composition of a country’s energy production, imports and exports through the conversion into energy people consume. The simulation allows users to explore the energy stories for different countries, and how these change over the decades.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".