Investigating the Relationship Between Power Plant Type and Regional Climate
Bibliographic record
Abstract
Climate change (UNSDG Goal 13) is one of the most pressing issues of our lifetime. Though recent years have given the world hope for the Earth’s future, we still rely on fossil fuels to produce a vast majority of our energy. Power plants, the “creator” of said energy, are found around the world, everywhere from Afghanistan to Zimbabwe. However, there are multiple types of power plants each of which leave different effects on the environment. At the same time, the world has wildly different climates. The cold winters of Northern Canada could not be more different than the tropical island climate of Indonesia. Since the weather is different, the power plant that can produce energy with the most efficiency could be affected. This raises the following question: Does the most common type of powerplant vary by region? We will investigate this, digging into each country’s primary power source and analyzing its similarity to its neighboring nations. If a relationship between these two variables is proven, a myriad of sub-questions become apparent. Is there a relationship between the general approach to clean energy and the most common type of power plant? Does this general trend correlate across different continents? If a relationship is not proven, questions about which variables affect the frequency of the power plant types will be raised. Once we have a better understanding of the frequency of power plants around the world, we as a collective can work to make all power plants both environmentally-friendly and efficient.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".