RENEWABLE ENERGY SOURCES - BENEFITS AND DRAWBACKS FROM THE PERSPECTIVE OF THE EXPERIENCES OF CHINA, BRAZIL, CANADA AND THE UNITED STATES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of the article was to identify actions, based on the experiences of China, Brazil, Canada, and the United States, that countries can implement to increase the share of hydro, solar, and wind energy in their economies. The analysis relied on a literature review and data obtained from the Our World In Data database. The findings indicate that there are effective strategies for clean energy adoption that can be applied worldwide. Key considerations include investing in appropriate infrastructure, developing new energy storage technologies, and implementing environmentally friendly methods for disposing of photovoltaic panels. It is essential to provide financial support for scientific research, particularly in assessing the long-term potential of renewable energy, considering geographic distribution, and evaluating public acceptance. Regulatory frameworks should strike a balance between promoting renewable energy expansion and avoiding excessive growth.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it