Bibliographic record
Abstract
As a result of the policies pursued by the different countries of the region and the local availability of natural resources, primary energy production in Latin America and the Caribbean has been mainly based on petroleum. Its share as an energy source has, however, fallen steadily since the 1970s and it accounted for 43% of total energy production in 2006 (down from 62% in 1970). On the other hand, in the early 1970s, natural gas accounted for 11% of primary energy production and its share has steadily increased since than, accounting for a quarter of total primary energy supply (TPES) in 2006. It is possible, then, that its share of total production will increase in the near future owing to greater availability and the stronger push by the countries of the Southern Common Market (MERCOSUR) to integrate their gas markets. Hydroelectric power peaked at 11.5% of the total in 2000. Since then, its share of total production has declined to stabilize at about 9%. This decline is due to reforms and the pattern of investments in the electricity industry, which has emphasized building fossil-fuel power plants (thermal, for example). Finally, geothermal and nuclear energy production is still minimal in the region (0.2% and 1% of total energy production, respectively).
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.156 | 0.053 |
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".