The Low-Cost Transition Towards Smart Grids in Low-Income Countries: The Case Study of Togo
Why this work is in the frame
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Bibliographic record
Abstract
Power grids must integrate information and communication technologies to become intelligent. This integration will enable power grids to be reliable, resilient, and environmentally friendly. The smart grid would help low-income countries to have a more stable power system to boost their development. However, implementing a smart grid is costly and requires specialized skills. This article aims to outline a low-cost transition from conventional power grids to smart grids in low-income countries. It examines the possibility of telecommunications networks participating in implementing smart grids in these countries, to minimize costs. A combination of quantitative and qualitative methods was used. Using Togo as an example, a conceptual scheme for a low-cost smart grid is proposed, with Togo's telecom operators as the telecoms network support. A transition plan to the smart grid is proposed, based on feedback from developed countries.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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