Solid state lighting for the developing world: the only solution
Bibliographic record
Abstract
Approximately two billion people, one third of humanity still has no access to electricity, and thus relies on fuel-based lighting, a dangerous alternative of last resort that is unhealthy, expensive, and offers very poor levels of illumination. This lack of light makes it difficult to perform most evening activities including studies by children and adults alike and therefore represents a significant barrier to human development. Over the past five years The Light Up The World Foundation (LUTW) has pioneered the use of the white light emitting diode (WLED) as an alternative home lighting solution, bringing clean, affordable light to thousands of non-electrified homes around the world. The information presented herein is intended to increase awareness of the enormous potential possessed by this emergent technology, "Solid State Lighting" (SSL), to improve the quality of life of millions of people around the world. The feasibility of its implementation is demonstrated with results from comprehensive field experience and laboratory research work. The mutual economic, social and environmental benefits for both stakeholders and SSL suppliers are discussed. Strategies conducive to the dissemination of this technology throughout the developing world are also presented.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.010 |
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".