Solving the Last mile Problem for energy self-forming nano-grids
Bibliographic record
Abstract
In the later part of the 1980s, the telephony industry struggled with the cost of connecting subscribers over the last mile to serve millions of people in the developing world. While the cost of switching and transmission lines is shared across hundreds or thousands of subscribers, this advantage diminishes at the edge of the network where the cost must be borne by fewer and ultimately individual subscribers. In power grids today a similar situation exists. The Last-mile Problem was resolved when the cost of cellular telephones was reduced by integrating functions and components into silicon circuits and when African entrepreneurs found novel financial models to enable even the poorest to acquire a cell phone. Using parallel principles to provide energy where the infrastructure and the required capital investment do not exist, the self-forming nano-grid project discussed in this paper can start from a single photovoltaic (PV) panel and battery each with attached inverters yet scale up to tens of kilowatts. This system also incorporates load management in a power distribution panel to set priority for load shedding to keep critical loads powered in the face of minimal generation and no conventional grid resources.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".