Bibliographic record
Abstract
KGM1 is a new brand for power generation that uses the most powerful green technology called Airborne Wind Energy (AWE) for off-grid populations, a growing market with 800 million users. Inuit, Sami, remote communities, islands, disaster lands; lots having low sun, high winds and clipping logistic problems, 60% of communities use diesel generators with power around 20kW. It offers a mobile wind energy system (10-25 kW rated) using kites, that is up to 50% cheaper and 90% weight-saving compared to current wind turbines. We target simple concepts, an easiness to transport and installation; last but not least, is the first prototype worldwide, tested under operating conditions, with an always positive energy production duty cycle. To make a simple comparison, this generation of technology works like a kite surfer who is kiting! It is now managed by GHIVA firm and structured on a less demanding Off-Grid market but will then reach the On-Grid market with larger, fully automated generators. Born from a “bottom-up” approach in my own cellar KGM1 has evolved as far as passion and free time could take it—with very low funding. Today, it drifts through the online world like a message in a bottle, awaiting its next chapter. Two web links to summarize 8 years of research: https://drive.google.com/file/d/1XwjtqPLi5PsAxo49bGlJG24GRubCFhxz/view?usp=drive_link https://docs.google.com/presentation/d/1EiyWFwV4oBhGxbAo7xr0kjq2aTWQbEvg/edit?usp=drive_link&ouid=108653029879271250506&rtpof=true&sd=true
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 0.017 |
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".