The Rush To Electrical Transportation Vs The Reality Of The Electrical Grid: The Consequences
Bibliographic record
Abstract
During the last months I have been inundated with a multitude of requests to resolve issues relating to charging points for electric vehicles; either concerning problems with those points already installed in privates and public sites or requests for new installation points in these places.The problems generated by these charging points vary from insufficient power availability to interferences in the network resulting in power interruptions and high level of harmonics.These issues have only developed recently, since the advent of widespread use of electric cars, so, to date, there is very little academic or scientific material available on the topic.Following a wide literature review searching for written material from various sources describing possible solutions to these issues, I found articles in technical papers lead me to the standards covering these issues and specially IEEE 519 and 1159, NFPA 70, IEC 50160, ANSI-IEEE-C37-2 [3], and power quality publications.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 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".