Bibliographic record
Abstract
For several years now, there has been a strong drive in the automotive industry to displace the NiMH batteries in Hybrid Electric Vehicles (HEV) with lighter, more durable, more powerful and potentially less expensive Li-Ion batteries. These efforts have been hampered mostly by the concerns over the safety of the Li-Ion batteries. Such concerns have been overblown by merely focusing on abusive testing without paying equal attention to assessing the risk posed by such batteries in the event of a mishap. History shows that the automotive industry has been very successful in managing the risk posed by gasoline, a highly combustible fluid with an energy density 100 times more than the most energy-dense of advance batteries. This paper discusses a methodology developed for the risk assessment of advance batteries. Although the focus here is on the batteries used in hybrid, electric, or plug-in vehicles, the methodology itself, called Hazard Modes & Risk Mitigation Analysis (HMRMA), is quite general and can be used in other applications for batteries as well as for other components & parts that maybe considered hazardous. In addition, the methodology quantifies the risk associated with each hazard and becomes a valuable design tool to develop the most effective way of reducing the risk.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".