Rapport technique : Locomotives électriques à batterie
Bibliographic record
Abstract
This report summarized the different types of batteries that could be used in the design of a battery powered locomotive and compiled literature about the ways lithium-ion batteries fail. Lithium-ion batteries are known for having a high energy and power density, as well as high cycle life and charge retention, and therefore are typically considered to be the best available battery technology for many applications, including powering locomotives. Research in Great Britain has shown that many of the hazards of a battery locomotive occur while charging the battery and are related to the battery system (fire, release of chemicals). There are also risks with the train becoming immobile during operation because of issues with the propulsion system (hybrid system). This poses a risk to operations as an immobile train can block track, causing delays and hazards to nearby operations. There is also a risk of the locomotive becoming immobile in a potentially dangerous location, such as a tunnel or on a high grade, further exacerbating the consequences. Only two hazards fell into the category of “intolerable”. One involved a collision between a battery powered locomotive and a road user, and the other involved a road user crashing into a charging station. Both of these could result in a fire or possible explosion. This risk is not limited to battery trains but to any trains. There are currently few codes, standards, or regulations that are specific to battery-electric locomotives. However, there are some that have been made in recent years for transporting the batteries, and although there are some standards for batteries with the chemistries used in battery electric locomotives, no standardization has been made for battery locomotives themselves. Voltage, capacity, chemistry, and form factors are all variables decided by the manufacturers and are not standardized. There are many standards from various sources and countries on locomotives in general, but not on battery-electric locomotives specifically.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.018 |
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".