Fuel Cells for Commercial Applications
Bibliographic record
Abstract
Abstract The use of fuel cell technology offers benefits to many applications beyond light‐duty vehicles, and many of these are currently commercially viable or have the potential to be in the near term. These include material‐handling equipment, construction equipment, handheld and portable power, telecom backup power, airport ground support equipment, aerospace power, and maritime power. In all of these applications, fuel cells can provide immediate benefits in terms of decreased fossil‐fuel use, reduced criteria pollutants and greenhouse gases, and delivery of new capabilities. Just as important, they can also be leveraged to facilitate the eventual introduction of fuel cell light‐duty vehicles by providing experience in both fuel cells and hydrogen that helps to refine products, drive down cost, reconcile codes and standards issues, make hydrogen fuel more available, and introduce familiarity with the technology to the public. In the words of theUS DOE's Fuel Cell Technologies Office Market Transformation subprogram, these near‐term applications “help overcome nontechnical challenges to the expansion of hydrogen and fuel cell technologies into the broader vehicular marketplace.” The applications mentioned earlier are in varying states of commercial viability and development yet all are contributing to these goals. As experience and performance enhancements continue, the opportunity for new applications increases.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.228 | 0.123 |
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".