Bibliographic record
Abstract
Abstract Polymer electrolyte fuel cells (PEFCs) are promising electrochemical devices for the direct conversion of chemical energy of a fuel into useful electrical work with vast applications in automotive, stationary, and autonomous power. It is widely recognized that progress in PEFC technology is a multi‐disciplinary challenge and hinges on Research and Development (R&D) breakthroughs in design, fabrication, and implementation of innovative materials, processes, and system optimization. Fuel cell modeling, in particular, has been the subject of intense research in the past two decades, as it is of great importance to design and process optimizations. Building upon the insights obtained in a European Collaborative Research Program, we present an analysis of fuel cell modeling R&D roadmap by focusing on technical and market attributes and the inter‐relations therein. The roadmap is driven by three distinct outcomes – alpha, beta and commercial versions, reflecting the maturity of the multi‐scale software. All roadmap entries are organized in layers, namely Market and Business; Services; Products; High Level Targets; Technology; Science; and Enablers and Resources. This study contributes to a much needed foundation for further planning of potential R&D and demonstration projects of fuel cells for automotive and other emerging sectors.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".