Auto-Stack - Implementing a European Automotive Fuel Cell Stack Cluster
Bibliographic record
Abstract
Currently, Europe is lacking competitive fuel cell stack development for automotive application. While some OEMs have their captive development activities in the US and Canada, others are not yet prioritizing the technology. The supply chain and research activities are fragmented and suffer from the associated strategic uncertainties. A lack of investment and consequently a lack of critical mass and momentum in automotive stack development is the consequence of the status quo. With this background, the Auto-Stack project combined key European players including automotive OEMs, component suppliers and research organizations in a structured approach to facilitate the development and commercialization of automotive fuel cells in Europe. The consortium assessed ways to identify and reduce the critical barriers for better collaboration between stakeholders and generate a more attractive business case for a European automotive stack industry during pre-commercial and early commercial phases. Activities included the development of a common OEM specification and stack platform concept, analysis of the potential for meeting the mid-term technical and cost targets by the European supply chain, the assessment of synergies with other applications and finally options for facilitating economies of scale.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".