Bibliographic record
Abstract
Fuel cells can be highly efficient energy conversion devices. However, the environmental benefit of utilising fuel cells for energy conversion is completely dependent on the source of the fuel. Hydrogen is the ideal fuel for fuel cells but the current most economical methods of producing hydrogen also result in the production of significant amounts of carbon dioxide. Utilising biomass to produce the fuel for fuel cell systems offers an option that is technically feasible, potentially economically attractive and greenhouse gas neutral. High-temperature fuel cells that are able to operate with carbon monoxide in the feed are well suited to these applications. Furthermore, because they do not require noble metal catalysts, the cost of high-temperature fuel cells has the greatest potential to become competitive in the near future compared to other types of fuel cells. It is, however, extremely difficult to assess the economic feasibility of biomass-fuelled fuel cell systems because of a lack of published cost information and uncertainty in the predicted cost per kW of the various types of fuel cells for large volume production methods. From the scant information available it appears that the current cost for fuel-cell systems operating on anaerobic digester gas is about US$2,500 per kW compared to a target price of US$1,200 required to compete with conventional technologies.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".