Bibliographic record
Abstract
In 1999, fuel cells cost 8-15 times as much as internal combustion engines (ICEs), but their prices are falling rapidly; some time in 2003, they should cost no more than twice as much as ICEs. Ballard Power Systems of Canada expects to sell fuel cell stacks for less than $4000 in 2003, and General Motors (GM), which is sharing fuel cell research with Toyota, aims for a $2000-3000 fuel cell cost. The first fuel cell cars to be offered for sale are expected within a few years. Ford and DaimlerChrysler each plan productions of small series of fuel cell cars in 2004. Honda is planning to make fuel cell vehicles available in 2003. In 1999, Peugeot and Renault announced their forthcoming joint fuel cell venture, but their relevant research is believed to be only rudimentary. Both Ford-DaimlerChrysler and GM-Toyota are working on fuel cells that use proton exchange membrane fuel cell technology to create electricity from hydrogen, which will power electric motors already being developed for electric vehicles. The difficult part is finding hydrogen supplies for fuel cells. Initially, manufacturers believe that hydrogen will have to be made from methanol or petrol in the car itself. At a later stage, reformers that change liquid fuel to hydrogen gas could be installed at service stations, to pump hydrogen into vehicles.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.155 | 0.056 |
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".