Bibliographic record
Abstract
have several significant advantages over other technologies, including zero-emission operation, a high degree of vehicle efficiency, and the potential to diversify fuel production using a wide variety of primary energy sources. Research and development in the area of alternative vehicle technologies has a long tradition at Ford. Ford's global goal is to offer zero-emission vehicles in the near future. In late 2004 the first vehicles of Ford's low-volume fuel cell vehicle production program went into service. Over a period of three years this vehicle fleet will be tested under real world driving and operating conditions, not by Ford employees but in customers' hands. The main fleet test locations are California, Florida, and Michigan in the United States, British Columbia and Victoria in Canada, and Germany in Europe, the latter in the context of the so-called Clean Energy Partnership, Berlin. This 3-year phase marks a milestone on the way toward the market introduction of fuel cell vehicles as it is the first time ever that fuel cell vehicles are operated by customers in every day service. Ford's global strategy on the way towards sustainable mobility also includes development work in the area of hydrogen internal combustion engines. For the transition period leading up to the market introduction of fuel cell vehicles this technology will help pave the way for fuel cell vehicles as it supports the development of new and advanced hydrogen storage systems, the establishment of safety standards for hydrogen, and the construction of a hydrogen infrastructure from a vehicle manufacturer side. This paper gives a detailed overview of Ford's European research, development, fleet test, and demonstration activities including: Ford's European activities embedded in its global strategy, hydrogen internal combustion as a bridging technology towards fuel cells, Europe's largest fleet test and demonstration project for hydrogen cars, and the latest fleet test results of Ford's European fuel cell vehicle fleet (A). For the covering abstract of the conference see ITRD E212343.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.022 |
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".