Fuel Cells Research in Canada and in Other Leading Countries: A Bibliometric Study
Bibliographic record
Abstract
• Canada ranks first in the multicriteria ranking of scientific research on fuel cells (first in papers per capita, first in scientific impact). • Canada follows leading Asian countries (Korea, Japan, Taiwan and China) in terms of research intensity (specialization). • At the world level, most publications that specify a fuel cell type mention SO or PEM fuel cells but biological fuel cell research is growing steadily. • Canada is unique in that it occupies the most advantageous position (high impact and high level of specialization) for both SOFCs and PEMFCs. • At the world level NRC is amongst the top ten governmental organizations and NGOs involved in FC research in terms of scientific output and is the leading scientific research institution in Canada. • Penn State is the leading university at the world level. • The most important collaboration hubs in Canada are NRC and UBC although scientific collaboration in Fuel Cell research is generally fragmented and mostly regional. • Canada occupies the most advantageous position in FC IP with above world average technological impact and specialization. • Ballard ranks second at the world level for its FC IP portfolio. Ballard ranks 5th for fuel cell papers among firms and also has an excellent scientific impact.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.095 | 0.344 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".