Technology roadmap and resource allocation methodology for the Canadian Institute for Fuel Cell Innovation (IFCI)
Bibliographic record
Abstract
A technology roadmap and research resource allocation methodology was develoded for the Canadian National Research Council's Institute for Fuel Cell Innovation (LFCI). This report outlines the roadmap and portfolio mapping tools developed, demonstrates how they were applied to the institute's 2005 portfolio of projects, and outlines a process by which the tools can be applied in the future. This work also includes a review of the relevant literature on technology roadmapping and research portfolio management as well as an internal and external analysis of IFCI. The technology roadmap and resource allocation methodology will aid IFCI in determining which projects to fund when faced with limited resources. The external analysis shows that IFCI is well positioned to make a substantial contribution to fuel cell commercialisation and to build a world-class reputation. However, being a young institute in an emerging field, IFCI is finding it challenging to define and implement a coherent strategy. IFCI is working to refine its strategic direction and build capabilities that match the needs of the cluster which it is intended to serve. This report closes with recommendations for further improvement.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".