Demonstration Project for a Multi-Material Lightweight Prototype Vehicle as Part of the Clean Energy Dialogue with Canada
Bibliographic record
Abstract
The intent of the Multi-Material Lightweight Vehicle (“MMLV”) was to assess the feasibility of achieving a significant level of vehicle mass reduction, enabling engine downsizing resulting in a tangible fuel reduction and environmental benefit. The MMLV project included the development of two (2) lightweight vehicle designs, referred to as Mach-I and Mach-II MMLV variants, based on a 2013 Ford production C/D segment production vehicle (Fusion). Weight comparison, life cycle assessment and limited full vehicle testing are included in the project scope. The Mach-I vehicle variant was comprised of materials and processes that are commercially available or previously demonstrated. The 363 kg mass reduction associated with the Mach-I design enabled use of a one-liter, three-cylinder, gasoline turbocharged direct injection engine, maintaining the performance and utility of the baseline vehicle. The full MMLV project produced seven (7) MMLV Mach-I “concept vehicles” which were used for testing and evaluation. The full vehicle tests confirmed that MMLV Mach-I concept vehicle performed approximately equivalent to the baseline 2013 Ford Fusion vehicle thereby validating the design of the multi material lightweight vehicle design. The results of the Life Cycle Assessment, conducted by third party consultant, indicated that if the MMLV Mach-I design was built and operated in North America for 250,000 km (155,343 miles) it would produce significant environmental and fuel economy benefits including a 16% reduction in Global Warming Potential (GWP) and 16% reduction in Total Primary Energy (TPE). The LCA calculations estimated the combined fuel economy of 34 mpg (6.9 l/100 km) associated with the MMLV Mach-I Design compared to 28 mpg (8.4 l/100 km) for the 2013 Ford Fusion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".