Investigating the development of a zero emission electric utility snowmobile
Bibliographic record
Abstract
This thesis addresses the question: can an electric snowmobile be one of the solutions to help lower snowmobile emissions and energy consumption? In addressing this question the performance limitations of current electric snowmobile prototypes are investigated and it is shown that, unless a huge leap is seen in current battery technology energy density, electric snowmobiles cannot perform on par with gasoline snowmobile on both range and performance simultaneously. Despite this, electric snowmobiles do have a certain number of niche applications where they can be useful. This thesis suggests that electric snowmobile powetrain modeling and simulation for these niche applications can potentially help overcome some of the challenges that exist in implanting such a vehicle for regular use. A complete, virtual electric snowmobile model was built and validated using actual electric snowmobile on-snow test data. Snowmobile emission and energy consumption simulation was performed and demonstrated that Canadian electric snowmobile fuel cycle emissions and energy consumptions were, in general, substantially lower than gasoline snowmobiles. However, this is closely linked with electricity generation techniques and should not be extrapolated to say that this is the case for all potential electric snowmobiles worldwide.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".