Exponential regression analysis of the Canadian Zero Emission Vehicle market’s effects on climate emissions in 2030
Bibliographic record
Abstract
The electric vehicle (EV) market has ballooned in sales in recent years with promises of emissions targets to inhibit the proliferation of symptoms of climate change. Canada, having acceded to the guidelines set by international climate preservation organizations, has set emissions targets for itself. As a result, the government has recognized the relevance of EVs in the Canadian auto market and has begun to subsidize their development and use. However, there is very little information available about the capacity of emissions that EVs can reduce. We explored how viable EVs could be as a solution to substantially reduce emissions from the transport industry. We used regression algorithms to identify the possibility of a 45% reduction in emissions from the transport industry as suggested by the Canadian government. Based on our research, we have concluded that it is highly unlikely that Canada will be able to meet its 2030 emissions reduction targets through the sale and use of EVs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".