A SHIFT TO TRUCKS LEAVES CANADIAN FUEL ECONOMY FLAT OVER THE PAST DECADE Highlights
Bibliographic record
Abstract
The recent spikes in gasoline prices have triggered some chatter about drivers shifting towards smaller, more fuel efficient vehicles. While Canadians tend to purchase smaller cars during periods of rapidly rising fuel costs, there has been an overall shift towards trucks over the last ten years. Consumer uptake of hybrid and electric vehicles has been quite slow. These alternative-powered vehicles still have a number of hurdles to overcome before they will comprise a significant share of the Canadian market. Despite ongoing efforts to improve the fuel efficiency of all vehicles, the rising demand for light trucks has left the overall fuel economy in Canada unchanged over the past decade. With gas prices still hovering at elevated levels, there has been a great deal of talk surrounding a shift toward more fuel efficient vehicles. While consumers do tend to purchase smaller cars during times of rapid gas price spikes, light trucks (which include crossover and sport utility vehicles, vans and pick-up trucks) have become a more popular choice among Canadian drivers over the years. What’s more, the overall fuel economy in Canada has remained quite constant over the past decade, despite ongoing efficiency gains created by automakers. Gas price spikes boost small car sales
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.003 |
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".