Developing a route calculator for e-bikes based on GPS data
Bibliographic record
Abstract
Electric-Assisted bikes, or e-bikes, are a low-carbon mode of transportation. Therefore, they could help in mitigating climate change by reducing greenhouse gas emissions caused by the transportation sector. Indeed, they could replace personal cars for some trips. However, even if the use of e-bikes is developing in Europe, it is less the case in North America. In particular, little information is available regarding the use of e-bikes in Québec (Canada). In order to include them into transportation planning, it would be useful to know more precisely the potential routes that e-bikes could follow. Using GPS data from e-bike trials in the six major regions of Québec (Canada), we develop a route calculator dedicated to e-bikes by computing the cost of each link thanks to the observed speeds in the dataset. This calculator takes into account the slope and road category. The computed itineraries are coherent with the dataset and with another dataset containing trips from the e-bike sharing system in Montreal (Québec Region, Canada).
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".