A public health dilemma: Urban bicycle-sharing schemes Dear Editor: In 2008, a pioneering, Canadian company, Bixi, launched an
Bibliographic record
Abstract
urban bicycle-sharing program. With six years of award-winning success, Bixi’s bicycles rode to the rescue of commuters, the environmentally-friendly and the health-conscious in Toronto, Ottawa and Montreal. Despite the acclaim, the company’s success came to an untimely end in February 2014 when it filed for bankruptcy. The city of Toronto has dedicated $5 million1 to rescuing the bicycle-sharing scheme. Bixi’s collapse has ignited the debate on the public health benefits of urban bicycle-sharing programs. Cardiovascular disease is a major burden on the health service. In 2011, it caused more than a quarter of all deaths and cost $21 billion in medical bills and decreased productivity across Canada.2 Physical inactivity is a major contributor to cardiovascular disease, with only 15 % of Canadians meeting the suggested targets for daily exercise.2 Bicycle-sharing schemes allow people to easily
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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.029 | 0.034 |
| Insufficient payload (model declined to judge) | 0.007 | 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".