Mobile Virtual Communities of Commuters
Bibliographic record
Abstract
Commuting forms an integral part of our lives, whether we are commuting for leisure or business. The use of location-based services and mobile computing has potentials to improve commuting experience and awareness. For instance, printed bus schedules have been only recently complemented with online systems to provide bus timing information for the community of public transport commuters. Commuters can nowadays inquire about bus timings by the use of telephony systems and the Internet. However, the information provided to users is statically produced, just like the still in-use old fashion bus route tables, and does not take into consideration delays and cancellations. The next step in the evolution of these schedules must produce live information, track bus movements, and alert commuters of bus arrivals and timings. The experience of commuting using taxis can also be improved beyond the use of telephony, while the most common way of asking for a taxi continues to be by hand waiving. Such improvements are more crucial for commuters that are not completely aware of their surrounding environment, such as tourists and business visitors. This article envisions the formation of networked organizations of commuters, through the use of mobile and location-based services. We discuss scenarios and use cases of such organizations and propose an example software implementation for the supporting services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".