On the recruitment of smart vehicles for urban sensing
Bibliographic record
Abstract
With the abundant on-board resources in intelligent vehicles, they have become major candidates for providing ubiquitous services, including urban sensing. This paper proposes an efficient recruitment scheme for vehicles in urban sensing applications. Our trajectory-based recruitment (TBR) scheme solves the problem of participant selection by considering spatiotemporal availability of participants. The aim of TBR is choosing the minimum number of vehicles that achieve a required level of coverage for the area of interest. TBR utilizes the easy-to-acquire trajectories of the candidate vehicles as indicators of the availability of participants, and applies a minimal-cover greedy algorithm for selection. The basic greedy algorithm is adapted to handle some practical scenarios, including departing vehicles and varying redundancy requirements. The paper also discusses two data acquisition models for retrieving the sensing data (on-demand and unsolicited). Assessment of TBR shows that it achieves high levels of coverage even when vehicles do not stick to their announced trajectories.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".