On the Provisioning of Vehicle-Based Public Sensing Services
Bibliographic record
Abstract
Thanks to their abundant on-board resources, ubiquity, and mobility, smart vehicles can be considered major candidates for providing pervasive information services. With the diversity of in-vehicle sensors along with abundant storage, processing, and communication capabilities, smart vehicles can bring a wide scope of applications into action under the public sensing paradigm outstripping other candidate mobile resources such as smartphones. In this paper, we propose the vehicular public sensing (VPS) platform that aims at utilizing the abundant resources of smart vehicles for providing ubiquitous public sensing services. The VPS platform encompasses underlying components that address the recruitment, communication, sensing, reporting, and data analytics functionalities of a typical public sensing process. Taking into account different environmental and practical setups, the VPS platform provides potential adjustments and different approaches for the operation of each component. We anticipate that by engaging smart vehicles in providing ubiquitous sensing-based services, a plethora of information services and applications will be unleashed bringing a new era of service provisioning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".