Monitoring air pollution by combining a static infrastructure with a participatory sensing approach: Design and performance evaluation
Bibliographic record
Abstract
Participatory Sensing (PS) is a new fast-growing sensing approach that involves the participation of mobile phone users, and the corresponding communication infrastructure, to create a large-scale monitoring system. Using PS-based system makes it possible to measure and detect variables and events with an improvement in spatial and time resolution over traditional monitoring system. Pollution-Spots proposes an air pollution monitoring solution by means of using an infrastructure of fixed low-cost sensing devices, and reporting the measurements using a PS approach. The sensing devices acquire the variables and the pedestrian forwards this information, completing the cycle with no extra cost of data transport and/or human resources. However, including humans to the sensing loop, rises new challenges, such as protecting user private data, motivating user's participation, and reducing mobile phone's power consumption, all while maintaining the quality of the collected data. Pollution-Spots proposes a combined algorithm that protects the participant's private information and also implements a gamification technique to encourage the participation without any monetary reward. The proposed system has proven to be energy efficient when compared to similar approaches, with the additional benefit of considering the quality of the collected information, which is normally affected by privacy protection algorithms.
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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 | 0.001 | 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 | 0.001 |
| 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".