A wireless sensor network for urban environmental health monitoring:<i>UrbanSense</i>
Bibliographic record
Abstract
Urban areas are generators of environmental emissions such as carbon dioxide (CO 2 ), harmful air pollutants and noise, all with the potential to negatively impact the health and wellbeing of its human and non-human inhabitants. There is an urgent need to understand the characteristics of urban areas associated with variability in emissions and the potential for exposure to potential harmful environmental conditions. UrbanSense is a wireless sensor network (WSN) infrastructure designed to monitor environmental conditions at different temporal and spatial scales. The scalable infrastructure includes an extended range outdoor wireless sensing and data aggregation system, a web-based data management and visualization platform, and real-time event-based data stream integration. Sensors monitor changes in carbon dioxide (CO 2 ), carbon monoxide (CO), noise (LA eq ), as well as several meteorological conditions including wind speed and direction, temperature, relative humidity and precipitation. The implementation will provide opportunities for real-time data integration and an analysis system for environmental quality assessment, and may be realized on top of products arising from spatio-temporal (statistical) analyses and remotely-acquired data products such as satellite data. Sensor swapping and co-location with sensors from projects with different aims (traffic volume modelling and human tracking research) will add value for research in transportation planning, environmental regulation and policy and epidemiological studies focused on associations between environmental exposures and health outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".