New technique monitoring and transmission environmental data with mobile systems
Bibliographic record
Abstract
The deterioration of air quality in urban areas has become a problem to be reckoned with, the greater levels pollution, in addition to subject the inhabitants at risks substantial for the health, poses a risk potential of degradation of the historical-artisticarchitecture. The Department of Engineering of Messina together with the Department of Energetic and Environmental Researches (DREAM) of the University of Palermo, in cooperation with the Municipal Transport Company of Messina has development a project called "Project SET: Eco-efficient and Environmental Friendly Transport Systems". The project aims to define an integrated system of services to facilitate the deployment of ITS (Intelligent Transport System) solutions and of new techniques for the assessment of the urban microclimate and pollution conditions using original data acquisition equipments installed on moving vehicles. These instruments, equipped with various kinds of sensors, are able to measure the most important weather date and climate quantities and the most relevant pollution parameters and to save the position of the acquired data (latitude, longitude, altitude, speed) by means of a Global Positioning System (GPS). Data can be downloaded from a remote station through the GSM/GPRS mobile phone network. These equipments, continuously moving across the urban area, could integrate the data coming from existing fixed stations, and create a virtual network of hundreds of measuring points. In the field of the pollution control they can contribute to the characterization of causes, the localization of sources and to the application and check of control strategies with a relevant spin-off to town planning.In this paper the research activities carried out by the Departments of the two Universities of Messina and Palermo within the SET project and prototype of a standalone unit for detection and remote data acquisition are described.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".