Smart monitoring of benzene through an urban mobile phone network
Bibliographic record
Abstract
Benzene is one of the main air pollutants because of its dispersion throughout the territory and its carcinogenicity.Thus, in accordance with the current European Directive 2008/50/EC, benzene is monitored punctually in EU countries.In this context, the University of Trento and Telecom Italia, in response to specific studies, have developed an approach that could be conceptually divided into two phases: (A) the first one assumes the distribution of low-cost sensors to a part of the local population for the monitoring of carbon monoxide that, in this case, could be used for its indirect role of tracer.These sensors, compatible with smartphones and therefore with the network, allow acquiring remotely a huge amount of data that could be used to create detailed maps of air quality after a process of validation/selection (based on algorithms already developed by Telecom).(B) The second phase is based on the fact that the correlation between carbon monoxide and benzene is scientifically proven in homogeneous areas; thus, through an ad hoc study, it is possible to set a specific correlation carbon monoxide -benzene for each pre-selected area.The result of this method is quantitative information on exposure of the resident population to benzene with a detail not reachable through conventional approaches and suitable for an enhanced activity of decision makers.For a full scale exploitation, this approach requires an economic effort achievable only with external financing as, presently, the official monitoring activity allows only conventional actions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 0.001 |
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".