On the Design of Atmospheric and Water Pollution Sensors for Deployment over Unmanned Vehicles
Bibliographic record
Abstract
In this work we present advances on a project oriented to design, test and deploy sensors on unmanned vehicles to measure air and water pollutants.The main objective of the project is to develop a flexible platform composed by mobile nodes that allows dynamic sensing of pollutants in air and water environments.In this way, areas of interest can be discovered during early stages of monitoring, and sampling of pollutants can be adjusted according to priority levels.The design of such sensor devices needs to consider several parameters such as the weight, size, geometry, energy consumption, connectivity, and communication protocols, to provide a smooth integration to the robotic vehicle.In this paper, details are presented about the design of an air particulate matter (PM) sensor and a water nitrite concentration sensor.These devices will be mounted on an aerial and an underwater unmanned vehicle respectively.Also, in this work we sketch a methodology to coordinate the efficient deployment of the dynamic nodes considering a set of robotic agents carrying the sensors.The results of experiments with the sensors taking measurements are shown, revealing their suitability to accomplish the intended task.
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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 | 0.000 |
| 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".