Mobile sensor networks for modelling environmental pollutant distribution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article proposes to deploy a group of mobile sensor agents to cover a polluted region so that they are able to retrieve the pollutant distribution. The deployed mobile sensor agents are capable of making point observation in the natural environment. There are two approaches to modelling the pollutant distribution proposed in this article. One is a model-based approach where the sensor agents sample environmental pollutant, build up an environmental pollutant model and move towards the region where high density pollutant exists. The modelling technique used is a distributed support vector regression and the motion control technique used is a distributed locational optimising algorithm (centroidal Voronoi tessellation). The other is a model-free approach where the sensor agents sample environmental pollutant and directly move towards the region where high density pollutant exists without building up a model. The motion control technique used is a bacteria chemotaxis behaviour. By combining this behaviour with a flocking behaviour, it is possible to form a spatial distribution matched to the underlying pollutant distribution. Both approaches are simulated and tested with a group of real robots.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.002 | 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 it