Modeling Trust in IoT Systems for Drinking-Water Management
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 study focuses on trust within water-treatment IoT plants, examining the collaboration between IoT devices, control systems, and skilled personnel. The main aim is to assess the levels of trust between these different critical elements based on specific criteria and to emphasize that trust is neither bidirectional nor transitive. To this end, we have developed a synthetic database representing the critical elements in the system, taking into account characteristics such as accuracy, reliability, and experience. Using a mathematical model based on the (AHP), we calculated levels of trust between these critical elements, taking into account temporal dynamics and the non-bidirectional nature of trust. Our experiments included anomalous scenarios, such as sudden fluctuations in IoT device reliability and significant variations in staff experience. These variations were incorporated to assess the robustness of our approach. The trust levels obtained provide a detailed insight into the relationships between critical elements, enhancing our understanding of trust in the context of water-treatment plants.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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