Extended wifi network design model for ubiquitous emergency events
Bibliographic record
Abstract
Telecommunication is the exchange of information and data \nover significant distance by electronic means. During \nextreme events such as natural disasters and urgent events it becomes more and more important to preserve the communication devices and infrastructure to exchange information between rescue teams and persons in damaged zone based on their area. When extreme event happens, many communication scenarios can be considered. We focus on a the case of destruction of traditional communication networks during an emergency event such as natural disasters in which it is important to find an \nalternative network architecture to prevent the death and \ninjury of thousands of people. The rescue teams are unable \nto locate and communicate with victims on right time. This \nwork presents network architectural design model to extend \nthe range of WIFI networks and help people access to \nInternet or get rescue when the damage affects the most \nexisting telecommunication networks. This model is validated by analyzing two communication scenarios.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".