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
Recent advances in wireless and sensing technologies have enabled the deployment of large scale Wireless Sensor Networks (WSNs) which have a wide range of scientific and commercial applications. However, due to the limited energy supply of sensor nodes, extending the network lifetime has become crucial for WSNs to deliver their promised benefits. Several proposals have aimed at this objective by designing energy efficient protocols at the physical, medium access, and network layers. While the proposed protocols achieve significant energy savings for individual sensor nodes, they fail to solve topology-related problems. An example of such problems is the bottlenecks around the sink, which is a direct result of multi-hop relaying: sensor nodes around the sink relay data generated all over the network which makes them deplete their energy much faster than other nodes. A natural solution to this problem is to have multiple mobile data collectors so that the load is distributed evenly among all nodes. We investigate this promising direction for balancing the load and, hence, prolonging the lifetime of the network. We design optimization schemes for routing and placement of mobile data collectors in WSNs. We show, by theoretical analysis and simulations, that our approach has the potential to prolong the lifetime of the network significantly.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.004 | 0.001 |
| 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