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 electronic miniaturization, software engineering and wireless communication technologies have enabled the deployment of low-power sensor nodes that are equipped with an embedded processing unit, memory, power-supply, on-board sensor, radio communication facilities (I. F. Akyildiz, W. An important characteristic of sensor nodes is their ability to sense specific phenomena in a target field and send their data to a central node, called the Base Station/sink, possibly through multihop wireless communication links. Since most data gathering applications are concerned with collection of physical data that is generated in the target area monitored by sensor nodes, therefore coverage becomes a core meaure of performance. A fundamental issue in coverage is the quality of monitoring provided by the network. This quality is usually measured by how well deployed sensors cover a target area. In its simplest form, 1-coverage means that every point inthe target area is monitored at least one sensor. In recent years, the problem of providing sensor coverage has received extensive attention from the research community in the context of 2D sensor networks However, most of the real world sensor network deployments often a follow 3D model. Examples of such deployments are environmental monitoring in forests In most cases such deployments follow a model where sensor nodes are placed in large quantities over a target region. Excessive deployment of sensor nodes is often desirable to protect the network from individual node failures. However keeping in mind the energy and bandwidth constraints for most applications, the coverage control problem translates to choosing a set of active nodes that ensure that the target region is sufficiently monitored.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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