Determining the robustness of sensor barriers
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
Various notions of coverage provided by wireless sensor networks have attracted considerable attention over the past few years. In general, coverage can be expressed geometrically, by relating the positions, and associated coverage regions, of individual sensors to some underlying surveillance domain. The most natural notion is area coverage, where the goal is to achieve coverage for all points in the surveillance domain by a static arrangement of sensors. A less demanding alternative is barrier coverage, where the goal is to ensure merely the absence of undetectable transitions between critical subsets of the surveillance domain (for example, between unsecured entry and exit points). An arbitrary arrangement A of sensors is said to form a barrier between regions S and T if every path joining a point in S to a point in T must intersect the coverage region associated with at least one sensor in A. Determining if an arrangement of unit disks in the plane (or unit spheres in 3-space) forms a barrier is straightforward; determining the robustness (or redundancy) of such a sensor barrier, however, is considerably more challenging.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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