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
Location based services in wireless sensor networks are quite demanding applications especially in indoors, such that accurate localization of objects and people in indoor environments has long been considered as one of important building blocks in wireless systems. In this paper, we investigate sensor location estimation problem where a target sensor measures inconsistent signals as received-signal-strength or time-of-arrival from anchor sensors with known locations, whereas target sensor location must be estimated. We know that even in large scale wireless sensor networks, information are relatively sparse compared with the number of sensors. In such networks, the localization problem can be recast as a sparse signal recovery problem in the discrete spatial domain from a small number of linear measurements by solving an under-determined linear system. By exploiting the compressive sensing theory, sparse signals can be recovered from far fewer samples than Nyquist sampling rate. Our approach uses a few number of inconsistent measurements to find the wireless device location over a non-symmetric spatial grid. In this method, an ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> -norm minimization program is used to recover the wireless user location. The performance of the proposed method is evaluated through simulations with synthetic and real measurements.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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