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
A fundamental problem in wireless sensor networks is localization -- the determination of the geographical locations of sensors. Most existing localization algorithms were designed to work well either in networks of static sensors or networks in which all sensors are mobile. In this paper, we propose two localization algorithms, MSL and MSL*, that work well when any number of sensors are static or mobile. MSL and MSL* are range-free algorithms -- they do not require that sensors are equipped with hardware to measure signal strengths, angles of arrival of signals or distances to other sensors. We present simulation results to demonstrate that MSL and MSL* outperform existing algorithms in terms of localization error in very different mobility conditions. MSL* outperforms MSL in most scenarios, but incurs a higher communication cost. MSL outperforms MSL* when there is significant irregularity in the radio range. We also point out some problems with a well known lower bound for the error in any range-free localization algorithm in static sensor networks.
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