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 cooperative spectrum‐sensing problem has been considered here, in which a network of secondary users (SUs) assists a fusion centre (FC) in detecting the presence of a primary user (PU). Assuming communication links with unlimited capacity of the SUs and FC and known channel gains and noise variances, the optimal Neyman–Pearson detector is derived. Assuming limited capacity between the SUs and FC and unknown channel gains and noise variances, three different spectrum‐sensing protocols have been studied; namely, amplify‐and‐forward (AF), compress‐and‐forward (CF) and detect‐and‐forward (DF), where each SU transmits an amplified or compressed version of its observed signal, or its local binary decision to the FC, respectively. The Edgeworth expansion is used to obtain novel expressions for the performance of these detectors. The theoretical analysis and numerical results show that the CF and OR detectors outperform the other proposed detectors. In addition, the simulation results show that the performance of the coded protocols (CF and DF) improves as the number of samples increases or as the noise variance at the SUs decreases, whereas such a behaviour cannot be guaranteed in the uncoded AF protocol.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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