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 nonlinear group-blind technique is developed for joint detection of some given users' data in a CDMA uplink environment with the presence of unknown interference. This method performs the so-called "slowest-descent search" over a likelihood function of the desired users, starting from the estimate closest to the unconstrained maximizer of the likelihood function, and along mutually orthogonal directions where this likelihood function drops to the slowest. Simulation results show that this new nonlinear technique offers substantial performance improvement over the previously proposed linear group-blind multiuser detectors with little attendant increase in computational complexity. The problem of group-blind multiuser detection in the presence of both unknown interference and impulsive ambient noise is also treated under the framework of slowest-descent search, with the aid of a novel subspace-based robust interference cancellation scheme. It is seen that this robust group-blind method significantly outperforms the robust blind multiuser detection scheme proposed previously.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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