GSECps: a diversity technique with improved performance-complexity tradeoff
Why this work is in the frame
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Bibliographic record
Abstract
To efficiently take advantage of the potential diversity benefits of diversity-rich environments, we need combining schemes that can achieve good performance-complexity tradeoff. In this paper, noting the performance limitation of the recently proposed generalized switch and examine combining (GSEC) scheme over the low signal to noise ratio (SNR) region, we develop a new low-complexity combining scheme. The new scheme, termed as GSEC with post-examine selection (GSECps), can offer improved performance over GSEC by operating in the same way as generalized selection combining (GSC) when fading condition is unfavorable. Through thorough performance and complexity analysis of GSECps over i.i.d. Rayleigh fading channels, we show that GSECps can obtain a better performance-complexity tradeoff than both GSEC and GSC schemes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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