Bibliographic record
Abstract
Diversity methods Two channels with different frequencies, polarizations, or physical locations experience fading independently of each other. By combining two or more such channels, fading can be reduced. This is called diversity . Diversity ensures that the same information reaches the receiver from statistically independent channels. There are two types of diversity: microdiversity that mitigates the effect of multipath fading, and macrodiversity that mitigates the effect of shadowing. For a fading channel, if we use two well-separated antennas, the probability of both the antennas being in a fading dip is low. Diversity is most efficient when multiple diversity channels carry independently fading copies of the same signal. This leads to a joint pdf being the product of the marginal pdfs for the channels. Correlation between the fading of the channels reduces the effectiveness of diversity, and correlation is characterized by the correlation coefficient, as discussed in Section 3.4.2. Note that for an AWGN channel, diversity does not improve performance. Common diversity methods for dealing with small-scale fading are spatial diversity (multiple antennas with space separation), temporal diversity (time division), frequency diversity (frequency division), angular diversity (multiple antennas using different antenna patterns), and polarization diversity (multiple antennas with different polarizations). Macrodiveristy is usually implemented by combining signals received by multiple BSs, repeaters or access points, and the coordination between them is part of the networking protocols.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.013 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".