Bibliographic record
Abstract
It is common for arrays to degrade as elements fail, resulting in high sidelobes. The sensitivity of sidelobe levels to element failures is examined for an arbitrarily shaded array. Using the difference of complex beampatterns, it is found that the beampattern, which can be associated with just the failed elements, controls the degraded array response in the deep sidelobe region. Using results for addition of weighted random phasers, expressions are presented for an upper bound, the mean and standard deviation of the power sidelobes of the degraded array in terms of the number and shading weights of the failed elements. The upper bound depends on the percent of elements that fail and is independent of array size. The average sidelobe level depends on both the failed-to-good ratio and the number of remaining good elements, making large arrays more robust for the same percentage of failed elements. The standard deviation of sidelobe levels is approximately equal to the mean. The ratio of failed to remaining good elements is analogous to the combined amplitude and phase variance for uncorrelated tolerance errors. Combined effects of element failures and random amplitude and phase errors are then presented.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".