Instability of a Large Coupled Microbeam Array Initialized at Its Two Ends
Bibliographic record
Abstract
A simple approximate method is suggested to determine the critical value for instability of a large parallel array of mutually attracting microbeams, based on instability analysis of a small array of only a few microbeams at the ends of the original large array. First, it is verified by a simplified spring system that equilibrium deflections of all intermediate microbeams (except those at the two ends of the parallel array) are negligibly small, and instability of the large microbeam array is initialized at the two ends of the array. Therefore, the critical value for instability of the original large array is determined by the critical value for instability of a small array of only a few microbeams at the two ends with its innermost microbeam fixed. The results obtained for the spring system show that the relative errors in the critical value between the original large array and the substitute small array are less than 2% when only three or four springs at each end are considered. In particular, the relative errors quickly converge to zero when the number of springs considered in the substitute small array further increases. This simple substitution method is used to approximately determine the critical value for instability of a large array of mutually attracting microbeams, and the results are compared with those obtained by other methods based on the instability analysis of the original large array, which contains a large number of microbeams. The present work offers a simple method to reduce the instability analysis of a large array of microbeams to a much simpler problem of a small array of only a few microbeams.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".