Analysis of power pattern in CLAS with the material thickness and properties error through interval arithmetic
Bibliographic record
Abstract
A novel analytical approach based on interval arithmetic is proposed to investigate the effect of material thickness and properties errors on the average power pattern in the conformal load‐bearing antenna structures (CLAS) for both local and global errors conditions. The uncertainties of the thickness or properties error of CLAS composite material are modelled as interval‐valued parameters. The dominant expressions between the thickness or properties error interval and the power pattern interval are derived by interval arithmetic along with some main electromagnetic characteristics (side‐lobe level, peak power, and half‐power beamwidth) expressed as intervals can be produced through interval analysis (IA). Some numerical examples are reported to validate the proposed approach and to show its reliability and efficiency when considering different thickness and property errors. The obtained results show that the proposed IA‐based approach offers tangible advantages and effectiveness against some traditional statistical techniques (such as the Monte Carlo method).
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".