Bibliographic record
Abstract
iAbstract A genetic algorithm for the design of passive filters and a distributed amplifier is presented. The characteristics of the algorithm are studied through simple design examples that include a resistor divider, a T-filter, and a 5th-order low-pass filter. In each case, the algorithm is able to meet all of the performance requirements. Preliminary results from the design of a distributed amplifier are presented. While the algorithm does not design an amplifier that meets the performance requirements within a reasonable design time, analysis of the evolution indicates that the design does improve with each generation. This demonstrates that the algorithm is able to evolve the circuit to meet the specification. ii Acknowledgements I would first like to thank Professor Anthony Chan Carusone for his guidance and support over this past year. My experience within his group has left a tremendously positive impact on me and has shaped my future ambitions. I would also like to thank my family for their ever-present support, and Diana for always believing in me. Finally, I would like to thank
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.619 | 0.313 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".