Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An adaptive predistortion technique has been presented and verified through the design and fabrication of practical filters in both the C and Ku bands. The method allows the realization of microwave filters at a lower cost, lighter mass, smaller volume, and better performance with minimum insertion loss penalties.The concept of lossy filters has been presented from a practical perspective. A simple lossy synthesis technique using any synthesized lossless (nontransversal) filter was shown, which can be used with hyperbolic rotations for loss distribution. Moreover, the limitation on the minimum Q of lossy resonators has been studied using a one-pole filter as a fundamental building block. Lossy four-pole Chebyshev and quasi-elliptic synthesis examples were presented. A four- pole Chebyshev lossy filter in the Ku band has been synthesized, modeled, and fabricated successfully using mixed combline and microstrip technologies. The design has the advantage of having all input-output paths going through more than one resonator, which minimizes unwanted source-to-load coupling, especially at high frequencies. The lossy approach is still at its early stages of development and needs more research and development effort to become as mature as the predistorted filters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| 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 | 0.002 |
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 it