No Noise Is Good Noise: Noise Matching, Noise Canceling, and Maybe a Bit of Both for Wide-Band LNAs
Bibliographic record
Abstract
Researchers have described the concept of noise matching since at least the 1950s, with studies demonstrating the interrelationships among the noise factor of a low-noise amplifier (LNA), the LNA's noise parameters, and the signal-source impedance Zs. Noise matching is accomplished when an LNA is driven by a signal source, the impedance (or admittance) of which is designed-perhaps using a matching network-to equal the LNA's optimum signal-source impedance for minimum noise, or Zopt. The complex Zopt= Gopt+ jXoptrepresents two of the four noise parameters that completely characterize the noise behavior of a linear two-port device, such as an LNA. The other two noise parameters are the minimum noise factor, Fmin, and the Lange invariant N, which is, arguably, a more fundamental parameter than the often-used equivalent noise resistance, Rn.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".