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
This paper presents a versatile bit-error-rate (BER) testing scheme to characterize the quality of communication interfaces. Traditionally, the presilicon BER is evaluated using time-consuming software simulations. The stand-alone BER test products for postsilicon evaluation are expensive and do not include channel emulators, which are essential to testing the BER under the presence of noise. For both the design and evaluation phases, we present a scheme for BER testing in field-programmable gate arrays (FPGAs) that consists of a BER tester (BERT) core and a novel additive white Gaussian noise (AWGN) generator core. The maximum output value of our AWGN generator is 53, whereas that of the existing solutions is less than 7. Therefore, our generator can better emulate the tail of a Gaussian distribution, which is suitable for exploring applications at very low BERs. We also present a pipelined structure that exploits the central limit theorem for speedups of four or more. Combining a BERT and an AWGN in FPGAs is orders of magnitude more efficient in cost, volume, and energy over the existing similar-speed stand-alone solutions and has a huge speed advantage over software simulations. We demonstrate the applications of our solution through two case studies.
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) | 0.000 | 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 it