Bibliographic record
Abstract
The PCIe Gen5 CEM RX compliance test presents significant challenges compared to traditional electrical margin tests for marginal systems due to the injection of additional jitter and noise during the RX compliance testing process. Currently, there exists a notable gap in standardized PCIeGen5 CEM RX compliance simulation methodologies available to system designers for evaluating platform design risks prior to PCB board fabrication or system builds. This deficiency often results in costly system board redesigns when RX compliance tests fail in validation stage. This study introduces a new PCIe Gen5 CEM RX compliance simulation methodology that addresses this critical issue. The proposed approach enables system designers to evaluate whether a platform design meets CEM RX compliance standards before PCB board tape-out (TO). By implementing this methodology, designers can conduct comprehensive risk assessments of their designs through RX compliance simulations. The advantage of the proposed RX compliance simulation methodology is its potential to yield substantial cost savings for product development and accelerate time-to-market for product launches. By shift-left identifying and mitigating compliance issues early in the design stage, this methodology offers a proactive approach to ensuring design validity and reducing redesign times.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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 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".