Proceedings of the 4th Analytic Virtual Integration of Cyber-Physical Systems Workshop December 3 Vancouver Canada
Bibliographic record
Abstract
We would like to welcome you to the 2013 Analytic Virtual Integration Cyber-Physical Systems (AVICPS) workshop.The workshop is focusing on analytic techniques that enable the early discovery of defects in CPS, before the system is integrated or its parts are built.The principal objective is to present and discuss novel ideas and results that help to discover and resolve problems early during the design and implementation phases.AVICPS 2013 aims at bringing together researchers, engineers, and application developers from both industry and academia to present their latest advances in this field.Our program is organized according to three themes: mathematical fundamentals, model integration, and model analysis.The program also includes time reserved for lively discussions; we hope that all attendees will benefit from these interactions.We received 13 submissions, from which 6 were accepted; 4 as position papers and 2 as full research papers.Our 13 internationally known PC members came from academia and industry and they have worked very hard to review the papers; most papers have received three reviews.We would like to thank the program committee members for their excellent work and for their suggestions in the selection of papers.We would like to thank all those who submitted papers for their efforts and for the quality of their submissions.Thank you for your active participation in AVICPS 2013.We hope you will find this event to be productive and enjoyable, and we look forward to seeing you next year at the next AVICPS.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.161 | 0.036 |
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".