Bibliographic record
Abstract
CPS are smart systems that encompass computational and physical components, seamlessly integrated and closely interacting to sense the context of the real world [1]. These systems involve a high degree of complexity at numerous spatial and temporal scales and controlling software and physical components with highly networked communications. Thus, CPS comprise tightly integrated networking, computing, controlling, sensing and actuation capabilities. The societal impact of CPS is enormous. Virtually every engineered system is affected by advances in these interconnected capabilities. Future CPS applications are expected to be more transformative than the IT revolution of the past three decades [2]. Today CPS R&D affords spectacular and transformative opportunities due to the convergence of analytical and cognitive capabilities, real-time and networked control, pervasive sensing and actuating, as well as compute and storage clouds. Advancement in CPS requires a new control and systems science that encompasses both physical and computational aspects [3]. Engineering and computer science research have provided a solid foundation for spectacular progress in IT, but now we need to address the unique scientific and technical challenges for this new systems and control science for CPS. This workshop will concentrate on selected CPS foundations, applications areas, and grand challenges including networked control, adaptive systems, energy, smart oceans, assistive technologies and medical care monitoring including elderly care, transportation and mobility, autonomous systems, smart materials, and wearable devices.
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".