Guest Editorial: Special Issue on Cyber-Physical Systems and Services
Bibliographic record
Abstract
The papers in this special section focus on cyber-physical systems and applications for their use. A cyber-physical system (CPS) integrates a vast variety of static and mobile resources, including sensor and actuator networks, swarms of robots, remote-controlled vehicles, critical infrastructures, control and decision software, static data and just-in-time information from sensors, knowledge, data analytics and fusion software, event driven supply chains, and humans, and offers great potential for achieving tasks that are far beyond the capabilities of existing systems [1]. Individual users, organizations, and various communities can transform the vast space of cyberphysical entities into capabilities that no single entity can achieve alone. However, these capabilities do not come easily. Intelligence is needed for just-in-time composition of resources into services. Associated challenges include how to manage the vast number and diverse varieties of static and mobile physical entities, how to describe the capabilities of the cyber-physical entities, how to decompose high level goals into low-level control commands for the individual entities, how to achieve intelligent coordination and manage information flow among the entities.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.025 | 0.019 |
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".