Bibliographic record
Abstract
Research activities on nanorobotics comprise an emerging interdisciplinary technology area raising new scientific challenges and promising revolutionary advancement in applications such as medicine, biology, and industrial manufacturing. Nanorobots can be defined as intelligent systems with overall dimensions at or below the micrometer range that are made of assemblies of nanoscale components while exploiting the physics at such a scale, or as larger platforms capable of robotic operations at the nanoscale. In an effort to disseminate the current advances in this specialized field of robotics, and to stimulate discussion on the future research directions while invigorating research interests towards the development and applications of nanorobotic systems, a special issue of this issue of IEEE TRANSACTIONS ON ROBOTICS (T-RO) has been dedicated to recent developments in nanorobotics. This Special Issue presents a total of 15 papers in the most active areas of research in nanorobotics. Six papers are dedicated to actuation presenting recent advances in the implementation, control, and modelling of actuation methods suited for such robots operating in low Reynolds hydrodynamic conditions and, more specifically, helical propulsion with the force being induced from a rotating magnetic field, resonant magnetic actuation, and self-propelled microjets and platinum catalytic mobile nanorobots. Four papers cover the very active field of research in nanorobotics is in biological and medical applications. The remainder look at industrial applications of micro/nanorobotic manipulation systems.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.024 | 0.017 |
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".