Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Soft robots outperform the conventional robots on enhanced safety for human–machine interaction, environmental adaptability, and continuous deformation. In this blooming area of fundamental and technological importance, liquid crystal polymer networks and liquid crystal elastomers (referred to as LCNs) have emerged as one of the most valuable candidates for soft robots due to their complex, large, and reversible shape change capabilities. To date, much research effort, mainly regarding chemical synthesis, fabrication technologies and soft robot design, has been dedicated to LCN robotic systems to endow them with versatile and complex actions controlled by various stimuli. Herein, starting with the principle that governs the stimuli‐responsiveness of LCNs, recent progress made in LCN soft robots is summarized while focusing on different robotic motions, such as grapping, walking, swimming, and oscillation. Especially, novel LCNs with intelligent functions such as reprocessability, reconfigurability, self‐regulating behavior and associative learning capability, are highlighted. This article aims to provide significant insights into the design and development of LCN‐based soft robots.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it