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
Getting the most out of muscles Materials that convert electrical, chemical, or thermal energy into a shape change can be used to form artificial muscles. Such materials include bimetallic strips or host-guest materials or coiled fibers or yarns (see the Perspective by Tawfick and Tang). Kanik et al. developed a polymer bimorph structure from an elastomer and a semicrystalline polymer where the difference in thermal expansion enabled thermally actuated artificial muscles. Iterative cold stretching of clad fibers could be used to tailor the dimensions and mechanical response, making it simple to produce hundreds of meters of coiled fibers. Mu et al. describe carbon nanotube yarns in which the volume-changing material is placed as a sheath outside the twisted or coiled fiber. This configuration can double the work capacity of tensile muscles. Yuan et al. produced polymer fiber torsional actuators with the ability to store energy that could be recovered on heating. Twisting mechanical deformation was applied to the fibers above the glass transition temperature and then stored via rapid quenching. Science , this issue p. 145 , p. 150 , p. 155 ; see also p. 125
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