Bibliographic record
Abstract
Introduction With the rapid advances of materials used in science and technology, various intelligent materials that can sense variations in the environment, process the information, and respond accordingly are being developed at a fast pace. Shape-memory alloys, piezoelectric materials, etc., fall into this category of intelligent materials. Polymers have attractive properties compared to inorganic materials. They are lightweight, inexpensive, fracture tolerant, pliable, and easily processed and manufactured [1]. An organic polymer that possesses the electrical, electronic, magnetic and optical properties of a metal while retaining the mechanical properties and processability, etc., commonly associated with a conventional polymer, is termed an “intrinsically conducting polymer” (ICP), or more commonly, a “synthetic metal” [2]. The unique properties of these materials are highly attractive for a wide range of applications such as actuators, supercapacitors, batteries, etc. With the development of nanotechnology, these materials can be engineered to develop a variety of multifunctional active materials for intelligent applications that were previously imaginable only in science fiction. Figure 8.1 shows an artistic interpretation of the Grand Challenge for EAP actuated robotics.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".