Bibliographic record
Abstract
Abstract Ultrasound sensors have been widely used in medical imaging, as well as structural health monitoring (SHM) and non-destructive testing (NDT) in civil and mechanical structures. Covering entire structures and imaging large areas requires multiplexing of many ultrasound sensors with single readout instrument, which can be difficult for traditional piezoelectric transducers. Optical fiber-based sensors offer numerous advantages such as being lightweight, small, the ability to be embedded, immunity to electro-magnetic interference, and the ability to be multiplexed and distributed ultrasound sensors. Fiber ultrasound sensors are regarded as an ideal sensing solution for SHM and NDT, and even most recently for medical imaging due to its broadband ultrasound response and distributed capability. Micro and nanofibers are made smaller than telecom fibers using a wider selection of sensing materials with higher bending capability, which makes them ideal for high frequency (hundreds of MHz) ultrasound detection of micrometer cracks and imaging biological tissues. New optical materials and fabrication techniques are shaping the future with exceptionally small ultrasound sensors and actuators, extending the range of applications in SHM, NDT and medical imaging with higher accuracy and better precision over larger areas.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".