Bibliographic record
Abstract
Abstract The performance of modern ultrasound imaging systems is primarily determined by the characteristics of the ultrasound transducer. Early imaging systems were based on single‐element transducers. These transducers had a fixed focal distance and had to be mechanically scanned to create an image. The systems were cumbersome to use, and the image quality was relatively poor. Much of the success of modern ultrasound imaging can be attributed to the development of efficient transducer arrays. The ultrasound beam produced by an array can be electronically steered and focused. Electronic steering avoids the need to mechanically scan the transducer, whereas electronic focusing provides improved resolution over a large imaging depth. Since transducer arrays were first introduced in the late 1970s, there has been a steady improvement in the efficiency and resolution of arrays. Transducer arrays are now available in a variety of geometries and operating frequencies, each optimized for a particular imaging application. As new transducer materials and improved fabrication techniques have become available, there has been a corresponding improvement in the quality of the ultrasound images. The pace of this development has not slowed, and improvements in ultrasound imaging can be expected for many years to come.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.101 | 0.088 |
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".