Bibliographic record
Abstract
Abstract Up to this point, it has generally been assumed that the excitation is sufficiently small so that linearity can be assumed. In practice, for both ultrasound diagnostic and therapeutic applications, this condition is frequently exceeded. For diagnostic applications where a short, high-amplitude transmit pulse is used to obtain good resolution and sensitivity, the amplitude is often sufficiently large that non Hnear effects become apparent [7-9]. As will be discussed in Chapter 8 (section 8.6), the presence of nonlinearity in B-mode imaging enables harmonic imaging to be achieved with the potential advantage of improved spatial resolution [10]. In therapeutic use, such as in lithotripsy, where a shock wave is generated near the focal zone for the purpose of kidney stone fragmentation, a high degree of nonlinearity occurs in the propagation process, especially as the wavefront approaches the focal zone. Similarly, when high-intensity focused ultrasound is used to raise the temperature of a localized zone, nonlinear effects often become important. Further details of the biomedical aspects of nonlinear ultrasound are contained in the reviews by Carstensen and Bacon [11] and Duck [12]. A number of excellent books and chapters devoted to nonlinear acoustics provide a much more detailed account at both the advanced and introductory levels [1-6].
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.082 | 0.047 |
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".