A new transducer receive transfer function calibration method: application to microbubble backscattering cross-section measurements at high frequency
Bibliographic record
Abstract
When comparing acoustic scattering experiments with theory, the relationship between the pressure generated by a scatterer at the surface of a transducer and the induced voltage must be known. Methods have been previously proposed to measure the receive transfer function that rely on several assumptions. A new, experimental method for measuring the acoustic response of a spherically-focused transducer, using a hydrophone at twice the focal distance, is proposed that requires a minimum number of assumptions and calculations. The receive transfer function of a spherically-focused, high-frequency transducer was calculated, and found to be within 10% of the receive transfer function calculated assuming reciprocity. Further, using the receive transfer function, the effective backscattering cross-section of bound microbubbles interrogated at 30 MHz was measured to be, on average, 65% of the geometric backscattering cross-section, with significant size-independent variability. These results give insight into selecting the optimal microbubble size distribution for linear microbubble imaging at high frequencies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".