Single cell size estimation from backscattered spectrum by using some weak acoustic scattering approximations
Bibliographic record
Abstract
A method for the sizing of a cell in suspension by ultrasonic means is discussed. The technique uses frequency minima of the backscatter intensity pattern for an acoustically weak scatterer and provides a simple formula for scatterer size estimation in the framework of the Born approximation. The technique has been implemented here to examine performance of the Born approximation and a modified Born approximation in predicting size of a cell in a suspension. This was done by comparing the mean diameter of a cell obtained from optical microscopic measurements over many cells and that determined by employing these approximations only using the minima of measured high-frequency (10-65 MHz) ultrasonic backscatter spectra. Both approximations in estimating size of a scatterer worked with high accuracy (error < 3%) for scatterers like PC-3 cells (ka &asyum; 0.55-3.58) and sea urchin oocytes (ka &asyum; 1.54-10.03) where, k and a are the wave number of the incident wave and scatterer size respectively. This study suggests that this simple method can be used to estimate cell size.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".