SU‐F‐E‐05: Determination of Breakeven Points of in Vitro Meats and in Vivo Mice Based On Tissue Temperature Enhancement Pattern
Bibliographic record
Abstract
Purpose: To determine the breakeven points in fresh commercial meat and in vivo mice using the tissue temperature enhancement pattern. Methods: A 1 cm length and 0.1 cm diameter gold rod were implanted in fresh chicken breast, beef, fish, in vivo Mus Musculus white mice (medial dorsal region) and insonated with ultrasound. The temperature enhancement of gold rods was measured with a needle type thermistor over a temperature range from 35–50 oC. From these results the breakeven points were determined by plotting the gold rod temperature versus ultrasound exposure time and determining the interception point of two curves fitted by a linear regression of thermal response above and below 43 °C. Results: The linear correlation coefficients for all fitted curves lie within 0.97 and 0.99. The breakeven points were found to be the same for all kinds of fresh meat (fish = 42.1 ±1.1, chicken breast = 42.3 ±0.9, beef = 42.6 ±0.8) and in vivo Mus Musculus white mice (43.3 ±0.6). These temperatures agree with the standardized value (e.g. equivalent minutes at 43 °C) for comparison of thermal treatments (Proc. SPIE Int. Soc. Opt. Eng. 2003 June 2; 4954: 37). Conclusion: The interception of the thermal response curves above and below 43 °C may be used as a fast method and useful dosimetric tool in clinical research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".