Fat emulsions as diffusive reference standards for tissue simulating phantoms?
Bibliographic record
Abstract
Intralipid 20% was recently suggested as a diffusive reference standard for tissue simulating phantoms. In this work, we extend previously obtained results to other fat emulsions, specifically Intralipid 10%, Intralipid 30%, Lipovenoes 10%, Lipovenoes 10% PhosphoLipid Reduced, Lipovenoes 20%, Lipofundin S 10%, and Lipofundin S 20%. Of particular importance for practical applications, our measurements carried out at a wavelength of 751 nm show the following features. First, these products show high stability and small batch-to-batch variations in their diffusive optical properties, similar to Intralipid 20%. Second, the absorption coefficient of Intralipid, Lipovenoes, and Lipofundin S are very similar and their measured values are within the experimental errors; moreover the reduced scattering coefficient of Intralipid 20%, Lipovenoes 20%, and Lipofundin S 20% are similar and their measured values are within 5%. Third, the reduced scattering coefficient of Intralipid 10% and Intralipid 30% can be scaled from that of Intralipid 20% with an error of 9% and 2%, respectively. A similar scaling property is valid for Lipovenoes and Lipofundin S. We have verified that this scaling property depends on the composition of the fat emulsions: If the ingredients exactly scale with the concentration then the reduced scattering coefficient almost exactly scale as well.
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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 | 0.006 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".