Density of Ocular Components of the Bovine Eye
Bibliographic record
Abstract
PURPOSE: Density is essential for acoustic characterization of tissues and provides a basic input for ultrasound backscatter and absorption models. Despite the existence of extensive compilations of acoustic properties, neither unified data on ocular density nor comparisons of the densities between all ocular components can be found. This study was undertaken to determine the mass density of all the ocular components of the bovine eye. METHODS: Liquid components were measured through mass/volume ratio, whereas solid tissues were measured with two different densitometry techniques based on Archimedes Principle. The first method determines the density by measuring dry and wet weight of the tissues. The second method consists of immersing the tissues in sucrose solutions of varying densities and observing their buoyancy. RESULTS: Although the mean densities for all tissues were found to be within 0.02 g/cm by both methods, only the sucrose solution method offered a consistent relative order for all measured ocular components, as well as a considerably smaller standard deviation (a maximum standard deviation of 0.004 g/cm for cornea). The lens was found to be the densest component, followed by the sclera, cornea, choroid, retina, aqueous, and vitreous humors. CONCLUSIONS: The consistent results of the sucrose solution tests suggest that the ocular mass density is a physical property that is more dependent on the compositional and structural characteristics of the tissue and than on population variability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".