Instrument for noninvasive photonic assessments of biological materials
Bibliographic record
Abstract
We have developed a new instrument for non-invasive assessments of biological materials. A new technique was implemented to measure the light-tissue interaction in samples using an efficient light delivery and detection method. The optical properties measured were, transmitted, forward scattered, diffusely reflected and specularly reflected light. Measurements were made using a white light source, as well as with spectrally-resolved signals. Using artificial, human, and rabbit corneas as models, measurements were made to determine correlations of the above optical properties in the different tissues. The instrument repeatability using non-biological controls, was between 0.1% and 0.2% for the measured optical properties. The repeatability was consistent even at low light conditions of 0.01 to 0.05 relative intensity. The instrument repeatability was better than the variability of samples within a test group. For both transmitted and reflected non-specular light, there was an equivalent correlation measured between artificial and human corneas. The instrument also proved useful in tracking time-dependant responses of biological tissues subjected to various insults. This new instrument is a reliable tool for measuring static and dynamic optical properties of various biological tissues. The ability to measure small relative changes in optical properties of tissues make it an invaluable diagnostic tool.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".