Bibliographic record
Abstract
Spatial frequency domain imaging (SFDI) is an emerging technique to measure tissue oxygenation, by sampling both the absorption and reduced scattering coefficients (μa and μs’). In this work, lasers are implemented into the SFDI system for the integration of laser speckle contrast imaging (LSCI), which provides simultaneous blood flow and oxygenation measurements. A LED-based SFDI system is first built and optimized to achieve a 2% spatial variation noise and a 14.6% and 6.6% average error for μa and μs’. Monte Carlo simulations are performed in search of a more accurate model than the diffuse approximation. A current sweep speckle reduction method with the vertical-cavity surface-emitting-lasers (VCSELs) is investigated to achieve a similar error (14.2% and 4.4% in μa and μs’ respectively) when compared to the LEDs. Lastly, qualitative results of a LSCI experiment agree with the prediction that speckle contrast reduces with a faster flow, while future studies are required.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".