An experimental model for the photon depth statistics of re-emitted near-infrared light (Conference Presentation)
Bibliographic record
Abstract
There is growing research on renal applications of near-infrared spectroscopy (NIRS), a non-invasive optical technique that measures tissue oxygen saturation. NIRS data can be more illuminating if clinicians know which anatomical structures it probes, but there is a lack of experimental data on the maximum and average photon depth of penetration in kidney tissue. Literature suggests that these parameters depend on the interoptode distance (IOD) between the light source and detector. To test these models, we made incremental cuts into a porcine renal sample secured on a NIRS sensor. We placed an opaque blade in the cut and graphed the attenuation of the optical signal. The maximum and average penetration depth was computed for 6 IODs between 16-40 mm. The maximum and the square root of the average depth of penetration had a linear relationship with the IOD, with an approximate slope of 0.25 and 0.02, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".