Design of a single-fiber, wavelength-resolved system for monitoring deep tissue oxygenation
Bibliographic record
Abstract
We propose a single-fiber, zero source-detector separation system with wavelength-resolved detection for measuring oxygen saturation in deep brain structures. The system consists of a white light emitting diode (LED) source, optics to couple light into a 240-μm-diameter fiber, a beam splitter to separate the collected from the delivered photons and a spectrometer for detection. Depth resolution is achieved by inserting the fiber, comparable in size to microelectrodes used for electrophysiology, into the tissue of interest. Since most of the diffuse reflected light travels through a small volume at the tip of the fiber, this arrangement allows efficient collection of signal. Fresnel reflections are minimized using polarizers. Monte Carlo simulations across 400-1000 nm indicate that ~0.5% of the incident light can be collected and effectively interrogate a ~0.02 mm(3) volume at the fiber tip. System design, characterization data and phantom experiments using an absorptive dye in scattering media are presented. The simple nature of the instrumentation can potentially lead to a miniaturized system capable of detecting oxygen saturation in deep brain structures in freely-moving animals.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".