From astronomy and telecommunications to biomedicine
Bibliographic record
Abstract
Photonics is an inherently interdisciplinary endeavor, as technologies and techniques invented or developed in one scientific field are often found to be applicable to other fields or disciplines. We present two case studies in which optical spectroscopy technologies originating from stellar astrophysics and optical telecommunications multiplexing have been successfully adapted for biomedical applications. The first case involves a design concept called the High Throughput Virtual Slit, or HTVS, which provides high spectral resolution without the throughput inefficiency typically associated with a narrow spectrometer slit. HTVS-enhanced spectrometers have been found to significantly improve the sensitivity and speed of fiber-fed Raman analysis systems, and the method is now being adapted for hyperspectral imaging for medical and biological sensing. The second example of technology transfer into biomedicine centers on integrated optics, in which optical waveguides are fabricated on to silicon substrates in a substantially similar fashion as integrated circuits in computer chips. We describe an architecture referred to as OCTANE which implements a small and robust "spectrometer-on-a-chip” which is optimized for optical coherence tomography (OCT). OCTANE-based OCT systems deliver three-dimensional imaging resolution at the micron scale with greater stability and lower cost than equivalent conventional OCT approaches. Both HTVS and OCTANE enable higher precision and improved reliability under environmental conditions that are typically found in a clinical or laboratory setting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".