Broadband SLED-based light source (BeST-SLED<sup>TM</sup>) and spectrometer
Bibliographic record
Abstract
A small footprint, low power, cost effective single mode fiber coupled broadband light source and spectrometer is presented. It is based on Super Luminescent Diode (SLED) devices and a compact design enables coverage of the 1250 nm-1750 nm region with a total optical power of 50 mW at the output of the fiber. This Broad Spectrum Tunable Super Luminescent (BeST-SLEDTM) light source can operate at temperatures ranging from -40°C to 60°C, and resides in a custom designed 26-pin package. The fiber is a polarization maintaining fiber with a FC/APC connector at the output. Three variations of the BeST-SLEDTM were developed, BEST-SLED™ Bands, BeST-SLEDTM Tunable and BeST-SLEDTM FTNIR. In the Bands version six SLEDs were packaged allowing for one SLED on at a time or any combination of the SLEDs on. In the Tunable version an Acoustic Optical Tunable Filter (AOTF) was integrated into the package allowing the user to select one wavelength at a time to pass into the fiber with resolution of ~1 nm @1550nm. In the FTNIR version, a Silicon Photonic based interferometer (the Nano-SpecTM) was integrated into the package for a Fourier Transform Near Infrared based Spectrometer and light source. The BeST-SLEDTM is being used in process control applications such as steam quality measurements, oil in water, gas composition and air quality monitoring.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.002 |
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".