Spectral imaging of microscopic samples with high-performance CCD array-based spectrometer
Bibliographic record
Abstract
The need for parallel spectral analysis of small details in microscopic samples is well recognized in many research fields. Many instruments were proposed for this purpose, some of them using direct projection of an image produced by a standard microscope onto entrance slit of a spectrometer. Typical scanning wavelength spectrometers using focusing reflective gratings have limited imaging performance. These spectrometers also suffer from low light coupling efficiency, poor spatial and spectral resolution, high acquisition times and low image quality. These significant concerns are now addressed by a coupling of a high performance imaging spectrometer to one of the readout ports of a microscope. This spectrometer uses refractive optics, transmission based volume phase holographic (VPH) diffraction gratings and is equipped with two-dimensional array of photodetectors. Such a system provides a significant advantage over most currently used microscope coupled spectrometers, resulting in a larger volume of extracted information, better spectral and spatial resolution, higher SNR and generally better image quality. This is illustrated with examples of spectral images of various biological samples.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".