Numerical Spectral Demulitplexing Microscopy of Measurements from an Anatomical Specimen
Bibliographic record
Abstract
Multispectral microscopy is a method of capturing spectral bands using a microscope, and is used to observe specimens on a micron or nano scale. However, these systems are limited because they cannot capture transient phenomena since they cannot capture simultaneous spectral information. We propose a new method called numerical spectral demultiplexing microscopy (NSDM) which utilizes a Raspberry Pi camera to capture RGB measurements and then infer narrow-band multispectral spectra. This is accomplished by training a non-linear regression random forest model based on the spectral sensitivity of the camera which allows for a low-cost, portable, and simultaneous capture multispectral microscopy system. We use the NSDM system as a bright-field multispectral microscope and a dark-field fluorescence multispectral microscope on an anatomical specimen and show that additional information can be gathered by combining a bright-field and dark-field fluorescence microscope.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".