Numerical Spectral Demulitplexing Microscopy of Measurements from an Anatomical Specimen
Bibliographic record
Abstract
<p>Multispectral microscopy is a method of capturing spectral bands<br />using a microscope, and is used to observe specimens on a micron<br />or nano scale. However, these systems are limited because they<br />cannot capture transient phenomena since they cannot capture simultaneous<br />spectral information. We propose a new method called<br />numerical spectral demultiplexing microscopy (NSDM) which utilizes<br />a Raspberry Pi camera to capture RGB measurements and<br />then infer narrow-band multispectral spectra. This is accomplished<br />by training a non-linear regression random forest model based on<br />the spectral sensitivity of the camera which allows for a low-cost,<br />portable, and simultaneous capture multispectral microscopy system.<br />We use the NSDM system as a bright-field multispectral microscope<br />and a dark-field fluorescence multispectral microscope<br />on an anatomical specimen and show that additional information<br />can be gathered by combining a bright-field and dark-field fluorescence<br />microscope.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".