Concurrent fluorescence macro-imaging across multiple spectral regions in the visible and the near infrared
Bibliographic record
Abstract
Fluorescent imaging, often synonymous with microscopic imaging, is an imaging modality whereby various features of a target are observed based on assignment of chemical labels. These labels are in most cases indirect tracers of specific structures or chemical compounds which cannot be otherwise identified. The tracers are excited by an illuminating source and they in turn emit light at specific wavelengths. This light is then captured by an imaging device and represented as an indirect observation of the specific feature in the sample. The process of excitation and imaging of the emitted light is performed sequentially and is proportional to the number of tracers or fluorescence species present in the sample. We present an imaging system that can image fluorescent tracers, in the visible and the near Infra-red, simultaneously. This system is capable of illuminating the target with different excitation light sources and capture the corresponding fluorescence images in one snapshot using a series of mirrors to capture different views of the sample. The simultaneously captured image are fused using a computational reconstruction process to present a coherent multispectral fluorescence image. The system is proposed for use in applications where the rapid enumeration of fluorescent species in a large field of view is paramount as opposed to their microscopic image in a narrow field of view. The system was tested using a controlled cocktail solution of four different types fluorescent microspheres and was able to enumerate the microspheres based on their different fluorescent signatures as captured by the system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".