Angular domain fluorescence imaging for small animal research
Bibliographic record
Abstract
We describe a novel macroscopic fluorescent imaging technique called angular domain fluorescence imaging (ADFI) applicable to the detection of fluorophores embedded in biological tissues. The method exploits the collimation detection capabilities of an angular filter array (AFA). The AFA uses the principle of acceptance angle filtration to extract minimally scattered photons emitted from fluorophores deep within tissue. Our goal was to develop an ADFI system for imaging near-infrared fluorescent markers for small animal imaging. According to the experimental results, the ADFI system offered higher resolution and contrast compared to a conventional lens and lens-pinhole fluorescent detection system. Furthermore, ADFI of a hairless mouse injected with a fluorescent bone marker revealed vertebral structural and morphometric data that correlated well with data derived from volumetric x-ray computed tomography images. The results suggested that ADFI is a useful technique for submillimeter mapping of the distribution of fluorescent biomarkers in small animals.
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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 | 0.003 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".