Quantification of bioluminescence images of point source objects using diffusion theory models
Bibliographic record
Abstract
A simple approach for estimating the location and power of a bioluminescent point source inside tissue is reported. The strategy consists of using a diffuse reflectance image at the emission wavelength to determine the optical properties of the tissue. Following this, bioluminescence images are modelled using a single point source and the optical properties from the reflectance image, and the depth and power are iteratively adjusted to find the best agreement with the experimental image. The forward models for light propagation are based on the diffusion approximation, with appropriate boundary conditions. The method was tested using Monte Carlo simulations, Intralipid tissue-simulating phantoms and ex vivo chicken muscle. Monte Carlo data showed that depth could be recovered within 6% for depth 4-12 mm, and the corresponding relative source power within 12%. In Intralipid, the depth could be estimated within 8% for depth 4-12 mm, and the relative source power, within 20%. For ex vivo tissue samples, source depths of 4.5 and 10 mm and their relative powers were correctly identified.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".