Quantification of bioluminescence images of point source objects using diffusion theory models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it