Post mortem evaluation of a new approach for quantitative bioluminescence imaging in small animals
Bibliographic record
Abstract
We report the performance of a simple method for making quantitative bioluminescence measurements of a point-like source embedded in small animals. In this method, video reflectometry is first used to obtain an estimate of the in situ optical properties of the tissue containing the bioluminescent source. A 2-dimensional image of the bioluminescence signal emitted from the surface of the animal is then acquired with a CCD. Using the measured optical properties, and a simple diffusion theory model, an inversion algorithm is applied to retrieve the source depth and power from a region of interest of the bioluminescence images. Two major factors determine the accuracy of the reconstruction: tissue heterogeneity and curvature of the imaged surface. The use of measured optical properties to characterize in situ tissue surmounts, to a degree, the heterogeneity problem: post mortem data from rats show that the relative power can be retrieved within a factor of 2 and frequently within 20 %, and the depth within 1.0 mm for implanted depths of 4-10 mm, when the curvature effects were eliminated. For depths shallower than 4 mm, the errors in the retrieved depth are consistently larger.
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.
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 | 0.002 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".