A New Cytogenetic Biodosimetry Image Repository for the Dicentric Assay
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
The BioDoseNet was founded by the World Health Organization as a global network of biodosimetry laboratories for building biodosimetry laboratory capacities in countries. The newly established BioDoseNet image repository is a databank of ~25 000 electronically captured images of metaphases from the dicentric assay, which have been previously analysed by international experts. The detailed scoring results and dose estimations have, in most cases, already been published. The compilation of these images into one image repository provides a valuable tool for training and research purposes in biological dosimetry. No special software is needed to view and score the image galleries. For those new to the dicentric assay, the BioDoseNet Image Repository provides an introduction to and training for the dicentric assay. It is an excellent instrument for intra-laboratory training purposes or inter-comparisons between laboratories, as recommended by the International Organization for Standardisation standards. In the event of a radiation accident, the repository can also increase the surge capacity and reduce the turnaround time for dose estimations. Finally, it provides a mechanism for the discussion of scoring discrepancies in difficult cases.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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