WHO 1st Consultation on the Development of a Global Biodosimetry Laboratories Network for Radiation Emergencies (BioDoseNet)
Bibliographic record
Abstract
Blakely, W. F., Carr, Z., Chu, M. C-M., Dayal-Drager, R., Fujimoto, K., Hopmeir, M., Kulka, U., Lillis-Hearne, P., Livingston, G. K., Lloyd, D. C., Maznyk, N., Perez, M. D. R., Romm, H., Takashima, Y., Voisin, P., Wilkins, R. C. and Yoshida, M. A. WHO 1st Consultation on the Development of a Global Biodosimetry Laboratories Network for Radiation Emergencies (BioDoseNet). Radiat. Res. 171, 127–139 (2009).The World Health Organization (WHO) held a consultation meeting at WHO Headquarters, Geneva, Switzerland, December 17–18, 2007, to develop the framework for a global biodosimetry network. The WHO network is envisioned to enable dose assessment using multiple methods [cytogenetics, electron paramagnetic resonance (EPR), radionuclide bioassays, etc.]; however, the initial discussion focused on the cytogenetic bioassay (i.e., metaphase-spread dicentric assay). Few regional cytogenetic biodosimetry networks have been established so far. The roles and resources available from United Nations (UN) agencies that provide international cooperation in biological dosimetry after radiological emergencies were reviewed. In addition, extensive reliance on the use of the relevant International Standards Organization (ISO) standards was emphasized. The results of a WHO survey of global cytogenetic biological dosimetry capability were reported, and while the survey indicates robust global capability, there was also a clear lack of global leadership and coordination. The expert group, which had a concentrated focus on cytogenetic biodosimetry, formulated the general scope and concept of operations for the development of a WHO global biodosimetry laboratory network for radiation emergencies (BioDoseNet). Follow-on meetings are planned to further develop technical details for this network.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.022 | 0.010 |
| Insufficient payload (model declined to judge) | 0.028 | 0.009 |
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".