IMBA expert(r): Internal dosimetry made simple
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
In 1997, a collaboration between British Nuclear Fuels plc (BNFL), Westlakes Research Institute and NRPB started, with the aim of producing IMBA (Integrated Modules for Bioassay Analysis), a suite of software modules that implement the new ICRP models for estimation of intakes and doses. This was partly in response to new UK regulations, and partly due to the requirement for a unified approach in estimating intakes and doses from bioassay measurements within the UK. Over the past 5 years, the IMBA modules have been developed further, have gone through extensive quality assurance, and are now used for routine dose assessment by approved dosimetry services throughout the UK. More recently, interest in the IMBA methodology has been shown by the United States Department of Energy (USDOE), and in 2001 an ambitious project to develop a software package (IMBA Expert USDOE Edition) which would meet the requirements of all of the major USDOE sites began. Interest in IMBA Expert is now being expressed in many other countries. The aim of this paper is to outline the origin and evolution of the IMBA modules (the past); to describe the full capabilities of the current IMBA Expert system (the present) and to indicate possible future directions in terms of capabilities and availability (the future).
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 | 0.001 | 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) | 0.004 | 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