IMBA Professional Plus: a flexible approach to internal dosimetry
Bibliographic record
Abstract
IMBA (Integrated Modules for Bioassay Analysis) is a suite of software modules that implement the current ICRP biokinetic and dosimetric models for estimation of intakes and doses. The IMBA modules have gone through extensive quality assurance, and are now used for routine formal dose assessment by Approved Dosimetry Services throughout the UK. HPA has continued to develop the IMBA modules. In addition, several projects, sponsored by organisations both in the USA and in Canada, have resulted in the development of customised user-friendly interfaces (IMBA Expert 'editions'). These enable users not only to use the standard ICRP models, but also to change many of the parameter values from ICRP defaults, and to apply sophisticated data handling techniques to internal dose calculations. These include: fitting measurement data with the maximum likelihood method; using multiple chronic and acute intakes; and dealing with different data types, such as urine, faces and whole body simultaneously. These interfaces were improved further as a result of user-feedback, and a general 'off-the-shelf' product, IMBA Professional, was developed and made available in January 2004. A new version, IMBA Professional Plus, was released in April 2005, which is both faster and more powerful than previous software. The aim of this paper is to describe the capabilities of IMBA Professional Plus, and the mathematical methods used.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.021 |
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".