The IAEA handbook on radionuclide transfer to wildlife
Bibliographic record
Abstract
An IAEA handbook presenting transfer parameter values for wildlife has recently been produced. Concentration ratios (CRwo-media) between the whole organism (fresh weight) and either soil (dry weight) or water were collated for a range of wildlife groups (classified taxonomically and by feeding strategy) in terrestrial, freshwater, marine and brackish generic ecosystems. The data have been compiled in an on line database, which will continue to be updated in the future providing the basis for subsequent revision of the Wildlife TRS values. An overview of the compilation and analysis, and discussion of the extent and limitations of the data is presented. Example comparisons of the CRwo-media values are given for polonium across all wildlife groups and ecosystems and for molluscs for all radionuclides. The CRwo-media values have also been compared with those currently used in the ERICA Tool which represented the most complete published database for wildlife transfer values prior to this work. The use of CRwo-media values is a pragmatic approach to predicting radionuclide activity concentrations in wildlife and is similar to that used for screening assessments for the human food chain. The CRwo-media values are most suitable for a screening application where there are several conservative assumptions built into the models which will, to varying extents, compensate for the variable data quality and quantity, and associated uncertainty.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".