Background dose-rates to reference animals and plants arising from exposure to naturally occurring radionuclides in aquatic environments
Bibliographic record
Abstract
In order to put dose-rates derived in environmental impact assessments into context, the International Commission on Radiological Protection (ICRP) has recommended the structuring of effects data according to background exposure levels. The ICRP has also recommended a suite of reference animals and plants (RAPs), including seven aquatic organisms, for use within their developing framework. In light of these propositions, the objective of this work was to collate information on activity concentrations of naturally occurring primordial radionuclides for marine and freshwater ecosystems and apply appropriate dosimetry models to derive absorbed dose-rates. Although coverage of activity concentration data is comprehensive for sediment and water, few, or in some cases no, data were found for some RAPs, e.g. for frogs (Ranidae) and freshwater grasses (Poaceae) for most radionuclides. The activity concentrations for individual radionuclides in both organisms and their habitat often exhibit standard deviations that are substantially greater than arithmetic mean values, reflecting large variability in activity concentrations. To take account of variability a probabilistic approach was adopted. The dominating radionuclides contributing to exposure in the RAPs are (40)K, (210)Po and (226)Ra. The mean unweighted and weighted dose-rates for aquatic RAPs are in the ranges 0.07-0.39 microGy h(-1) and 0.37-1.9 microGy h(-1) respectively.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".