The tubercular badger and the uncertain curve:- The need for a multiple stressor approach in environmental radiation protection
Bibliographic record
Abstract
This article presents the results of a workshop held in Stirling, Scotland in June 2018, called to examine critically the effects of low-dose ionising radiation on the ecosphere. The meeting brought together participants from the fields of low- and high-dose radiobiology and those working in radioecology to discuss the effects that low doses of radiation have on non-human biota. In particular, the shape of the low-dose response relationship and the extent to which the effects of low-dose and chronic exposure may be predicted from high dose rate exposures were discussed. It was concluded that high dose effects were not predictive of low dose effects. It followed that the tools presently available were deemed insufficient to reliably predict risk of low dose exposures in ecosystems. The workshop participants agreed on three major recommendations for a path forward. First, as treating radiation as a single or unique stressor was considered insufficient, the development of a multidisciplinary approach is suggested to address key concerns about multiple stressors in the ecosphere. Second, agreed definitions are needed to deal with the multiplicity of factors determining outcome to low dose exposures as a term can have different meanings in different disciplines. Third, appropriate tools need to be developed to deal with the different time, space and organisation level scales. These recommendations permit a more accurate picture of prospective risks.
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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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 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".