Development of ecocentric radiation protection: issues, challenges and approaches.
Bibliographic record
Abstract
OBJECTIVE: This position paper results from an International Union of Radioecology symposium aimed at identifying challenges to develop eco-centric and holistic approaches to understanding ionizing radiation impacts on ecosystems. An ecosystem approach is particularly relevant today not only because of the triple planetary crisis of climate change, biodiversity loss, and pollution, which make single-stressor approaches unrealistic, but because of renewed interest in nuclear power as a potential solution to transition away from fossil fuels. For example, there are proposals to site small modular reactors in remote and pristine areas. The focus of the symposium was to expand the boundaries of existing approaches in radioecology and look at issues like ecosystem complexity and multiple stressors, which complicate single-stressor approaches. CONCLUSIONS: Discussion centered around existing tools for radiation protection e.g. Adverse Outcome Pathway (AOP) analysis, biomarkers, use of microcosms and mesocosms and modeling approaches. These approaches were discussed with emphasis on identifying gaps, boundaries, and where leaps into the unknown might be beneficial. Identified challenges with biomarker and AOP approaches were that the individual level is generally addressed while interrelatedness of ecosystem components is difficult to capture. Novel ideas suggested were to construct multiple-stressor AOPs which capture key interactions and consider time as a critical component, or to exploit 'ecological network analysis' metrics which have been extensively used in ecological science. Other discussions centered on complexity and chaos modeling. The use of microcosms, focused field studies, and harnessing ecosystem information and communication systems were suggested to bridge the gap between individual and population-level responses.
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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 | 0.023 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".