Addressing ecological effects of radiation on populations and ecosystems to improve protection of the environment against radiation: Agreed statements from a Consensus Symposium
Bibliographic record
Abstract
This paper reports the output of a consensus symposium organized by the International Union of Radioecology in November 2015. The symposium gathered an academically diverse group of 30 scientists to consider the still debated ecological impact of radiation on populations and ecosystems. Stimulated by the Chernobyl and Fukushima disasters' accidental contamination of the environment, there is increasing interest in developing environmental radiation protection frameworks. Scientific research conducted in a variety of laboratory and field settings has improved our knowledge of the effects of ionizing radiation on the environment. However, the results from such studies sometimes appear contradictory and there is disagreement about the implications for risk assessment. The Symposium discussions therefore focused on issues that might lead to different interpretations of the results, such as laboratory versus field approaches, organism versus population and ecosystemic inference strategies, dose estimation approaches and their significance under chronic exposure conditions. The participating scientists, from across the spectrum of disciplines and research areas, extending also beyond the traditional radioecology community, successfully developed a constructive spirit directed at understanding discrepancies. From the discussions, the group has derived seven consensus statements related to environmental protection against radiation, which are supplemented with some recommendations. Each of these statements is contextualized and discussed in view of contributing to the orientation and integration of future research, the results of which should yield better consensus on the ecological impact of radiation and consolidate suitable approaches for efficient radiological protection of the environment.
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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 | 0.204 | 0.151 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.007 | 0.026 |
| Research integrity | 0.022 | 0.039 |
| Insufficient payload (model declined to judge) | 0.002 | 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".