Global variability and controls on the accumulation of fallout radionuclides in cryoconite
Bibliographic record
Abstract
The accumulation of fallout radionuclides (FRNs) from nuclear weapons testing and nuclear accidents has been evaluated for over half a century in natural environments; however, until recently their distribution and abundance within glaciers have been poorly understood. Following a series of individual studies of FRNs, specifically 137Cs, 241Am and 210Pb, deposited on the surface of glaciers, we now understand that cryoconite, a material commonly found in the supraglacial environment, is a highly efficient accumulator of FRNs, both artificial and natural. However, the variability of FRN activity concentrations in cryoconite across the global cryosphere has never been assessed. This study thus aims to both synthesize current knowledge on FRNs in cryoconite and assess the controls on variability of activity concentrations. We present a global database of new and previously published data based on gamma spectrometry of cryoconite and proglacial sediments, and assess the extent to which a suite of environmental and physical factors can explain spatial variability in FRN activity concentrations in cryoconite. We show that FRNs are not only found in cryoconite on glaciers within close proximity to specific sources of radioactivity, but across the global cryosphere, and at activity concentrations up to three orders of magnitude higher than those found in soils and sediments in the surrounding environment. We also show that the organic content of cryoconite exerts a strong control on accumulation of FRNs, and that activity concentrations in cryoconite are some of the highest ever described in environmental matrices outside of nuclear exclusion zones, occasionally in excess of 10,000 Bq kg−1. These findings highlight a need for significant improvements in the understanding of the fate of legacy contaminants within glaciated catchments. Future interdisciplinary research is required on the mechanisms governing their accumulation, storage, and mobility, and their potential to create time-dependent impacts on downstream water quality and ecosystem sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".