Radon and radium isotope signatures on a massive ice- and permafrost-rich coastline
Bibliographic record
Abstract
Rapid warming in the Arctic is degrading permafrost and massive ice deposits, which releases solutes previously trapped in ice and frozen soils into groundwater, rivers, and the coastal ocean. Radium and radon isotopes are enriched in groundwaters and may therefore have utility as tracers for the delivery of permafrost-derived solutes to the ocean, particularly if massive ice- or permafrost-derived meltwaters have a distinct isotopic ratio. The ratio of radium isotopes within massive ice deposits can also be used to constrain timescales of ice segregation, as the four isotopes will decay with known rates once removed from contact with sediments. Here we present the first measurements of radium and radon in massive ice deposits along the Beaufort Sea coast in the Northwest Territories, Canada. Measurable levels of short-lived radium and radon isotopes indicate relatively recent ice segregation and/or inputs from sediment grains frozen within the ice lenses; the radium-224/radium-228 ratio is used to identify ice samples that must have experienced segregation within the last five years, and desorption experiments provide evidence that sediment within the ice can support the radium activities observed in older ice. Short- to long-lived isotope ratios were low in ice and increased in meltwater and groundwater, while coastal ocean ratios were similar to those of the nearby Mackenzie River. Thus, we find that the radium isotope ratio of massive ice is not preserved during transport, and the river is the main control on the coastal radionuclide distribution in this region. A distinctly low (<1) radium-224/radium-228 ratio was measured in massive ice deposits, similar to previous observations in permafrost, suggesting that this low ratio may be characteristic of cryogenic environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".