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Record W4410392698 · doi:10.1130/g53021.1

Glaciation-induced radionuclide release: Implications for geological disposal of nuclear waste

2025· article· en· W4410392698 on OpenAlexaff
Ian Burron, Mostafa Fayek

Bibliographic record

VenueGeology · 2025
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsCanadian Nuclear Safety CommissionUniversity of Manitoba
Fundersnot available
KeywordsRadionuclideRadioactive wasteGeologyGlacial periodGeochemistryMining engineeringEarth scienceWaste managementGeomorphologyEngineeringNuclear physics

Abstract

fetched live from OpenAlex

Abstract Deep geological disposal of used nuclear fuel requires knowledge of how radionuclides behave over ~100 k.y. to 1 m.y. timeframes. New data from the Kiggavik uranium deposits, a natural analogue for deep geological disposal of nuclear waste, shows that oxidized subglacial waters may be injected deeply into the subsurface along fracture systems, resulting in limited radionuclide mobilization. Periods of radionuclide mobility correspond to episodes of climate change during glacial periods and occurred over ca. 36–471 ka time scales comparable to the evolution of Homo sapiens. While oxidized subglacial waters may pose a hazard to geologic disposal, resulting uranium transport is millions of times slower than glacial movements and short-range (<~5 cm). Reduction by preexisting uranium minerals and titanium oxides and adsorption onto clay minerals have effectively trapped radionuclides.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.246
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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