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Record W4416759425 · doi:10.1038/s41467-025-65625-y

Thorium-234 as a tracer for deep-sea mining sediment plume deposition

2025· article· en· W4416759425 on OpenAlexaff
Bryan J. O’Malley, Patrick Schwing, Sophia Chernoch, Rebekka A. Larson, Michael F. Clarke, Leigh Marsh, Alastair Lough, Gregg R. Brooks

Bibliographic record

VenueNature Communications · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsImperial Metals (Canada)
FundersGoddard Space Flight CenterUniversity of South Florida
KeywordsPlumeSedimentationDeposition (geology)SedimentSedimentary rockBaseline (sea)TRACERLead (geology)

Abstract

fetched live from OpenAlex

Deep-sea mining for polymetallic nodules is currently exploratory, but commercial-scale operations require indicators of environmental change to support regulatory thresholds and inform adaptive management. In the Clarion-Clipperton Zone, where background sedimentation rates are low, seafloor imagery has validated mining plume deposition but cannot resolve repeated sedimentation as nodules become buried. Thorium-234 (234Th), a naturally occurring radionuclide with a 24.1-day half-life and strong particle reactivity, serves as a high-resolution geochemical tracer. Here we apply sedimentary 234Th to identify the spatial extent of plume deposition following the NORI-D mining test. Excess 234Th (234Thxs) activity was low at baseline but elevated after mining and declined to background within 1–2 km of the directly mined area. Results suggest that mining plumes scavenge and redistribute 234Thxs, establishing a geochemical benchmark for plume extent and an operational tool for tracing recent sedimentation under future commercial-scale mining scenarios. Thorium-234 provides a time-sensitive geochemical tracer to identify the extent of deep-sea mining sediment plume deposition, enabling detection of recent impacts and supporting long-term environmental management.

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 score0.441
Threshold uncertainty score0.408

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 head0.277
Teacher spread0.266 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

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