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Record W4383988912 · doi:10.1038/s41467-023-39922-3

Overlooked riverine contributions of dissolved neodymium and hafnium to the Amazon estuary and oceans

2023· article· en· W4383988912 on OpenAlexafffund
Antao Xu, Ed C. Hathorne, Georgi Laukert, Martin Frank

Bibliographic record

VenueNature Communications · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsDalhousie University
FundersCanada First Research Excellence FundOcean Frontier InstituteChina Scholarship Council
KeywordsEstuaryPlumeEnvironmental scienceAmazon rainforestOceanographyFlux (metallurgy)Environmental chemistryGenetic algorithmSalinityGeologyChemistryEcologyGeography

Abstract

fetched live from OpenAlex

The Amazon River accounts for 20% of global freshwater runoff and supplies vital trace metals to the Atlantic Ocean. Suspended particles within its plume are thought to partially dissolve, constituting a large potential source of metals, which is, however, not well constrained. Here we used combined neodymium (Nd) and hafnium (Hf) isotopes to disprove the release of Nd and Hf from particles as the cause of the observed dissolved concentration increases and isotopic variability across the plume. Instead, the changes reflect admixture of nearby Pará River freshwater with exceptionally high dissolved Nd and Hf concentrations contributing 45-100% of the riverine fraction to the southern and outer estuary. This result led us to develop an empirical relationship between riverine Nd concentration and pH to revise the global dissolved riverine Nd flux, which accordingly is at least three times higher than commonly used estimates. Future work should focus on contributions of low-pH rivers to global metal fluxes.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.342

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 head0.009
GPT teacher head0.249
Teacher spread0.240 · 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

Citations21
Published2023
Admission routes2
Has abstractyes

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