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Record W1622911508 · doi:10.1029/2007gb003136

The accumulation of silver in marine sediments: A link to biogenic Ba and marine productivity

2008· article· en· W1622911508 on OpenAlexafffundabout
J. L. McKay, T. F. Pedersen

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversity of Victoria
FundersCanadian Foundation for Climate and Atmospheric Sciences
KeywordsSedimentSulfateEnvironmental chemistrySedimentary rockScavengingContinental shelfRedoxTrace metalGeologyOceanographyMineralogyChemistryMetalGeochemistryPaleontologyInorganic chemistry

Abstract

fetched live from OpenAlex

The concentrations of Ag and a suite of redox‐sensitive trace metals (Re, Cd, and Mo) were measured in surface sediments from the Western Canadian, Mexican, Peruvian, and Chilean continental margins. In all regions, Ag content increases from ∼80 ng g −1 (i.e., lithogenic values) on the shelf up to as high as 1483 ng g −1 on the lower slope. However, the trend of increasing Ag with increasing water depth breaks down at deepwater sites (>2500 m) where only lithogenic concentrations are documented. Silver content does not correlate with the distributions of redox‐sensitive trace metals, suggesting that sedimentary redox conditions are not the primary control on Ag accumulation. Instead, a positive correlation between Ag and Ba in surface and near‐surface sediments suggests that Ag is scavenged by and delivered to the sediment with the organic particle flux. Scavenging probably results from the precipitation of Ag 2 S within the organic particles due to the development of anoxia and sulfate reduction. If this hypothesis is correct, then Ag has the potential to be a paleoproductivity proxy.

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 score0.029
Threshold uncertainty score0.463

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 science0.0000.001
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 head0.022
GPT teacher head0.270
Teacher spread0.248 · 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

Citations49
Published2008
Admission routes3
Has abstractyes

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