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Record W1526859590 · doi:10.1029/2003gb002140

Biological fractionation of silicon isotopes in Southern Ocean surface waters

2004· article· en· W1526859590 on OpenAlexaff
Diana E. Varela, Carol Pride, Mark A. Brzezinski

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSilicic acidEquilibrium fractionationSiliconFractionationDiatomIsotopes of siliconGeologyMass-independent fractionationSedimentIsotopeStable isotope ratioIsotope fractionationSurface waterBiogenic silicaEnvironmental chemistryOceanographyEnvironmental scienceChemistryPaleontology

Abstract

fetched live from OpenAlex

The fractionation of silicon isotopes by diatoms during silicification (i.e., opaline cell wall formation) provides a new tool for paleoceanographic studies of the silicon cycle. Here we examine the natural variations of the 30 Si: 28 Si ratio in silicic acid (Si(OH) 4 ) and biogenic silica (bSiO 2 ) in surface waters of the Antarctic Circumpolar Current (ACC) along 170°W. The results provide direct evidence of biologically mediated fractionation of silicon isotopes, with an enrichment factor (ɛ) of between −1.1 and −1.9‰, depending on the model employed. Comparison of the mass flux of bSiO 2 captured in sediment traps deployed in the study area with the silicon isotopic composition of that material establishes a direct linkage between diatom dynamics in surface waters and the isotopic signature of exported particles. We calculated Si(OH) 4 :NO 3 − utilization ratios from variations in silicon and nitrogen isotopic ratios in surface waters and sediment traps that agree well with direct observations of 4:1 in this region. This work supports the use of variations in silicon isotopes in sedimentary opal as a proxy for relative Si(OH) 4 utilization in surface waters.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.018
GPT teacher head0.248
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations191
Published2004
Admission routes1
Has abstractyes

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