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Record W4241273082 · doi:10.1515/9783110545630-017

16 Good Golly, Why Moly? THE STABLE ISOTOPE GEOCHEMISTRY OF MOLYBDENUM

2017· book-chapter· en· W4241273082 on OpenAlexafffund
B. E. Kendall, Tais W. Dahl, Ariel D. Anbar

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicMineralogy and Gemology Studies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaVillum FondenNational Science Foundation
KeywordsMolybdenumGeochemistryIsotopeGeologyIsotope geochemistryChemistryInorganic chemistryPhysics

Abstract

fetched live from OpenAlex

The subsequent ~15 years of research yielded an emphatic answer of "yes", centered in particular on paleoceanographic applications, but also extending to the solid Earth geosciences and other areas.This review provides an overview of this maturing isotope system, with an emphasis on paleoredox applications that dominate the literature.It is intended as an update of the reviews written when the Mo isotope system was still emerging (Anbar, 2004;Anbar and Rouxel, 2007).Section 2 covers analytical methodology.Sections 3 and 4 provide the necessary context for Mo isotope studies by reviewing Mo biogeochemistry and Mo isotope fractionation factors.Section 5 explores Mo isotope variations in meteorites and Earth reservoirs, with an emphasis on the large database for marine sediments.In the context of modern observations of the ocean Mo cycle, the use of Mo isotopes as a local and global ocean paleoredox proxy is synthesized in section 6.In section 7, we explore the rapidly growing application of Mo isotopes to ore deposits, oil, and anthropogenic tracing, areas that are expected to see strong growth in the near future. ANALYTICAL CONSIDERATIONS Data ReportingMolybdenum stable isotope fractionation is conventionally reported in δ 98 Mo notation as parts per thousand deviation of the 98 Mo/ 95 Mo ratio relative to a universal reference material.Older data were reported relative to in-house reference materials thought to be identical in composition.However, the analytical precision has improved since then and a common reference material is necessary because various in-house reference materials now differ by up to 0.37‰ (Goldberg et al., 2013).The Mo standard solution, NIST-SRM-3134, has been defined as an international reference material, and is assigned a distinct δ 98 Mo value of 0.25‰ to account for its offset from the most common in-house standards used previously (Nägler et al., 2014).On this scale, the Mo isotope composition of samples can be calculated as follows:δ 98 Mo = [( 98 Mo/ 95 Mo)sample /( 98 Mo/ 95 Mo)NIST-SRM-3134) -1] × 1000 + 0.25 [‰] If the δ 98 Mo of the in-house reference material relative to the NIST-SRM-3134 standard is known, then it is possible to re-normalize the Mo isotope composition of a sample from the inhouse reference scale to the NIST-SRM-3134 scale.If the isotopic offset between the in-house and NIST-SRM-3134 standards is not known, it is still possible to convert between the two scales by measuring a well-known secondary standard such as seawater (e.g., IAPSO) or the USGS rock reference material SDO-1, which has δ 98 Mo = 1.05 ± 0.14‰ (2σ = 2 standard deviations) on the NIST-SRM-3134 scale (Goldberg et al. 2013;Nägler et al., 2014).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.058
Threshold uncertainty score0.969

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0320.000

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 head0.019
GPT teacher head0.207
Teacher spread0.188 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations12
Published2017
Admission routes2
Has abstractyes

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