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Record W2963434090 · doi:10.1088/1681-7575/ab2995

Determination of the isotopic composition of hafnium using MC-ICPMS

2019· article· en· W2963434090 on OpenAlex
Shuoyun Tong, Juris Meija, Lian Zhou, Zoltán Mester, Lu Yang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMetrologia · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsNational Research Council Canada
FundersNational Institute of Standards and TechnologyChina Scholarship CouncilNational Institutes of Natural SciencesNational Natural Science Foundation of China
KeywordsHafniumIsotopeFractionationRheniumIsotope fractionationInductively coupled plasma mass spectrometryMass spectrometryAnalytical Chemistry (journal)Isotope analysisAtomic massChemistryRadiochemistryGeologyEnvironmental chemistryZirconiumNuclear physicsInorganic chemistryChromatographyPhysics

Abstract

fetched live from OpenAlex

Abstract Despite the numerous important applications of hafnium isotopes in geological science, and the advances in multi-collector inductively coupled plasma mass spectrometry (MC-ICPMS), hafnium still lacks calibrated measurements of its isotope ratios and, in turn, isotopic abundances and atomic weight. In this study, we determined the isotopic composition of hafnium in four commercial hafnium reagents, including a commonly used hafnium standard (JMC-475) and a NRC candidate isotopic reference material (HALF-1) by MC-ICPMS. The state-of-the-art regression model with NIST SRM 989 isotopic rhenium as calibrator was used to correct instrumental isotopic fractionation without reliance on other hafnium standards, normalizing isotope ratios, or exponential mass-bias fractionation model.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.999

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)0.0020.000

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 head0.013
GPT teacher head0.202
Teacher spread0.189 · 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