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Record W2132337918 · doi:10.1002/sia.3467

A new approach to measuring D/H ratios with the Cameca IMS‐7F

2010· article· en· W2132337918 on OpenAlexafffund
Rong Liu, Sharon Hull, Mostafa Fayek

Bibliographic record

VenueSurface and Interface Analysis · 2010
Typearticle
Languageen
FieldEngineering
TopicIon-surface interactions and analysis
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryAnalytical Chemistry (journal)MicroprobeHydrogenIonDeuteriumIsotopeHydrideSecondary ion mass spectrometryIon beamProtonAcceleration voltageMineralogyAtomic physicsElectronCathode rayNuclear physicsEnvironmental chemistry

Abstract

fetched live from OpenAlex

Abstract SIMS offers spatial resolution on the scale of micrometers and consumes an exceedingly small amount of sample. In addition, easy sample preparation, rapid analysis and high sensitivity allow SIMS to be applied to hydrous minerals. Hydrogen isotopes normally can be measured as negative or positive secondary ions using either a Cs + or an O − primary ion beam, respectively. The hydrogen negative secondary ion yields are only marginally higher than hydrogen positive secondary ion yields. There are advantages and disadvantages to using either method to analyze hydrogen isotopes by SIMS. For insulating samples, O − is commonly used to avoid using the electron gun. The main disadvantage in using an O − beam is hydride interference (H 2 + ) associated with deuterium (D) that must be resolved using a mass resolving power of ∼1000–2000. We have developed a method where we combine a lower mass resolution and a small voltage offset to obtain rapid (12 min), high‐precision (∼2‰, 2σ) D/H ratios in turquoise using a Cameca IMS‐7F ion microprobe. This technique can be applied to a variety of hydrous minerals in geological materials. Copyright © 2010 John Wiley & Sons, Ltd.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.217
Teacher spread0.208 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations9
Published2010
Admission routes2
Has abstractyes

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