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Record W3026504314 · doi:10.1017/exp.2020.15

Oxidized silver cups can skew oxygen isotope results of small samples

2020· article· en· W3026504314 on OpenAlexaff
Man‐Yin Tsang, Weiqi Yao, Kevin C. Tse

Bibliographic record

VenueExperimental Results · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChemistryOxygenCarbon fibersPyrolysisAnalytical Chemistry (journal)IsotopeMineralogyMaterials scienceEnvironmental chemistry

Abstract

fetched live from OpenAlex

Abstract One of the commonly used analytical approaches for measuring oxygen isotope ratios δ 18 O of solids (organic and inorganic) is to pyrolyze the samples to gaseous phases and then send the gas into an isotope ratio mass spectrometer system. Solid samples for δ 18 O measurements are usually stored in silver cups because of its low reactivity towards oxygen and other oxidants. Samples in silver cups can be dropped directly into the carbon column of the pyrolysis furnace. However, the silver cups can tarnish and then be oxidized over a prolonged storage period. We find that while a small amount of silver oxides does not affect measurements with appreciable sample sizes, it can skew isotope results of small samples. We thus recommend careful storage of samples in silver cups to minimize oxidation, such as under an air-isolated condition, and avoiding prolonged storage for accurate δ 18 O measurements.

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)0.004
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.034
GPT teacher head0.260
Teacher spread0.226 · 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
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

Citations4
Published2020
Admission routes1
Has abstractyes

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