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Record W3134777659 · doi:10.1111/ggr.12378

Silicon Isotope Analyses of Soil and Plant Reference Materials: An Inter‐Comparison of Seven Laboratories

2021· article· en· W3134777659 on OpenAlexaff
Camille Delvigne, Abel Guihou, Jan A. Schuessler, Paul S. Savage, Franck Poitrasson, Sebastian Fischer, Jade Hatton, Katharine Hendry, Germain Bayon, Emmanuel Ponzevera, R. Bastian Georg, Alisson Akerman, Oleg S. Pokrovsky, Jean‐Dominique Meunier, Pierre Deschamps, Isabelle Basile‐Doelsch

Bibliographic record

VenueGeostandards and Geoanalytical Research · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsTrent University
FundersNatural Environment Research CouncilU.S. Geological SurveyH2020 European Research CouncilAgence Nationale de la RechercheUniversity of BristolSight Research UKEquipex
KeywordsIsotopeEnvironmental scienceIsotopes of siliconSiliconMineralogyAnalytical Chemistry (journal)Materials scienceEnvironmental chemistryChemistryPhysicsMetallurgy

Abstract

fetched live from OpenAlex

The use of silicon (Si) isotopes has led to major advances in our understanding of Si cycling in modern and past environments. This inter‐laboratory comparison exercise provides the community with the first set of soil and plant reference materials with an analytically challenging matrix containing organic material that is known to induce isotopic bias, for use as secondary reference materials in Si isotope measurement. Seven laboratories analysed four soil reference materials (GBW‐07401, GBW‐07404, GBW‐07407, TILL‐1) and one plant reference material (ERM‐CD281). Participating laboratories employed a range of chemical preparation methods and analytical setups but all analyses were performed by MC‐ICP‐MS. Irrespective of the chemical preparation method or analytical conditions, the results show excellent agreement among laboratories within 2 s for at least three replicates. Data were combined together to calculate δ 29 Si and δ 30 Si mean values (relative to NBS 28) and their expanded uncertainties ( U , coverage factor k = 2). The δ 30 Si values are as follow: GBW‐07401: −0.27 ± 0.06‰, GBW‐07404: −0.76 ± 0.12‰, GBW‐07407: −1.82 ± 0.17‰, TILL‐1: −0.16 ± 0.06‰ and ERM‐CD281: −0.28 ± 0.11‰. Also, a compilation of published data provides an up‐to‐date mean δ 30 Si for BHVO‐2 of −0.28 ± 0.08‰.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.153
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.126
GPT teacher head0.423
Teacher spread0.297 · 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.

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

Citations13
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueGeostandards and Geoanalytical ResearchSame topicSilicon Effects in AgricultureFrench-language works237,207