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U‐Pb Detrital Zircon Analysis – Results of an Inter‐laboratory Comparison

2013· article· en· W1999527175 on OpenAlexaff
Jan Košler, Jiří Sláma, Елена Белоусова, Fernando Corfú, George E. Gehrels, Axel Gerdes, Matthew Horstwood, Keith Sircombe, Paul Sylvester, Massimo Tiepolo, Martin J. Whitehouse, Jon Woodhead

Bibliographic record

VenueGeostandards and Geoanalytical Research · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsMemorial University of Newfoundland
FundersNatural Environment Research CouncilSight Research UK
KeywordsZirconProvenanceGeologyMineralogyFractionationGeochemistryChemistry

Abstract

fetched live from OpenAlex

Inter‐laboratory comparison of laser ablation ICP ‐ MS and SIMS U‐Pb dating of synthetic detrital zircon samples provides an insight into the state‐of‐the art of sedimentary provenance studies. Here, we report results obtained from ten laboratories that routinely perform this type of work. The achieved level of bias was mostly within ± 2% relative to the ID ‐ TIMS U‐Pb ages of zircons in the detrital sample, and the variation is likely to be attributed to variable Pb/U elemental fractionation due to zircon matrix differences between the samples and the reference materials used for standardisation. It has been determined that ~ 5% age difference between adjacent age peaks is currently at the limit of what can be routinely resolved by the in situ dating of detrital zircon samples. Precision of individual zircon age determination mostly reflects the data reduction and procedures of measurement uncertainty propagation, and it is largely independent of the instrumentation, analytical technique and reference samples used for standardisation. All laboratories showed a bias towards selection of larger zircon grains for analysis. The experiment confirms the previously published estimates of the minimum number of grains that have to be analysed in order to detect minor zircon age populations in detrital samples.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.152
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.037
GPT teacher head0.331
Teacher spread0.294 · 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 designObservational
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

Citations116
Published2013
Admission routes1
Has abstractyes

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