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Application of Laser Ablation ICP‐MS to U‐Th‐Pb Dating of Monazite

2001· article· en· W2100706966 on OpenAlexafffund
Jan Košler, Mike Tubrett, Paul Sylvester

Bibliographic record

VenueGeostandards and Geoanalytical Research · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMonaziteLaser ablationIsotopeAnalytical Chemistry (journal)LaserAccuracy and precisionAblationTRACERMineralogyRadiochemistryInductively coupled plasma mass spectrometryChemistryGeologyMass spectrometryOpticsNuclear physicsZirconPhysicsGeochemistryMathematicsChromatographyStatistics

Abstract

fetched live from OpenAlex

Recent advances in laser ablation ICP‐MS techniques allow accurate U‐Th‐Pb age dating of monazites that are as young as several tens of million years to a precision better than 2%. Accuracy of the age determinations has been improved by true real‐time mass bias correction via nebulisation of a solution containing enriched 233 U and natural Tl isotopes. The Tl‐U tracer solution eliminates possible effects of variable sample matrices on the precision and accuracy of measured isotopic ratios. Mass bias corrections based on measured 205 Tl/ 233 U ratios in the tracer solution allow direct measurement of 235 U in monazite. Combined with high‐sensitivity laser ablation ICP‐MS measurements, direct measurement of 235 U particularly improves the precision of U‐Pb dating of young monazites. Correction for laser‐induced Pb/U and Pb/Th elemental fractionation is based on a mathematical treatment of time resolved count‐rate data that is independent of laser ablation characteristics, does not require external standardisation and allows variable laser pit size or raster patterns for each measurement. The new procedures make the LA ICP‐MS technique more flexible for in situ U‐Th‐Pb analysis.

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 score0.189
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0040.000

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 head0.036
GPT teacher head0.327
Teacher spread0.291 · 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

Citations77
Published2001
Admission routes2
Has abstractyes

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