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Record W2301094479 · doi:10.1144/jgs2015-114

Dealing with discordance: a novel approach for analysing U–Pb detrital zircon datasets

2016· article· en· W2301094479 on OpenAlexaff
Jesse Reimink, Joshua H.F.L. Davies, John W.F. Waldron, Xavier Rojas

Bibliographic record

VenueJournal of the Geological Society · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsZirconGeologyGeochemistry

Abstract

fetched live from OpenAlex

Detrital zircon U–Pb geochronology is a rapidly expanding and useful technique for addressing the sedimentary rock record. However, because of difficulties in evaluating discordant analyses, common in most datasets, a high proportion of results may be discarded. These analyses, if interpreted correctly, can provide valuable information regarding the deformation, alteration or metamorphism of the zircon population, as these events are all capable of producing U–Pb discordance. A novel modelling procedure permits analysis of probabilistic relationships within U–Pb datasets to deconvolve the age information present within discordant analyses by assessing the relative likelihood of potential discordia lines. Additionally, because we retrieve useful information from discordant data, a stricter filter can be used to assess concordance, increasing confidence in those distributions. The validity of this modelling method is demonstrated using two previously published cases from the Caledonide orogen where clearly discordant analyses exist. In the Southern Uplands of Scotland, these analyses indicate resetting of the U–Pb systematics in metasedimentary rocks in the Grampian orogen prior to Ordovician erosion and redeposition. In the second case, from the Greenland Caledonides, discordant data provide additional constraints on previously proposed in situ resetting during Scandian deformation and fluid flow events. Supplementary material: The code used for writing the model, along with an instruction guide, is available at http://doi.org/10.6084/m9.figshare.c.2182197 .

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.212
Teacher spread0.190 · 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 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

Citations49
Published2016
Admission routes1
Has abstractyes

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