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Record W4393097319 · doi:10.1130/b37294.1

Application of the 187Re-187Os geochronometer to crustal materials: Systematics, methodology, data reporting, and interpretation

2024· article· en· W4393097319 on OpenAlexafffund
Alan D. Rooney, Danny Hnatyshin, Jonathan Toma, Nicolas J. Saintilan, Alexie E.G. Millikin, David Selby, Robert A. Creaser

Bibliographic record

VenueGeological Society of America Bulletin · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of AlbertaGeological Survey of CanadaNatural Resources Canada
FundersNatural Sciences and Engineering Research Council of CanadaYale UniversityNational Science Foundation
KeywordsGeologySystematicsGeochronologyInterpretation (philosophy)Geochemistry

Abstract

fetched live from OpenAlex

Abstract The rhenium-osmium (187Re-187Os) system is a highly versatile chronometer that is regularly applied to a wide range of geological and extraterrestrial materials. In addition to providing geo- or cosmo-chronological information, the Re-Os system can also be used as a tracer of processes across a range of temporal (millennial to gigayear) and spatial scales (lower mantle to cryosphere). An increasing number of sulfide minerals are now routinely dated, which further expands the ability of this system to refine mineral exploration models as society moves toward a new, green economy with related technological needs. An expanding range of natural materials amenable to Re-Os geochronology brings additional complexities in data interpretation and the resultant translation of measured isotopic ratios to a properly contextualized age. Herein, we provide an overview of the 187Re-187Os system as applied to sedimentary rocks, sulfides, and other crustal materials and highlight further innovations on the horizon. Additionally, we outline next steps and best practices required to improve the precision of the chronometer and establish community-wide data reduction procedures, such as the decay constant, regression technique, and software packages to use. These best practices will expand the utility and viability of published results and essential metadata to ensure that such data conform to evolving standards of being findable, accessible, interoperable, and reusable (FAIR).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.047
GPT teacher head0.277
Teacher spread0.230 · 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

Citations18
Published2024
Admission routes2
Has abstractyes

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