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Record W3134114067 · doi:10.1093/gji/ggab090

First palaeointensity data from the cryogenian and their potential implications for inner core nucleation age

2021· article· en· W3134114067 on OpenAlexaffabout
Simon J. Lloyd, Andrew J. Biggin, H. C. Halls, Mimi J. Hill

Bibliographic record

VenueGeophysical Journal International · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsUniversity of Toronto
FundersLeverhulme Trust
KeywordsGeologyDynamoDynamo theoryInner coreNucleationPaleomagnetismVolcanoSillPaleontologyGeophysicsEarth sciencePetrologyPhysicsMagnetic field

Abstract

fetched live from OpenAlex

SUMMARY The timing of inner core nucleation is a hugely significant event in Earth's evolution and has been the subject of intense debate. Some of the most recent theoretical estimates for the age of nucleation fall throughout the Neoproterozoic era; much younger than previously thought. A young inner core requires faster recent core cooling rates and a likely hotter early core; knowledge of its age would be invaluable in understanding Earth's thermal history and total energy budget. Predictions generated by numerical dynamo models need to be tested against such data, but records are currently much too sparse to constrain the event to a precise period of time. Here, we present results from 720 Ma dolerite dykes (and one sill) from the Franklin Large Igneous Province, which fall within a crucial 300 Myr gap in palaeointensity records. This study uses three independent techniques on whole rocks from 11 sites spread across High Arctic Canada and Greenland to produce virtual dipole moments ranging from 5 to 20 ZAm2 (mean 11 ZAm2); almost one order of magnitude lower than the present-day field. These weak-field results agree with recent ultralow palaeointensity data obtained from Ediacaran rocks formed ∼150 Myr later and may support that the dynamo was on the brink of collapse in the Neoproterozoic prior to a young inner core formation date.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.032
GPT teacher head0.268
Teacher spread0.236 · 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

Citations29
Published2021
Admission routes2
Has abstractyes

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