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Record W4312122443 · doi:10.3389/feart.2022.1079229

Impact of turbulence on magnetic alignment in sediments

2022· article· en· W4312122443 on OpenAlexafffundabout
Édouard Philippe, Jean‐Pierre Valet, Guillaume St‐Onge, Ramón Egli

Bibliographic record

VenueFrontiers in Earth Science · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsUniversité du Québec à Rimouski
FundersFP7 Ideas: European Research CouncilNatural Sciences and Engineering Research Council of CanadaInstitut de Physique du Globe de ParisEuropean Commission
KeywordsNatural remanent magnetizationGeologyRemanenceSedimentary rockSedimentGrain sizePaleomagnetismRock magnetismMagnetizationMagnetic dipDeposition (geology)MineralogyGeophysicsGeomorphologyGeochemistryMagnetic fieldPhysics

Abstract

fetched live from OpenAlex

Rapidly deposited layers (RDL) such as turbidites or hyperpycnites are mostly studied for their sedimentological properties, but are carefully avoided in paleomagnetic studies due to the disturbances caused by such sudden and rapid sediment accumulation. Therefore, these layers can also be seen as potential indicators of sediment parameters susceptible of affecting the alignment of magnetic grains and ultimately the acquisition of the natural remanent magnetization (NRM). We have compiled 13 Holocene rapidly deposited layers from core MD99-2222 in the Saguenay Fjord, eastern Canada (St-Onge and al., 2004) with varying thicknesses (from 7.1 cm to 1,510 cm) and 4 Quaternary turbidites of different origins, to document the influence of sedimentary and magnetic parameters on natural remanent magnetization acquisition. We found a logarithmic relationship between rapidly deposited layers thickness on the one hand, and the amplitude of inclination changes and magnetic grain sizes on the other. Inclination and magnetic grain sizes are themselves correlated to each other by a logarithmic law. As there is no relationship between inclination deviation and stratigraphic depth, compaction alone cannot account for such large effects on inclination. Flocculation is grain size sensitive, but it is expected to affect mainly the natural remanent magnetization intensity, rather than its direction. Turbulence that prevails during the rapid deposition of sediments during such events is most likely the dominant factor.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.235
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.

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

Citations4
Published2022
Admission routes3
Has abstractyes

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