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Record W2886578445 · doi:10.1029/2018gc007651

Speleothems as Magnetic Archives: Paleosecular Variation and a Relative Paleointensity Record From a Portuguese Speleothem

2018· article· en· W2886578445 on OpenAlexaff
Jorge Ponte, Eric Font, Cristina Veiga‐Pires, Claude Hillaire‐Marcel

Bibliographic record

VenueGeochemistry Geophysics Geosystems · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsUniversité du Québec à Montréal
FundersFundação para a Ciência e a TecnologiaUniversidade de Lisboa
KeywordsSpeleothemGeologyPaleomagnetismSecular variationMagnetic declinationDeclinationNatural remanent magnetizationPaleontologyRock magnetismEarth's magnetic fieldGeophysicsRemanenceCaveMagnetizationMagnetic fieldGeographyArchaeologyPhysics

Abstract

fetched live from OpenAlex

Abstract We provide a high‐resolution and complete paleomagnetic study from a middle‐Holocene (~4,100–3,300 years Before Common Era, BCE) dome‐shaped speleothem (SPAIV) from Algarve, Portugal. Our results show that the SPAIV speleothem carries a primary and stable remanent magnetization, for which directions are similar to other speleothems from the western Alps. Magnetic declination and inclination curves are also comparable to current paleosecular variation models (SHA.DIF.14k, CALS10k.1b, and pfm9k.1a), one of them (SHA.DIF.14k) fitting better with the present data set. Relative paleointensity was estimated using two different methods: conventional normalization of natural remanent magnetization by anhysteretic and isothermal, and the pseudo‐Thellier method, which is being tested here for the first time in a speleothem. Both methods show similar results, with a minimum intensity peak at ~3,850 years BCE. This low relative intensity is observed in all samples pertaining to the same respective calcite laminae. These results suggest that high growth rate speleothems are good high‐resolution recorders of the direction and intensity of the Earth's magnetic field.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.202
Teacher spread0.196 · 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 designBench or experimental
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

Citations12
Published2018
Admission routes1
Has abstractyes

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