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Record W1554829979 · doi:10.1002/2014gl060080

Derivation of paleolongitude from the geometric parametrization of apparent polar wander path: Implication for absolute plate motion reconstruction

2014· article· en· W1554829979 on OpenAlexafffund
Lei Wu, Vadim A. Kravchinsky

Bibliographic record

VenueGeophysical Research Letters · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsApparent polar wanderPaleomagnetismGeologyGeodesyReference framePolar wanderRotation (mathematics)Great circleGeometryPaleontologyMathematicsFrame (networking)Computer science

Abstract

fetched live from OpenAlex

Abstract Obtaining ancient longitude position of continents in the past has always been a challenge for plate tectonic reconstructions. Paleomagnetism has been commonly used to reconstruct paleolatitudes and relative rotations but not paleolongitudes. In this work, we present a synthesized method to derive paleolongitude by geometrically parametrizing apparent polar wander path (APWP). Great and small circle modeling are implemented concurrently to the identified APWP tracks to calculate the paleomagnetic Euler parameters (stage rotation pole and rotation angle). From the Euler parameters of the optimal fitting option, the absolute motion history can be restored for the reference geometries. Using our method as well as the results from relative plate motion studies, we reevaluate the dispersion history of East Gondwana since 140 Ma. To further test the validity of our method, we compare the predictions from four other absolute motion models mainly in paleolatitude movement, longitudinal variation, and great circle distance, which suggest the most similarity with the global hybrid reference frame.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.855
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.025
GPT teacher head0.284
Teacher spread0.258 · 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 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

Citations15
Published2014
Admission routes2
Has abstractyes

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