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Investigating the lowermost mantle using migrations of long-period<i>S</i>-<i>ScS</i>data

2006· article· en· W2144870609 on OpenAlexaboutno aff
Kit Chambers, John Woodhouse

Bibliographic record

VenueGeophysical Journal International · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyMantle (geology)ScatteringCore–mantle boundarySlabDiscontinuity (linguistics)Mantle wedgeTransition zoneSeismologyGeophysicsGeometrySubductionPhysicsTectonicsOpticsMathematical analysis

Abstract

fetched live from OpenAlex

The lowermost mantle is investigated using a data set of high-quality waveforms and a phase stripping technique, which removes the S and ScS phases in order to isolate more subtle arrivals. Two migration methods were applied to locate scattering bodies in the lower mantle. The first, a simple backprojection, is useful for identifying regions of strong scattering. The second migration scheme uses weights based on the generalized Radon transform. We present results for a region of lower mantle beneath Alaska and western Canada, and test the influence of experimental geometry on the migration results by migrating synthetic data sets generated for various distributions of point scatterers. We show that, although the exact geometry of scattering bodies is poorly constrained, the majority of the features in the data can be recreated with a few simple structures. The results show the presence of a D″ discontinuity, which can be simulated using a flat scattering body ∼250 km above the core–mantle boundary near the Alaska–Canada border. We also present evidence for positive velocity anomalies in the bottom 100 km of the mantle, beneath the D″ reflector and near the Aleutians. The association of these scattering structures with a region of high velocity is consistent with a cause related to lower mantle heterogeneity introduced by a subducting slab.

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 score0.432
Threshold uncertainty score0.861

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.255
Teacher spread0.228 · 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

Citations13
Published2006
Admission routes1
Has abstractyes

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