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Record W2133389771 · doi:10.1190/1.2194514

Joint inversion of gravity and magnetic data for two-layer models

2006· article· en· W2133389771 on OpenAlexaffabout
Mark Pilkington

Bibliographic record

VenueGeophysics · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsInversion (geology)GeologyMagnetic anomalyInverse problemInverseGeodesyGravity anomalyDensity contrastImpact craterNonlinear systemGeometrySeismologyGeophysicsMathematical analysisMathematicsPhysics

Abstract

fetched live from OpenAlex

Abstract Gravity and magnetic data are inverted jointly in terms of a model consisting of an interface separating two layers having a constant density and magnetization contrast. A damped least-squares inversion is used to determine the topography of the interface. The inversion requires knowledge of the physical property contrasts across the interface and its average depth. Since the relationship between model parameters and data is weakly nonlinear, a constant damped least-squares inverse is used during the iterative solution search. The effect of this inverse is closely related to a downward continuation of the field to the average interface depth. The method is used to map the base of the Sept-Iles mafic intrusion, Quebec, Canada, and the shape of the central uplift at the Chicxulub impact crater, Yucatan, Mexico. At Sept-Iles, the intrusion reaches a thickness of >7km, coincident with the maximum gravity anomaly, south of the intrusion center. At Chicxulub, the top of the central uplift is modeled to be >3.5km deep and has a single peak form.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.504

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 head0.026
GPT teacher head0.239
Teacher spread0.214 · 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

Citations49
Published2006
Admission routes2
Has abstractyes

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