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Record W1970428575 · doi:10.1190/1.3447778

Effect of signal amplitude on magnetic depth estimations

2010· article· en· W1970428575 on OpenAlexaff
Madeline Lee, Bill Morris, Hernan Ugalde

Bibliographic record

VenueThe Leading Edge · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAmplitudeRemanenceMagnetizationSIGNAL (programming language)Function (biology)Magnetic susceptibilityAcousticsPhysicsNuclear magnetic resonanceComputational physicsCondensed matter physicsMagnetic fieldOpticsComputer scienceQuantum mechanicsBiology

Abstract

fetched live from OpenAlex

The magnetic signal generated over any source can be analyzed in terms of the two basic components of any wave: frequency (λ) and amplitude (A). With respect to magnetics, the frequency will be a function of the magnetic source body depth and geometry, while the amplitude is a function of magnetization intensity (magnetic susceptibility and natural remanent magnetization, NRM, if present). Many processing and interpretation methods developed over the last 50 years take advantage of the intrinsic relationship between frequency and depth to generate a variety of depth-estimation routines. Furthermore, many methods are independent of magnetic susceptibility (and therefore amplitude) contrasts since the methods incorporate some sort of mathematical expression that nulls the effect of varying susceptibilities.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.986
Threshold uncertainty score1.000

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)0.0010.001

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 head0.015
GPT teacher head0.274
Teacher spread0.259 · 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 designOther design
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

Citations10
Published2010
Admission routes1
Has abstractyes

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