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Record W2007956999 · doi:10.1190/1.1876036

Gridding aeromagnetic data using longitudinal and transverse horizontal gradients with the minimum curvature operator

2005· article· en· W2007956999 on OpenAlexaboutno aff
Michael D. O’Connell, Richard S. Smith, Marc A. Vallée

Bibliographic record

VenueThe Leading Edge · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsnot available
Fundersnot available
KeywordsTransverse planeCurvatureGeologyGeodesyOperator (biology)GeometryMathematicsEngineeringStructural engineering

Abstract

fetched live from OpenAlex

The measurement of gradients of the earth's magnetic field from an airborne platform has been advocated for many years. In 1954 Wickerham proposed the use of what is essentially a gradient along the flight direction (longitudinal gradient). Hood's 1965 suggestion was to measure the vertical gradient using a total-field magnetometer mounted in a tail stinger and a second magnetometer in a towed bird below the tail stinger. In fact, the system finally built by Hood's group at the Geological Survey of Canada used two tail stingers separated by 2 m. The advantages of this type of total-field vertical gradiometry were advocated so convincingly that vertical gradiometers was also developed for helicopter surveying. One of the key developments that allowed the collection of high-quality vertical-gradient data from a fixed-wing aircraft was the development of aircraft compensation schemes for gradiometer arrays.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.910
Threshold uncertainty score0.341

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 head0.046
GPT teacher head0.266
Teacher spread0.220 · 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 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

Citations29
Published2005
Admission routes1
Has abstractyes

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