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Record W2040208889 · doi:10.1190/1.2370393

The quest for the perfect gravity anomaly: Part 2 — Mass effects and anomaly inversion

2006· article· en· W2040208889 on OpenAlexaboutno aff
G. Randy Keller, Thomas G. Hildenbrand, William J. Hinze, Xiong Li, D. Ravat, Michael W. Webring

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsnot available
Fundersnot available
KeywordsGravimeterInversion (geology)GeologyAnomaly (physics)Free-air gravity anomalyGravity anomalyInstrumentation (computer programming)GeophysicsGeodesyComputer scienceSeismologyBouguer anomalyPhysicsProgramming language

Abstract

fetched live from OpenAlex

Gravity anomalies have become an important tool for geologic studies since the widespread use of high‐precision gravimeters after the Second World War. More recently the development of instrumentation for airborne gravity observations, procedures for acquiring data from satellite platforms, the readily available Global Positioning System for precise vertical and horizontal control, improved global data bases, and enhancement of computational hardware and software have accelerated the use of the gravity method. As a result, efforts are being made to improve the gravity databases that are made available to the geoscience community by broadening their observational holdings and increasing the accuracy and precision of the included data. Currently the North American Gravity Database as well as the individual databases of Canada, Mexico, and the United States of America are being revised using new formats and standards. The objective of this paper is to describe the use of the revised standards for gravity data processing and modeling and there impact on geological interpretations.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.715

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 studies0.0010.000
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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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

Citations7
Published2006
Admission routes1
Has abstractyes

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