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Record W2321267244 · doi:10.1190/segam2015-5873054.1

3D potential field inversion for wireframe surface geometry

2015· article· en· W2321267244 on OpenAlexaff
Peter G. Lelièvre, Colin G. Farquharson, Rodrigo Bijani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsInversion (geology)GeometryPotential fieldSurface (topology)Computer scienceGeologyMathematicsGeophysics

Abstract

fetched live from OpenAlex

Summary Geologists’ interpretations about the Earth typically involve distinct rock units with contacts between them. In contrast, standard minimum-structure inversions recover smooth models inconsistent with such interpretations. We are researching a fundamentally different type of inversion that parameterizes the Earth in terms of contact surfaces between rock units. 3D geological Earth models typically comprise wireframe surfaces of tessellated triangles. Our inversions parameterize the wireframe surfaces as the coordinates of the facet vertices. We solve for the vertex locations through global optimization strategies and we perform stochastic sampling to provide likelihood information. To provide computationally feasible inversion methods, we reduce the dimensionality of the problem by solving for a coarse representation of the geological wireframe model, which is refined through surface subdivision.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.250
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations20
Published2015
Admission routes1
Has abstractyes

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