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Record W2410179287 · doi:10.1063/1.4951819

Implicitly modelled stratigraphic surfaces using generalized interpolation

2016· article· en· W2410179287 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAIP conference proceedings · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsInterpolation (computer graphics)Surface (topology)Topology (electrical circuits)PlanarComputer scienceAlgorithmGeologyGeometryMathematicsArtificial intelligenceComputer graphics (images)Image (mathematics)

Abstract

fetched live from OpenAlex

Stratigraphic surfaces implicitly modelled using a generalized interpolation approach in various geological settings is presented to demonstrate its modelling capabilities and limitations. The generalized interpolation approach provides a useful mathematical framework in modelling continuous surfaces from scattered data consisting of the following geological constraints: contact locations and planar orientations. Examples are presented to show the effectiveness of the method in generating plausible representations of geological structures in sparse data environments. One of the major advantages of implicit surface modelling has long been claimed as its ability to model geometries with arbitrary topology. It is, however, demonstrated that this is in fact a disadvantage in robustly generating geologically realistic surfaces in structurally complex domains with a known topology.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.999

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)0.0020.000

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 head0.051
GPT teacher head0.246
Teacher spread0.195 · 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