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Record W2110302715 · doi:10.1071/aseg2013ab256

Hybrid 1D/3D geologically constrained inversion of airborne TEM data

2013· article· pt· W2110302715 on OpenAlex
Peter K. Fullagar, Glenn Pears, James Reid

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

VenueASEG Extended Abstracts · 2013
Typearticle
Languagept
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsMira Geoscience (Canada)
Fundersnot available
KeywordsInversion (geology)GeologyHomogeneousConductivityGeophysicsComputer scienceSeismologyTectonicsMathematics

Abstract

fetched live from OpenAlex

TEM data are best interpreted in tight integration with geological data. A computer program, VPem1D, has been written to perform 1D TEM inversion in a 3D geological framework. The fact that VPem1D operates on a geological model is advantageous both because it reduces interpretational ambiguity and because it facilitates a variety of inversion styles. If one or more geological units are considered uniform in conductivity, the optimal conductivities can be determined for the entire survey area via homogeneous unit inversion. Moreover, because geological interfaces are captured in the model, geometry inversion can be used to adjust interfacial shape, hence define depth to basement for example. If conductivity varies within geological units, heterogeneous unit inversion can be applied.VPem1D inversion is directly applicable to data from variety of systems including (but not limited to) GEOTEM, TEMPEST, VTEM, Spectrem, SkyTEM, MegaTEM and Hoistem.This paper will illustrate the different inversion options as applied to a variety of case study data sets.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.004

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 head0.035
GPT teacher head0.256
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