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Record W2109464393 · doi:10.1071/aseg2015ab051

The effect of highly magnetic material on ZTEM data

2015· article· en· W2109464393 on OpenAlexaff
Daniel Sattel, Ken Witherly

Bibliographic record

VenueASEG Extended Abstracts · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsCondor Petroleum (Canada)
Fundersnot available
KeywordsAmplitudeMagnetic fieldMagnetic surveyResistive touchscreenMagnetic anomalyInversion (geology)ConductivityCondensed matter physicsNuclear magnetic resonanceComputational physicsPhysicsGeologyGeophysicsOpticsSeismologyElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

ZTEM data acquired across the Humble magnetic anomaly of almost 30,000 nT were analyzed for the presence of a magnetic gradient response and the effects from elevated magnetic susceptibilities.The response of moving the receiver coil through the magnetic-field gradient peaks at 0.01 Hz and drops off strongly with frequency. Lacking information about the field strength at the base station precludes the comparison of amplitudes between computed gradient responses and the survey data, but the comparison of response shapes suggests that the gradient responses are too small to have a noticeable effect on the survey data.The 3D inversion of the magnetic survey data indicates magnetic susceptibility values as high as 2.0 (SI). Forward-modeling the ZTEM response for these K-values combined with resistive half-spaces indicates that the response amplitudes and shapes strongly depend on the background resistivities. Ignoring the elevated K-values during an inversion can result in the underestimation of conductivities and other artifacts, such as the mapping of patterns that resemble crop circles. For an environment such as Humble, with deep-seated zones of elevated K-values, the shallow inverted conductivity structure appears to be reliable, but the deeper structure should be interpreted with caution.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.997
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.027
GPT teacher head0.266
Teacher spread0.239 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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