MétaCan
Menu
Back to cohort
Record W2967747226 · doi:10.1190/segam2019-3215437.1

A brief analysis of MobileMT data

2019· article· en· W2967747226 on OpenAlexaffabout
Daniel Sattel, Ken Witherly, Vlad Kaminski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsCondor Petroleum (Canada)
Fundersnot available
KeywordsConductivityInversion (geology)Electrical conductorConductorMagnetic fieldBase stationGeologyRemote sensingComputer scienceElectrical engineeringEngineeringMaterials scienceTelecommunicationsPhysicsSeismology

Abstract

fetched live from OpenAlex

The MobileMT system measures natural-field EM data, acquiring three-component airborne magnetic-field data while monitoring the horizontal electric field at a base station. Data sensitivities of this configuration are similar to those of TE-mode MT data. Synthetic 2D modeling results across a conductive body demonstrate how the host conductivity and the location of the base station affect the conductor response. The strong sensitivity of the data to the conductivity structure at the base station results in inversion results being quite sensitive to the inversion start models used. MobileMT survey data from the Grindstone Copper-Nickel-Cobalt project, New Brunswick indicate a strong correlation between conductivity highs and magnetic anomalies. 2D inversions suggest conductivity structures to be detected up to 1000–2000 m depth. Model results from a VMS exploration survey across the Broken Evil prospect, Northern Ontario show good correlation with conductivity structures derived from a previous VTEM survey, including the location of a suspected VMS body. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 8:30 AM Presentation Time: 11:00 AM Location: 301B Presentation Type: Oral

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0190.000

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 head0.028
GPT teacher head0.268
Teacher spread0.240 · 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.

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

Citations13
Published2019
Admission routes2
Has abstractyes

Explore more

Same topicGeophysical and Geoelectrical MethodsFrench-language works237,207