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Record W1974021426 · doi:10.1190/1.3011019

Using reversed polarity airborne transient electromagnetic data to map tailings around mine sites

2008· article· en· W1974021426 on OpenAlexaff
Richard S. Smith, Li Zhen Cheng, Michel Chouteau

Bibliographic record

VenueThe Leading Edge · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsPolytechnique MontréalUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsTransient (computer programming)PolarizabilityConductivityConstant (computer programming)Polarity (international relations)Transient responseLoop (graph theory)Electrical resistivity and conductivityTailingsSign (mathematics)Frequency responseFunction (biology)GeologyGeophysicsPhysicsMechanicsMaterials scienceMathematical analysisMathematicsChemistryComputer scienceElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

The response measured by transient electromagnetic (TEM) systems is normally of a predictable sign. This is particularly true for coincident-loop ground TEM systems, where the response should in theory always be positive when the ground conductivity is a constant function of frequency and the quasi-static assumption is satisfied. In most cases, these are good assumptions, but, occasionally, coincident-loop negative transients are ob-served. These negatives can be explained if material in the ground has a conductivity that changes with frequency, or, equivalently, has nonzero polarizability.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.890

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 science0.0010.000
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 head0.113
GPT teacher head0.296
Teacher spread0.182 · 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 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

Citations12
Published2008
Admission routes1
Has abstractyes

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