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Record W2154288127 · doi:10.3997/1873-0604.2009002

Trends in waterborne electrical and EM induction methods for high resolution sub‐bottom imaging

2009· article· en· W2154288127 on OpenAlexaff
Karl E. Butler

Bibliographic record

VenueNear Surface Geophysics · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsElectrical resistivity tomographyGeologyEnvironmental geologyEconomic geologyHydrogeologyGroundwaterEngineering geologyGeobiologyElectrical resistivity and conductivityRegional geologyPalaeogeographyGeophysicsIgneous petrologyTelmatologyGeotechnical engineeringVolcanismTectonicsSeismologyEngineering

Abstract

fetched live from OpenAlex

ABSTRACT Shallow water applications of electrical and electromagnetic geophysical methods have grown in recent years with recognition of the information these methods can provide regarding groundwater‐surface water interaction, geotechnical engineering, exploration, marine geology and other fields. In many applications, spatial variations in resistivity are useful as a proxy for variations in another bulk material property such as pore water salinity, clay content, porosity, or temperature. Applications of galvanic resistivity methods have been buoyed by the development of marine configurations that are now commercially available. In contrast, with two notable exceptions, most applications of EM induction methods have involved experimental adaptations of instruments originally designed for use on land. Methods for shallow water resistivity and EM induction surveys are at an exciting stage of development where several promising applications have been demonstrated but the suite of tools and components commercially available and widely tested remains relatively small.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.002
Scholarly communication0.0030.005
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.003

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 head0.016
GPT teacher head0.279
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

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

Citations23
Published2009
Admission routes1
Has abstractyes

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