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Record W1981521025 · doi:10.4133/jeeg8.3.167

Time-Domain Electromagnetic Resistivity Mapping of a Fjord-Head Delta

2003· article· en· W1981521025 on OpenAlexaff
Melvyn E. Best, Jeff E. Gutsell, John J. Clague

Bibliographic record

VenueJournal of Environmental and Engineering Geophysics · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGeologyDeltaBedrockFjordGeomorphologySedimentSiltDepth soundingResistive touchscreenSubaerialHead (geology)Beach morphodynamicsRiver deltaSediment transportGeophysicsPaleontologyOceanography

Abstract

fetched live from OpenAlex

An electromagnetic (EM) survey was conducted on the Zeballos River delta, approximately 300kilometers north of Victoria, British Columbia, to determine whether EM methods can help delineate the three-dimensional architecture of a fjord-head delta. The EM data were collected using a Geonics EM-47 system with a 40-m transmitter loop. The data were downloaded and subsequently edited and interpreted using the TEMIXGL layered earth inversion package from Interpex. The interpretation provided a picture of the three-dimensional resistivity structure of the delta. Throughout most of the surveyed area, a relatively thick layer of resistive (>400ohm-m) sediment (gravel and sand—delta topset and foreset) overlies more conductive (10–20ohm-m) sediment (silt and clay—delta bottom-set), which in turn overlies resistive (>2,000ohm-m) bedrock. Shallow sediments in the intertidal zone are saturated with sea water and hence are very conductive. However, we were able to determine the depth to more resistive (fresh water) gravel and sand and, in some areas, what we interpret to be resistive bedrock. The results of this study indicate that EM resistivity surveying can provide constraints on sediment type and thickness which, when used in conjunction with geological mapping and borehole logs, help improve our understanding of the three-dimensional architecture of these deltas.

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 score0.977
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

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

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 head0.005
GPT teacher head0.169
Teacher spread0.164 · 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

Citations1
Published2003
Admission routes1
Has abstractyes

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