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Record W2888695869 · doi:10.2113/jeeg22.1.25

Mapping Buried Aquifers with HTEM in the Fort McMurray, Alberta Region

2017· article· en· W2888695869 on OpenAlexaffabout
Timothy Eadie, Alexander Prikhodko, Carlos Izarra

Bibliographic record

VenueJournal of Environmental and Engineering Geophysics · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsPetro Geotech (Canada)
Fundersnot available
KeywordsGeologyAquiferElectrical resistivity and conductivityInversion (geology)Regional geologyElectrical resistivity tomographyEconomic geologyHydrogeologyGroundwaterGeomorphologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Heliborne time-domain electromagnetic systems (HTEM) have proven to be effective tools in mapping sub-surface resistivity. As these systems are able to measure increasingly earlier time channels after the waveform's turn-off, they improve their ability to resolve resistivity contrasts in near-surface geology. This is demonstrated with the VTEM system over the Aspen property, near Fort McMurray, Alberta. This area provides a useful setting in mapping near-surface resistivity variations, specifically the location and geometry of the Pemmican Valley aquifer. This begins with the a Tau constant analysis of the earliest time channels of the VTEM decay curve that laterally mapped the Pemmican Valley aquifer and the existence of a shallower, previously unmapped, east-west trending aquifer. This is confirmed through 1D inversions of the dataset. The 1D inversion models accurately resolve the main near-surface geo-electrical units within the Aspen property when compared to resistivity well logs and a ground DC resistivity survey. Analysis of the stitched 1D inversion section maps provides useful resistivity maps that reflect the geological model.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.176
Teacher spread0.167 · 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
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

Citations7
Published2017
Admission routes2
Has abstractyes

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