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Record W4254259829 · doi:10.4133/1.3176745

Mapping Buried Valley Aquifers in SW Manitoba Using a Vibrating Source/Landstreamer Seismic Reflection System

2009· article· en· W4254259829 on OpenAlexaffabout
A J -M Pugin, S E Pullan, M J Hinton, T Cartwright, M Douma, Robert A. Burns

Bibliographic record

VenueSymposium on the Application of Geophysics to Engineering and Environmental Problems 2009 · 2009
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsGeologyAquiferReflection (computer programming)SeismologyGeomorphologyGeotechnical engineeringGroundwaterComputer science

Abstract

fetched live from OpenAlex

In southwest Manitoba, Canada, sand and gravel aquifers within buried valleys of Pleistocene and/or Tertiary age eroded into underlying Cretaceous bedrock have been developed for municipal, pipeline and farm water supplies. These valleys have little or no surface expression and the sedimentary architecture is poorly known; the extent of the valleys and their aquifers has been only partially delineated by boreholes. A program of seismic reflection surveys conducted primarily in compressional (P‐) mode has investigated three different buried valley systems and imaged their thick Pleistocene glacial sequences. The efficiency and effectiveness of the vibratory source/landstreamer system was demonstrated during a production P‐wave survey in which 38 line‐km of seismic reflection profiles were collected in 9 days. The seismic data provide an assessment of the subsurface architecture, and the thickness and properties of both the valley fill and the overlying sediments to depths of ∼100m. The profiles can also be used to locate optimum sites for groundwater well placements within buried valleys. This survey demonstrates that the seismic reflection method can now be considered a viable reconnaissance or regional mapping tool.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.723

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 head0.007
GPT teacher head0.178
Teacher spread0.171 · 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 designSimulation or modeling
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
Published2009
Admission routes2
Has abstractyes

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