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Record W7039526185

Mapping Groundwater with Geophysics: Locating and characterising aquifers in the Beaufort Watershed with electrical resistivity surveys

2023· other· en· W7039526185 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsBedrockGroundwaterWatershedBeaufort seaAquiferHydrology (agriculture)Sedimentary rockSaltwater intrusion
DOInot available

Abstract

fetched live from OpenAlex

In the Beaufort Watershed study area on eastern Vancouver Island, groundwater is the primary freshwater supply. More data are needed to improve characterization of the groundwater resource, in order to protect and manage it. Electrical resistivity surveying is a cost-effective remote sensing technique that allows the types of subsurface materials to be inferred. Working with the Beaufort Watershed Stewards, I collected, modelled, and interpreted 11 vertical 1D profiles (~83 m deep) and two vertical 2D profiles (81 m long x 17 m deep) in the study area. I was able to distinguish between different types of geological materials such as sand and gravel, till, clay, and various bedrock types and also whether these materials were dry or water-saturated. Extensive sand and gravel units, which are commonly major sources of groundwater, were only identified throughout the southern part of the study area, with most being water-bearing. A potential source of groundwater spanning the entire study area was the sedimentary bedrock identified in nearly all profiles. The 2D profiles, on either side of the Ships Point peninsula near Fanny Bay, both indicated the presence of saltwater within a sand and gravel unit, implying the occurrence of subsurface seawater proximal to shorelines.

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)0.001
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 score0.631
Threshold uncertainty score0.743

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.248
Teacher spread0.224 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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