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Record W2946118393 · doi:10.11575/prism/36404

A Conceptual Model for the Development and Persistence of Soap Holes (Unique Prairie Groundwater Discharge Features)

2019· dissertation· en· W2946118393 on OpenAlexaboutno aff
Landon Woods

Bibliographic record

VenuePRISM (University of Calgary) · 2019
Typedissertation
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsnot available
Fundersnot available
KeywordsPersistence (discontinuity)SOAPGroundwaterEnvironmental scienceHydrology (agriculture)Conceptual modelComputer scienceEngineeringGeotechnical engineeringWorld Wide WebDatabase

Abstract

fetched live from OpenAlex

Soap holes manifest as localized areas of water-saturated, weak sediments which render farmland uncroppable and hazardous. The formation mechanisms and global distribution are largely unknown, despite being inferred as groundwater discharge phenomena on the Alberta prairies over 50 years ago. Here, the first conceptual model for soap hole development and persistence is created using time-series hydraulic and temperature data, electrical resistivity tomography, sediment analyses, and geochemical data (sediment and groundwater) from two locations in Alberta, Canada. The soap holes developed in smectite-rich, dominantly colloidal-size glacial sediments under strong flowing artesian conditions, resulting in quick conditions and liquefaction. Geoelectric and hydraulic conductivity contrasts (2-5 orders of magnitude), as well as groundwater geochemistry, suggest preferential groundwater flow of artesian aquifer provenance through a low permeability confining layer. In addition to natural occurrences, a proportion of soap holes may be the consequence of explosive-source seismic conducted for oil and gas exploration in Alberta.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.203
Teacher spread0.186 · 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 designTheoretical or conceptual
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
Published2019
Admission routes1
Has abstractyes

Explore more

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