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Record W4366502795 · doi:10.11159/icgre23.131

Hydrogeochemical Analysis as a Tool to Verify Seepage Flow Paths in an Earth Dam

2023· article· en· W4366502795 on OpenAlexvenueno aff
Zorany S. Zapata, Maria C. Sierra, Adriana M. Blanco

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsnot available
Fundersnot available
KeywordsEarth (classical element)Flow (mathematics)GeologyComputer scienceHydrology (agriculture)Geotechnical engineeringMechanicsMathematics

Abstract

fetched live from OpenAlex

In this paper the hydrogeochemical characteristics of the infiltration waters detected in an earth core rockfill dam and its water reservoir are analyzed.The assessment focused on applying different methods of hydrogeological representations and from that, the type of water, its potential origin, and the interaction processes that they have with the rock mass and the reservoir were established.These hydrogeological samplings were carried out later after a geophysical study that suggested preferential flow routes.The hydrogeochemical tests consisted of the determination of in-situ parameters and major ions.The geophysical test was based on the application of an electric current to detect the points with the highest conductivity, and which was related to the presence of water.To complement this, x-ray fluorescence and x-ray diffraction tests were carried out on the solid deposits found in one of the samples.In the end, it was found that both studies suggested that there were no defects in the dam's waterproofing system and that water detected in the downstream embankment flowed first through the abutments.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.199
Teacher spread0.194 · 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

Explore more

Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicGroundwater flow and contamination studiesFrench-language works237,207