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Record W4406799766 · doi:10.1111/gwat.13465

Impact of Long Well Screens on Monitoring of the Freshwater‐Saltwater Transition Zone

2025· article· en· W4406799766 on OpenAlexaff
Frédérik Croteau, C. Coulon, John Molson, Jean‐Michel Lemieux

Bibliographic record

VenueGround Water · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsCenter for Northern StudiesUniversité Laval
Fundersnot available
KeywordsBoreholeAquiferTransition zoneGeologyGroundwaterHydraulic conductivityWater wellGroundwater flowSalinityFlow (mathematics)Displacement (psychology)Soil scienceHydrology (agriculture)Geotechnical engineeringGeophysicsMechanicsOceanography

Abstract

fetched live from OpenAlex

Deep monitoring wells with long screens crossing the transition zone between freshwater and saltwater are often used in coastal areas to characterize fresh groundwater resources and the depth of saline groundwater. However, past studies have demonstrated that long-screen wells can lead to biased observations of the transition zone, since vertical flow within the borehole can modify the shape and elevation of the transition zone in and around the borehole compared to undisturbed conditions without a well. Here, field observations and variable-density numerical flow simulations are used to evaluate, under natural flow conditions, how the installation of long-screen wells can provide time-varying biased observations of the freshwater-saltwater transition zone, and how various aquifer and well parameters affect the magnitude of these biases. Results show that long-screen wells can lead to a more dispersed interface, an upward displacement of the transition zone of between 5 and 10 m, and a salinity decrease in the saltwater portion of the well on the order of 10 to 15 g/L. The perturbations take up to 5 years to fully develop and stabilize. The degree of displacement depends on the screen diameter, screen length, aquifer anisotropy, and hydraulic conductivity, whereas the displacement is independent of the distance of the well from the coast. This analysis provides insight into which well and aquifer characteristics increase the risk of obtaining biased observations in long-screen wells, and provides orders of magnitude for these biases.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.370

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 head0.010
GPT teacher head0.238
Teacher spread0.228 · 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 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

Citations3
Published2025
Admission routes1
Has abstractyes

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