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Record W4385582980 · doi:10.1029/2023gl104642

Impacts of Evaporation‐Induced Groundwater Upwelling on Mixing Dynamics in Shallow Wetlands

2023· article· en· W4385582980 on OpenAlexaff
Xiaolong Geng, Michel C. Boufadel, Hailong Li, Viravid Na Nagara, Kenneth Lee

Bibliographic record

VenueGeophysical Research Letters · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersNational Science Foundation
KeywordsBiogeochemical cycleUpwellingGroundwaterWetlandEvaporationGeologyMixing (physics)Surface waterHydrology (agriculture)Groundwater flowEnvironmental scienceSoil scienceOceanographyAquiferEnvironmental chemistryEcologyChemistryEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract Groundwater mixing dynamics play a crucial role in the biogeochemical cycling of shallow wetlands. In this paper, we conducted groundwater simulations to investigate the combined effects of evaporation and local heterogeneity on mixing dynamics in shallow wetland sediments. The results show that evaporation causes groundwater and solutes to upwell from deep sediments to the surface. As the solute reaches the surface, evaporation enhances the accumulation of the solute near the surface, resulting in a higher solute concentration than in deep sediments. Mapping of flow topology reveals that local heterogeneity generates spatially varied mixing patterns mainly along preferential flow pathways. The upwelling of groundwater induced by surface evaporation through heterogeneous sediments is likely to create distinct mixing hotspots that differ spatially from those generated by lateral preferential flows driven by large‐scale hydraulic gradients, which enhances the overall mixing in the subsurface. These findings have strong implications for biogeochemical processing in wetlands.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
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 head0.041
GPT teacher head0.313
Teacher spread0.272 · 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

Citations22
Published2023
Admission routes1
Has abstractyes

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