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Record W3081075437 · doi:10.1029/2020gl089612

Heterogeneity Affects Intertidal Flow Topology in Coastal Beach Aquifers

2020· article· en· W3081075437 on OpenAlexaff
Xiaolong Geng, Holly A. Michael, Michel C. Boufadel, Fred J. Molz, Firas Gerges, Kenneth Lee

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsFisheries and Oceans Canada
FundersNational Science Foundation
KeywordsIntertidal zoneAquiferBiogeochemical cycleGeologySaltwater intrusionGroundwaterMixing (physics)Groundwater flowEnvironmental scienceHydrology (agriculture)OceanographyEcologyGeotechnical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Intertidal aquifers are hotspots of biogeochemical cycling where nutrients and contaminants are processed prior to discharge to the ocean. The nature of the dynamic subsurface mixing zone is a critical control on mitigating reactions. Simulation of density‐dependent, variably saturated flow and salt transport incorporating realistic representations of aquifer heterogeneity was conducted within a Monte Carlo framework to investigate influence of nonuniform permeability on intertidal groundwater flow and salt transport dynamics. Results show that heterogeneity coupled with tides creates transient preferential flow paths within the intertidal zone, evolving multiple circulation cells and fingering‐type salinity distributions. Due to heterogeneity, strain‐dominated (intense mixing) and vorticity‐dominated (low mixing) flow regions coexist at small spatial scales, and their spatial extent reaches peaks at high tide and low tide. Such topological characteristics reveal complex tempo‐spatial mixing patterns for intertidal flow with localized areas of high and low mixing intensities, which have implications for intertidal biogeochemical processing.

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 score0.162
Threshold uncertainty score0.860

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.038
GPT teacher head0.284
Teacher spread0.246 · 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

Citations64
Published2020
Admission routes1
Has abstractyes

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