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Record W1659454050 · doi:10.1029/2006wr004861

Influence of stream bank seepage during low‐flow conditions on riparian zone hydrology

2006· article· en· W1659454050 on OpenAlexaffabout
Tim P. Duval, Alan R. Hill

Bibliographic record

VenueWater Resources Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsMcMaster UniversityYork University
Fundersnot available
KeywordsRiparian zoneHydrology (agriculture)Perennial streamSTREAMSBankInflowEnvironmental scienceGeologyWater tableGroundwaterGeomorphologyEcologyGeotechnical engineering

Abstract

fetched live from OpenAlex

We examined the effect of sustained stream bank seepage during base flow conditions on the hydrology of two riparian zones in lowland agricultural areas in southern Ontario, Canada. Hydrometric data and subsurface chloride patterns over a 2‐year period indicated that stream inflow to the riparian zone sustained a reversed water table gradient inland for periods of up to 4 months in summer and autumn at one of the riparian sites. Stream bank seepage occurred throughout the year at the second riparian site where hillslope inflow was restricted by an upslope spur. Despite high evapotranspiration rates in summer, stream inflow maintained a zone of saturated riparian sediments that extended up to 25 m inland. A bromide tracer injection at the stream bank interface indicated that bank seepage occurred along preferential flow paths in a poorly sorted gravel layer at the two riparian sites. Conceptual models of humid temperate riparian zones have focused on hillslope to stream hydrologic flow paths. However, our results suggest that sustained stream bank inflow during low‐flow conditions can exert a dominant control on riparian hydrology in lowland landscapes where level riparian zones bounded by perennial streams receive limited subsurface inflows from adjacent slopes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations39
Published2006
Admission routes2
Has abstractyes

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