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Record W2001509031 · doi:10.1002/hyp.7249

Beaver dams along an agricultural stream in southern Ontario, Canada: their impact on riparian zone hydrology and nitrogen chemistry

2009· article· en· W2001509031 on OpenAlexaffabout
Alan R. Hill, Tim P. Duval

Bibliographic record

VenueHydrological Processes · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsMcMaster UniversityYork University
Fundersnot available
KeywordsRiparian zoneBeaverHydrology (agriculture)Water tableGroundwaterEnvironmental scienceNitrateWetlandGeologyEcologyHabitat

Abstract

fetched live from OpenAlex

Abstract The hydrology and nitrogen biogeochemistry of a riparian zone were compared before and after the construction of beaver dams along an agricultural stream in southern Ontario, Canada. The beaver dams increased surface flooding and raised the riparian water table by up to 1·0 m. Increased hydraulic gradients inland from the stream limited the entry of oxic nitrate‐rich subsurface water from adjacent cropland. Permeable riparian sediments overlying dense till remained saturated during the summer and autumn months, whereas before dam construction a large area of the riparian zone was unsaturated in these seasons each year. Beaver dam construction produced significant changes in riparian groundwater chemistry. Median dissolved oxygen concentrations were lower in riparian groundwater after dam construction (0·9–2·1 mg L −1 ) than in the pre‐dam period (2·3–3·9 mg L −1 ). Median NO 3 ‐N concentrations in autumn and spring were also lower in the post‐dam (0·03–0·07 mg L −1 ) versus the pre‐dam period (0·1–0·3 mg L −1 ). In contrast, median NH 4 ‐N concentrations in autumn and spring months were higher after dam construction (0·3–0·4 mg L −1 ) than before construction (0·13–0·14 mg L −1 ). Results suggest that beaver dams can increase stream inflow to riparian areas that limit water table declines and increase depths of saturated riparian soils which become more anaerobic. These changes in subsurface hydrology and chemistry have the potential to affect the transport and transformation of nitrate fluxes from adjacent cropland in agricultural landscapes. Copyright © 2009 John Wiley & Sons, Ltd.

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 score0.450
Threshold uncertainty score0.805

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)0.0010.000

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 head0.009
GPT teacher head0.194
Teacher spread0.185 · 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

Citations53
Published2009
Admission routes2
Has abstractyes

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