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Record W3121281496 · doi:10.1088/1748-9326/abe007

Effects of stream restoration by legacy sediment removal and floodplain reconnection on water quality

2021· article· en· W3121281496 on OpenAlexfundno aff
Patrick Lasater McMahon, Vanessa B. Beauchamp, R. Casey, Christopher J. Salice, Kyle Bucher, Melinda Marsh, Joel Moore

Bibliographic record

VenueEnvironmental Research Letters · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
FundersMcGill UniversityChesapeake Bay TrustNational Science Foundation
KeywordsEnvironmental scienceFloodplainBaseflowWater qualitySedimentHydrology (agriculture)STREAMSDeposition (geology)Urban streamStream restorationRestoration ecologyErosionDrainage basinEcologyStreamflowGeologyGeography

Abstract

fetched live from OpenAlex

Abstract The effectiveness of many stream restorations in improving water quality is unmeasured. In the Mid-Atlantic region of the United States, activity by European settlers resulted in upland erosion and deposition of sediments 1–3 m in thickness in stream valleys. Subsequently, streams incised those legacy sediments creating steep, exposed banks, infrequent floodplain inundation, and water tables disconnected from floodplains. Legacy sediment removal (LSR) and floodplain reconnection (FR) proposes water quality improvement by restoration to a hydrological state closer to pre-European. We investigated water quality at nine sites, six restored with LSR/FR and three comparison sites. Nitrogen baseflow concentrations and fluxes were elevated in urban and agricultural watersheds with little apparent effect due to restoration. Denitrification appeared to be constrained by carbon availability. Ion concentrations were elevated in all watersheds compared to a forested reference and represent a substantial ecological stressor for the post-restoration aquatic community. Storm event data from one site suggest small reductions in nutrient and sediment loads across the restored reach. High-frequency time series indicate that restoration effects are not observable at larger scales. The effects of restoration, particularly for denitrification, may not be observable for years and can be obscured by weather and climate-driven variability.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
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 studies0.0000.000
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 head0.015
GPT teacher head0.275
Teacher spread0.259 · 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.

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

Citations22
Published2021
Admission routes1
Has abstractyes

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