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Record W583106755

Stream Restoration: Its Effects on Stream-Subsurface Water Interaction

2006· article· en· W583106755 on OpenAlexaboutno aff
Tamao Kasahara

Bibliographic record

VenueDigital Commons - USU (Utah State University) · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStream restorationEnvironmental scienceUrban streamHydrology (agriculture)GeologySTREAMSComputer science
DOInot available

Abstract

fetched live from OpenAlex

Channel restoration projects focus on restoring surface channel and floodplain features and seldom consider the rehabilitation of the subsurface environment. However, it is increasingly recognized that the stream hyporheic zone, a subsurface region of streamsubsurface water exchange, is an important component of the stream ecosystem. Streamsubsurface water exchange enhances the mass transfer of dissolved and particulate substances between a stream and the streambed, moderates the fluctuation of stream water temperature, provides habitat and spawning gravels for stream organisms, and the hyporheic zone can influence stream chemistry as a result of the transformation and retention of nutrients, organic matter and trace metals. Thus, recognition of hyporheic processes in stream restoration is important. Case studies conducted in urban and agricultural streams near Toronto revealed that the stream channel restoration projects induced both vertical and lateral stream-subsurface water interaction. However, the areas of biogeochemical transformation were limited to the zone near the water-sediment interface as oxygen and nitrate were depleted in a short distance, and the impact of hyporheic processes on stream ecosystem functioning was not considerably enhanced by the restoration projects.

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 score0.008
Threshold uncertainty score0.016

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 studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.190
Teacher spread0.181 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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