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A pilot study of the efficacy of residuum lodges for managing sediment delivery to impoundment reservoirs

2008· article· en· W2052979989 on OpenAlexaboutno aff
Paul Kay, Alona Armstrong, Adrian McDonald, Daniel R. Parsons, Jim Best, Jeff Peakall, Andrew Walker, Miles Foulger, Sarah Gledhill, Martin R. Tillotson

Bibliographic record

VenueWater and Environment Journal · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsnot available
Fundersnot available
KeywordsResiduumSedimentSTREAMSHydrology (agriculture)ClearanceEnvironmental scienceCurrent (fluid)Water qualityGeologyOceanographyEcologyGeomorphologyGeotechnical engineeringBiology

Abstract

fetched live from OpenAlex

Abstract Residuum lodges comprise small dams constructed on feeder streams immediately before they enter a reservoir, behind which ponds form, where sediment is deposited. Despite their construction on many impoundment reservoirs (IRE) and catchwaters, little research has previously investigated their efficacy at removing sediments from feeder streams. The current pilot study has, therefore, been carried out at an IRE supplying Halifax, West Yorkshire, UK, where a residuum lodge was recently cleaned out. Sediment concentrations reaching the reservoir were reduced by up to 42% although no certain impacts were noted on the other water quality variables that were measured. Moreover, it was found that the clearance operation did not result in the release of excessive quantities of sediment into the reservoir. It was estimated that the cleared residuum lodge would take 12 years to refill. A survey of other residuum lodges in the Yorkshire region showed there to be considerable differences in their remaining capacities.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.209
Teacher spread0.171 · 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

Citations5
Published2008
Admission routes1
Has abstractyes

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