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Record W2313949321 · doi:10.2166/wst.2014.122

Improving sediment removal in standard stormwater sumps

2014· article· en· W2313949321 on OpenAlexaff
Yiyi Ma, David Z. Zhu

Bibliographic record

VenueWater Science & Technology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsStormwaterSedimentSurface runoffEnvironmental scienceHydrology (agriculture)Environmental engineeringDrainageGeotechnical engineeringGeologyGeomorphology

Abstract

fetched live from OpenAlex

Standard sumps are an important component of our stormwater drainage system. Recently they have received significant attention as a stormwater pre-treatment device to remove sediment from stormwater runoff. The objective of this research is to explore some simple structures to be installed inside standard sumps to improve sediment removal efficiency. A number of structures were tested and two structures were found to be most effective in sediment removal. Both structures can increase the sediment removal rate by around 20-25% for sediment sizes of 80-140 μm and 110-170 μm under all tested flowrates, and 10-20% for sediment of 160-240 μm. The flow patterns in these structures were simulated using a numerical model, and the energy loss was also examined. The results of this study offer a new direction for the development of stormwater treatment devices.

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 score0.339
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
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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