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Record W2166241551 · doi:10.1061/9780784479087.264

Stormwater Sediment Filtration Using Sand versus Synthetic Fibers

2015· article· en· W2166241551 on OpenAlexaboutno aff
Milind V. Khire, Duraisamy S. Saravanathiiban, Mark Verwiel, Christopher Prucha, Terry L. Johnson

Bibliographic record

VenueIFCEE 2015 · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsTurbidityCloggingFiltration (mathematics)Hydraulic conductivityEffluentStormwaterSedimentSand filterFilter (signal processing)Environmental scienceSlow sand filterVolume (thermodynamics)Environmental engineeringWastewaterGeologyWater treatmentSoil scienceSurface runoffSoil water

Abstract

fetched live from OpenAlex

Ottawa sand and a synthetic fiber were tested for filtration of stormwater sediments. Laboratory column tests were performed using water spiked with Bentonite clay as a surrogate for sediment. Filter media column height was varied from 20 to 90 cm. 10 to 20 pore volumes of Bentonite spiked water was permeated through each filter medium. Influent turbidity and effluent turbidity were monitored. Ratios of effluent turbidity to influent turbidity and hydraulic conductivity as a function of pore volumes of flow were evaluated for each filter medium as an indicator of hydraulic efficiency. Hydraulic conductivity of Ottawa sand and Synthetic Fiber was 0.08 and 0.20 cm/s, respectively. Increase in filter column height of Ottawa sand increased the filtration efficiency as quantified by lower turbidity effluent versus pore volume. However, hydraulic conductivity decreased with increasing filtration efficiency for Ottawa sand due to clogging. Compared to Ottawa sand, filtration efficiency and hydraulic conductivity of synthetic fibers was higher throughout the experiments.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score1.000

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)0.0010.002

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 head0.062
GPT teacher head0.271
Teacher spread0.210 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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