MétaCan
Menu
Back to cohort
Record W2039611291 · doi:10.2166/wst.2011.346

Residence time and contact volume in sloped compost and compost/vegetated filter beds

2011· article· en· W2039611291 on OpenAlexaff
Kelly Mae Coulson, Royann J. Petrell, Darius Chiu

Bibliographic record

VenueWater Science & Technology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCompostResidence time (fluid dynamics)Volume (thermodynamics)Volumetric flow rateEnvironmental scienceHydrology (agriculture)Flow (mathematics)Environmental engineeringEffluentSurface runoffFilter (signal processing)ChemistrySoil scienceGeologyGeotechnical engineeringWaste managementEcologyMechanicsEngineering

Abstract

fetched live from OpenAlex

Little is known about transport mechanisms in sloped dormant vegetated and compost only filters for roadway runoff. Residence time experiments were carried out in triplicate in 0.254 m wide × 0.65 m long by 0.10 m deep beds using a bromide tracer. Bed slope was 12°. Only at the lowest flow rate tested (0.276 l/min per m of filter width) were mean residence times in compost beds with and without dormant grasses different. Pools formed ahead of beds at higher flow rate, and pool depth reached bed depth at 3.54 l/min/m. The ideal model of a well-mixed pool in series with a plug flow porous bed was a good predictor of effluent concentration data at flows ≥2.66 l/min/m. Theoretical contact volume within the beds increased with flow rate to reach approx. 30% of available pore space, while free drainage volume declined. Data shows that designs for sloped compost filter beds must consider flow, bed depth and length, and whether or not areas for pooling are needed.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
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
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.012
GPT teacher head0.195
Teacher spread0.183 · 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 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
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueWater Science & TechnologySame topicUrban Stormwater Management SolutionsFrench-language works237,207