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Record W2331026742 · doi:10.2166/wst.2013.108

Saturated and unsaturated flow through sloped compost filter beds of different particle sizes

2013· article· en· W2331026742 on OpenAlexaff
Royann J. Petrell, Anastasia Gumulia

Bibliographic record

VenueWater Science & Technology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSurface runoffCompostParticle (ecology)DrainageFlow (mathematics)Environmental scienceParticle sizeTurbulenceVolume (thermodynamics)Hydrology (agriculture)Flow conditionsEnvironmental engineeringSoil scienceChemistryGeotechnical engineeringGeologyMechanicsWaste management

Abstract

fetched live from OpenAlex

Little is known about the hydraulics of sloped compost beds having active free and non-flowing zones, and used for runoff erosion and volume control, and heavy metal removal. Water sorption tests on yard waste compost indicated that water transfer between the two zones would be slow (6 hr for a 0.04 m rise). The free flowing zone in ≈1 m long sloped (15°) beds increased in depth (0.01-0.08 m) with decreasing particle size and increasing flow. Particle size and flow (0.08-0.3 L/s/m) affected bed stability. Drainage volume increased with flow while drainage time remained fairly constant. Saturated flow occurred depending on the particle size above 0.02-0.165 L/s/m. Data indicate that sheet runoff from low intensity storms would most likely create unsaturated but stable bed conditions. Concentrated flows as from downspouts would likely create saturated conditions and have to be managed to prevent washout. A model based on porous media theory indicated that flow regime under saturated flow is turbulent. Results can be used to design compost beds for various runoff rates and to develop a heavy metal sorption model.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
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
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.009
GPT teacher head0.205
Teacher spread0.195 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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